JP7079790B2 - CNA guided care to improve clinical outcomes and reduce total treatment costs - Google Patents

Description

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

As the lifespan of the general population increases, most medical costs associated with the elderly population are increasing. The costs associated with serious and chronic illnesses such as cancer are growing unsustainably. For example, the cost of cancer is expected to be the highest growing area of health care if there is no commensurate improvement in outcome. In the United States alone, approximately $ 125 billion will be spent on cancer treatment in 2010, and it is estimated that approximately 15-30% of medical costs can be classified as a result of unnecessary or inadequate treatment, in this case. Too little or too much treatment for individual patients leads to suboptimal clinical outcomes and over-expenses (hereinafter "disadvantageous differences").

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

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

Current pre-approved infrastructure that allows payers to determine that health services are benefits guaranteed by patient health insurance is expensive, time consuming, and delays the treatment required, resulting in clinical outcomes. May have a negative impact.

As technology and medicine continue to advance, scientific and clinical practice for the treatment of diseases (cancer, heart disease, lung disease, and behavioral health science) is evolving rapidly. Healthcare professionals (eg, oncologists) often have difficulty keeping up with these advances. These advances, such as next-generation gene sequencing, are usually complex and can pose 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) operations. Similarly, healthcare professionals (eg, physicians) will need more decision support tools to provide the best possible practice and keep the business going.

The Clinical Outcome Tracking and Analysis (COTA) module, for example, allows healthcare professionals and / or other users to better practice, better manage and retrieve specific information related to disease and / or patients, and costs. A tool that can help improve management.

Parameters for clinical outcome follow-up and analysis include sorting, outcome follow-up, Eastern Cooperative Oncology Group (ECOG) performance status, toxicity to treatment, and treatment costs. In one embodiment, the method and system receive one or more parameters for sorting multiple data records from a client device operated by the user, and in response to this reception, to the received parameters. Includes a COTA module that sorts data records based on. A nodal address indicating one or more variables is applied to the sorted set of patient records to provide one or more clinically relevant patient records. Determined as a sorted patient medical record set that satisfies the variables of. The COTA module then analyzes the clinically relevant set of patient medical records and communicates to the client device to display at least a portion of the classified and sorted data records and updated data records. do.

Each data record contains data related to the disease and data related to patients who are currently suffering from the disease or who have had it 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 allows comparison of data or follow-up outcomes between patients, can identify a particular patient as a candidate for a particular treatment or drug, and conveys an analysis tool to a client device, eg, for example. It facilitates the analysis of classified and sorted data records, or allows comparison of Kaplan-Meier curves, and based on follow-up, a particular physician involved with a patient treats another (similar) patient with another physician. It is possible to determine whether or not the patient is being treated according to the treatment method of.

The COTA module can also send an alert to the client device when a trigger occurs. The trigger can be, for example, at the time of diagnosis, at the time of progression, at the time of dose change, at the time of drug change, at the time of toxicity, when there is a tendency for variation from the desired outcome, and / or at a specific time.

The COTA module tracks the results of treatment choices and reports on the outcomes associated with the use of COTA Nodal Addresses (CNA). It preliminarily considers biological variability by grouping patients within the patient population, effectively eliminating biological variability as a factor in the value of treatment, and treatment variability as the dominant factor in treatment outcomes. Leave. The COTA module receives and collects personal health information from each patient in the patient population, records it in a database, and sorts the personal health information. By classifying 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 is a discrete punctuation digit sequence containing a prefix, middle part, and suffix that represents a preselected set of variables that divides the sorted and categorized information into clinically relevant health information sets. It is expressed as (discrete prefixed reading of variables). Multiple CNAs have reduced trillions of possible sequences to a reduced number of clinically meaningful sequences based on a discrete punctuation sequence representing each nodal address, which is initially behavioral. And then a variation from the ideal value of the resulting clinical and cost outcome, expressed as the best clinical outcome at the lowest possible cost, with respect to the care needed and the avoidance of unnecessary care. Allows analysis in the inevitable time required to increase the value of treatment, which means a better clinical outcome at the lowest possible cost; a discrete punctuation sequence representing each nodal address. Reduce processing requirements and processing time to streamline real-time monitoring of healthcare provider capabilities, based on and on the basis of reduced sequence; key points in time where behavioral variability may occur. To enable prediction and avoid behavioral fluctuations, interrupt the flow of treatment 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 CNA through healthcare providers, payers, and triads of sick patients. to enable.

The first aspect of the activation 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 communicably linked to the first COTA over a network. And the interaction with the payer. This allows the payer to consider the patient whose health insurance benefits guarantee service over the network via a first client device with a second COTA module communicatively linked to the first COTA module of the processor. Identify medical services; indicate that this service is for diagnostic or therapeutic purposes; allow communications to be transmitted to the processor to identify variables 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 to patients at each nodal address at key points in time during treatment. Disadvantageous difference data of each healthcare provider in each CNA, including both non-existent necessary and unnecessary medical care, which contributes to the disadvantageous difference of the person, (iii) Treat each patient in the patient population assigned to the CNA. Cost reports containing real-time cost data for, and (iv) (1) treatment costs for clinical outcomes, (2) toxicity incidence and severity for treatment costs and clinical outcomes, (3) therapy and quality of life. Information can be transmitted, including graph analysis that correlates one or more of them. Based on this information, the payer at a key point in time, (a) considers the potential benefits and harms to the patient, that the medical service is an appropriate provision or level of service, ( b) Health 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 interventions. The payer can ensure that it is cost-effective for the medical condition and clinical outcome to be treated, and that this service follows generally accepted medical practices, and the payer will provide medical care for this patient's medical service. Can approve payment to a person.

From the payer's point of view, this activation tool can be effective in eliminating prior permission for testing, diagnosis, and treatment, eliminating pre-approval, and eliminating the need for referrals. .. From a healthcare provider's perspective, this tool reduces the time required to pay a provider for healthcare. Thus, activation tools allow healthcare to optimize per-medical payment-based refunds and shift from per-medical payments to risk-based treatment refunds (eg, by bundling services). ..

The second aspect of CNA-guided medical care is with 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 over a network, and a patient. Includes interactions between. Through a series of communications between the 1st COTA module and the patient, CNA-guided treatment is primarily for the patient to obtain a complete medical assessment of his or her disease and for the 1st COTA module to assign the CNA to the patient. Patients set up to provide patients with sufficient information and, secondly, to use CNA to select geography, clinical outcomes, costs, and treatments optimized to avoid disadvantages. Based on the other criteria given, the patient is shown treatment options and, thirdly, the patient's community of care providers who follow CNA-guided care with the best outcome at an acceptable cost of treatment.

