JP2020514888A - CNA-guided care to improve clinical outcomes and reduce total treatment costs - Google Patents

Abstract

The invention described herein, when implemented on a processor, uses CNA-guided care to improve the clinical outcome at a particular patient level and reduce total treatment costs at a population level in a first COTA module. Provided are non-transitory computer-readable media, methods, and systems for storing computer program instructions for execution. By proactively accounting for biological variability by grouping patients within a patient population to a first clinical outcome tracking and analysis module on a processor with a first clinical outcome tracking and analysis module, thereby , Remove biological variability as a factor in the value of treatment, and leave treatment variability as a controlling factor in treatment outcome, which receives and collects personal health information from each patient in the patient population and The latter is done by generating and assigning multiple clinical outcome and analysis Nodal Addresses (CNAs) within the first clinical outcome tracking and analysis module, each CNA being , Sorted and categorized information as a sequence of discrete punctuation digits, including a prefix, a middle part, and a suffix, representing a preselected set of variables that divides into a clinically relevant set of health information. A first clinical outcomes tracking and analysis (COTA) module tracks the outcome of treatment selection and reports on outcomes associated with the use of CNAs. CNA guided care has two aspects, operates through the application of CNAs, and is formed by a triad of healthcare providers, payers, and patients suffering from a disease. The first aspect, an activation tool, includes a healthcare provider, a computer including a processor with a first clinical outcome tracking and analysis (COTA) module, and a first communicatively linked to the first COTA module via a network. It includes the interaction between the first client device with the 2COTA module and the payer. The payer comprises, through a second COTA module, a first COTA module, through a second COTA module, with information identifying a medical service under consideration for a patient whose health insurance benefit guarantees the service and other variables selected by the payer. Sent to the processor. In response, the first COTA module assigns to the second COTA module (1) appropriate CNA clinical outcome data, (2) behavioral change data for each healthcare provider in each CNA, and (3) appropriate CNA. A cost report including real-time cost data for treating each patient in the patient population, and (4) one or more graphical analyzes correlating treatment costs with clinical outcomes. This information indicates that the medical service is an appropriate provision or level of service, considering the potential benefits and harms to the patient, that the medical service improves clinical outcomes and reduces total treatment costs. Is effective in improving health outcomes, that this service is cost-effective for the medical condition and clinical outcome being treated, as compared to alternative health interventions or no intervention, and The payer may authorize payment to the health care provider for this health care service, provided that it is sufficient to establish that, in accordance with generally accepted medical practices. A second aspect including interaction between a patient, a computer including a processor having a first COTA module, a second client device having a third COTA module communicatively linked to the first COTA module via a network, and a patient. Includes a processor with a first COTA module and a third COTA module with a communicatively linked third that provides the patient with sufficient information for a complete medical evaluation of the patient and to assign a CNA to the patient. Provides communication with client devices. Using CNA, patients with the illness then have an optimal care plan with a low risk of adverse differences, as well as healthcare professionals based on geography, clinical outcomes, costs within the CNA, and other criteria set by the patient. Will be able to select. Assigned CNAs can be associated with one or more bundles of pre-determined patient care services for treatment of illness and can provide a given course of treatment, cost certainty, or both. .. Once the patient selects a health care worker who meets one or more of the patient's geography, cost, and outcome needs, the first COTA module can communicate to a computing device in the selected health care worker's office. Can facilitate scheduling appointments. [Selection diagram]

Description

This application claims the benefit of priority to US Application No. 15 / 351,099 filed Nov. 14, 2016, the entire contents of which are incorporated herein by reference.
The present disclosure relates to CNA-guided care to improve clinical outcomes at the particular patient level and reduce total treatment costs at the population level.

  As the life of the general population increases, most of the health care costs associated with the aging population are increasing. The costs associated with serious and chronic diseases such as cancer are growing unsustainably. For example, cancer costs are projected to be the highest growing area of health care costs without a corresponding improvement in outcome. In the United States alone, approximately $ 125 billion was spent on cancer treatment in 2010, with an estimate that approximately 15-30% of healthcare costs could be classified as a result of unnecessary or inadequate treatment, in which case , Too little or too much treatment of individual patients has led to suboptimal clinical outcomes and excess costs (hereinafter “disadvantages”).

  In the current system, there is an average 20% disadvantage ratio per 100 severe medical conditions being treated, which means that there was too little or too much treatment for individual patients. To do. Therefore, suboptimal clinical outcomes occur in 20% of patients being treated, causing an increase in total treatment costs at the population level of 100% at that time.

  Traditional approaches to controlling costs, such as clinical pathways and disease management, are usually ineffective and there are no high quality alternatives on the market today.

  The current pre-licensing infrastructure that allows payers to determine that health care services are guaranteed benefits of their patients' health insurance has resulted in high costs, time consuming, and delays in the treatment required resulting in clinical outcomes. May have a negative impact.

  As technology and medicine continue to evolve, scientific and clinical practice for the treatment of diseases (cancer, heart disease, lung disease, and behavioral health) is evolving rapidly. Often, health care professionals (eg, oncologists) have difficulty keeping up with these advances. These advances, such as next-generation gene sequencing, are usually complex and can be a major problem for health insurance and healthcare professionals. As a result, health insurance may require additional tools and support for managing medical (eg, oncology) practices. Similarly, healthcare professionals (eg, physicians) will need more decision support tools to provide the best care and keep the business going.

  The Clinical Outcomes Tracking and Analysis (COTA) module may, for example, allow healthcare professionals and / or other users to better manage, better manage and retrieve specific information related to disease and / or patients, and reduce costs. It is a tool that allows for improved management.

  Parameters of clinical outcome tracking and analysis include sorting, outcome tracking, United States East Coast Cancer Clinical Trials Group (ECOG) performance status, treatment toxicity, and treatment cost. In one aspect, a method and system receives one or more parameters for sorting a plurality of data records from a client device operated by a user, and in response to receiving the received parameters. It includes a COTA module that sorts the data records based on. A nodal address representing one or more variables is applied to a sorted set of patient records to produce a clinically relevant set of patient records of one or more. It is determined as a sorted medical record set of patients that satisfies the variable of. The COTA module then analyzes a clinically relevant set of patient medical records and communicates to the client device for displaying at least a portion of the sorted and sorted data records and the updated data records. To do.

  Each data record includes data related to the disease and data related to a patient who is currently ill or has been ill in the past. The COTA module can receive data from electronic medical records (EMRs), users, healthcare professionals, professionals, or any other source.

  The COTA module allows the user to perform various analyzes on one or more of the data records. For example, the COTA module can enable comparison of data or follow-up outcomes between patients, identify a particular patient as a candidate for a particular treatment or drug, and communicate analysis tools to a client device to, for example, Facilitates the analysis of sorted and sorted data records, or allows the comparison of Kaplan-Meier curves, and based on the tracking, a particular physician involved in a patient treats other (similar) patients with other physicians. Can be determined according to the treatment method of.

  The COTA module can also send an alert to the client device when the trigger occurs. The trigger can be, for example, at diagnosis, progression, dose modification, drug modification, toxicity, propensity for variability from the desired outcome, and / or a particular time.

  The COTA module tracks the outcome of treatment selection and reports on the outcomes associated with the use of COTA Nodal Addresses (CNA). This is done by grouping patients within a patient population to proactively consider biological variability and effectively eliminate biological variability as a factor in treatment value and therapeutic variability as a controlling factor in treatment outcome. Leave. The COTA module receives and collects personal health information from each patient in the patient population, records it in a database, and sorts personal health information. By categorizing similar personal health information and generating and assigning multiple nodal addresses within the COTA module, the types of patients within the patient population are grouped based on the personal health information. Each CNA represents a sequence of preselected variables that divides the sorted and categorized information into clinically relevant sets of health information, a discrete punctuation digit sequence containing a prefix, a middle part, and a suffix. (Discrete punctuated string of digits). Multiple CNAs reduce trillions of possible permutations to a reduced number of clinically meaningful permutations based on a discrete punctuation sequence representing each nodal address, which is initially behavioral. And then warn of the resulting clinical and cost outcomes from ideal values, expressed as the best possible clinical outcome at the lowest possible cost, with regard to required care and avoidance of unnecessary care. In the inevitable time needed to do so, increasing the value of the treatment, which means a better clinical outcome at the lowest possible cost; a discrete punctuation string representing each nodal address On the basis of and on the basis of the reduction of permutation, reduce the processing requirements and the processing time in order to make the real-time monitoring of the health care provider's capacity efficient; a key point in time when behavioral fluctuations may occur. To enable predictability and avoid behavioral variability, interrupting treatment flow to prevent overuse / underuse of care.

  The invention described herein provides CNA guided care. CNA-guided care has two dimensions, which together provide value-based healthcare delivery in the United States through the application of CNAs through the triad of healthcare providers, payers, and patients suffering from illness. to enable.

  A first aspect, an enabling tool is a healthcare provider, a computer including a processor with a first COTA module, and a first client device with a second COTA module communicatively linked to a first COTA via a network. And the interaction with the payer. This allows the payer to consider the patient's health insurance benefits assured service on the network via the first client device with the second COTA module communicatively linked to the first COTA module of the processor. A medical service of the user; indicating that the service is for the purpose of diagnosing or treating the medical condition; and enabling communication to the processor to identify the variable selected by the payer. Upon receiving the communication from the payer, the first COTA module provides the second COTA module with (i) clinical outcome data for each CNA, (ii) medical care for patients at each nodal address at key points in time during treatment. Disadvantage data for each provider in each CNA, including both non-existent and unnecessary medical care, which contributes to the individual's disadvantage, (iii) treat each patient in the patient population assigned to the CNA Cost reports including real-time cost data for (iv) (1) treatment costs for clinical outcomes, (2) treatment costs and toxicity incidence and severity for clinical outcomes of treatments, (3) therapy and quality of life Information can be sent that includes a graph analysis correlating one or more of them. Based on this information, the payer is at the key point in time that: (a) the medical service is an appropriate offering or level of service, taking into account the potential benefits and harms to the patient; b) Healthcare services are effective in improving health outcomes by improving clinical outcomes and reducing total treatment costs, as compared to the absence of alternative health interventions or the absence of this service. Being able to establish that it is cost-effective for the medical condition and clinical outcome being treated, and that this service complies with generally accepted medical practices, the payer will be able to provide medical care for this patient's medical services. Can approve payments to the person.

  From the payer's perspective, this enablement tool can be effective to eliminate pre-authorization for testing, diagnosis, and treatment, pre-approval, and eliminate the need for referrals. .. From the healthcare provider's perspective, this tool reduces the time required to pay the provider for healthcare. Thus, the enablement tool allows healthcare to optimize per-treatment pay-based reimbursement and transition from per-care pay to risk-based treatment reimbursement (eg, by bundling services) ..

  A second aspect of CNA guided medicine is a computer including a processor with a first COTA module, a second client device with a third COTA module communicatively linked to the first COTA module via a network, and a patient. Including interactions between. Through a series of communications between the first COTA module and the patient, the CNA-guided treatment is primarily for the patient to obtain a complete medical assessment of his illness and for the first COTA module to assign the CNA to the patient. Patient-configured to provide sufficient information to the patient and, secondly, to use CNA to allow geography, clinical outcomes, costs, and patient selection of optimized treatments to avoid adverse differences Patients are presented with treatment options based on other criteria, and thirdly, patients are presented with healthcare providers within their region who are following CNA-guided care with the best outcome at acceptable treatment costs.

US Application No. 15 / 351,099

  The invention described herein, when implemented on a processor, uses CNA-guided care to improve the clinical outcome at a particular patient level and reduce total treatment costs at a population level in a first COTA module. Provided are non-transitory computer-readable media, methods, and systems that store computer program instructions for execution.

According to one aspect, the invention described herein provides a method for improving clinical outcomes at a particular patient level and reducing total treatment costs at a population level. A healthcare provider, a computer including a processor with a first clinical outcome tracking and analysis module, and a second clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module via a network. The interaction between the first client device provided and the payer, and B. A computer including a processor having a first clinical outcome tracking and analysis module and a second client having a third clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module via a network. Comprising a device and an interaction between the patient and the computer, the computer comprising a processor executing the first clinical outcome tracking and analysis module on a processor comprising the first clinical outcome tracking and analysis module, the steps comprising: , (A) proactively consider biological variability by grouping patients within a patient population, thereby effectively eliminating biological variability as a factor in the value of treatment and in treating outcome. Including the treatment variability as a controlling factor, which is done by (i) receiving and collecting personal health information from each patient in the patient population and recording it in a database, , (Ii) contains information that influences survival, prognosis, or treatment based on current parameters that characterize each patient in the patient population and current scientific knowledge and medical guidelines, and (ii) for each patient in the patient population. The personal health information is sorted using a sorting filter, resulting in a sorted personal health information set for this population, identifying patients within the patient population that meet each parameter, and (iii) a first clinical outcome tracker. And categorizing similar personal health information based on the personal health information associated with the patient population by generating and assigning multiple clinical outcomes and analysis Nodal Addresses (CNA) within the analysis module. , Grouping patient types within a patient population and generating and assigning multiple nodal addresses: (1) Represent each CNA as a discrete punctuation string that includes a prefix, a middle part, and a suffix. Stages, which represent a preselected set of variables that divide the sorted and classified information into clinically relevant sets of health information, (2) trillions of possible permutations. To a reduced number of clinically meaningful permutations based on the discrete punctuation sequence representing each nodal address, which is initially behavioral and then the resulting clinical and cost outcomes. Of deviations from the ideal value, expressed as the best possible clinical outcome at the lowest possible cost, can be analyzed in the inevitable time needed to warn about required care and avoidance of unnecessary care. This allows you to maximize the potential (3) Real-time monitoring of the health care provider's ability based on the discrete punctuation sequence representing each CNA and based on the reduction of permutation, which also enhances the value of treatment, which means better clinical outcomes at lower cost. In order to be efficient, reduce processing requirements and processing time, and (4) enable prediction of key points in time when behavioral fluctuations may occur, and avoid treatment fluctuations in order to avoid behavioral fluctuations. Interrupting flow to prevent overuse / underuse of care, and (iv) for one or more patients within a subset of the patient population, clinically relevant personal health of the subset of patient population By measuring the clinical outcome of each CNA by analyzing the information set, (v) treatment, testing, follow-up, adherence to prescribed drugs, cost of each patient within the patient population assigned to each CNA, Measuring the behavioral variation of each caregiver for each patient within the patient population assigned to each CNA by comparing the differences between one caregiver and another
The above steps are
(B) a first client with a second clinical outcome tracking and analysis module that communicates identifying a medical service under consideration with respect to a patient whose health insurance benefits guarantee the medical service and variables selected by the payer. Upon receipt from the payer via the device, the second clinical outcome tracking and analysis module is sent information including: (1 ′) (iv) clinical outcome data for each CNA, (2 ′) (v). Behavioral data of each healthcare provider in each CNA of (3 ') (iii), a cost report containing real-time cost data assigned to the CNA for treating each patient in the patient population, (4' ) One or more graphical analyzes correlating treatment costs to clinical outcomes, where this information allows the payer to (a ') consider potential benefits and harms to the patient at key time points. And that the medical service is an appropriate provision or level of service, and (b ') the medical service is effective in improving the health outcome by improving the clinical outcome and reducing the total treatment cost. , (C ') that this service is cost-effective for the medical condition and clinical outcome being treated, as compared to alternative health interventions or no intervention, (d') this service is generally accepted The payer may authorize payment to the health care provider for this health care service, if sufficient to establish compliance with the medical practice, and the above steps further include (c) third clinical outcome tracking. And communicating a series of communications with the patient via a second client device with an analysis module, providing sufficient information for the complete medical evaluation of the patient and to assign the CNA to the patient, Used to determine an optimal care level with a low risk of adverse differences and to select a provider and desired level of CNA-guided care, a series of communications (i '') first clinical outcomes tracking and analysis module. Responsive to the first communication from the patient reporting a health concern to the processor of the computer server with a second client device with a third clinical outcome tracking and analysis module, the health concern as a medical condition. From the patient, including in a response, a second communication containing a list of tests required to diagnose and including the results of tests required to diagnose (ii ″) (i ″) health concerns. Further diagnosing the medical condition and classifying the medical condition in a second client device with a third clinical outcome tracking and analysis module in response to the third communication of In response to transmitting a fourth communication containing a list of additional tests required to do, and in response to a fifth communication from the patient containing the results of the additional tests of (iii ″) (i ″), One CNA for the patient from the multiple CNAs available in (a '') (iii) based on the results of the test, the diagnosis of (ii ''), and the list of additional tests of (ii '') Assigning, but the assigned CNA includes a set of health information clinically relevant to the patient, and (b ″) to a second client device with a third clinical outcome tracking module, a sixth communication including: A geographically-organized list of healthcare professionals who send and (1 '') assigned CNAs and (2 '') assigned CNAs to treat patients, this list of healthcare professionals being: Optimal care plans categorized by one or more of geography, clinical outcome, or cost and with low risk of (iv '') disadvantageous differences, and at least one or more of the geography, cost, and outcome needs of the patient or In response to a seventh communication from the patient selecting a health care professional who meets two or more, the patient is in the office of the selected health care professional, the computing device comprising a third clinical outcome tracking and analysis module. Is communicatively linked to and facilitates scheduling appointments for selected healthcare workers and patients.