US Application No. 15 / 351,099

The invention described herein uses CNA guided care to improve clinical outcomes at the specific patient level and reduce total treatment costs at the population level in the first COTA module when performed on a processor. Provided are non-temporary computer-readable media, methods, and systems for storing computer program instructions to be executed.

According to one embodiment, the invention described herein provides a method for improving clinical outcomes at a particular patient level and reducing total treatment costs at the population level. A second clinical outcome tracking and analysis module that is communicably linked to a first clinical outcome tracking and analysis module over a network with a healthcare provider and a computer that includes a processor with a first clinical outcome tracking and analysis module. The interaction between the first client device provided and the payer, and B.I. A computer with a processor equipped with a first clinical outcome tracking and analysis module and a second client with a third clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module over a network. Including the interaction between the device and the patient, the computer comprises a processor running the first clinical outcome tracking and analysis module on a processor with the first clinical outcome tracking and analysis module, the stage is , (A) By grouping patients within a patient population, biological variability is considered in advance, thereby effectively eliminating biological variability as a factor in the value of treatment and in the outcome of treatment. It involves the step of leaving treatment variability as a dominant factor, which is done by: (i) receiving, collecting and recording personal health information from each patient in the patient population in a database, personal health information , Each parameter that characterizes each patient in the patient population, and information that affects survival, prognosis, or treatment, based on the latest scientific knowledge and medical guidelines, (ii) of each patient in the patient population. Personal health information is sorted using a sorting filter to provide a sorted personal health information set for this population, identifying patients who meet each parameter within the patient population, and (iii) first clinical outcome tracking. And by generating and assigning multiple clinical outcomes and analytical nodal addresses (CNAs) within the analysis module, similar personal health information is categorized based on personal health information relevant to the patient population. The steps of grouping the types of patients in a patient population and generating and assigning multiple nodal addresses are as follows: (1) Represent each CNA as a discrete punctuation sequence containing prefixes, middle parts, and suffixes. Including stages, these represent a preselected set of variables that divide the sorted and categorized information into clinically relevant health information sets, (2) trillions of possible sequences. Is reduced to a clinically meaningful sequence of reduced numbers based on a discrete punctuation sequence representing each nodal address, which is the first behavioral and then the resulting clinical and cost outcome. To be able to analyze the variation from the ideal value, expressed as the best clinical outcome at the lowest possible cost, in the inevitable time needed to warn about necessary care and avoidance of unnecessary care. And this is the most possible Increase the value of treatment, which also means better clinical outcomes at lower cost, and (3) real-time monitoring of healthcare provider capacity based on discrete punctuation numbers representing each CNA and based on reduced sequence. To be efficient, reduce processing requirements and processing time, (4) to enable prediction of key points over time where behavioral fluctuations may occur, and to avoid behavioral fluctuations in treatment. It interrupts the flow to prevent overuse / underuse of care, and (iv) for one or more patients within the patient population subset, the clinically relevant personal health of the patient population subset. By analyzing the information set, the clinical outcome of each CNA is measured, and (v) in treatment, testing, follow-up, adherence to prescribed medications, and the cost of each patient within the patient population assigned to each CNA. By comparing the differences between one healthcare provider and another, the behavioral variability of each healthcare provider in each patient within the patient population assigned to each CNA is measured.
The above steps are further
(B) A first client with a second clinical outcome tracking and analysis module that identifies the medical service under consideration for a patient whose health insurance benefits guarantee the medical services and variables selected by the payer. Upon receipt from the payer via the device, information including the following is sent to the Second Clinical Outcome Tracking and Analysis Module, the clinical outcome data for each CNA in (1') (iv), (2') (v). Behavioral variability data for each health provider in each CNA, Cost Report (4'), which includes real-time cost data for treating each patient in the patient population, assigned to the CNA in (3') (iii). ) A graph analysis of one or more that correlates treatment costs to clinical outcomes, in which the payer considers (a') potential benefits and harms to the patient at key points in time. 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') This service is more cost effective for the medical condition and clinical outcome being treated, compared to the absence of alternative health interventions or interventions, (d') This service is generally accepted. If sufficient to ensure compliance with the medical practice, the payer may approve payment to the health provider for this medical service, and the above steps further include (c) Third clinical outcome tracking. And a series of communications with the patient via a second client device equipped with an analysis module provides sufficient information for the patient's complete medical evaluation and to assign the CNA to the patient. It is used to determine the optimal level of care with a low risk of adverse differences, select the care provider and the desired level of CNA-guided care, and a series of communications is (i'') the first clinical outcome tracking and analysis module. A second client device equipped with a third clinical outcome tracking and analysis module in response to a first communication from a patient reporting a health concern to a computer server processor equipped with a health concern as a medical condition. From the patient, a second communication containing a list of tests necessary for diagnosis is sent in response, including the results of the tests necessary to diagnose the health concerns of (ii'') (i''). Further classify the diagnosis and condition of the condition into a second client device equipped with a third clinical outcome tracking and analysis module in response to the third communication of. In response to the fifth communication from the patient, including the results of the additional tests (iii'') (i''), send a fourth communication containing a list of additional tests required to do so. Based on the results of the tests, the diagnosis of (ii''), and the list of additional tests of (ii''), one CNA per patient from multiple CNAs available in (a'') (iii). The assigned CNA contains a patient-clinically relevant health information set and (b'') sends a sixth communication to a second client device with a third clinical outcome tracking module, including: This list of medical personnel is a geographically organized list of medical personnel who treat patients within (1'') assigned CNA, (2'') assigned CNA. Optimal care plan, categorized by one or more of geography, clinical outcome, or cost, with low risk of (iv'') adverse differences, and at least one or one of the patient's geography, cost, and outcome needs. In response to a seventh communication from the patient, selecting a healthcare worker who meets two or more, the patient is in the selected healthcare worker's office, a computing device with a third clinical outcome tracking and analysis module. Linked communicably to facilitates scheduling of appointments for selected healthcare professionals and patients.

In some embodiments of the method, the sorting parameters of (a): (a) (ii) are gender, age, ethnicity, coexistence, smoking, health source, medical record number, family doctor, contract doctor. , Hospitals, licensed service vendors, disease-specific clinical molecular phenotypes, therapeutic objectives, stages of treatment, biomarkers, and one or more of the cost of treatment, or (b): (a) (iii). The preselected set of variables in (1) comprises a disease-specific clinical molecular phenotype, and the sequence of numbers representing the phenotype is determined based on a directed graph, or (c): step (a) (iii). Key points in time of (4) and steps (b) (1') (4') are changes from diagnosis, progression, dose change, drug change, toxicity, and desired outcome. The steps that include one or more of the trends or correlate the cost of treatment of (d): (b) (1') (4') with clinical outcome are the incidence of toxicity, severity of toxicity, therapy, and The stage of correlating one or more of the quality of life with treatment costs and outcomes, or receiving transmissions from client devices in (e): (b) or (c), is human. The CNA assigned through the user or technical process of (f): (c) (iii ") (b") (1 ") is a predetermined patient care service for the treatment of the disease. The clinical outcome of (g): (c) (iii ") (b") (2 ") associated with one or more bundles is the therapeutic agent received, delivery dose intensity, dosing interval, dosing duration. , Includes one or more of life quality metrics, therapeutic toxicity, progression-free survival, overall survival, response metrics, and death, or (h): stage (c) (iii ") (b). The list of medical personnel in ") (2") 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 the treatment of an illness, or both.