  In some embodiments of the method, the parameters for sorting (a) :( a) (ii) are gender, age, ethnicity, coexistence, smoking, health source, medical record number, family doctor, referring physician. , One or more of: hospital, licensed service vendor, disease-specific clinical molecular phenotype, therapeutic purpose, stage of therapy, biomarker, and cost of treatment, or (b): (a) (iii). The preselected set of variables in (1) includes the disease-specific clinical molecular phenotype, and the number string representing the phenotype is determined based on a directed graph, or (c): steps (a) (iii). The key time points of (4) and step (b) (1 ') (4') are the changes in the time of diagnosis, progression, dose change, drug change, toxicity, and change from the desired outcome. Correlating one or more of the trends, or correlating the treatment cost of (d) :( b) (1 ′) (4 ′) with clinical outcome, may include incidence of toxicity, severity of toxicity, therapy, and Correlating one or more of the quality of life with treatment costs and treatment outcomes, or receiving a transmission from a client device in (e) :( b) or (c) is a human being. CNAs assigned via the user's or technical process, or at (f) :( c) (iii ") (b") (1 "), are of pre-determined patient care services for the treatment of illness. The clinical outcome of (g) :( c) (iii ") (b") (2 ") associated with one or more bundles, or (g) :( c) (iii") (b ") (2"), is treatment received, dose strength delivered, dosing interval, dosing period , Quality of life metrics, toxicity to treatment, progression-free survival, overall survival, response metrics, and mortality, or (h): stage (c) (iii ”) (b). The list of healthcare workers in ") (2)" is visually categorized by clinical outcome.

  In some embodiments, one or more bundles of a given patient care service provide a predetermined course of treatment, cost certainty for treatment of an illness, or both.

  In some embodiments, the method ranks both clinical outcome and treatment cost in one or more bundles of a given patient care service to provide the best clinical outcome at the lowest treatment cost in the CNA. The method further includes the step of identifying.

  In some embodiments, the visual categorization of the list of health care workers by clinical outcome is: (a) green indicates above-average clinical outcome, (b) yellow indicates average clinical outcome, and (c) ) Red indicates below-average clinical outcome.

  In some embodiments, the method, with permission from the patient and the selected health care professional, from the first clinical outcomes tracking and analysis module, the fourth clinical outcomes tracking at the selected health care worker's office. And further transmitting the personal health information of the patient to the computing device comprising the analysis module.

  In some embodiments, the behavioral variability data for each health care provider in each CNA in (c) contributes to the health care provider's disadvantage to the patient at each CNA at key time points during treatment. Yes, including both non-existent and unnecessary medical care.

  In some embodiments, the method further comprises, in step (d), indicating that the service is for the purpose of diagnosing or treating the medical condition.

  In some embodiments, the cost report is a comparison of average costs per revenue between physicians, or a comparison of hospital contributions in dollars and percentages, or average revenue per patient at hospital, or per patient at hospital. Average cost, or average cost per case for each doctor, or weight per average, or average cost of imaging, laboratory, evaluation and management, medicines, medical supplies, and other for each doctor Costs, or a combination thereof.

  In some embodiments, the variable selected by the payer is a variable that identifies a particular attribute of the personal health information as the desired set of characteristics, added to the personal health information by the payer to obtain the personal health information for each patient. Includes one or more attributes, or both, that identify <RTIgt; as </ RTI> an equal level of importance with other health information in the patient population database.

  According to another aspect, the invention described herein provides a system for improving clinical outcome at a particular patient level and reducing total treatment costs at a population level, the system comprising: A processor of a computer server comprising a database containing population personal health information and data, a first clinical outcomes tracking and analysis module communicatively linked to the database and a network, and a memory for storing computer program instructions. A first client device comprising: (B) a processing unit; a memory; and a second clinical outcome tracking and analysis module communicatively linked to a first clinical outcome tracking and analysis module of the processor of the computer server. And (C) a processing unit, a memory, and a third clinical outcome tracking and analysis module communicatively linked to a first clinical outcome tracking and analysis module of the processor of the computer server, And (A) computer program instructions, when executed on a processor, cause the first clinical outcome tracking and analysis module to perform operations including: (a) grouping patients within a patient population. Thereby proactively accounting for biological variability, thereby effectively eliminating biological variability as a factor in the value of treatment and leaving therapeutic variability as the controlling factor in treatment outcome, which , (I) receiving and collecting personal health information from each patient in the patient population and recording in a database, the personal health information including each parameter characterizing each patient in the patient population, and the latest (Ii) personal health information for each patient in the patient population, sorted using a sorting filter, including information that affects survival, prognosis, or treatment based on scientific knowledge and medical guidelines of Providing a sorted personal health information set for this population, identifying patients in the patient population that meet each parameter, and (iii) multiple clinical outcome tracking and analysis nodals within the first clinical outcome tracking and analysis module. Generating and assigning addresses (Nodal Addresses (CNA)) to categorize similar personal health information based on the personal health information related to the patient population and group the types of patients within the patient population. , Generating and assigning a plurality of CNAs, (1) representing each CNA as a sequence of discrete punctuation digits including a prefix, a middle part, and a suffix , Which represent a preselected set of variables that divide the sorted and categorized information into clinically relevant sets of health information, (2) representing trillions of possible permutations. , A reduced number of clinically meaningful permutations based on the discrete punctuation sequence representing each CNA, which is possible for first behavioral and then consequent clinical and cost outcomes. Allow deviations from ideal values, expressed as the best possible clinical outcomes at the lowest possible cost, to be analyzed in the necessary time to warn about required care and avoidance of unnecessary care, which Enhances the value of treatment, which means better clinical outcome at the lowest possible cost, and (3) based on the discrete punctuation sequence representing each CNA and based on the reduction of permutation In order to make the real-time monitoring of the ability of the system efficient, the processing requirements and processing time are reduced, and (4) it is possible to predict key points on the time when the behavior variation may occur, and avoid the behavior variation. To prevent overuse / underuse of care to interrupt the flow of treatment, and (iv) for one or more patients within a subset of the patient population, the clinical relevance of the subset of the patient population Determine the clinical outcome of each CNA by analyzing a personal health information set that includes (v) treatment, testing, follow-up, adherence to prescribed medications, and each patient within the patient population assigned to each CNA. Measures the behavioral variability of each caregiver for each patient within the patient population assigned to each CNA by comparing the differences between one caregiver and another at , (B) a communication with a second clinical outcome tracking and analysis module for identifying a medical service under consideration for a patient whose health insurance benefit guarantees the medical service and variables selected by the payer, a first with a clinical outcome tracking and analysis module. Upon receipt from the payer via the client device, the second clinical outcome tracking and analysis module is sent information including: (1 ′) (iv) clinical outcome data for each CNA, (2 ′) (v). ) Behavioral data of each health care provider in each CNA, cost reports containing real-time cost data for treating each patient in the patient population assigned to the CNA in (3 ′) (iii), (4) ') One or more graphical analyzes that correlate treatment costs with clinical outcomes, where this information provides (a') potential benefits and benefits to patients at key points in time for the payer. Considering the harmfulness, the medical service is an appropriate provision or level of the service, and (b ') the medical service improves the clinical outcome by reducing the total treatment cost, thereby improving the health outcome. Be effective, (c ') that this service is more cost effective for the medical condition and clinical outcome being treated, as compared to alternative health interventions or no intervention, (d') The payer may authorize payment to the health care provider for this health care service, provided that it is sufficient to establish that, following generally accepted medical practices, (c) Third Clinical Outcome Tracking. And communicating a series of communications with the patient via a second client device with an analysis module, providing sufficient information for the complete medical evaluation of the patient and to assign the CNA to the patient, Used to determine the optimal level of care with a low risk of adverse differences, to select the provider and the desired level of CNA-guided care, a series of communications (i '') for the first clinical outcome tracking and analysis The second client device with a third clinical outcome tracking and analysis module is responsive to the first communication from the patient reporting the health concern to the processor of the computer server with the module, and the medical condition with the medical condition. A response including a second communication containing a list of tests required to diagnose as, and including results of tests required to diagnose (ii '') (i '') health concerns In response to a third communication from the third client device with a third clinical outcome tracking and analysis module, a fourth communication containing a list of additional tests required to diagnose and further classify the medical condition. In response to the fifth communication from the patient, including the result of the additional test of (iii ″) (i ″), in response to the test result, the diagnosis of (ii ″), and (ii). Based on the list of additional tests in ″), one CNA is assigned to the patient from the multiple CNAs available in (a ″) (iii), but the assigned CNA is clinically relevant to the patient. Sending a sixth communication including (b '') a third clinical outcome tracking module to a second client device that includes a certain health information set and includes (1 '') an assigned CNA, (2 '') A geographically organized list of healthcare professionals treating patients within their assigned CNAs, this list of healthcare professionals categorized by one or more of geography, clinical outcome, or cost. And (iv '') In response to a seventh communication from the patient selecting an optimal care plan with a low risk of interest margin and a health care professional who meets at least one or more of the patient's geography, cost, and outcome needs, The patient is communicatively linked to a computing device with a third clinical outcome tracking and analysis module in the office of the selected healthcare provider to schedule appointments for the selected healthcare provider and patient. make it easier.

  In some embodiments, the parameters for sorting (a) :( a) (ii) are gender, age, ethnicity, coexistence, smoking, health resource, medical record number, family doctor, referring physician, hospital. , Licensed service vendor, disease-specific clinical molecular phenotype, therapeutic purpose, stage of treatment, biomarker, and treatment cost, or (b): (a) (iii) (1) preselected set of variables Is a disease-specific clinical molecular phenotype, and the numerical sequence representing the phenotype is determined based on a directed graph, or (c): step (a) (iii) (4) and step (b) (1 ′). ) (4 ′) key points in time include one or more of the tendency of change at the time of diagnosis, progression, dose change, drug change, toxicity, and desired outcome, or ( d): correlating the treatment cost of (b) (1 ′) (4 ′) with clinical outcome, one or more of the incidence of toxicity, severity of toxicity, therapy, and quality of life: Correlating with treatment cost and treatment outcome, or receiving the transmission from the client device in (e) :( b) or (c), via a human user or technical process, or ( f): The CNA assigned in (c) (iii ″) (b ″) (1 ″) is associated with one or more bundles of predetermined patient care services for the treatment of illness. Or (g) :( c) (iii ″) (b ″) (2 ″) the clinical outcome is: received therapeutic agent, delivered dose intensity, dosing interval, dosing period, quality of life metrics, Includes one or more of treatment toxicity, progression-free survival, overall survival, response metrics, and death, or (h): stage (c) (iii '') (b '') ( The 2 '') list of health care workers is visually categorized by clinical outcome.

  In some embodiments, one or more bundles of a given patient care service provide a given course of treatment, cost certainty for treatment of an illness, or both.

  In some embodiments, the system ranks both clinical outcomes and treatment costs in one or more bundles of a given patient care service to optimize the best treatment cost in the assigned CNA. The method further includes identifying a clinical outcome.

  In some embodiments, the visual categorization of the list of health care workers by clinical outcome is: (a) green indicates above-average clinical outcome, (b) yellow indicates average clinical outcome, and (c) ) Red indicates below-average clinical outcome.

  In some embodiments, the system obtains permission from the patient and the selected healthcare practitioner from the first clinical outcomes tracking and analysis module to the fourth clinical outcomes tracking at the selected healthcare practitioner's office. And further transmitting the personal health information of the patient to the computing device comprising the analysis module.

  In some embodiments, the behavioral variability data for each health care provider in each CNA in (c) contributes to the health care provider's disadvantage to the patient at each CNA at key time points during treatment. Yes, including both non-existent and unnecessary medical care.

  In some embodiments, the system further comprises the step of indicating that the service is for the purpose of diagnosing or treating the medical condition.

  In some embodiments, the cost report is a comparison of average costs per revenue between physicians, or a comparison of hospital contributions in dollars and percentages, or average revenue per patient at hospital, or per patient at hospital. Average cost, or average cost per case for each doctor, or weight per average, or average cost for imaging, laboratory tests, assessments and management, pharmaceuticals, medical supplies, and other for each doctor Costs, or a combination thereof.

  In some embodiments, the variable selected by the payer is a variable that identifies a particular attribute of the personal health information as the desired set of characteristics, added to the personal health information by the payer to obtain the personal health information for each patient. Includes one or more attributes, or both, that identify <RTIgt; as </ RTI> an equal level of importance with other health information in the patient population database.

  According to another aspect, the invention described herein includes (A) a healthcare provider, a computer including a processor with a first clinical outcome tracking and analysis module, a first clinical outcome tracking and analysis module via a network. A first client device with a second clinical outcome tracking and analysis module communicatively linked to the payer, and (B) a computer including a processor with a first clinical outcome tracking and analysis module, a network Computer program instructions for facilitating interaction between a second client device with a third clinical outcome tracking and analysis module communicatively linked to a first clinical outcome tracking and analysis module via a patient, and a patient. Providing a non-transitory computer-readable medium for storing and causing the first clinical outcome tracking and analysis module to perform operations including the following when executed on a processor with a first clinical outcome tracking and analysis module: (A) By preliminarily considering biological variability by grouping patients within a patient population, thereby eliminating biological variability as a factor in the value of treatment and as a controlling factor in treatment outcome. Retaining treatment variability, which includes the steps of (i) receiving, collecting and recording in a database personal health information from each patient in the patient population, the personal health information being (Ii) personal health information for each patient in the patient population, including information that affects survival, prognosis, or treatment, based on the parameters that characterize each patient and on the latest scientific knowledge and medical guidelines. , Using a sorting filter to yield a sorted personal health information set for this population, identifying patients within the patient population that meet each parameter, and (iii) inside the first clinical outcome tracking and analysis module. Generating and assigning multiple clinical outcomes and analysis Nodal Addresses (CNAs) in to classify similar personal health information based on the personal health information related to the patient population to within the patient population. Grouping patient types and generating and assigning multiple nodal addresses includes: (1) representing each CNA as a discrete punctuation string that includes a prefix, a middle part, and a suffix. , Which represent a preselected set of variables that divide the sorted and classified information into clinically relevant sets of health information, (2) trillions of possible orders. The sequence is reduced to a reduced number of clinically meaningful permutations based on the discrete punctuation sequence representing each CNA, which is initially behavioral and then the resulting clinical and cost outcomes. Of deviations from the ideal value, expressed as the best possible clinical outcome at the lowest possible cost, can be analyzed in the inevitable time needed to warn about required care and avoidance of unnecessary care. Thereby increasing the value of the treatment, which means a better clinical outcome at the lowest possible cost, and (3) based on the discrete punctuation sequence representing each CNA and on the basis of permutation reduction, In order to make the real-time monitoring of the health care provider's ability efficient, reduce the processing requirements and processing time, and (4) enable the prediction of key points in time when behavior fluctuation may occur, and To avoid variability, interrupting the flow of treatment to prevent over- / under-utilization of care, and (iv) for one or more patients within a subset of the patient population, a clinical subset of the patient population Measure the clinical outcome of each CNA by analyzing a personal health information set that is relevant to (v) treatment, testing, follow-up, adherence to prescribed medications, and within the patient population assigned to each CNA. Behavior of each caregiver of each patient within the patient population assigned to each CNA by comparing the differences between one caregiver and another at the cost of each patient in And (b) a communication identifying a medical service under consideration with respect to the patient whose health insurance benefits of the patient guarantee the medical service and variables selected by the payer, a second clinical outcome tracking and analysis module. Upon receipt from the payer via the first client device comprising: a second clinical outcome tracking and analysis module, sending information including: (1 ′) (iv) clinical outcome data for each CNA, ( 2 ') (v) Behavioral change data for each healthcare provider in each CNA, including real-time cost data for treating each patient in the patient population assigned to the CNA in (3') (iii) A cost report, (4 ') one or more graphical analyzes correlating treatment costs to clinical outcomes, where this information is (a') potential benefit to the patient at the payer's key time point. Sex and harm, the medical service is an appropriate provision or level of service, and (b ') the medical service improves clinical outcomes and reduces total treatment costs. Effective in improving health outcomes, (c ') that the service is cost effective for the medical condition and clinical outcome treated, as compared to alternative health interventions or no intervention; (D ') If this service is sufficient to establish that it complies with generally accepted medical practices, the payer may authorize payment to the healthcare provider for this medical service, and (C) interacting with the patient via a second client device with a third clinical outcome tracking and analysis module is sufficient for a complete medical evaluation of the patient and for assigning a CNA to the patient. CNA to determine the optimal level of care with a low risk of adverse differences, select the provider and the desired level of CNA-guided care, and the sequence of communications (i '' ) A second client device with a third clinical outcome tracking and analysis module in response to a first communication from a patient reporting a health concern to a processor of a computer server with a first clinical outcome tracking and analysis module. A second communication containing a list of tests necessary for diagnosing a health concern as a medical condition in response, and necessary for diagnosing the (ii '') (i '') health concern. Additional tests required to diagnose and further classify the medical condition in a second client device with a third clinical outcome tracking and analysis module in response to a third communication from the patient, including the results of various tests. In response, a fourth communication including a list of (iii ') and (i' ') including a result of the additional test of (iii' ') and (i' ') is received in response to the fifth communication from the patient. Based on the list of ') diagnoses and (ii' ') additional tests, assign one CNA to the patient from the multiple CNAs available in (a' ') (iii), but the assigned CNA is , (B '') sending to a second client device with a third clinical outcome tracking module a health information set that is clinically relevant to the patient, including (1 ''): ) Assigned CNA, (2 '') Within the assigned Nodal Address, a geographically organized list of healthcare professionals treating patients, the list of healthcare professionals including geography, clinical outcome, Or (1) more than one of the costs and (iv '') an optimal care plan with a low risk of unfavorable differences, and medical services that meet at least one or more of the patient's geography, cost and outcome needs Select worker In response to the seventh communication from the patient, the patient is communicatively linked to a computing device at a selected healthcare professional's office with a third clinical outcome tracking and analysis module, Facilitates scheduling appointments for selected healthcare workers and patients.

  In some embodiments, the parameters for sorting (a) :( a) (ii) are gender, age, ethnicity, coexistence, smoking, health resource, medical record number, family doctor, referring physician, hospital. , Licensed service vendor, disease-specific clinical molecular phenotype, therapeutic purpose, stage of treatment, biomarker, and treatment cost, or (b): (a) (iii) (1) preselected set of variables Is a disease-specific clinical molecular phenotype, and the numerical sequence representing the phenotype is determined based on a directed graph, or (c): step (a) (iii) (4) and step (b) (1 ′). ) (4 ′) key points in time include one or more of the tendency of change at the time of diagnosis, progression, dose change, drug change, toxicity, and desired outcome, or ( d): correlating the treatment cost of (b) (1 ′) (4 ′) with clinical outcome, one or more of the incidence of toxicity, severity of toxicity, therapy, and quality of life: Correlating with treatment cost and treatment outcome, or receiving the transmission from the client device in (e) :( b) or (c), via a human user or technical process, or ( f): The CNA assigned in (c) (iii ″) (b ″) (1 ″) is associated with one or more bundles of predetermined patient care services for the treatment of illness. Or (g) :( c) (iii ″) (b ″) (2 ″) the clinical outcome is: received therapeutic agent, delivered dose intensity, dosing interval, dosing period, quality of life metrics, Includes one or more of treatment toxicity, progression-free survival, overall survival, response metrics, and death, or (h): stage (c) (iii '') (b '') ( The 2 '') list of health care workers is visually categorized by clinical outcome.

  In some embodiments, one or more bundles of a given patient care service provide a given course of treatment, cost certainty for treatment of an illness, or both.

  In some embodiments, the non-transitory computer-readable medium provides the lowest treatment cost in the CNA by ranking both clinical outcomes and treatment costs in one or more bundles of a given patient care service. The method further comprises identifying the best clinical outcome.

  In some embodiments, the visual categorization of the list of health care workers by clinical outcome is: (a) green indicates above-average clinical outcome, (b) yellow indicates average clinical outcome, and (c) ) Red indicates below-average clinical outcome.

  In some embodiments, the non-transitory computer readable medium is at the selected healthcare provider's office from the first clinical outcomes tracking and analysis module with permission from the patient and the selected healthcare provider. The method further comprises transmitting the patient's personal health information to a computing device having a fourth clinical outcome tracking and analysis module.

  In some embodiments, the behavioral variability data for each health care provider in each CNA in (c) contributes to the health care provider's disadvantage to the patient at each CNA at key time points during treatment. Yes, including both non-existent and unnecessary medical care.

  In some embodiments, the non-transitory computer readable medium further comprises the step of indicating that the service is for the purpose of diagnosing or treating a medical condition.

  In some embodiments, the cost report is a comparison of average costs per revenue between physicians, or a comparison of hospital contributions in dollars and percentages, or average revenue per patient at hospital, or per patient at hospital. Average cost, or average cost per case for each doctor, or weight per average, or average cost for imaging, laboratory tests, assessments and management, pharmaceuticals, medical supplies, and other for each doctor Costs, or a combination thereof.

  In some embodiments, the variable selected by the payer is a variable that identifies a particular attribute of the personal health information as the desired set of characteristics, added to the personal health information by the payer to obtain the personal health information for each patient. Includes one or more attributes, or both, that identify <RTIgt; as </ RTI> an equal level of importance with other health information in the patient population database.

  These and other aspects and embodiments will be apparent to those of ordinary skill in the art by reference to the following detailed description and the accompanying drawings.

  In the drawings, which are not to scale, like reference numerals indicate like elements throughout the drawings.

FIG. 6 is a block diagram of some examples of pressure in the cancer market and some potential solutions. FIG. 3 is a block diagram illustrating a server computer in communication with one or more computers over a network to provide a clinical outcome tracking and analysis (COTA) module to one or more user computers, according to embodiments of the disclosure. FIG. 6 is a block diagram illustrating some functionality provided by a COTA module, according to embodiments of the disclosure. FIG. 6 is a block diagram illustrating the use of a COTA module to sort data related to colon cancer patients, according to embodiments of the disclosure. FIG. 7 is a flow diagram of a COTA module that sorts data through specific node generation, according to embodiments of the disclosure. FIG. 6 is a block diagram illustrating a directed graph for determining a digit sequence representing phenotypic characteristics for nodal addressing, according to an embodiment of the present disclosure. 6 is a flowchart illustrating steps performed by a COTA module according to embodiments of the disclosure. 6 is a flow chart showing steps for enabling a patient diagnosed with a disease to optimize treatment options, according to embodiments of the disclosure. FIG. 6 is a flow diagram of a COTA module that sends an alert in response to a trigger, according to embodiments of the disclosure. 6 is a graphical representation of a mobile device summarizing received alerts, according to an embodiment of the disclosure. 6 is a graphical representation of disease prevalence by cancer subtype according to embodiments of the disclosure. 6 is a graphical representation of a search refined by variable input to a COTA module, according to embodiments of the disclosure. FIG. 6 illustrates a list display of multiple variables associated with a particular disease, according to embodiments of the disclosure. 6 is a graphical representation including a real-time Kaplan-Meier curve with confidence intervals for pancreatic cancer, according to embodiments of the disclosure. 6 is a graphical representation showing Kaplan-Meier curves for each progression of disease, according to embodiments of the disclosure. 6 is a real-time benchmarking graph representation of bilateral outcomes, according to embodiments of the disclosure. 6 is a graphical representation of a cost report according to an embodiment of the disclosure. 6 is a graphical representation of a treatment interface according to embodiments of the disclosure. 6 is a graphical representation of a treatment interface according to embodiments of the disclosure. 6 is a graphical representation of an outcome screen, according to an embodiment of the disclosure. 6 is a graphical representation of a treatment detail report screen according to embodiments of the disclosure. 6 is a graphical representation of an analysis screen comparing toxicity and cost according to embodiments of the disclosure. 6 is a graphical representation of an analysis screen comparing treatment and quality of life according to embodiments of the disclosure. FIG. 6 is a flow diagram of feedback support provided to a healthcare professional according to embodiments of the disclosure. FIG. 4 illustrates an embodiment of a graphical representation of different diagnostic types according to embodiments of the disclosure. FIG. 4 illustrates an embodiment of a graphical representation of different diagnostic types according to embodiments of the disclosure. FIG. 4 illustrates an embodiment of a graphical representation of different diagnostic types according to embodiments of the disclosure. 6 is a graphical representation showing COTA module data generation and sorting for breast oncology-Breast cancer with histology from invasive ductal carcinoma from 2008 to 2013 according to embodiments of the disclosure. 6 is a graphical representation showing data generation and sorting of COTA modules for Breast Oncology-Tumor Grade and Stage of Breast Cancer 2008-2013, according to embodiments of the disclosure. 6 is a graphical representation showing data generation and sorting of COTA modules for Breast Cancer-2008 to 2013 Stage IIB- according to embodiments of the disclosure. 6 is a graphical representation showing an overall survival outcome for breast cancer patients according to embodiments of the present disclosure. 6 is a graphical representation showing a breast cancer outcome-two-way comparison-according to embodiments of the present disclosure. FIG. 3 illustrates an example of a conceptual diagram of a client device according to an embodiment of the present disclosure. FIG. 16 is a block diagram illustrating an internal architecture of a computer according to an embodiment of the present disclosure. 3 is a flowchart illustrating a patient aspect of CNA guided care for a breast cancer patient according to an embodiment of the present disclosure. FIG. 6 is a block diagram illustrating the benefits of two aspects of CNA guided care. Provider-Payer aspects include elimination of prior authorization for examination, diagnosis, and treatment selection, elimination of prior authorization and need for recommendations to optimize per-treatment, payment-based reimbursement, per-treatment payments To a risk-based refund. Patient aspects of patients using CNA-guided care include three things: First, a complete assessment of their illness (eg, physical assessment, laboratory assessment, imaging) so that the patient can be assigned a CNA. Etc.) to provide information to the patient, secondly, indicating patient treatment options for medical conditions for optimizing treatment and reducing the risk of adverse differences, and third, CNA-guided care. Refer the patient to a physician within the patient's community who has the best outcome with acceptable treatment costs, according to.

  Embodiments will now be described in more detail with reference to the drawings that accompany the present application. In the accompanying drawings, similar and / or corresponding elements are designated with similar reference numbers.

  Although various embodiments are disclosed herein, the disclosed embodiments and user interfaces as shown are merely illustrative of the disclosure and may be embodied in various forms. Additionally, each of the examples provided in connection with various embodiments is illustrative and not intended to be limiting. Moreover, the drawings are not necessarily to scale and some features may be exaggerated to show details of particular elements (and all sizes, materials, and similar details shown in the figures may vary). It is illustrative and not intended to be limiting). Therefore, the specific structural and functional details disclosed herein are not to be construed as limitations, but are exemplary bases for teaching one skilled in the art to variously utilize the disclosed embodiments. Nothing more.

  The present invention is described below with reference to block diagrams and operational illustrations of methods and devices to selectively present media associated with a particular topic. It is to be understood that each block in the block diagrams or operational illustrations and combinations of blocks in the block diagrams or operational illustrations can be implemented using analog or digital hardware and computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, a special purpose computer, an ASIC, or other programmable data processing device so that they are executed via the processor of the computer or other programmable data processing device. The implemented instructions perform the functions / acts specified in the block diagrams or one or more operational blocks.

  In some alternative implementations, the functions / acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may actually be executed at substantially the same time, or they may be executed in some reverse order, depending on the functions / acts involved. It may be done. Moreover, the method embodiments presented and described in the flow chart of the present disclosure are provided as examples to provide a more complete understanding of the present technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as part of the larger operation are performed separately.

  Although the clinical outcome treatment analysis described here is related to the symptoms of cancer, any clinical manifestations (eg, cardiovascular, metabolic (diabetes), immune (eg, lupus, rheumatoid arthritis)) , Organ transplantation, neurodegenerative disorder, lung disease, infectious disease, liver disorder, behavioral health science). The practitioner will be aware of the parameters of each such symptom.

  Throughout this specification and claims, terms may have meanings that are presented or implied in context, beyond the explicit meanings. Similarly, the phrase "in one embodiment" as used herein does not necessarily refer to the same embodiment, and the phrase "in another embodiment" as used herein does not necessarily mean a different implementation. It does not refer to morphology. For example, the claimed subject matter is intended to include, in whole or in part, combinations of the exemplary embodiments.

  In general, the terms can be understood, at least in part, from their use in context. For example, the terms "and (and)", "or (or)" or "and / or (and / or)" as used herein are at least partly in the context in which such terms are used. Various meanings can be included depending on the. Generally, when "or" is used to associate a list such as A, B, or C, it is used in the inclusive sense of A, B, and C, and A, B, Or, it is used in an exclusive meaning with C. Additionally, the term “one or more” as used herein is used to describe any feature, structure, or characteristic in the singular sense, at least in part, depending on the context. Can be used, or can be used to describe a combination of features, structures, or characteristics in multiple senses. Similarly, words such as “a”, “an”, or “the” can be understood to convey a singular use or a plurality of uses, at least in part depending on the context. In addition, it can be understood that the term "based on" is not necessarily intended to convey an exclusive set of factors, and, again, in this case as well, at least partially in context. Accordingly, the presence of additional factors not necessarily explicitly stated can be allowed.

  The pharmaceutical industry has invested much of its R & D investment in specialty compounds with oncology as a major category. For example, approximately 30-35% of Phase 3 pipelines are oncology. These compounds are specialized targeted therapies based on the latest scientific advances and are likely to require different commercial and development models than exist today. The current structure of pharmaceutical companies is generally inefficient and is likely to be unsupported by future products.

  Diagnostic equipment companies developing new companion diagnostic tests for a new generation of treatment will need new methods to foster physicians and efficient marketing and distribution channels.

  The US refund model is likely to change from a pay-as-you-go model to a value-based pay model. With the Affordable Care Act, certain elements (for example, accountable medical organizations (ACO) and patient centered medical homes for primary care) are provided. ) (PCMHs model) is accelerating, and there is a payer move towards bundling payments within a specialty (eg, orthopedics). The current pay-per-care model is likely to be unsustainable for governments, employees, other payers, and / or physicians. Many physicians are also finding that the economy of pay-per-use models is not sustainable. As mentioned above, the government is likely to move to a value-based payment model.

  FIG. 1, for example, shows a block diagram of some examples of pressure in the cancer market and some potential solutions. Oncologists 105 face financial pressures, and many are unable to continue their work in the current model, trying to find leverage in new ways. A viable oncologist solution 110 includes bundle-like aggregation and novel payment models. Pharmaceutical companies (illustrated as “Pharma”) 115 typically have many or all of their cancer pipelines as targeted treatments. Additionally, the era of blockbuster drugs is ending. Moreover, an "outdated" business model is no longer suitable and too costly. Potential pharmaceutical solutions 120 include identifying patients and changing their business models. Health insurance 125 generally has an ever increasing need to "manage" oncology. Also, there are no internal trusted tools or functions to perform this management. Additionally, medical personnel, such as doctors, cannot support health insurance. The potential health insurance solution 130 includes a new payment model (eg, bundle) and cost control.

  2 communicates with a user computer (also referred to herein as a client device) 210 and a user computer (also referred to herein as a computing device) 230 over a network 215 to track clinical outcomes, according to one embodiment. And a block diagram of a server computer 205 (hereinafter also referred to as server 205) that provides an analysis (COTA) module 220 to user computer 210 and / or user computer 230. The server 205 can generate and / or provide a web page displayed by a browser (not shown) of the user computer 210 and / or the user computer 230 from a network 215 such as the Internet. In one embodiment, the COTA module 220 is a web page (or a portion of a web page) and is therefore accessed by a user of user computer 210 and / or user computer 230 via a web browser. In another embodiment, the COTA module 220 is a software application, such as a mobile “app”, downloadable from the server computer 205 to the user computer 210 and / or user computer 230. In a further embodiment, the COTA module 220 provides a user interface to enable the functionality described herein.