In some embodiments, the method provides the best clinical outcome with the lowest treatment cost in CNA by ranking both clinical outcomes and treatment costs in one or more bundles of a given patient care service. Further includes a step of identification.

In some embodiments, the visual classification of the healthcare professional list by clinical outcome is (a) green with above-average clinical outcome, (b) yellow with average clinical outcome, and (c). ) Red indicates subaverage clinical outcome.

In some embodiments, the method, with permission from the patient and selected healthcare professional, is from the first clinical outcome tracking and analysis module to the fourth clinical outcome tracking in the selected healthcare professional's office. And further includes the step of transmitting the patient's personal health information to a computing device equipped with an analysis module.

In some embodiments, the behavioral variability data for each healthcare provider in each CNA in (c) contributes to the health care provider's disadvantageous differences for each CNA patient at key points in time during treatment. Includes both necessary and unnecessary medical care that does not exist.

In some embodiments, the method further comprises, in step (d), a step 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 income between doctors, or a comparison of dollars and percentages of hospital contributions, or average income per hospital patient, or per hospital patient. Average cost, or average cost per case for each doctor, or weight per average, or average cost for contrast, clinical examination, evaluation and management, medicines, medical supplies, and others for each doctor Includes the cost of, or a combination thereof.

In some embodiments, the variable selected by the payer is a variable that identifies a particular attribute of personal health information as a desired set of characteristics, which is added to the personal health information by the payer to provide personal health information for each patient. Includes one or more attributes that identify as having the same level of importance as other health information in the patient population database, or both.

According to another aspect, the invention described herein provides a system for improving clinical outcomes at a particular patient level and reducing total treatment costs at the population level, where the system is (A) subject. A computer server processor with a database containing personal health information and data for the population, a first clinical outcome tracking and analysis module communicatively linked to the database and network, and memory for storing computer program instructions. And (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. (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. The computer program instruction of (A) causes the first clinical outcome tracking and analysis module to perform actions including the following when executed on the processor, and (a) groups the patients in the patient population. Thereby, including the step of preliminarily considering biological variability, thereby effectively eliminating the biological variability as a factor in the value of treatment and leaving the therapeutic variability as the dominant factor in the treatment outcome. (I) Receive, collect and record personal health information from each patient in the patient population in a database, where the personal health information is the parameters that characterize each patient in the patient population, and the latest. Based on the scientific knowledge and medical guidelines of, (ii) personal health information of each patient in the patient population is sorted using a sorting filter, including information that affects survival, prognosis, or treatment. It provides a sorted personal health information set for this population, identifies patients who meet each parameter within the patient population, and (iii) multiple clinical outcome tracking and analysis nodals within the First Clinical Outcome Tracking and Analysis Module. By generating and assigning addresses (Nodal Adsresses (CNA)), similar personal health information is categorized and the types of patients within the patient population are grouped based on the personal health information associated with the patient population. , The stage of generating and assigning multiple CNAs is (1) the stage of expressing each CNA as a discrete punctuation number string including a prefix, an intermediate part, and a suffix. These represent a preselected set of variables that divide the sorted and categorized information into clinically relevant health information sets, including (2) trillions of possible sequences. Based on the discrete punctuation sequence representing each CNA, the reduced number is reduced to a clinically meaningful sequence, which is possible, first behavioral and then the resulting clinical and cost outcome. Fluctuations from ideal values, expressed as the best clinical outcome at the lowest possible cost, can be analyzed in the inevitable time needed to warn about necessary care and avoidance of unnecessary care. Increases the value of treatment, which means better clinical outcomes at the lowest possible cost, and (3) health providers based on discrete punctuation numbers representing each CNA and on the basis of reduced order. To improve the efficiency of real-time monitoring of the ability of the patient, the processing requirements and processing time are reduced, and (4) it is possible to predict the key points in time when the behavioral fluctuation may occur, and avoid the behavioral fluctuation. To prevent overuse / underuse of care by interrupting the flow of treatment, (iv) clinical relevance of the patient population subset for one or more patients within the patient population subset. By analyzing a set of personal health information, the clinical outcome of each CNA is measured and (v) treatment, testing, follow-up, adherence to prescribed medications, each patient within the patient population assigned to each CNA. By comparing the differences between one provider and another in terms of cost, behavioral variability of each patient within the patient population assigned to each CNA is measured. , (B) Communication identifying the medical service under consideration for the patient whose health insurance benefits guarantee the medical services and variables selected by the payer, first with a second clinical outcome tracking and analysis module. Upon receipt from the payer via the client device, information including the following is sent to the Second Clinical Outcome Tracking and Analysis Module, the clinical outcome data for each CNA in (1') (iv), (2') (v). ) Behavioral variation data of each health provider in each CNA, cost report (4'), which includes real-time cost data for treating each patient in the patient population, assigned to the CNA in (iii). ') A graph analysis of one or more that correlates treatment costs to clinical outcomes, where this information is at key points in time for the payer to (a') potential benefits and presence for the patient. In consideration of harm, medical services are at the appropriate provision or level of service, and (b') medical services improve health outcomes by improving clinical outcomes and reducing total treatment costs. Effectiveness, (c') this service is more cost effective for the medical condition and clinical outcome being treated, compared to the absence of alternative health interventions or interventions, (d') this service If sufficient to ensure compliance with generally accepted medical practices, the payer may approve payment to the health care provider for this medical service, and (c) follow up on the third clinical outcome. And a series of communications with the patient via a second client device equipped with an analysis module provides sufficient information for the patient's complete medical evaluation and to assign the CNA to the patient. It is used to determine the optimal level of care with a low risk of disadvantage, select the care provider and the desired level of CNA-guided care, and a series of communications (i'') first clinical outcome tracking and analysis. Report a health concern to the processor of a computer server equipped with a module In response to a first communication from a patient, a second client device equipped with a third clinical outcome tracking and analysis module is diagnosed with a health concern. A second communication containing the list of tests required to diagnose as (ii'') (i'') contains the results of the tests necessary to diagnose the health concerns of the patient. In response to a third communication from, a second client device equipped with a third clinical outcome tracking and analysis module receives a fourth communication containing a list of additional tests necessary to further classify the diagnosis and condition of the condition. In response to a fifth communication from the patient, including the results of the additional tests (iii'') (i'') sent in response, the results of the test, 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. There is a health information set containing, (b'') a sixth communication including the following to a second client device equipped with a third clinical outcome tracking module, (1'') assigned CNA, (2). '') Within the assigned CNA, a geographically organized list of medical personnel treating patients, this medical personnel list is categorized by geography, clinical outcome, or cost 1 or 2 or greater. And (iv'') not In response to a seventh communication from the patient, select an optimal care plan with a low risk of interest differences and a healthcare professional who meets at least one or more of the patient's geography, cost, and outcome needs. Patients are communicably linked to a computing device in the selected healthcare professional's office with a third clinical outcome tracking and analysis module to schedule appointments for the selected healthcare professional and patient. make it easier.