  Computing devices such as server computer 205, user computer 210, and user computer 230 are capable of sending and receiving signals over wired or wireless networks or the like, or process or store signals in memory such as physical memory states. Have the ability to A device capable of acting as a server is, for example, a dedicated rack mount server, desktop computer, laptop computer, set top box, an integrated combination of various functions such as two or more functions of the above-mentioned devices. Devices and the like can be included. Servers can vary greatly in configuration or function, but in general, a server can include one or more central processing units and memory. The server may also be one or more mass storage devices, one or more power supplies, one or more wired or wireless network interfaces, one or more input / output interfaces, or a Windows® server, Mac. (Registered trademark) One or more operating systems such as OS X (registered trademark), Unix (registered trademark), Linux (registered trademark), and FreeBSD (registered trademark) can also be included.

  The server 205 can include devices that include settings for providing content to other devices over the network. The server 205 can host sites, such as, for example, social networking sites, examples of which include, but are not limited to, Flickr®, Twitter®, Facebook®, It can include LinkdIn® or personal user sites (blogs, video blogs, etc.). The server 205 may also host a variety of other sites including, but not limited to, business sites, education sites, dictionary sites, encyclopedia sites, wikis, financial sites, government sites, etc.

  The server 205 includes, but is not limited to, web services, third party services, audio services, video services, email services, instant messaging (IM) services, SMS services, MMS services, FTP services, voice over IP (VOIP) services, Various services including a calendar service and a photo service can be further provided. Examples of content may include text, images, audio, video, etc., which are processed in the form of physical signals, eg electrical signals, or in memory, eg physical states. Can be stored. Examples of devices that can act as servers include desktop computers, multiprocessor systems, microprocessor-type or programmable consumer electronics, and the like.

  In one embodiment, server 205 hosts or is in communication with database 240. Database 240 can be stored locally from server 205 or remotely. In one embodiment, the COTA module 220 accesses or retrieves or sorts the data stored in the database 240. COTA module 220 can also retrieve information over network 215 (eg, from the Internet). The database 240 can store patient data or other patient medical information. For example, the data entered into the database or COTA module 220 should come from experts in their respective fields (eg, oncologists with 5, 10, 15, 20, 25, 30 years of experience). You can The data can be entered manually or automatically in the database 240 and / or the COTA module 220.

  The network may couple the devices so that they can communicate, such as between a server and client devices or other types of devices, including, for example, wireless devices coupled via a wireless network. .. The network may also include mass storage, such as, for example, network attached storage (NAS), storage area network (SAN), or other form of computer or machine readable medium. The network may include the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), wired type connections, wireless type connections, or any combination thereof. .. Similarly, sub-networks that may utilize different architectures or comply or be compatible with different protocols may interoperate within a larger network. For example, various types of devices may be made available to provide interoperability with different architectures or protocols. As one illustrative example, a router may provide a link between otherwise separate and independent LANs.

  Communication links or channels are, for example, analog telephone lines such as twisted pair lines, coaxial cables, full or fractional digital lines including T1, T2, T3 or T4 type lines, integrated services digital networks (ISDN), digital subscriber lines. (DSL), wireless links including satellite links, or other communication links or channels known to those skilled in the art. Further, a computing device or other related electronic device can be remotely coupled to the network, such as via a telephone line or link.

  A wireless network may couple client devices with the network. The wireless network may be a stand-alone ad hoc network, a mesh network, a wireless LAN (WLAN) network, a cellular network, or the like. The wireless network may further include systems such as terminals, gateways, routers, etc., coupled by wireless radio links, etc., which may move freely randomly or organize themselves so that the network topology is , Sometimes it changes quite rapidly. Wireless networks include multiple network access technologies including Long Term Evolution (LTE), WLAN, Wireless Router (WR) mesh, or second, third, or fourth generation (2G, 3G, or 4G) cellular technologies. Further utilization is possible. Network access technologies can enable wide area coverage for devices, such as client devices with varying mobilities, for example.

  For example, the network may be a global system for mobile communication (GSM (registered trademark)), universal mobile telecommunication system (UMTS), general packet radio service (GPRS), enhanced data GSM (registered trademark) Environment (EDGE). Via one or more network access technologies such as 3GGP Long Term Evolution (LTE), LTE Advanced, Wideband Code Division Multiple Access (WCDMA (R)), Bluetooth (R), 802.11b / g / n, etc. RF or wireless type communication can be enabled. A wireless network can virtually include any type of wireless communication mechanism capable of communicating signals between devices, such as client devices or computing devices, between networks, within networks, and so on.

  In one embodiment and as described herein, the user computer 210 and / or the user computer 230 is a smartphone. In another embodiment, user computer 210 and / or user computer 230 is a tablet. User computer 210 and / or user computer 230 can also be a computer, music player, set-top box, smart TV, or any other computing device capable of transmitting information.

  The COTA module 220 can establish an effective way to manage a patient, leading to better outcomes at a controlled cost. In one embodiment, the COTA module 220 is a connector or interface between a third party and a healthcare professional (eg, oncologist). In one embodiment, the COTA module 220 is an analytical tool that sorts cancers to the highest levels of clinical and molecular fidelity. The COTA module 220 then tracks outcomes such as overall survival (OS), progression-free survival (PFS), and cost in real time.

  The overall survival time can be the study endpoint, which is usually expressed in terms of time period (lifetime), eg, months. Often intermediate values are used so that the study endpoint can be calculated once 50% of the subjects reach the endpoint. An example is disease-free survival, which is usually used to analyze the results of treatments for local diseases, such as surgery or surgery and postoperative adjuvant therapy, that make patients apparently disease-free. It In disease-free survival, the event is recurrence rather than death. A person who relapses, although still alive, is no longer considered disease free.

  Progression free survival is the period during and after medication or treatment while the disease (eg, cancer) being treated is not aggravated. It may be used as a metric to study the health of people suffering from the disease in order to determine how well the new therapies work.

  As used herein, the term "real-time" or "real time" means that there is no perceptible delay or information that is delivered immediately after collection or processing. These terms also include delays (eg, near real time) introduced by automated processing.

  In one embodiment, the COTA module 220 can alert the user of the user computer 210 and / or the user computer 230 (eg, a healthcare professional) to provide relevant information at key moments. The COTA module 220 may also enable communication and collaboration between healthcare personnel and content publishing (eg, by healthcare personnel). In one embodiment, the COTA module 220 allows a healthcare professional to perform risky contracts with payers (eg, bundled payments).

  Although the COTA module 220 is described in the context of cancer, the COTA module 220 can be advantageously used to manage any disease or medical condition.

  In one embodiment, the COTA descriptive elements include sorting, outcome tracking, performance status / quality of life metrics, treatment toxicity, and treatment cost.

  FIG. 3 is a block diagram illustrating the functionality 300 provided by the COTA module 220 according to one embodiment.

  In one embodiment, COTA module 220 performs COTA sorting 310 to identify patients who meet one or more parameters. Parameters include, for example, demographic parameters such as gender, age, ethnicity, co-morbidity, smoking, medical record number, health source, family doctor, referring doctor, hospital, licensed service vendor (eg, pharmacy), disease. Specific clinical molecular phenotype, therapeutic purpose, stage of treatment for disease progression, and biomarkers can be included. The parameters can be simple indicators (eg positive, negative, no access), numerical based parameters (eg tumor size), standard based parameters (eg tumor grade), etc. The parameters can be received by the COTA module 220 as user-selected inputs. Since each patient has different mortality, morbidity, treatment, and cost, 310 can be sorted to the highest levels of clinical and molecular fidelity at diagnosis. The term "highest level of clinical and molecular fidelity" refers to the highest level of patient information available in accordance with current scientific and / or medical guidelines as recognized in the relevant fields. For example, if there are 10 tests available for lung cancer, the results of the 10 tests represent the highest level of lung cancer fidelity. The COTA module 220 can sort lung cancer patients by any combination of these 10 results. The COTA module 220 may include additional scientific and / or medical guidelines as they become recognized in the relevant areas. In one embodiment, the COTA module 220 collects all information that impacts patient survival and / or prognosis and / or treatment based on current scientific and / or medical guidelines.

  In addition, the COTA module 220 performs 320 outcome tracking and analysis. The COTA module 220 tracks outcomes in real time. In one embodiment, the Outcome Tracking element provides progression-free survival, overall survival, performance status / quality of life metrics, incidence / severity of toxicity (eg, a drug or drug causes damage to an individual). %), Mortality, and drug usage (eg, delivered dose intensity, dosing interval, and duration of treatment). Other types of outcomes are also envisioned.

  The ECOG performance status / quality of life metric element refers to a method by which long-term patient quality of life can be tracked. It is part of the disease-specific clinical molecular phenotype of the demographic parameters, ie the stage of the patient's health at the start of treatment and included within the sorting. For example, comparison of ECOG at the beginning of treatment (eg, ECOG3) with post-treatment ECOG (eg, ECOG2) reflects the effect of treatment.

  In one embodiment, exemplary parameters for the Toxity to Therapy component are incidence and severity. In one embodiment, the COTA module 220 enables risky financial contracts between payers and suppliers, thus reducing diversity, waste, and inefficiencies, but nevertheless. Achieve the desired outcome.

  The COTA module 220 also communicates alerts 330, etc. at key points, such as during diagnosis, progression, during dose / drug changes / toxicity, and / or when prone to variability from the desired outcome. It may also be sent in real time to a healthcare professional (eg, a doctor) (or, in another embodiment, the patient's insurance company or any other operator). In one embodiment, the COTA module 220 provides alerts to the healthcare practitioner that identify the particular patient that the practitioner is searching for. For example, the COTA module 220 can provide real-time alerts to pharmaceutical companies looking for a particular patient to administer a particular (eg, new) drug or drug candidate. The alert can identify a particular patient that is an appropriate candidate for a particular drug.

  FIG. 4A is a block diagram illustrating the steps of sorting data associated with colon cancer patients, according to one embodiment. Although described in the context of cancer, eg, colon cancer, the description and figures are applicable to any type of cancer, or in another embodiment, any type of disease for which there is patient-related data. Can be applied to.

  Data 410 is collected for all cancers (or, in another embodiment, multiple types of cancers, or, in another embodiment, heart, lung, gastrointestinal, nervous system, etc.) data 410. Are narrowed to a subset 420 associated with colon cancer, for example. In one embodiment, the subset 420 of data associated with colon cancer is then analyzed and sorted by the COTA module 220 to produce a sorted colon cancer dataset 430. The sorted colon cancer dataset 430 can include one or more groups, each group including data related to patients with a particular colon cancer of the same type. Therefore, the COTA module 220 can sort cancers to the highest level of fidelity.

  Patient information is typically stored in electronic medical records (EMRs). However, the EMR may contain too much information, and thus it is difficult for the healthcare professional to find the particular information of interest from the vast amount of information stored in the EMR. Moreover, most of the information in the EMR is not relevant to the information the healthcare professional is searching for. Unlike EMRs, which are intended to capture all or most of the patient-related data entering and leaving the clinic, the COTA module 220 allows the user to search for specific data related to a particular patient, Modules are targeted. Thus, the COTA module 220 can sort the data to retrieve certain special information. The data that the COTA module 220 receives is typically via a web page and is individual (eg, typically the user has one or more choices in a drop-down menu or via one or more checkboxes). Provided by choosing).

  The COTA module categorizes, sorts, and smooths groups of patient types based on the results of these variables in the specification of unique COTA nodal addresses (CNA) and implements these categorical variables. In one embodiment, data is captured into the system via a human user or technical process, for example, an API, a layer within an application (meaning a part of an application that performs a particular function) For example, if the information is correct, if the information is corrupted, what information is present, what information is missing / holes in the information, how is the information It is formatted, spelled, evaluated, and the application detects any problems with the information it has detected so far and assigns a CNA to the information set. In one embodiment, a CNA is an address for classifying similar data. In one embodiment, the COTA module identifies relationships between different characteristics within a group, allowing the COTA to classify information about every patient within the group. In one embodiment, the information sets contained within the database are pre-assigned with CNAs. In one embodiment, the COTA module captures a large amount of information encompassing many different attributes, allowing the user to identify some of the attributes as a set of characteristics, which information is then transferred to other information in the database. An attribute is added to the information to indicate that it is similar, ie this information is the same type / value as another information. Thus, a nodal address is a numerical value that allows a user to specifically compare similar patients to similar patients. This can minimize the biodiversity of the outcome, resulting in greater accuracy as to the effect of the therapeutic agent on outcome.

  In one embodiment, the user wants to validate the personal health information (PHI) from the patient to ensure that the information is correct in all respects, and then assign the appropriate CNA. Personal Health Information (PHI), as used herein, is created, used, or disclosed in the process of providing a medical service such as a diagnosis or treatment that can be used to identify an individual's patient. , Refers to any information within a medical record or specified set of records. Examples of personal identifiers within a PHI include, but are not limited to, names, street addresses, cities, counties, provinces, geographic areas smaller than states, including postal codes, directly related to an individual, date of birth, date of admission, discharge from a hospital. All elements of the date (excluding years) including days, death dates, and all ages above 89, and all elements of dates indicating such ages (including years), telephone numbers, fax numbers, emails Vehicle identifier and serial number including address, social security number, medical record number, health insurance recipient number, account number, certificate number / license number, license plate number, device identifier and serial number, Universal Resource Locators (URL) on the web , Internet Protocol (IP) address numbers, biometric identifiers including fingerprints and voiceprints, front images and any equivalent images, and any other unique identification number, characteristic, or code (but encoding data). It is not a unique code assigned by the researcher). This PHI is entered into the browser for patient A. The PHI is sent to the classification layer to be assigned a CNA, which defines the attributes of patient A's record and is then put into the database, ie, the set of patient attributes classified into this CNA type is CNA. Be tied to. Once this is complete, the next time the user logs into the application and accesses the database, the database will return all of Patient A's information and the assigned CNA. Therefore, the user immediately understands how to treat this patient's symptoms / attributes, that is, the user has an overview of how this type of patient relates to other patients whose information is contained in the database. obtain.

  FIG. 4B is a flow diagram of COTA for classifying and sorting data as described above through specific node generation, according to one embodiment. As shown in FIG. 4B, the expert may be gender or gender 440 (variable A), race 445 (variable B) ,. ． ． And a variable called KRAS450 (variable G). K-Ras is a protein encoded by the KRAS gene in the human body. Although the protein product of the normal KRAS gene plays an essential function in normal tissue signaling, mutations in the KRAS gene are an important step in the development of many cancers.

  The COTA module 220 analyzes the sorted and sorted data 430 with respect to these variables (eg, variables 440, 445, 450) and produces a unique COTA node 455. The COTA module 220 applies these nodes to the sorted and sorted data to produce clinically more relevant results. Nodes are created as a preselected set of variables and these nodes are applied to further filter the sorted and sorted data. Nodes are represented as nodal addresses that indicate preselected variables. Variables can include, for example, diagnostics, demographics, outcomes, phenotypes, and the like. A phenotype is a compound of human observable properties or characteristics, such as human morphology, development, biochemical or physiological properties, phenology, behavior, and product of behavior. The phenotype results from the expression of human genes, as well as the effects of environmental factors and the interaction between the two. In one embodiment, the variables of the nodes are selected by experts in the relevant field to split the data into clinically relevant results.

  COTA node 455 is represented as a nodal address within COTA module 220. In one embodiment, the nodal address is represented as a list of selected variables (as a function of the letters that represent the variables and the numbers that represent the choices within the variables). For example, as shown in FIG. 4B, the node 455 is A1-2 (A represents a gender or a gender variable, and 1-2 is represented by a block that surrounds both the female variable and the male variable of the gender variable A. Represents female and male patients). Node 455 also contains B1-4 as it contains a race variable with all of the selected sub-variables (indicated by the boxes surrounding all of the race variables). Node 455 also includes G1 in relation to the KRAS variable, and only Mut + is selected (enclosed). Therefore, the node 455 has the node addresses of A1-2, B1-4, and G1.

  In another embodiment, the node address is represented as a series of digits separated by periods, each digit string having one or more variables (eg, disease, phenotype, treatment type, progression / tracking, gender). Etc.). For example, the first string of digits can represent a particular disease, the second string of digits can represent a type of disease, the third string of digits can represent a subtype of a disease, and a fourth string of digits. The string of numbers can represent a phenotype. Thus, in this example, the first number sequence may be 01 indicating cancer, the second number sequence may be 02 indicating breast oncology, and the third number sequence may indicate breast cancer. 01, and the fourth digit string can be 1201 representing certain characteristics of the phenotype, so that the nodal address is 01.02.01.1201. It should be understood that the nodal address can include any number of digit sequences and is not limited to four digit sequences.

  In one embodiment, the string of numbers representing the phenotype can be provided by representing the characteristics of the phenotype as a directed graph. FIG. 4C depicts a directed graph 460 showing phenotypic characteristics to provide a string of numbers representing the phenotype, according to one embodiment. The directed graph 460 includes nodes that represent phenotypes and edges that represent relationships between the nodes. The graph starts at the root "start" node and is traced to the node of the selected phenotype. A number is associated with each edge. A string of numbers representing the phenotype for the node address is provided as a combination of numbers. For example, the digit string for the selected phenotype representing white male would be represented as 11. Other types of combinations can also be utilized. Advantageously, expressing the phenotypic characteristics as a directed graph allows the addition of other nodes without changing the overall structure. The appearance of the screen is the result of CNA and the appearance can be changed, but it is desirable to present the information.

  Node 455 provides COTA module 220 with the ability to match resources and alerts that are unique to each associated phenotype. Resources can be information, content, links to live support, etc. Each patient is classified into one or more nodal addresses. One or more nodes can also be associated with each disease. In one embodiment, the resource is "tagged" with the appropriate associated node. In one embodiment, nodes can be replaced over time to keep up with scientific / medical advances.

  Each nodal address can be associated with one or more bundles of a given patient care service (eg, treatment plan). Each bundle can also be associated with one or more nodes. The services included in each bundle can be determined by one or more healthcare workers, hospitals, groups, insurance companies, etc. to optimize patient care and / or cost. In one example, the bundle can show a number of image scans, medications or medication selections, schedules for when medications should be administered, surgery or procedures, number and frequency of follow-up visits, and so on. Bundling of patient care services can be particularly useful for risk contracts. For example, each bundle corresponding to a nodal address (associated with a particular disease) has a predetermined cost that allows the user (eg, doctor, patient, etc.) to select the appropriate bundle. Costs can be determined or negotiated based on historical data associated with this particular disease or nodal address. Advantageously, bundling of services provides insurance companies and / or hospitals with cost certainty for a particular disease. It also reduces the cost of processing and maintaining records. Additionally, the health care professional will know in advance a predetermined course of treatment, which will motivate physicians to obtain better outcomes at a lower cost.

  International Statistical Classification of Diseases and Related health Problems 10th Revision (ICD-10) is a classification of diseases, signs, symptoms, symptoms, and symptoms as classified by the World Health Organization (WHO). , Discomfort, social conditions, and external factors of injury or illness. These code sets are considered classification code sets and have a higher level of information than some other medical code set, such as the Systemized Nomenclature of Medicine (SNOMED) used by the federal system for electronic exchange of clinical health information. Is. In one advantageous embodiment, the nodal address is used to identify the course of treatment instead of using the ICD-10 (International Classification of Diseases, 10th Edition) code to identify and treat the disease. For example, this allows a more effective course of treatment than the conventional ICD-10 code, especially when it comes to treatment plans where the patient is misdiagnosed.

  FIG. 5A is a flowchart illustrating steps performed by the COTA module 220, according to one embodiment. In step 505, the COTA module 220 collects data records. Each data record contains data associated with a disease (eg, cancer). The data record can include patient data for patients who have or previously had the disease. For example, the data record can include diagnostics, demographics, outcomes, costs, or other relevant information. Data records can be collected from an electronic database (eg, electronic medical record) provided by a user (eg, medical practitioner, professional, specialist, etc.) or from any other source. In one embodiment, the COTA module 220 can store the data record in the database 240.

  In step 510, COTA module 220 receives one or more parameters for sorting the data records. One or more parameters may be received from user computer 210 as a user-selected input. The one or more parameters can include, for example, diagnostics, demographics, outcomes, costs, or any other parameter.

  In step 515, the COTA module 220 sorts the data records based on one or more parameters. Sorting identifies patients who meet one or more parameters. Patients are sorted to the highest level of clinical and / or molecular fidelity based on current scientific and / or medical guidelines recognized in the relevant fields. In one embodiment, sorting is performed in real time.

  At step 520, the sorted and sorted data records are filtered according to the nodal address. The nodal address represents a variable preselected by the user to provide a clinically relevant set of patients. In one embodiment, the nodal address variable is selected by an expert in the field. Nodal addresses can be represented as a series of numbers, each separated by a period. Each digit string can represent one or more variables (eg, disease, disease type, disease subtype, phenotype, or any other relevant variable). Other representations of nodal addresses are also envisioned.

  At step 525, clinically relevant patient data records are analyzed. Analysis of the data record can include tracking (eg, in real time) the clinical outcome of the patient associated with the disease. Outcomes include, for example, delivered dose intensity, therapeutic agent received, dose, dosing interval and duration, incidence and severity of toxicity, cost, progression free survival (PFS), overall survival (OS), performance. Efficiency etc. can be included. The COTA module 220 can compare tracked outcomes between patients. The COTA module 220 may also, based on the tracking, determine whether the particular physician associated with the tracked patient is treating the patient according to the treatment of the other physician treating other (similar) patients. You can also In one embodiment, the COTA module 220 determines this based on the outcome of many patients.

  In another embodiment, analyzing the data record may include updating (eg, in real time) at least a portion of the data record based on the tracked outcome. For example, the COTA module 220 can determine that patient ABC has colon cancer, has been prescribed drug XYZ, has been on the drug for 2 years, and is currently in remission for the last 3 years. If the COTA module 220 determines this information from tracking the patient ABC, the module 220 can update the data record associated with the patient ABC with this information.

  In another embodiment, analysis of the data record comprises performing the analysis to determine patient survival, such as by creating a Kaplan-Meier curve. The Kaplan-Meier curve is, for example, a curve indicating a 5-year survival rate that can occur with respect to one doctor (or medical staff) or a group of doctors (or medical staff). Kaplan-Meier curves can be generated for overall survival and / or progression-free survival. Other types of analysis are also envisioned.

  To facilitate analysis, COTA module 220 may also include analysis tools for user computer 210 and / or user computer 230. The analysis tool can be a user interface accessible via a web page, a tab on an existing web page, a software application, an app, etc. The user interface as shown in the figures herein is exemplary. This analysis tool allows the user to compare, analyze, or further sort the data records.

  At stage 530, the COTA module 220 provides communication based on the analysis. The communication can be in the form of an alert to the user. In one embodiment, the COTA module 220 communicates the sorted and sorted data records and / or the updated data records to the user computer 210 and / or user computer 230. For example, the COTA module 220 communicates tables, charts, lists, links, etc. that allow a user to access sorted or updated data records. In another embodiment, the COTA module 220 can send a notification (eg, associated therewith) to the user computer 210 and / or user computer 230 along with the data record. In another embodiment, the COTA module 220 can identify a particular patient as a candidate for a particular treatment or drug. This information can be useful, for example, for pharmaceutical companies, health insurance, managed medical associations, insurance companies and the like. COTA module 220 may send communications (eg, via network 215) to user computer 210, user computer 230, or any other entity.

  The COTA module 220 can be used by and benefit many people, workers, and / or businesses. For example and as mentioned above, a highly specialized pipeline of a pharmaceutical company has many aspects (eg, development / postmarketing research, including phase 4 trials, marketing, sales, pricing, and contracts). A new business model seems necessary. In one embodiment, pharmaceutical company professionals can use the COTA module 220 to facilitate this new business model. For example, the COTA module 220 can match the right patient to the right medication. The COTA module 220 can enable accurate patient identification via its own sorting and nodal addressing capabilities. In one embodiment, the COTA module 220 allows one or more patients to be users (eg, pharmaceuticals) who are or will be good candidates for a particular drug that a pharmaceutical company has released or is developing. Provides a matching function that allows a company to search (in real time).

  Further, the COTA module 220 can benefit health insurance. As mentioned above, the treatment of cancer can be more complex and it is not efficient for health insurance to continue direct management. In one embodiment, health insurance outsources cancer treatment to the COTA module 220 (similar to what health insurance used to do with drug benefits). This provides cost savings, such as by reducing total treatment costs, and offsetting costs by replacing pathways, reducing expensive pre-licensing infrastructure, and reducing the number of other "people who manage cancer". Additionally, the US Affordable Care Act clause states that 85% of insurance premiums must be devoted to clinical practice-related activities, as opposed to administrative costs. There is. In one embodiment, the COTA module 220 provides an analytical interface with a connection to insurance claim data to support health insurance in the management of an oncologist.

  In one embodiment, COTA module 220 can benefit an organization engaged in a diagnostic method or tool. Organizations engaged in diagnostics or tools, such as those involved in next-generation gene sequencing, will likely require efficient education, marketing, and distribution / distribution channels. The use of COTA module 220 can benefit such organizations because COTA module 220 can accurately sort and identify patients and send time-based alerts to physicians (or other healthcare professionals). be able to.

  FIG. 5B is a flow chart showing steps for enabling a patient diagnosed with a disease to optimize their treatment options, according to an embodiment. The steps of FIG. 5B can be performed by the COTA module 220 running on the server computer 205. The COTA module 220 running on the server computer 205 may interact with a user (eg, a patient) via the COTA module 220 running on the user computer 210 and / or on the user computer 230. It is possible to interact with a user (eg, a healthcare professional) through the network 215 via the running COTA module 220. COTA module 220 may include multiple instances of COTA module 220 for execution on server computer 205, user computer 210, and user computer 230.

  At stage 550, the COTA module 220 running on the server computer 205 receives a communication from the patient user computer 210. For example, the communication may include a treatment option, an indication that the patient is looking for a healthcare professional, or any other indication. The COTA module 220 running on the server computer 205 is communicatively linked to a database 240 containing personal health information for the patient population, which may or may not include this patient's health information. The COTA module 220 running on the server computer 205 is configured to analyze personal health information stored in the database 240. This can be done as a pre-processing stage before receiving the communication from the patient. For example, the COTA module 220 running on the server computer 205 sorts and classifies personal health information, assigns multiple nodal addresses to each patient in the patient population, and measures the clinical outcome of each nodal address. However, by measuring the behavioral changes of each health care provider assigned to each nodal address, personal health information can be analyzed as described herein.

  At step 555, patient health information is received. Health information can be received from the COTA module 220 running on the user computer 210. The patient's health information can include the disease for which the patient was diagnosed. In one embodiment, the illness is selected by the patient from the list and sub-list of illnesses available in the database 240 of the computer server 205 via the COTA module 220 running on the user computer 210, with specificity. .. The COTA module 220 running on the server computer 205 determines a test list for further categorization of the illness based on the received patient health information.

  In step 560, the COTA module 220 running on the server computer 205 sends the list of tests needed to further classify the illness to the COTA module 220 running on the user computer 210.

  At step 565, the results of the test are received from the COTA module 220 running on the user computer 210. The results of the test can be sent to the health care professional from the patient after completion of the initial treatment and with permission from the patient to provide the result of the test to the COTA module 220 running on the server computer 205.

  At step 570, a nodal address is assigned to the patient based on the results of the examination. The assigned nodal addresses can be assigned from a plurality of nodal addresses generated for the patient population during the pre-processing phase by the COTA module 220 running on the server computer 205. The assigned nodal address contains a set of preselected variables that are effective in dividing the sorted and categorized personal health information into clinically relevant sets of health information. In one embodiment, the assigned nodal address is associated with one or more bundles of predetermined patient care services (eg, treatment plan) for treatment of the condition. The predetermined patient care service can be determined by one or more of a healthcare professional, hospital, medical group, or insurance company. The predefined patient care service bundle may provide a predefined course of treatment. Pre-defined patient care service bundles can provide cost certainty for illness.

  In step 575, the COTA module 220, running on the server computer 205, assigns the assigned nodal address and a geographically summarized list of medical personnel who treat patients within the assigned nodal address. Send to the COTA module 220 running on the user computer 210. The geographically summarized list of health care workers can be visually categorized by clinical outcome. In one embodiment, the geographically organized list of health care workers can be visually categorized by clinical outcome by color. For example, a geographically summarized list of health care workers may show that green indicates better than average clinical outcome, yellow indicates average clinical outcome, and red indicates poorer than average clinical outcome. , Can be visually classified.

  At step 580, a selection of candidate health care workers is received. The candidate health care workers selected may be selected by the patient to meet one or more of geographical restrictions, clinical outcome criteria, and cost criteria set by the patient. In one embodiment, at the patient's discretion, after receiving the selection of candidate health care workers, each health care worker is further classified within the group of candidate health care workers based on treatment costs. For example, each health care worker may be further classified as being above, below, or substantially equivalent to the selected health care worker candidate.

  In one embodiment, the patient user computer 210 is communicatively linked to a healthcare professional user computer 230 (eg, in the office of the selected healthcare provider candidate) to select the selected healthcare provider candidate. To facilitate the schedule setting for medical appointments with. In another embodiment, the COTA module 220 running on the server computer 205 sends the patient's personal health information to the COTA module 220 running on the healthcare professional user computer 230.

  Advantageously, the steps of FIG. 5B allow a patient diagnosed with a disease to optimize treatment options based on geographic restrictions, clinical outcomes, and patient cost criteria.

  FIG. 6 is a flow diagram 600 of alerts provided by the COTA module 220, according to an embodiment. In one embodiment, a physician or other health care professional is alerted based on their preferences. These preferences can be set by a healthcare professional / physician and can include, for example, a trigger 610 for an alert, and / or the technique used to provide the alert. Triggers for alerts include, for example, new patient diagnosis 615, updates to diagnosis, real-time scheduled events, group member changes (eg, new genes identified as potentially changing grouping, and / Or someone leaves the group), toxicity and / or dose intensity changes 620, disease progression 625, administration of a particular drug, trend 630 of variability from desired outcome, and / or expected time or cycle. Dependent alerts 635 (eg, side effect alerts and / or diagnostic test reminders) may be included. The alert may include a text message 640 or email 645 sent to the user computer 210. Sent using other types of alerts, such as phone calls to user computer 210, updates on web pages, social media updates, Twitter®, Facebook®, or other social media sites. Messages, software libraries or the addition of content to web pages, and / or any other message or communication sent to or accessed by the user computer 210 may be envisioned. Although the trigger is described above as providing an alert, it can be any action that causes the COTA module 220 to perform any other action.

  FIG. 7 is a graphical representation showing a mobile device 705 (eg, user computer 210) summarizing alerts received by device 705, according to one embodiment. As shown in FIG. 7, the received COTA alerts are New Colon CA 710, New Renal Cell CA 715, Dose Adjustment 720, Drug Discontinuation. 725, New Progression 730, New Breast CA 735, CHOP 3rd cycle alert 740, Neutropenia risk alert 745, and available clinical trials 750, such as title or theme. Listed for each. CHOP is an abbreviation for a combination of drugs used in chemotherapy including cyclophosphamide (Cytoxan / Neosar), doxorubicin (or adriamycin), vincristine (oncobin), and prednisolone, for example, treating non-Hodgkin's lymphoma. It is used to

  The COTA module 220 may provide a particular disease data set (eg, on-demand, in real time), including, for example, disease prevalence (eg, by COTA sorting), progression-free survival, and / or overall survival. ) Can be provided. In one embodiment, the COTA module 220 may provide a drug utilization data set, such as data related to total or partial treatment, toxicity, and / or modification of treatment.

  FIG. 8 illustrates a graphical representation 800 of disease prevalence by cancer subtype that can be provided by the COTA module 220, according to one embodiment. Here, the COTA graph 800 relates to lymphoma from 2010 to 2013. The user can utilize the graph search input section 810 to narrow down the information to be graphed. Graphical search input section 810 may include, for example, report subject selection (eg, minimal diagnosis, complete diagnosis, and / or audited patient), diagnosis type, cancer site / subtype, ICD9 (International Disease Classification, 9th Section). Version) code, comorbidity, disease progression, sex, age, date range, race, diabetes, smoking history, past chemotherapy and radiation therapy history, etc.