In some embodiments, the sorting parameters of (a): (a) (ii) are gender, age, ethnicity, coexistence, smoking, health source, medical record number, family doctor, contract doctor, hospital. , Approved service vendor, disease-specific clinical molecular phenotype, therapeutic objectives, stage of treatment, biomarkers, and cost of treatment, or (b): (a) (iii) (1). Includes disease-specific clinical molecular phenotypes, and the sequence of numbers representing the phenotypes is determined based on a directed graph, or (c): steps (a) (iii) (4) and steps (b) (1'). ) (4') time key points include one or more of the propensity for variation from diagnosis, progression, dose change, drug change, toxicity, and desired outcome, or ( d): The step of correlating the cost of treatment in (b) (1') (4') with clinical outcome is one or more of the incidence of toxicity, severity of toxicity, therapy, and quality of life. The step of including a step that correlates with the cost of treatment and the outcome of the treatment, or receiving a transmission from the client device in (e): (b) or (c), is 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 the disease. Or (g): (c) (iii'') (b'') (2'') clinical outcomes are the therapeutic agent received, delivery dose intensity, dosing interval, dosing duration, life quality metrics, Includes one or more of therapeutic toxicity, progression-free survival, overall survival, response metrics, and death, or (h): stage (c) (iii'') (b'') ( The list of 2'') medical personnel 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 the treatment of an illness, or both.

In some embodiments, the system best at the lowest treatment cost in the assigned CNA by ranking both clinical outcomes and treatment costs in one or more bundles of given patient care services. Further includes the step of identifying clinical outcomes.

In some embodiments, the visual classification of the healthcare professional list by clinical outcome is (a) green with above-average clinical outcome, (b) yellow with average clinical outcome, and (c). ) Red indicates subaverage clinical outcome.

In some embodiments, the system, with permission from the patient and selected healthcare professional, from the first clinical outcome tracking and analysis module to the fourth clinical outcome tracking in the selected healthcare professional's office. And further includes the step of transmitting the patient's personal health information to a computing device equipped with an analysis module.

In some embodiments, the behavioral variability data for each healthcare provider in each CNA in (c) contributes to the health care provider's disadvantageous differences for each CNA patient at key points in time during treatment. Includes both necessary and unnecessary medical care that does not exist.

In some embodiments, the system 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 income between doctors, or a comparison of dollars and percentages of hospital contributions, or average income per hospital patient, or per hospital patient. Average cost, or average cost per case for each doctor, or weight average, or average cost for imaging, clinical testing, evaluation and management, medicines, medical supplies, and others for each doctor. Includes the cost of, or a combination thereof.

In some embodiments, the variable selected by the payer is a variable that identifies a particular attribute of personal health information as a desired set of characteristics, which is added to the personal health information by the payer to provide personal health information for each patient. Includes one or more attributes that identify as having the same level of importance as other health information in the patient population database, or both.

According to another aspect, the invention described herein is: (A) a first clinical outcome tracking and analysis module via a network, a computer comprising a processor with a first clinical outcome tracking and analysis module. A first client device with a second clinical outcome tracking and analysis module communicatively linked to, and between payers, and (B) a computer, network containing a processor with a first clinical outcome tracking and analysis module. A second client device with a third clinical outcome tracking and analysis module that is communicably linked to the first clinical outcome tracking and analysis module, and computer program instructions to facilitate interaction between patients. Provides a non-temporary computer-readable medium for storing, and causes the first clinical outcome tracking and analysis module to perform actions including the following when performed on a processor equipped with the first clinical outcome tracking and analysis module. (A) By grouping patients within a patient population, biological variability is considered in advance, thereby eliminating biological variability as a factor in the value of treatment and as a dominant factor in treatment outcomes. This includes the step of leaving treatment variability, which is done by (i) receiving, collecting and recording personal health information from each patient in the patient population in a database, where personal health information is in the patient population. Based on the parameters that characterize each patient, as well as information that affects survival, prognosis, or treatment, based on the latest scientific knowledge and medical guidelines, (ii) personal health information for each patient in the patient population. , Sorted using a sorting filter to provide a sorted personal health information set for this population, identify patients who meet each parameter within the patient population, and (iii) inside the first clinical outcome tracking and analysis module. By generating and assigning multiple clinical outcomes and analytical nodal addresses (CNAs) in, similar personal health information is categorized within the patient population based on the personal health information associated with the patient population. The steps of grouping the types of patients in the group to generate and assign multiple nodal addresses include (1) representing each CNA as a discrete punctuation number sequence containing prefixes, middle parts, and suffixes. , These represent a preselected set of variables that divide the sorted and categorized information into clinically relevant health information sets, (2) in the order of trillions of possibilities. The columns are reduced to a clinically meaningful sequence of reduced numbers based on a discrete punctuation sequence representing each CNA, which is initially behavioral and then the resulting clinical and cost outcome. To be able to analyze the variation from the ideal value, expressed as the best clinical outcome at the lowest possible cost, in the inevitable time needed to warn about necessary care and avoidance of unnecessary care. And thereby increasing the value of treatment, which means a better clinical outcome at the lowest possible cost, (3) based on the discrete punctuation number sequence representing each CNA and based on the reduction of the sequence. To streamline real-time monitoring of the ability of the care provider, reduce processing requirements and processing time, and (4) enable the prediction of key points in time where behavioral fluctuations may occur, and behave. To avoid variability, interrupt the flow of treatment to prevent overuse / underuse of care, and (iv) clinically with one or more patients within the patient population subset. The clinical outcome of each CNA is measured by analyzing the personal health information set associated with (v) treatment, testing, follow-up, adherence to prescribed medications, within the patient population assigned to each CNA. Behavioral variation of each patient within the patient population assigned to each CNA by comparing the differences between one patient and another in the cost of each patient. The second clinical outcome tracking and analysis module provides communication to identify the medical services under consideration for the patient whose health insurance benefits guarantee the medical services and variables selected by the payer. Upon receipt from the payer via the first client device comprising, send information to the second clinical outcome tracking and analysis module, including the following, the clinical outcome data for each CNA of (1') (iv), (1'). 2') Includes behavioral variability data for each care provider in each CNA in (v) and real-time cost data for treating each patient in the patient population assigned to the CNA in (3') (iii). A cost report, (4') a graph analysis of one or more that correlates treatment costs to clinical outcomes, where this information is potentially beneficial to (a') patients at key points in time for the payer. By ensuring that the medical service is the appropriate provision or level of service, taking into account sex and harm, (b') the medical service improves clinical outcomes and reduces total treatment costs. It is effective in improving health outcomes, and (c') this service is more cost effective for the medical condition and clinical outcomes being treated, compared to the absence of alternative health interventions or interventions. (D') If sufficient to ensure that this service complies with generally accepted medical practices, the payer may approve payment to the health provider for this medical service and further. , (C) A series of communications with the patient via a second client device equipped with a third clinical outcome tracking and analysis module is sufficient for the patient's complete medical evaluation and to assign the CNA to the patient. Providing information and using CNA to determine the optimal level of care with a low risk of adverse differences, selecting the health provider and the desired level of CNA-guided care, the series of communications is (i''. ) A second client device equipped with a third clinical outcome tracking and analysis module in response to a first communication from a patient reporting a health concern to the processor of a computer server equipped with a first clinical outcome tracking and analysis module. Necessary for diagnosing the health concerns of (ii'') (i'') by sending a second communication in response containing a list of tests necessary to diagnose the health concerns as a medical condition. Additional tests required to further classify the diagnosis and condition of the condition on a second client device equipped 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 to the fifth communication from the patient, including the result of the additional test of (iii'') (i''), the result of the test, (iii' Based on the diagnosis of') and the list of additional tests in (iii''), one CNA is assigned to the patient from multiple CNAs available in (a'') (iii), but the assigned CNA is , Contains a set of health information that is clinically relevant to the patient, and (b'') sends a sixth communication, including (1''), to a second client device equipped with a third clinical outcome tracking module, including: ) Assigned CNA, (2'') A geographically organized list of medical personnel treating patients within an assigned Nodal address, this list of medical personnel is geography, clinical outcomes, Or, classified by cost 1 or 2 or more, (iv'') optimal care plan with low risk of disadvantage, and medical care that meets at least 1 or 2 of the patient's geography, cost, and outcome needs. Select a worker In response to the seventh communication from the patient of choice, the patient is communicably linked to a computing device with a third clinical outcome tracking and analysis module in the selected healthcare professional's office. Facilitates scheduling of appointments for selected healthcare professionals and patients.