  FIG. 9 is a graphical representation 900 of a sort that can be provided by the COTA module 220 based on variable inputs to the COTA module 220, according to one embodiment. Graphical representation 900 shows a COTA graph for Hodgkin lymphoma 2010-2013, segmented by gender. Graphical representation 900 shows statistics 910 of various patients suffering from this disease graphed in representation 900. FIG. 10 illustrates an exemplary listing of a plurality of variables 1005 related to a particular disease (the variables shown here being for lymphoma), according to one embodiment.

  FIG. 11 is a graphical representation 1100 that includes real-time Kaplan-Meier curves with confidence intervals for pancreatic cancer that can be provided by the COTA module 220, according to one embodiment. As described above, the Kaplan-Meier curve is a curve showing the 5-year survival rate that can be developed, for example, with respect to one doctor (or medical staff) or a group of doctors (or medical staff). Kaplan-Meier curves can be generated for overall survival and / or progression-free survival. The user indicates his graph search variables in the graph search input section 1110.

  FIG. 12 is a graphical representation 1200 showing Kaplan-Meier curves for each disease progression that can be provided by the COTA module 220, according to one embodiment. Line 1205 relates to all pancreatic cancers and bold line 1210 relates to pancreatic cancer with initial progression.

  FIG. 13 is a graphical representation 1300 of real-time benchmarking of bilateral outcomes that can be provided by the COTA module 220, according to one embodiment. The graph 1300 includes a curve 1305 regarding the outcome of Dr. John Doe treating pancreatic cancer and a curve 1310 regarding the outcome of the remaining doctors treating pancreatic cancer. FIG. 13 also includes a meter 1320 that measures whether Dr. John Doe's outcome goes positive or negative.

  FIG. 14 is a graphical representation of a cost report 1400 associated with (eg, provided by) the COTA module 220, according to one embodiment. The appearance of the screen is the result of CNA and its appearance can be changed, however, it is desirable to present information. The cost report 1400 can be associated with the cost tab 1220 of FIG. The cost report 1400 can be used, for example, in estimating treatment costs, acquiring knowledge, and / or converting knowledge into a particular implementation. In one embodiment, the COTA module 220 tracks the costs of various treatments, doctors, hospitals, etc. in real time. As shown in FIG. 14, the cost report 1400 shows a comparison of average costs per revenue between physicians. The cost report 1400 also includes hospital dollar and percent contribution margins, average hospital revenues and costs (eg, average revenue per patient, average cost per patient), average costs per doctor's case (eg, for each physician). Average cost per case, weight peer average), average cost per physician's income (eg imaging, laboratory, assessment and management, pharmaceuticals, medical supplies, and other costs for each physician) Other comparisons such as

  15A and 15B are graphical representations of a treatment interface 1500 associated with (eg, provided by) a COTA module 220 for facilitating a link between outcome and treatment, according to one embodiment. .. As shown in FIG. 15A, the treatment interface 1500 provides a list of different types of treatments administered to (or declined by) patients with breast cancer, such as surgery, antineoplastic agents, cell therapy, radiation therapy. Can be included. Treatment can be arranged according to the exacerbation of the disease. For example, drugs in oncology are usually given in cycles, and any number of drugs can be given in any one cycle. In one embodiment, the user can select an exacerbation (eg, represented as exacerbation 0 to exacerbation 4) where exacerbation 0 is after initial diagnosis, and selects drugs within or from multiple categories. can do.

  In FIG. 15B, in another embodiment, the treatment interface 1510 can include treatment plans for one or more treatments, illustrated as tabs 1515 on the treatment interface 151. The treatment interface 1510 can include fields to show start and end data for a plan, dose intensity, treatment description, particular drug brand, and the like. The treatment plan may be graphically represented as a treatment list in table 1520. The table 1520 can include action icons 1505 for each treatment. The action icon 1505 can facilitate actions such as, for example, editing, closing, displaying elements, and so on. In one embodiment, the action icon 1505 can be a shortcut for performing a complex task (eg, requiring multiple clicks or selections) with a single selection. For example, the icons in the diagnostics column can take the user to the diagnostics screen.

  FIG. 16 is a graphical representation of an outcome screen 1600 for facilitating outcome tracking, according to one embodiment. The outcome screen 1600 can facilitate outcome tracking from, for example, diagnosis (ie, zero exacerbations), initial exacerbations, second to fourth exacerbations, where each exacerbation is considered a different illness. The tabs on the outcome screen include the date of diagnosis (eg, in one or more drop-down menus or other fields), the start and end dates of the treatment, the response to the treatment (eg, complete, incomplete, stable), and response. Fields may be included for days, notes for reactions (eg, incomplete field, CR-RA-Pet Negative field, CR field, etc.), and end-of-track data that may include final contact and death. The outcome screen 1600 may also input toxicity of the drug treatment, occurrence (eg, discontinuation, continuation, no change, drug dose change, and number), number of delays, number of drug changes, and / or number of reductions. An input area for doing so can also be included. In one embodiment, the user of the COTA module 220 can flag the patient.

  FIG. 17 is a graphical representation of a treatment detail report screen 1700 showing a comparison between costs and outcomes, according to one embodiment. The treatment detail report screen 1700 correlates treatment costs with clinical outcomes to optimize treatment value. Cost and financial data can be collected and analyzed by hospitals, by physicians, over a given time period (eg, 5 years). The cost and financial data can be represented in one or more cost ranges. In one embodiment, the cost range includes a cost range 1705 that is greater than $ 25,000, a cost range 1710 that is $ 10,000 to $ 25,000, and a cost range 1715 that is less than $ 10,000. Combined with clinical data, the COTA module 220 can provide cost data for different treatments for a given time period based on different clinical sorts.

  FIG. 18 is a graphical representation of an analysis screen 1800 provided by COTA module 220 showing a comparison between toxicity and cost, according to one embodiment. The appearance of the screen is the result of CNA and the appearance can be changed, but it is desirable to present the information. The analysis screen 1800 correlates the incidence and severity of toxicity with treatment costs and treatment outcomes. Toxicity can be expressed numerically (eg, in range), by criteria (eg, grade), etc. For example, as shown in Figure 18, toxicity is represented as toxicity grades 1-4 based on the Common Terminology Criteria for Adverse Events (CTCAE) classification. Toxicity grades are compared graphically with costs. The analysis screen 1800 can be used to optimize the value and effectiveness of treatment, where value is effectiveness / cost. In one embodiment, the COTA module 220 attempts to obtain high effectiveness and low cost.

  FIG. 19 is a graphical representation of an analysis screen 1900 provided by COTA module 220 showing a comparison between treatment and quality of life according to one embodiment. The treatment can be displayed for each therapeutic agent on the analysis screen 1900. However, other forms of treatment are also envisaged, eg surgery, procedures. In one embodiment, treatment includes treatment incidence, severity, and toxicity. Quality of life can be measured based on the average ECOG (US East Coast Cancer Clinical Trials Group) scale, grade 0 (ie, healthy) to grade 5 (ie, death). Quality of life can also be measured using any suitable metric. The analysis screen 1900 can facilitate an assessment of how advanced a patient's disease is, how the disease affects a patient's daily living ability, and appropriate treatment and prognosis.

  FIG. 20 is a graphical representation 2000 of alerts provided to a healthcare professional, according to one embodiment. The appearance of the screen is the result of CNA and the appearance can be changed, but it is desirable to present the information. In one embodiment, the information in the alert helps the user make decisions in the future. In one embodiment, the information in the alert provides a set of attributes that have occurred in the past time period. In one embodiment, this proactively influences the user's decisions and reactively provides a digest report of how the healthcare practitioner / doctor did in the last week, last month, and previous quarter. In one embodiment, there are different alerts for different users, each of which may affect the decisions the user makes. Alerts can be used for real-time trajectory modification to drive maximum value, for example, when the administered regimen tends to fluctuate from the desired outcome. At block 2005, a definition is triggered based on the clinical data. Definitions can be triggered using any criteria such as, for example, new disease diagnosis, disease progression, patient response, patient characteristic changes, dose / drug toxicity changes, trend of variability from desired outcome. can do. The criteria can be adjusted based on the disease and its parameters. Alerts 2010-A, 2010-B, 2010-C (collectively referred to as alerts 2010) are sent based on the triggered definitions. It should be appreciated that alerts 2010 can include any number of alerts. Alerts 2010 can include content or links to content. The alert 2010 can be sent to the attending physician, other healthcare professional, hospital, pharmaceutical company, or any other person or entity.

  The contents 2015-A, 2015-B, 2015-C (collectively referred to as contents 2015) are displayed using, for example, the user computer 210 for providing an alert. Content 2015 may include patient data, comparisons, or any other relevant content associated with alert 2010. In one embodiment, the comparison can be, for example, between physicians, between one physician's patient and the entire patient population, between one physician at a particular location and all physicians, and so on. .. The comparison can indicate, based on trend analysis, where the treatment is prevalent and whether the treatment is off course (ie, the results are not as good as the baseline). The comparison can be displayed graphically as one or more curves on the graph. In one embodiment, COTA module 220 is utilized in cloud-based computing. COTA module 220 may also enable or utilize a connection to hospital records.

  In one embodiment, the content 2015 may include feedback support to healthcare personnel having a traffic light feedback indicator (not shown) on the display. For example, blue can mean very good performance (ie above standard), green can mean standard performance, yellow is good performance but needs attention. It can mean that there is one, and red can mean that the user needs to pay attention to what the healthcare professional's approach to this disease is. Other implementations of the feedback indicator are available.

  21-24 illustrate graphical representations of different diagnostic types according to one or more embodiments. FIG. 21 shows a diagnostic screen 2100 for gastrointestinal oncology (eg, colon cancer). FIG. 22 shows a diagnostic screen 2200 for breast oncology (eg breast cancer). FIG. 23 shows a diagnostic screen 2300 for breast oncology (eg, lung cancer). The diagnostic screens 2100, 2200, 2300 include many different parameters such as the examination or appearance of a disease. The parameters can be represented as simple indicators, numerical based parameters, standard based parameters, etc.

  FIG. 24 shows a graphical representation of a reporting screen 2400 showing data generation and sorting of the COTA module 220 for breast oncology. Report screen 2400 shows breast cancer from 2008 to 2013 by histology, ie, with invasive ductal carcinoma, according to one embodiment. The reporting screen 2400 allows for selection of breast cancer patients in real time based on stage, age, progression, or any other parameter. Advantageously, the reporting screen 2400 allows for classification in a clinically relevant manner.

  FIG. 25 shows a graphical representation of a reporting screen 2500 showing the data generation and sorting of the COTA module 220 for breast oncology. Report screen 2500 shows stage-by-stage tumors for all grade 2 breast cancers from 2008 to 2013, according to one embodiment.

  FIG. 26 shows a graphical representation of a reporting screen 2600 showing data generation and sorting of COTA module 220 for breast cancer. Report screen 2600 shows all stage IIB breast cancers from 2008 to 2013, according to one embodiment. Graph 2605 on report screen 2600 shows all stage IIB breast cancers by progesterone receptor status.

  FIG. 27 shows a graphical representation of an analysis screen 2700 showing overall survival outcomes for breast cancer patients, according to one embodiment. FIG. 28 shows a graphical representation 2800 showing breast cancer survival outcomes as a comparison between Dr. John Doe (thick line) and grand total (non-thick line), according to one embodiment.

  In one embodiment, the "nodes" described above represent any possible permutation of the variables shown in one or more of the graph representations (eg, one or more of Figures 21-27).

  As shown in the example of FIG. 29, client device 2905 can include one or more processing units (also referred to herein as CPUs) 2922, which interface with at least one computer bus 2925. The client device 2905 can be, for example, the user computer 210. Memory 2930 can be persistent storage and interfaces with computer bus 2925. The memory 2930 includes a RAM 2932 and a ROM 2934. The ROM 2934 includes the BIOS 2940. The memory 2930 is an operating system 2941, application programs 2942, device drivers, and / or program code, and / or functions described herein, such as, for example, computer-implemented implementation of one or more of the process flows described herein. Interfaces with computer bus 2925 to provide information stored in memory 2930 to CPU 2922 during execution of software programs such as software modules 2943, 2945 that include possible processing steps. CPU 2922 first loads computer-executable processing steps from storage, such as memory 2932, one or more data storage media 2944, removable media drives, and / or other storage devices. The CPU 2922 can then execute the stored processing steps to execute the loaded computer-executable processing steps. Stored data, eg, data stored by a storage device, can be accessed by CPU 2922 during execution of computer-executable processing steps.

  One or more persistent storage media 2944 are computer-readable storage media that can be used to store software and data, such as an operating system and one or more application programs. One or more persistent storage media 2944 may also be a device driver, web page, content file, play, such as one or more of a digital camera driver, monitor driver, printer driver, scanner driver, or other device driver. It can also be used to store lists and other files. One or more persistent storage media 2206 may further include program modules and data files used to implement one or more embodiments of the present disclosure.

  For purposes of this disclosure, a computer-readable medium stores computer data, which may include computer-executable computer program code in a machine-readable form. By way of example, and not limitation, computer readable media may comprise computer readable storage media for storage of tangible or fixed data, or communication media for temporary interpretation of signals containing code. .. Computer-readable storage media, as used herein, refers to physical or tangible storage (as opposed to signals) including, but not limited to, computer-readable instructions, data structures, program modules, or other data. Volatile and non-volatile, removable and non-removable media implemented by any method or technique for tangible storage of information such as. Computer readable storage media include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory, or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage. , Or other magnetic storage device, or any other physical or material medium usable to tangibly store desired information or data or instructions and accessible by a computer or processor.

  The client device 2905 also includes a power supply 2926, a network interface 2950, an audio interface 2952, a display 2954 (eg, monitor or screen), a keypad 2956, an illuminator 2958, an I / O interface 2960, a haptic interface 2962, a GPS 2964, a microphone. 2966, video camera, TV / radio tuner, audio / video capture card, sound card, analog audio input with A / D converter, modem, digital media input (HDMI (registered trademark), optical link), digital I / O port ( RS232, USB, FireWire, Thunderbolt) and expansion slots (PCMCIA, ExpressCard, PCI, PCIe) can also be included.

  For the purposes of this disclosure, a module may be any software, hardware, or firmware (or combination thereof) system, process, or function, or process, feature, and / or function described herein (human interoperability). These components that perform or facilitate (with or without action or augmentation). Modules can include sub-modules. The software components of the modules can be stored on computer readable media. A module can be integrated with one or more servers or can be loaded and executed by one or more servers. One or more modules can be grouped into an engine or an application.

  FIG. 30 is a block diagram illustrating the internal architecture of an example of a computer, such as server computer 205, user computer 210, and / or user computer 230, according to one or more embodiments of the present disclosure. A computer, as referred to herein, refers to any device that comprises a processor capable of executing logical or encoded instructions, to name a few, servers, personal computers, set-top boxes. , Tablet, smartphone, pad computer, or media device. As shown in the example of FIG. 30, internal architecture 3000 includes one or more processing units (also referred to herein as CPUs) 3012 that interface with at least one computer bus 3002. Interfacing with computer bus 302 further includes one or more persistent storage media 3006, network interface 3014, memory technology 3004, such as random access memory (RAM), run-time temporary memory, read-only memory (ROM), floppy, etc. A media disk drive interface 2308, which is an interface for drives that can read and write to and from media, including removable media such as CD-ROM, DVD, etc., and a display, which is an interface for monitors or other display devices. Interface 3010, keyboard interface 3016 for a keyboard, pointing device interface 3018 for a mouse or other pointing device, and various other interfaces not shown separately, such as parallel and serial port interfaces, There is a universal serial bus (USB) interface and the like.

  The memory 3004 may be a computer-executable process that incorporates an operating system, application programs, device drivers, and / or program code, and / or functionality described herein, such as one or more of the process flows described herein. Interfaces with computer bus 3002 to provide information stored in memory 3004 to CPU 3012 during execution of software programs, such as software modules that include steps. The CPU 3012 first loads computer-executable processing steps from storage, such as memory 3004, one or more data storage media 3006, removable media drives, and / or other storage devices. The CPU 3012 can then execute the stored processing steps to execute the loaded computer-executable processing steps. Stored data, eg, data stored by a storage device, can be accessed by CPU 3012 during execution of computer-executable processing steps.

  As mentioned above, one or more persistent storage media 3006 are computer-readable storage media that can be used to store software and data, such as an operating system and one or more application programs. One or more persistent storage media 3006 may also be one or two of a digital camera driver, monitor driver, printer driver, scanner driver, or other device driver, web page, content file, playlist, and other file. It can also be used to store the above device drivers. One or more persistent storage media 3006 may further include program modules and data files used to implement one or more embodiments of the present disclosure.

  The computer's internal architecture 3000 includes a microphone (as described above), a video camera, a TV / radio tuner, an audio / video capture card, a sound card, an analog audio input with A / D converter, a modem, a digital media input (HDMI (registered). Trademark, optical link), digital I / O port (RS232, USB, FireWire, Thunderbolt), expansion slot (PCMCIA, ExpressCard, PCI, PCIe).

  CNA-guided care has two aspects, which together enable the provision of value-based medical care in the United States through the application of CNA through healthcare providers, payers, and triads of sick patients. To

  A first aspect, an enabling tool is a healthcare provider, a computer including a processor with a first COTA module, and a first client device with a second COTA module communicatively linked to a first COTA via a network. And the interaction with the payer. This allows the payer to consider the patient's health insurance benefits assured service on the network via the first client device with the second COTA module communicatively linked to the first COTA module of the processor. A medical service of the user; indicating that the service is for the purpose of diagnosing or treating the medical condition; and enabling communication to the processor to identify the variable selected by the payer. Upon receiving the communication from the payer, the first COTA module provides the second COTA module with (i) clinical outcome data for each CNA, (ii) medical care for patients at each nodal address at key points in time during treatment. Disadvantage data for each provider in each CNA, including both non-existent and unnecessary medical care, which contributes to the individual's disadvantage, (iii) treat each patient in the patient population assigned to the CNA Cost reports including real-time cost data for (iv) (1) treatment costs for clinical outcomes, (2) treatment costs and toxicity incidence and severity for clinical outcomes of treatments, (3) therapy and quality of life Information can be sent that includes a graph analysis correlating one or more of them. Based on this information, the payer is at the key point in time that: (a) the medical service is an appropriate offering or level of service, taking into account the potential benefits and harms to the patient; b) Healthcare services are effective in improving health outcomes by improving clinical outcomes and reducing total treatment costs, as compared to the absence of alternative health interventions or the absence of this service. Being able to establish that it is cost-effective for the medical condition and clinical outcome being treated, and that this service complies with generally accepted medical practices, the payer will be able to provide medical care for this patient's medical services. Can approve payments to the person.

  As shown in the example of FIG. 31, a second aspect of CNA-guided medicine according to embodiments of the present disclosure for breast cancer patients is a computer including a processor with a first COTA module and a first COTA module via a network. Includes interaction between a patient and a second client device with a third COTA module communicatively linked. When interacting with a patient through a second client device with a third clinical outcome tracking and analysis module, the first COTA module does three things: The patient has a complete medical assessment of his or her illness ( (E.g., physical assessment, laboratory assessment, imaging, etc.) to inform patients so that they can be assigned CNAs; the assigned CNAs can be used to optimize care and reduce the risk of adverse differences. Inform the patient of treatment options for the illness that will be alleviated; by providing the patient with a physician within the patient's geographical region who has the best outcome at an acceptable treatment cost, in accordance with CNA-guided care Allows individuals and their desired level of CNA guided care to be selected.

  As shown in FIG. 32, from the payer's point of view, this validation tool is designed to eliminate pre-authorization, pre-approval, and referral needs for testing, diagnosis, and treatment. Can be effective. From the healthcare provider's perspective, this tool reduces the time required to pay the provider for healthcare. Thus, the enablement tool allows healthcare to optimize per-treatment pay-based reimbursement and transition from per-care pay to risk-based treatment reimbursement (eg, by bundling services) .. From a patient's perspective, CNA-guided care has three parts: first, enough for the patient to get a complete medical assessment of their illness and for the first COTA module to assign a CNA to the patient. Patient-provided, and secondly, using CNAs to allow geography, clinical outcomes, costs, and patient selection of optimized treatments to avoid adverse differences Based on other criteria, show treatment options to the patient and, third, show the healthcare provider within the patient's area to follow CNA-guided care with acceptable treatment cost and best outcome.

  One of ordinary skill in the art will understand that the methods and systems of the present disclosure can be implemented in numerous ways and are therefore not limited by the exemplary embodiments and examples described above. In other words, in various combinations of hardware and software or firmware, functional elements performed by single or multiple components, and individual functions may be performed either on the user computing device, the server, or both. It can be distributed among software applications. In this regard, any number of the features of the various embodiments described herein may be combined in one or more embodiments and may be less than or more than all of the features described herein. Alternative embodiments having many are possible. The functions may also be distributed in whole or in part among the components in a manner now known or will become known in the future. Thus, myriad combinations of software / hardware / firmware are possible in implementing the functions, features, interfaces, and preferences described herein. Moreover, the scope of the disclosure is in addition to the features and functions and interfaces described herein, as well as to the hardware or software or firmware components described herein, as will be understood by those skilled in the art now and hereafter. Ranging from conventional to known methods for carrying out possible variations and modifications.

  Although systems and methods have been described with respect to one or more embodiments, it should be understood that the present disclosure need not be limited to the embodiments disclosed herein. It is intended to cover various modifications and similar arrangements included within the spirit and scope of the following claims, which scope should be construed in its broadest interpretation so as to encompass all such modifications and similar structures. . This disclosure includes any and all embodiments of the following claims.

Claims (30)