In some embodiments, the sorting parameters of (a): (a) (ii) are gender, age, ethnicity, coexistence, smoking, health source, medical record number, family doctor, contract doctor, hospital. , Approved service vendor, disease-specific clinical molecular phenotype, therapeutic objectives, stage of treatment, biomarkers, and cost of treatment, or (b): (a) (iii) (1). Includes disease-specific clinical molecular phenotypes, and the sequence of numbers representing the phenotypes is determined based on a directed graph, or (c): steps (a) (iii) (4) and steps (b) (1'). ) (4') time key points include one or more of the propensity for variation from diagnosis, progression, dose change, drug change, toxicity, and desired outcome, or ( d): The step of correlating the cost of treatment in (b) (1') (4') with clinical outcome is one or more of the incidence of toxicity, severity of toxicity, therapy, and quality of life. The step of including a step that correlates with the cost of treatment and the outcome of the treatment, or receiving a transmission from the client device in (e): (b) or (c), is 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 the disease. Or (g): (c) (iii'') (b'') (2'') clinical outcomes are the therapeutic agent received, delivery dose intensity, dosing interval, dosing duration, life quality metrics, Includes one or more of therapeutic toxicity, progression-free survival, overall survival, response metrics, and death, or (h): stage (c) (iii'') (b'') ( The list of 2'') medical personnel 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 the treatment of an illness, or both.

In some embodiments, non-temporary computer-readable media have the lowest treatment costs in CNA by ranking both clinical outcomes and treatment costs in one or more bundles of a given patient care service. Further includes the steps to identify the best clinical outcome.

In some embodiments, the visual classification of the healthcare professional list by clinical outcome is (a) green with above-average clinical outcome, (b) yellow with average clinical outcome, and (c). ) Red indicates subaverage clinical outcome.

In some embodiments, the non-temporary computer-readable medium is in the selected healthcare professional's office from the First Clinical Outcome Tracking and Analysis Module with the permission of the patient and the selected healthcare professional. 4. Further includes the step of transmitting the patient's personal health information to a computing device equipped with a clinical outcome tracking and analysis module.

In some embodiments, the behavioral variability data for each healthcare provider in each CNA in (c) contributes to the health care provider's disadvantageous differences for each CNA patient at key points in time during treatment. Includes both necessary and unnecessary medical care that does not exist.

In some embodiments, the non-temporary computer-readable medium 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 income between doctors, or a comparison of dollars and percentages of hospital contributions, or average income per hospital patient, or per hospital patient. Average cost, or average cost per case for each doctor, or weight average, or average cost for imaging, clinical testing, evaluation and management, medicines, medical supplies, and others for each doctor. Includes the cost of, or a combination thereof.

In some embodiments, the variable selected by the payer is a variable that identifies a particular attribute of personal health information as a desired set of characteristics, which is added to the personal health information by the payer to provide personal health information for each patient. Includes one or more attributes that identify as having the same level of importance as other health information in the patient population database, or both.

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

Not in scale In the figure below, similar reference numbers indicate similar elements across multiple figures.