A method for improving clinical outcomes at a specific patient level and reducing total treatment costs at a population level, comprising:
A. A healthcare provider, a computer including a processor with a first clinical outcome tracking and analysis module, and a second clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module via a network. Interaction between a first client device with a payer and a payer,
B. A computer including the processor having a first clinical outcome tracking and analysis module and a third clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module via the network. An interaction between the second client device and the patient,
The computer includes the processor executing the first clinical outcome tracking and analysis module on the processor including the first clinical outcome tracking and analysis module;
(A) By grouping patients within a patient population to proactively account for biological variability, thereby effectively eliminating biological variability as a factor in the value of treatment and controlling in treatment outcome. Including leaving treatment variability as a factor, which is done by:
(I) Receive, collect and record personal health information from each patient in the patient population in a database, the personal health information including each parameter characterizing each patient in the patient population and the latest science. Contains information that affects survival, prognosis, or treatment based on intellectual knowledge and medical guidelines,
(Ii) The personal health information of each patient in the patient population is sorted using a sorting filter to provide a sorted set of personal health information for this population, and patients that meet each parameter in the patient population are sorted. Identify and
(Iii) based on the personal health information associated with the patient population by generating and assigning multiple clinical outcome and analysis Nodal Addresses (CNAs) within the first clinical outcome tracking and analysis module. Classifying similar personal health information to group patient types within the patient population and generating and assigning the plurality of clinical outcome and analysis nodal addresses.
(1) Representing each CNA as a sequence of discrete punctuation digits including a prefix, a middle part, and a suffix, which sets the sorted and classified information to a clinically relevant health information set. Represents a set of preselected variables to divide into
(2) Reduce trillions of possible permutations to a reduced number of clinically meaningful permutations based on the discrete punctuation sequence representing each nodal address, which is initially behavioral. And then the resulting clinical and cost outcomes from their ideal values, expressed as the best possible clinical outcome at the lowest possible cost, with respect to necessary and unnecessary care avoidance. Enable analysis in the inevitable time needed to warn, thereby increasing the value of treatment, which means better clinical outcome at the lowest possible cost
(3) reduce processing requirements and processing time for efficient real-time monitoring of the health care provider's ability based on the discrete punctuation sequence representing each CNA and based on the reduction in permutation;
(4) It is possible to predict key points in time where behavioral fluctuations may occur, and to avoid the behavioral fluctuations, interrupt the flow of treatment to prevent overuse / underutilization of care, Furthermore,
(Iv) clinical analysis of each CNA by analyzing the clinically relevant personal health information set of the subset of patient populations for one or more patients in the subset of the patient population Measure the outcome,
(V) Treatment, examination, follow-up, adherence to prescribed medications, cost of each patient within the patient population assigned to each CNA, between one provider and another. Measuring the behavioral variability of each healthcare provider of each patient within the patient population assigned to each CNA by comparing the differences,
The steps are further
(B) comprising a communication identifying the medical service under consideration for the patient for which the patient's health insurance benefit guarantees the medical service and variables selected by the payer, the second clinical outcome tracking and analysis module. Upon receipt from the payer via the first client device, sending information to the second clinical outcome tracking and analysis module, including:
(1 ′) (iv) the clinical outcome data of each CNA,
(2 ′) (v) The behavior change data of each medical provider in each CNA,
(3 ′) (iii) a cost report comprising real-time cost data assigned to the CNA for treating each patient in the patient population,
(4 ') one or more graphical analyzes correlating treatment costs to clinical outcomes, wherein this information is provided by the payer at the key point in time:
(A ') the medical service is an appropriate provision or level of service in consideration of potential benefits and harms to the patient,
(B ') the medical service is effective in improving health outcomes by improving clinical outcomes and reducing total treatment costs;
(C ') the service is cost-effective for the medical condition being treated and the clinical outcome, as compared to alternative health interventions or no intervention;
(D ') the payer may authorize payment to the health care provider for the medical service if it is sufficient to establish that the service complies with generally accepted medical practices. ,
The step further comprises: (c) communicating a series of communications with the patient via the second client device with the third clinical outcome tracking and analysis module,
Providing sufficient information for a complete medical evaluation of the patient and for assigning a CNA to the patient,
Using the CNA to determine an optimal care level with a low risk of adverse differences, select a health care provider and a desired level of CNA guided care,
The series of communication is
(I '') in response to a first communication from the patient reporting a health concern to the processor of the computer server comprising the first clinical outcome tracking and analysis module,
In response to the second client device with the third clinical outcome tracking and analysis module, a second communication including a list of tests required to diagnose a health condition as a medical condition is transmitted,
(Ii '') (i '') in response to a third communication from the patient containing the results of the test necessary to diagnose the health concern,
In response to sending a fourth communication to the second client device comprising the third clinical outcome tracking and analysis module, the list of additional tests required to further diagnose the condition and further classify the condition,
(Iii ″) (i ″) in response to a fifth communication from the patient, including the result of the additional test, the result of the test, the diagnosis of (ii ″), and (ii ′). ') Based on the above list of additional tests
Assigning a CNA to the patient from the plurality of CNAs available in (a ″) (iii), the assigned CNA comprising the clinically relevant health information set for the patient. ,
(B '') send a sixth communication to the second client device with the third clinical outcome tracking module, including:
(1 '') the assigned CNA,
(2 '') A geographically organized list of healthcare workers treating patients within the assigned CNA, wherein the list of healthcare workers is one or more of geography, clinical outcome, or cost. Classified by
(Iv '') Optimal care plans with low risk of unfavorable differences, and selecting a healthcare professional who meets at least one or more of patient geography, cost, and outcome needs, Responsive to the communication, communicatively linked to a computing device in the office of the selected healthcare worker, the computing device comprising the third clinical outcome tracking and analysis module, and the selected healthcare worker A method that facilitates the scheduling of a patient appointment. (A): Sorting parameters of (a) (ii) are sex, age, ethnicity, coexistence, smoking, health supply source, medical record number, family doctor, contract doctor, hospital, licensed service vendor, disease Including one or more of an inherent clinical molecular phenotype, therapeutic purpose, stage of treatment, biomarker, and treatment cost, or (b): (a) (iii) (1) above preselected The set of variables includes a disease-specific clinical molecular phenotype, and the number string representing the phenotype is determined based on a directed graph, or (c): step (a) (iii) (4) and step (B) The above-mentioned key point in time of (1 ′) (4 ′) is one of the tendency of fluctuation at the time of diagnosis, progress, dose change, drug change, toxicity, and desired outcome, or Two or more, or (d): (b) (1 ') (4') correlating the treatment cost with clinical outcome, the steps of determining the incidence of toxicity, severity of toxicity, therapy, and quality of life. Correlating one or more of these with treatment costs and treatment outcomes, or (e): receiving the transmission from the client device in (b) or (c) comprises: Through the user or technical process, or (f) :( c) (iii ") (b") (1 ") the assigned CNAs are predetermined patient care services for the treatment of the disease. Associated with one or more bundles of, or (g) :( c) (iii ") (b") (2 ") said clinical outcome is: received therapeutic agent, delivered dose intensity, dosing interval, Includes one or more of duration of administration, quality of life metrics, toxicity to treatment, progression free survival, overall survival, response metrics, and death, or (h): stage (c) (iii ”) The method of claim 1, wherein the list of (b ″) (2 ″) health care workers is visually categorized by clinical outcome. The one or more bundles of given patient care services provide a given course of treatment, cost certainty for treatment of the condition, or both.
The method according to claim 2 (f). Further comprising identifying the best clinical outcome at the lowest treatment cost in the CNA by ranking both clinical outcomes and treatment costs in the one or more bundles of a given patient care service. ,
The method according to claim 2 (f). The visual categorization of the list of healthcare workers by clinical outcome is:
(A) Green indicates above average clinical outcome,
(B) Yellow indicates average clinical outcome, and (c) Red indicates sub-average clinical outcome,
The method according to claim 2 (h). With permission from the patient and the selected healthcare professional,
Transmitting the personal health information of the patient from the first clinical outcome tracking and analysis module to a computing device in the selected healthcare professional's office with a fourth clinical outcome tracking and analysis module. In addition,
The method of claim 1. The behavioral variation data for each health care provider in each CNA in (c) contributes to the disadvantageous difference of the health care provider for each CNA patient at the key point in time during treatment, which does not exist Including both required and unnecessary medical care,
The method according to step (d) (2) of claim 1. Further comprising the step of indicating that the service is for the purpose of diagnosing or treating a medical condition
The method according to step (d) of claim 1. The cost report is
Average cost-per-revenue comparison between doctors, or dollar-percentage comparison of hospital contributions, or average revenue per patient at hospital, or average cost per patient at hospital, or average per case for each physician. Cost, weight per average, or average cost of imaging, clinical examination, evaluation and management, or pharmaceuticals, medical supplies, and other costs for each physician, or a combination thereof,
The method of claim 1, step (d) (3 '). The variable selected by the payer is a variable that identifies a particular attribute of the personal health information as a desired set of characteristics and is added to the personal health information by the payer to provide the personal health information for each patient. Including one or more attributes or both that identify as having an equal level of importance as other health information in the patient population database,
The method according to step (b) (i ') of claim 1. A system for improving clinical outcomes at a specific patient level and reducing total treatment costs at a population level, comprising:
(A) A database containing personal health information and data of a subject group,
A first clinical outcome tracking and analysis module communicatively linked to the database and network;
A memory for storing computer program instructions,
A computer server processor having
(B) a first client device comprising a processing unit, a memory, and a second clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module of the processor of the computer server. When,
(C) A second client device comprising a processing unit, a memory, and a third clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module of the processor of the computer server. And including,
The computer program instructions of (A), when executed on the processor, cause the first clinical outcome tracking and analysis module to perform operations including:
(A) By grouping patients within a patient population to proactively account for biological variability, thereby effectively eliminating biological variability as a factor in the value of treatment and controlling in treatment outcome. Including the treatment variability as a factor, which is performed by (i) receiving and collecting personal health information from each patient in the patient population and recording in a database, said personal health information comprising: Includes information that affects survival, prognosis, or treatment based on each parameter that characterizes each patient in the patient population, and current scientific knowledge and medical guidelines,
(Ii) Sorting the personal health information of each patient in the patient population using a sorting filter, resulting in a sorted personal health information set for the population, satisfying each parameter in the patient population. Identify the patient,
(Iii) assigning a plurality of clinical outcome tracking and analysis Nodal Addresses (CNAs) within the first clinical outcome tracking and analysis module to the personal health information associated with the patient population. Categorizing similar personal health information, grouping patient types within the patient population, and generating and assigning the plurality of CNAs,
(1) Representing each CNA as a sequence of discrete punctuation digits including a prefix, a middle part, and a suffix, which sets the sorted and classified information to a clinically relevant health information set. Represents a set of preselected variables to divide into
(2) Reduce trillions of possible permutations into a reduced number of clinically meaningful permutations based on the discrete punctuation sequence representing each CNA, which is initially behavioral. And then warn of the resulting clinical and cost outcomes from ideal values, expressed as the best possible clinical outcome at the lowest possible cost, with regard to required care and avoidance of unnecessary care. To increase the value of the treatment, which means better clinical outcomes at the lowest possible cost, and
(3) reduce processing requirements and processing time for efficient real-time monitoring of the health care provider's ability based on the discrete punctuation sequence representing each CNA and based on the reduction in permutation;
(4) It is possible to predict key points in time where behavioral fluctuations may occur, and to avoid the behavioral fluctuations, interrupt the flow of treatment to prevent overuse / underutilization of care,
(Iv) clinical analysis of each CNA by analyzing the clinically relevant personal health information set of the subset of patient populations for one or more patients in the subset of the patient population Measure the outcome,
(V) Treatment, examination, follow-up, adherence to prescribed medications, cost of each patient within the patient population assigned to each CNA, between one provider and another. Measuring the behavioral variability of each healthcare provider of each patient within the patient population assigned to each CNA by comparing the differences,
(B) comprising a communication identifying the medical service under consideration for the patient for which the patient's health insurance benefit guarantees the medical service and variables selected by the payer, the second clinical outcome tracking and analysis module. Upon receipt from the payer via the first client device, sending information to the second clinical outcome tracking and analysis module, including:
(1 ′) (iv) clinical outcome data for each CNA,
(2 ′) (v) The behavior change data of each medical provider in each CNA,
A cost report comprising real-time cost data for treating each patient in the patient population assigned to the CNA at (3 ′) (iii);
(4 ') one or more graphical analyzes correlating treatment costs to clinical outcomes, wherein the information is:
(A ') the medical service is an appropriate provision or level of service in consideration of potential benefits and harms to the patient,
(B ') the medical service is effective in improving health outcomes by improving clinical outcomes and reducing total treatment costs;
(C ') the service is cost-effective for the medical condition being treated and the clinical outcome, as compared to alternative health interventions or no intervention;
(D ') the service follows generally accepted medical practices,
The payer may authorize payment to the health care provider for the health care service,
(C) interacting with the patient via the second client device with the third clinical outcome tracking and analysis module,
Providing sufficient information for a complete medical evaluation of the patient and for assigning a CNA to the patient,
Using the CNA to determine an optimal care level with a low risk of adverse differences, select a health care provider and a desired level of CNA guided care,
The series of communication is
(I '') in response to a first communication from the patient reporting a health concern to the processor of the computer server comprising the first clinical outcome tracking and analysis module,
In response to the second client device with the third clinical outcome tracking and analysis module, a second communication including a list of tests required to diagnose the health concern as a medical condition is sent in response,
(Ii '') (i '') in response to a third communication from the patient containing the results of the test necessary to diagnose the health concern,
In response to sending a fourth communication to the second client device comprising the third clinical outcome tracking and analysis module, the list of additional tests required to further diagnose the condition and further classify the condition,
(Iii '') (i '') in response to a fifth communication from the patient, including the results of the additional test, the results of the test, the diagnosis of (ii ''), and (ii). )) Based on the above list of additional tests,
Assigning a CNA to the patient from the plurality of CNAs available in (a ″) (iii), the assigned CNA comprising the clinically relevant health information set for the patient. ,
(B '') send a sixth communication to the second client device with the third clinical outcome tracking module, including:
(1 '') the assigned CNA,
(2 '') A geographically organized list of healthcare workers treating patients within the assigned CNA, wherein the list of healthcare workers is one or more of geography, clinical outcome, or cost. Classified by
(Iv '') an optimal care plan with a low risk of adverse differences and a first selection from the patient who selects a health care worker who meets at least one or more of the patient's geographic, cost, and outcome needs. In response to 7 communication,
The patient is communicatively linked to a computing device in the selected healthcare professional's office with a third clinical outcome tracking and analysis module to schedule appointments with the selected healthcare worker and the patient. Facilitating the scheduling of,
A system characterized by that. (A): Sorting parameters of (a) (ii) are sex, age, ethnicity, coexistence, smoking, health supply source, medical record number, family doctor, contract doctor, hospital, licensed service vendor, disease Including one or more of an inherent clinical molecular phenotype, therapeutic purpose, stage of treatment, biomarker, and treatment cost, or (b): (a) (iii) (1) above preselected The set of variables includes a disease-specific clinical molecular phenotype, and the number string representing the phenotype is determined based on a directed graph, or (c): step (a) (iii) (4) and step (B) The above-mentioned key point in time of (1 ′) (4 ′) is one of the tendency of fluctuation at the time of diagnosis, progress, dose change, drug change, toxicity, and desired outcome, or Two or more, or (d): (b) (1 ') (4') correlating the treatment cost with clinical outcome, the steps of determining the incidence of toxicity, severity of toxicity, therapy, and quality of life. Correlating one or more of these with treatment costs and treatment outcomes, or (e): receiving the transmission from the client device in (b) or (c) comprises: Through the user or technical process, or (f) :( c) (iii ") (b") (1 ") the assigned CNAs are predetermined patient care services for the treatment of the disease. Associated with one or more bundles of, or (g) :( c) (iii ") (b") (2 ") said clinical outcome is: received therapeutic agent, delivered dose intensity, dosing interval, Includes one or more of duration of administration, quality of life metrics, toxicity to treatment, progression free survival, overall survival, response metrics, and death, or (h): stage (c) (iii ”) The list of (b ″) (2 ″) healthcare workers is visually categorized by clinical outcome,
The system according to claim 11. The one or more bundles of given patient care services provide a given course of treatment, cost certainty for treatment of the condition, or both.
The system of claim 12 (f). Further comprising identifying the best clinical outcome at the lowest treatment cost in the CNA by ranking both clinical outcomes and treatment costs in the one or more bundles of a given patient care service. ,
The system of claim 12 (f). The visual categorization of the list of healthcare workers by clinical outcome is:
(A) Green indicates above average clinical outcome,
(B) Yellow indicates average clinical outcome, and (c) Red indicates sub-average clinical outcome,
The system according to claim 12 (h). With permission from the patient and the selected healthcare professional,
Transmitting the personal health information of the patient from the first clinical outcome tracking and analysis module to a computing device in the selected healthcare professional's office with a fourth clinical outcome tracking and analysis module. In addition,
The system according to claim 11. The behavioral variation data for each health care provider in each CNA in (c) contributes to the disadvantageous difference of the health care provider for each CNA patient at the key point in time during treatment, which does not exist Including both required and unnecessary medical care,
System according to step (d) (2) of claim 11. Further comprising the step of indicating that the service is for the purpose of diagnosing or treating a medical condition
The system according to step (d) of claim 11. The cost report is
Average cost-per-revenue comparison between doctors, or dollar-percentage comparison of hospital contributions, or average revenue per patient at hospital, or average cost per patient at hospital, or average per case for each physician. Cost, weight per average, or average cost of imaging, clinical examination, evaluation and management, or pharmaceuticals, medical supplies, and other costs for each physician, or a combination thereof,
The system of claim 11, step (d) (3). The variable selected by the payer is a variable that identifies a particular attribute of the personal health information as a desired set of characteristics and is added to the personal health information by the payer to provide the personal health information for each patient. Including one or more attributes or both that identify as having an equal level of importance as other health information in the patient population database,
The system according to step (b) (i ') of claim 11. (A) a healthcare provider, a computer including a processor with a first clinical outcome tracking and analysis module, a second clinical outcome tracking and analysis communicatively linked to the first clinical outcome tracking and analysis module via a network Interaction between a first client device with a module and a payer, and (B) the computer including the processor with a first clinical outcome tracking and analysis module, the first clinical outcome tracking via a network And a second client device comprising a third clinical outcome tracking and analysis module communicatively linked to the analysis module, and a non-transitory computer storing computer program instructions for facilitating interaction between the patient. A readable medium,
Causing the first clinical outcome tracking and analysis module to perform operations including the following steps when executed on the processor with the first clinical outcome tracking and analysis module:
(A) By grouping patients within a patient population to proactively account for biological variability, thereby effectively eliminating biological variability as a factor in the value of treatment and controlling in treatment outcome. The stage of leaving treatment fluctuation as a factor,
This is done by
(I) Receive, collect and record personal health information from each patient in the patient population in a database, the personal health information including each parameter characterizing each patient in the patient population and the latest science. Contains information that affects survival, prognosis, or treatment based on intellectual knowledge and medical guidelines,
(Ii) Sorting the personal health information of each patient in the patient population using a sorting filter, resulting in a sorted personal health information set for the population, satisfying each parameter in the patient population. Identify the patient,
(Iii) assigning a plurality of clinical outcome tracking and analysis Nodal Addresses (CNAs) within the first clinical outcome tracking and analysis module to the personal health information associated with the patient population. Categorizing similar personal health information based on the patient types, grouping patient types within the patient population, and generating and assigning the plurality of nodal addresses.
(1) Representing each CNA as a sequence of discrete punctuation digits including a prefix, a middle part, and a suffix, which sets the sorted and classified information to a clinically relevant health information set. Represents a set of preselected variables to divide into
(2) Reduce trillions of possible permutations to a reduced number of clinically meaningful permutations based on the discrete punctuation digit sequence representing each CNA, which is initially behavioral. And then warn of the resulting clinical and cost outcomes from ideal values, expressed as the best possible clinical outcome at the lowest possible cost, with regard to required care and avoidance of unnecessary care. In the time required to do so, increasing the value of care, which means better clinical outcomes at the lowest possible cost,
(3) reduce processing requirements and processing time for efficient real-time monitoring of the health care provider's ability based on the discrete punctuation sequence representing each CNA and based on the reduction in permutation;
(4) It is possible to predict key points in time where behavioral fluctuations may occur, and to avoid the behavioral fluctuations, interrupt the flow of treatment to prevent overuse / underutilization of care,
(Iv) clinical analysis of each CNA by analyzing the clinically relevant personal health information set of the subset of patient populations for one or more patients in the subset of the patient population Measure the outcome,
(V) Treatment, examination, follow-up, adherence to prescribed medications, cost of each patient within the patient population assigned to each CNA, between one provider and another. Measuring the behavioral variability of each healthcare provider of each patient within the patient population assigned to each CNA by comparing the differences,
Furthermore,
(B) comprising a communication identifying the medical service under consideration for the patient for which the health care benefit of the patient guarantees the variable selected by the medical service and the payer, the second clinical outcome tracking and analysis module. Upon receipt from the payer via the first client device, sending information to the second clinical outcome tracking and analysis module including:
(1 ′) (iv) clinical outcome data for each CNA,
(2 ′) (v) The behavior change data of each medical provider in each CNA,
A cost report comprising real-time cost data for treating each patient in the patient population assigned to the CNA at (3 ′) (iii);
(4 ′) One or more graphical analyzes correlating treatment costs to clinical outcomes, which information is provided by the payer at key points in time,
(A ') the medical service is an appropriate provision or level of service in consideration of potential benefits and harms to the patient,
(B ') the medical service is effective in improving health outcomes by improving clinical outcomes and reducing total treatment costs;
(C ') the service is cost-effective for the medical condition being treated and the clinical outcome, as compared to alternative health interventions or no intervention;
(D ') the payer may authorize payment to the health care provider for the medical service if it is sufficient to establish that the service complies with generally accepted medical practices. ,
Furthermore,
(C) interacting with the patient via the second client device with the third clinical outcome tracking and analysis module,
Providing sufficient information for a complete medical evaluation of the patient and for assigning a CNA to the patient,
Using the CNA to determine an optimal care level with a low risk of adverse differences, and to select a health care provider and a desired level of CNA-guided care;
Including,
The series of communication is
(I '') in response to a first communication from the patient reporting a health concern to the processor of the computer server comprising the first clinical outcome tracking and analysis module,
In response to the second client device with the third clinical outcome tracking and analysis module, a second communication including a list of tests required to diagnose a health concern as a medical condition is sent,
(Ii '') (i '') in response to a third communication from the patient containing the results of the test necessary to diagnose the health concern,
In response to sending a fourth communication to the second client device comprising the third clinical outcome tracking and analysis module, the list of additional tests required to further diagnose the condition and further classify the condition,
(Iii ″) (i ″) in response to a fifth communication from the patient, including the result of the additional test, the result of the test, the diagnosis of (ii ″), and (ii ′). ') Based on the above list of additional tests
Assigning a CNA to the patient from the plurality of CNAs available in (a ″) (iii), the assigned CNA comprising the clinically relevant health information set for the patient. ,
(B '') send a sixth communication to the second client device with the third clinical outcome tracking module, including:
(1 '') the assigned CNA,
(2 '') A geographically organized list of healthcare workers treating patients within the assigned nodal address, where the list of healthcare workers is one of geography, clinical outcome, or cost. Classified into 2 or more,
(Iv '') Optimal care plans with low risk of unfavorable differences, and selecting a healthcare professional who meets at least one or more of patient geography, cost, and outcome needs, In response to the communication,
A patient is communicatively linked to a computing device in the office of the selected healthcare provider, the computing device comprising the third clinical outcome tracking and analysis module, to visit the selected healthcare provider and the patient. A non-transitory computer-readable medium that facilitates scheduling appointments. (A): Sorting parameters of (a) (ii) are sex, age, ethnicity, coexistence, smoking, health supply source, medical record number, family doctor, contract doctor, hospital, licensed service vendor, disease Including one or more of an inherent clinical molecular phenotype, therapeutic purpose, stage of treatment, biomarker, and treatment cost, or (b): (a) (iii) (1) above preselected The set of variables includes a disease-specific clinical molecular phenotype, and the number string representing the phenotype is determined based on a directed graph, or (c): step (a) (iii) (4) and step (B) The above-mentioned key point in time of (1 ′) (4 ′) is one of the tendency of fluctuation at the time of diagnosis, progress, dose change, drug change, toxicity, and desired outcome, or Two or more, or (d): (b) (1 ') (4') correlating the treatment cost with clinical outcome, the steps of determining the incidence of toxicity, severity of toxicity, therapy, and quality of life. Correlating one or more of these with treatment costs and treatment outcomes, or (e): receiving the transmission from the client device in (b) or (c) comprises: Through the user or technical process, or (f) :( c) (iii ") (b") (1 ") the assigned CNAs are predetermined patient care services for the treatment of the disease. Associated with one or more bundles of, or (g) :( c) (iii ") (b") (2 ") said clinical outcome is: received therapeutic agent, delivered dose intensity, dosing interval, Includes one or more of duration of administration, quality of life metrics, toxicity to treatment, progression free survival, overall survival, response metrics, and death, or (h): stage (c) (iii ”) The list of (b ″) (2 ″) healthcare workers is visually categorized by clinical outcome,
The non-transitory computer readable medium of claim 21. The one or more bundles of given patient care services provide a given course of treatment, cost certainty for treatment of the condition, or both.
The non-transitory computer readable medium of claim 22 (f). Further comprising identifying the best clinical outcome at the lowest treatment cost in the CNA by ranking both clinical outcomes and treatment costs in the one or more bundles of a given patient care service. ,
The non-transitory computer readable medium of claim 22 (f). The visual categorization of the list of healthcare workers by clinical outcome is:
(A) Green indicates above average clinical outcome,
(B) Yellow indicates average clinical outcome, and (c) Red indicates sub-average clinical outcome,
The non-transitory computer readable medium of claim 2 (h). With permission from the patient and the selected healthcare professional,
Transmitting the personal health information of the patient from the first clinical outcome tracking and analysis module to a computing device in the selected healthcare professional's office with a fourth clinical outcome tracking and analysis module. In addition,
The non-transitory computer readable medium of claim 21. The behavioral variation data for each health care provider in each CNA in (c) contributes to the disadvantageous difference of the health care provider for each CNA patient at the key point in time during treatment, which does not exist Including both required and unnecessary medical care,
The non-transitory computer readable medium of claim 21, step (d) (2). Further comprising the step of indicating that the service is for the purpose of diagnosing or treating a medical condition
The non-transitory computer readable medium of claim 21, step (d). The cost report is
Average cost-per-revenue comparison between doctors, or dollar-percentage comparison of hospital contributions, or average revenue per patient at hospital, or average cost per patient at hospital, or average per case for each physician. Cost, weight per average, or average cost of imaging, clinical examination, evaluation and management, or pharmaceuticals, medical supplies, and other costs for each physician, or a combination thereof,
The non-transitory computer readable medium of claim 21, step (d) (3). The variable selected by the payer is a variable that identifies a particular attribute of the personal health information as a desired set of characteristics and is added to the personal health information by the payer to provide the personal health information for each patient. Including one or more attributes or both that identify as having an equal level of importance as other health information in the patient population database,
The non-transitory computer readable medium of claim 21, step (b) (i ').

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