It 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 according to an embodiment of the present disclosure that communicates with one or more computers on a network to provide a clinical outcome tracking and analysis (COTA) module to one or more user computers. FIG. 3 is a block diagram showing some of the functions provided by the COTA module according to embodiments of the present disclosure. FIG. 3 is a block diagram illustrating the use of the COTA module to sort data related to colon cancer patients according to embodiments of the present disclosure. FIG. 3 is a flow diagram of a COTA module that sorts data through specific node generation according to an embodiment of the present disclosure. FIG. 3 is a block diagram showing a directed graph for determining a numeric sequence representing a phenotypic characteristic for nodal addressing according to an embodiment of the present disclosure. It is a flowchart which shows the stage performed by a COTA module by embodiment of this disclosure. It is a flowchart which shows the step for allowing a patient diagnosed with a certain disease to optimize a treatment option according to an embodiment of this disclosure. FIG. 3 is a flow chart of a COTA module that sends an alert in response to a trigger according to an embodiment of the present disclosure. It is a graph representation which shows the mobile device which summarized the received alerts by embodiment of this disclosure. FIG. 3 is a graph representation of the prevalence of disease by subtype of cancer according to embodiments of the present disclosure. It is a graph representation of the search refined by the variable input to the COTA module according to the embodiment of the present disclosure. It is a figure which shows the list display of a plurality of variables which are related to a specific disease by embodiment of this disclosure. A graphical representation according to an embodiment of the present disclosure comprising a real-time Kaplan-Meier curve with confidence intervals for pancreatic cancer. FIG. 6 is a graph representation showing a Kaplan-Meier curve for each disease progression according to an embodiment of the present disclosure. It is a real-time benchmarking graph representation of the outcome between the two according to an embodiment of the present disclosure. It is a graph representation of a cost report according to an embodiment of the present disclosure. It is a graph representation of a treatment interface according to an embodiment of the present disclosure. It is a graph representation of a treatment interface according to an embodiment of the present disclosure. It is a graph representation of the outcome screen according to the embodiment of the present disclosure. It is a graph representation of the treatment detail report screen by embodiment of this disclosure. FIG. 6 is a graph representation of an analysis screen comparing toxicity and cost according to embodiments of the present disclosure. FIG. 6 is a graph representation of an analysis screen comparing treatment and quality of life according to an embodiment of the present disclosure. It is a flow chart of the feedback support provided to the medical staff by the embodiment of this disclosure. It is a figure which displays the embodiment of the graph representation of a different diagnostic type according to the embodiment of this disclosure. It is a figure which displays the embodiment of the graph representation of a different diagnostic type according to the embodiment of this disclosure. It is a figure which displays the embodiment of the graph representation of a different diagnostic type according to the embodiment of this disclosure. It is a graph representation showing the data generation and sorting of the COTA module for breast oncology-breast cancer with histology due to invasive ductal carcinoma from 2008 to 2013 according to the embodiments of the present disclosure. It is a graph representation showing data generation and sorting of the COTA module for breast oncology-tumor grades and stages of breast cancer from 2008 to 2013 according to embodiments of the present disclosure. It is a graph representation showing the data generation and sorting of the COTA module for breast cancer-stage IIB- from 2008 to 2013 according to the embodiments of the present disclosure. FIG. 6 is a graphical representation showing the overall survival outcome of a breast cancer patient according to an embodiment of the present disclosure. It is a graph representation showing the outcome of breast cancer according to the embodiment of the present disclosure-comparison between the two. It is a figure which shows one example of the conceptual diagram of the client device by embodiment of this disclosure. It is a block diagram which shows the internal architecture of the computer by embodiment of this disclosure. It is a flowchart which shows the patient aspect of CNA guided care by embodiment of this disclosure for a breast cancer patient. FIG. 6 is a block diagram showing the advantages of two aspects of CNA guided care. The provider-payer aspect is the elimination of pre-authorization for testing, diagnosis, and treatment selection, the elimination of pre-approval and need for recommendations to optimize per-medical payment-based refunds, and per-medical payments. Allows the transition from to risk-based refunds. The patient aspects of patients using CNA guided care include three things: First, a complete assessment of their illness (eg, physical assessment, laboratory assessment, contrast, so that they can be assigned a CNA. Etc.) to provide information to patients so that they can be obtained, secondly to show patient treatment options for medical conditions that optimize treatment and reduce the risk of disadvantageous differences, and thirdly, CNA guided care. In accordance with the procedure, show the patient a doctor in the area of the patient who has the best outcome at an acceptable cost of treatment.

Here, embodiments will be described in more detail with reference to the drawings attached to this application. In the accompanying drawings, similar and / or corresponding elements are indicated by similar reference numbers.

Although various embodiments are disclosed herein, the disclosed embodiments and the user interface as shown herein are merely exemplary of the present disclosure and can be embodied in various forms. In addition, each of the examples given in connection with the various embodiments is exemplary and is not intended to be limiting. Moreover, the drawings are not necessarily to scale and some features may be exaggerated to show details of a particular element (and any size, material, and similar details shown in the figure. It is an example and is not intended to be limited). Accordingly, the particular structural and functional details disclosed herein should not be construed as limiting and are a typical basis for teaching one of ordinary skill in the art to make various use of the disclosed embodiments. It's just that.

The present invention will be described below with reference to block diagrams and operational diagrams of methods and devices, and media related to a particular topic will be selected and presented. It should be understood that each block in the block diagram or operation diagram and the combination of blocks in the block diagram or operation diagram can be implemented using analog or digital hardware and computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, dedicated computer, ASIC, or other programmable data processor, and as a result, run through the processor of the computer or other programmable data processor. The instructions given perform the function / operation specified in the block diagram or one or more operation blocks.

In some alternative implementation configurations, the functions / operations shown within the block can be performed out of the order shown in the operation explanatory diagram. For example, two blocks shown in succession can actually be executed at substantially the same time, or these blocks may be executed in reverse order, depending on the function / operation involved. It may be done. Further, embodiments of the methods presented and described as flowcharts in the present disclosure are shown as examples to provide a more complete understanding of the art. The disclosed method is not limited to the operation and logical flow presented herein. An alternative embodiment is contemplated in which the order of the various movements is changed and the subordinate movements described as part of the larger movements are performed separately.

The clinical outcome treatment analysis described here is described in relation to the symptoms of cancer, but any clinical symptoms (eg, cardiovascular disease, metabolic disease (diabetes), immune disease (eg, cyst, rheumatoid arthritis)). , Organ transplantation, neurodegenerative disorders, lung diseases, infectious diseases, liver disorders, behavioral health sciences). The practitioner will be aware of the parameters of each of these symptoms.

Throughout the specification and claims, terms may have meanings presented or suggested in context beyond the expressly indicated 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 refer to a different embodiment. It does not refer to form. For example, the subject matter described in the claims is intended to include all or part of a combination of exemplary embodiments.

In general, 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 partial in the context in which such terms are used. It can include various meanings depending on the situation. Generally, when "or (or)" is used to associate a list such as A, B, or C, it is used in an inclusive sense with A, B, and C, and A, B, Or it is used in an exclusive sense with C. In addition, the term "one or more" as used herein is used to describe any feature, structure, or feature in a singular sense, at least in part, depending on the context. It can be used, or it can be used to represent a feature, structure, or combination of properties in multiple ways. Similarly, words such as "a," "an," or "the" can be understood to convey a singular use or multiple uses, at least in part, depending on the context. In addition, it can be understood that the word "based on" is not necessarily intended to convey an exclusive set of factors, and instead, again, in this case as well, at least in part in context. Accordingly, the presence of additional factors that are not necessarily explicitly indicated can be tolerated.

The pharmaceutical industry has invested much of its R & D investment in specialty compounds with oncology as the main category. For example, approximately 30-35% of the Phase 3 pipeline is oncology. These compounds are specialized, targeted treatments based on the latest scientific advances and are likely to require different commercial and development models than those that exist today. The current regime 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 ways to develop physicians and efficient sales and distribution channels.

The US refund model is likely to change from a per-medical payment model to a value-based payment model. Due to the Affordable Care Act, this certain element (eg, accountable care organizations (ACO) for primary care & patient centered medical home). ) (PCMHs) model) is accelerating, and there is a payer's move towards bundling payments within a specialty (eg, orthopedics). Current per-medical payment models are likely to be unsustainable for governments, employees, other payers, and / or doctors. Many physicians are also finding that the economy of per-medical payment models is unsustainable. As mentioned above, the government appears to be moving to a value-based payment model.

FIG. 1 shows, for example, a block diagram of some examples of pressure in the cancer market and some potential solutions. Faced with financial pressure, oncologist 105 is often unable to continue working with current models and is trying to find leverage in new ways. A viable oncologist solution 110 includes bundle-like aggregation and new payment models. Pharmaceutical companies (shown as "Pharma") 115 typically have much or all of their cancer pipeline as targeted treatment. In addition, the era of blockbuster medicine is ending. Moreover, "obsolete" business models are no longer appropriate and costly. Potential pharmaceutical solutions 120 include patient identification and changes in their business model. Health insurance 125 generally has an increasing need to "manage" oncology. Also, there is no internal reliable tool or function to perform this management. In addition, health care workers such as doctors cannot support health insurance. Potential health insurance solution 130 includes new payment models (eg, bundles) and cost control.

FIG. 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 on network 215 to track clinical outcomes, according to one embodiment. A block diagram of the server computer 205 (hereinafter also referred to as the server 205) that provides the analysis (COTA) module 220 to the user computer 210 and / or the user computer 230 is shown. The server 205 can generate and / or provide a service of 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 part of a web page) and is therefore accessed by the user of the user computer 210 and / or the user computer 230 via a web browser. In another embodiment, the COTA module 220 is a software application such as a mobile "app" that can be downloaded from the server computer 205 to the user computer 210 and / or the user computer 230. In a further embodiment, the COTA module 220 provides a user interface to enable the functions described herein.

Computing devices such as the server computer 205, the user computer 210, and the user computer 230 are capable of transmitting and receiving signals via a wired or wireless network, or process or store signals in memory such as physical memory states. Have the ability to do. Devices capable of acting as servers are integrated with a variety of features, such as dedicated rack-mounted servers, desktop computers, laptop computers, set-top boxes, and two or more features of the devices described above. Can include devices and the like. Servers can vary widely in terms of configuration or functionality, but in general, servers 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 Windows® servers, Mac. (Registered Trademarks) One or more operating systems such as OS X (Registered Trademarks), Unix® (Registered Trademarks), Linux® (Registered Trademarks), FreeBSD® (Registered Trademarks), etc. can also be included.

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

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 calendar service, photo service, etc. can be further provided. Examples of content can include text, images, audio, video, etc., which are processed in the form of physical signals, such as electrical signals, or in memory, such as physical states. Can be stored. Examples of devices that can act as servers include desktop computers, multiprocessor systems, microprocessor type or programmable household appliances and the like.

In one embodiment, server 205 hosts or communicates with database 240. The database 240 can be stored locally or remotely from the server 205. In one embodiment, the COTA module 220 accesses, retrieves, or sorts the data stored in the database 240. The COTA module 220 can also read information on network 215 (eg, from the Internet). Database 240 can store patient data or other patient medical information. For example, the data entered in the database or COTA module 220 should come from specialists in their respective fields (eg, oncologists with experience such as 5, 10, 15, 20, 25, 30 years or more). Can be done. Data can be manually or automatically entered into the database 240 and / or the COTA module 220.

The network can bind devices so that they can communicate, for example between a server and a client device or other type of device, including between wireless devices coupled over a wireless network. .. Networks can also include, for example, mass storage such as network attached storage (NAS), storage area networks (SAN), or other forms of computer or machine readable media. The network can include the Internet, one or more local area networks (LANs), one or more wide area networks (WAN), wired type connections, wireless type connections, or any combination thereof. .. Similarly, subnetworks that can utilize different architectures or that comply with or are compatible with different protocols can interoperate within a larger network. For example, different types of devices can be made available to provide interoperability capabilities for different architectures or protocols. As an exemplary embodiment, routers can otherwise provide links between 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 of skill in the art. In addition, computing devices or other related electronic devices can be remotely coupled to the network, for example via telephone lines or links.

Wireless networks can connect client devices to the network. As the wireless network, a stand-alone ad hoc network, a mesh network, a wireless LAN (WLAN) network, a cellular network, or the like can be used. A network topology can further include systems such as terminals, gateways, routers, etc. coupled by wireless radio links, etc., which move freely and randomly or organize themselves arbitrarily, so that the network topology , Sometimes it can change 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 technology. It can be used further. Network access technology can enable wide area coverage for devices such as client devices with varying degrees of mobility, for example.

For example, the network is a global system for mobile communications (GSM®), universal mobile telecommunications (UMTS), general packet radio service (GPRS), Enhanced Data GSM® Environment (EDGE). , 3GGP Long Term Evolution (LTE), LTE Advanced, Wideband Code Division Multiple Access (WCDMA®), Wireless®, 802.1b / g / n, etc. via one or more network access technologies RF or wireless type communication can be enabled. The 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 or within networks.

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

The COTA module 220 can establish an effective method for managing patients and yields 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, an oncologist). In one embodiment, the COTA module 220 is an analytical tool that sorts cancer into 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.

Overall survival can be a time-to-live (survival), eg, a test endpoint usually expressed in months. Often, intermediate values are used so that the test endpoint can be calculated once 50% of the subjects have reached the endpoint. An example is disease-free survival, which is usually used to analyze the outcome of treatment of topical diseases that make patients apparently disease-free, such as surgery or surgery and postoperative adjuvant therapy. To. In disease-free survival, the event is more relapsed than death. People who have relapsed are still alive, but are no longer considered disease-free.

Progression-free survival is the period during and after dosing or treatment while the disease being treated (eg, cancer) is not exacerbated. It may be used as a metric to study the health of people with illness to determine how well new treatments work.

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

In one embodiment, the COTA module 220 can warn the user of the user computer 210 and / or the user of the user computer 230 (eg, a healthcare professional) to provide relevant information at a key moment. The COTA module 220 can also enable communication and coordination between healthcare professionals, as well as content publishing (eg, by healthcare professionals). In one embodiment, the COTA module 220 allows healthcare professionals to make risky contracts with payers (eg, bundled payments).

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

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

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

In one embodiment, the 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, comorbidities, smoking, medical record numbers, health sources, family doctors, contract doctors, hospitals, licensed service vendors (eg pharmacies), illnesses. It can include a unique clinical molecular phenotype, therapeutic purpose, stage of treatment for disease progression, and biomarkers. The parameters can be simple indicators (eg, positive, negative, no access), numerically based parameters (eg, tumor size), standard based parameters (eg, tumor grade), and the like. The parameters can be received by the COTA module 220 as user-selected inputs. Each patient has different mortality, morbidity, treatment, and cost, so it can be sorted to the highest levels of clinical and molecular fidelity at diagnosis 310. The term "highest level clinical and molecular fidelity" refers to the highest level of patient information available in accordance with the latest scientific and / or medical guidelines as recognized in the relevant discipline. For example, if there are 10 tests available for lung cancer, the results of the 10 tests represent the highest level of fidelity for lung cancer. 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 field. In one embodiment, the COTA module 220 collects all information that affects patient survival and / or prognosis and / or treatment, based on the latest scientific and / or medical guidelines.

In addition, the COTA module 220 performs outcome tracking and analysis 320. The COTA module 220 tracks outcomes in real time. In one embodiment, the Outcome Tracking element is progression-free survival, overall survival, performance status / quality of life metrics, incidence / severity of toxicity (eg, drug or drug damaging an individual). Includes (how much), mortality, and actual drug use (eg, delivery dose intensity, dosing interval, and duration of treatment). Other types of outcomes are also envisioned.

The ECOG performance status / quality of life metric refers to a way in which a patient's quality of life can be tracked over time. It is part of the disease-specific clinical molecular phenotype of the demographic parameter, i.e., the stage of patient health at the start of treatment and is included within sorting. For example, a comparison of the ECOG at the start of treatment (eg, ECOG3) with the post-treatment ECOG (eg, ECOG2) reflects the effect of the treatment.

In one embodiment, the exemplary parameters of the Toxiity to Therapy component are expression rate and severity. In one embodiment, the COTA module 220 enables risky financial contracts between payers and suppliers, so that both reduce diversity, waste, and inefficiency, but nonetheless. Can achieve the desired outcome.

The COTA module 220 also communicates such as alert 330 when there is a tendency for variation from key points such as diagnostics, progression, dose changes / drug changes / toxicity, and / or desired outcomes. It can also be transmitted 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 the healthcare professional with an alert that identifies a particular patient being searched 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. Alerts can identify specific patients who are good candidates for a particular drug.

FIG. 4A is a block diagram showing a step of sorting data related to a colon cancer patient according to one embodiment. Although described in relation to cancer, such as colon cancer, the description and illustration can be applied to any type of cancer, or in another embodiment, any type of disease for which patient-related data is present. Can be applied to.

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

Patient information is typically stored in an electronic medical record (EMR). However, the information contained in the EMR can be too much, and it is therefore difficult for healthcare professionals to find the particular information of interest from the vast amount of information stored within the EMR. Moreover, most of the information in the EMR is not related to the information that healthcare professionals are searching for. Unlike EMR, which aims to retrieve all or most of the patient-related data in and out of the clinic, the COTA module 220 allows the user to search for specific data related to a particular patient. Modules are targeted. Therefore, the COTA module 220 can sort the data to retrieve certain special information. The data received by the COTA module 220 is usually via a web page and is individual (eg, usually the user has one or more choices in the drop-down menu or via one or more checkboxes. Provided by selection).

The COTA module classifies, sorts, and facilitates groups of patient types based on these variable results in a unique COTA nodal address (CNA) designation and embodies these classifying variables. In one embodiment, data is captured into a system via a human user or technical process, eg, an API, a layer within an application (meaning a portion of an application performing a particular function) of information. For example, whether the information is correct, whether the information is corrupted, what information exists, what information is missing / hole in the information, how the information is. It looks at the spelling to see if it is formatted, evaluates it, fixes any problems with the information the application has detected so far, and assigns a CNA to that information set. In one embodiment, the CNA is an address for classifying similar data. In one embodiment, the COTA module identifies relationships between different characteristics within a group, allowing COTA to classify information about any patient within the group. In one embodiment, the information set contained within the database is pre-assigned with CNA. In one embodiment, the COTA module captures a large amount of information containing many different attributes, allowing the user to identify some of the attributes as a set of characteristics, which information becomes other information in the database. Add an attribute to the information to indicate that it is similar, i.e., this information is of the same type / value as another piece of information. Thus, a nodal address is a number that allows a user to specifically compare a similar patient with a similar patient. This allows the biodiversity of outcomes to be minimized, resulting in greater accuracy in the effect of the therapeutic agent on the outcome.

In one embodiment, the user wants to validate the personal health information (PHI) from the patient, ensure that the information is correct in all respects, and then assign the appropriate CNA. Personal Health Information (PHI), as used herein, can be used to identify an individual patient and is created, used, or disclosed in the process of providing medical services such as diagnosis or treatment. , Medical records or any information in a designated recordset. Examples of personal identifiers in PHI are, but not limited to, geographic divisions that are finer than state, including name, location, address, city, county, province, and postal code, directly related to the individual, birthday, date of admission, and discharge. All elements of the date including the date, date of death, and all ages over 89 (excluding the year), and all elements of the date indicating such age (including the year), phone number, fax number, email. Address, social security number, medical record number, health insurance beneficiary number, account number, certificate number / license number, vehicle identifier and serial number including number 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, frontal images and any equivalent images, and any other unique identification number, characteristic, or code (but encodes the data). (Not a unique code assigned by the investigator). This PHI is entered into the browser for patient A. The PHI is sent to the classification layer, the CNA is assigned, the CNA defines the attributes of the patient A's record, and then is put into the database, i.e., the set of patient attributes classified into this CNA type is the CNA. Is tied to. Once this is done, the next time the user logs in to 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 handle the symptoms / attributes of this patient, i.e., the user has an overview of how this type of patient is associated with other patients whose information is contained in the database. obtain.

FIG. 4B is a COTA flow diagram for classifying and sorting data as described above through the generation of specific nodes according to one embodiment. As shown in FIG. 4B, the experts are gender or gender 440 (variable A), race 445 (variable B) ,. .. .. And select a variable called KRAS450 (variable G). K-Ras is a protein encoded by the KRAS gene in the human body. While the protein products of the normal KRAS gene perform essential functions 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 data 430 classified and sorted for these variables (eg, variables 440, 445, 450) to generate a unique COTA node 455. The COTA module 220 applies these nodes to the classified and sorted data to produce more clinically relevant results. Nodes are created as a set of preselected variables and these nodes are applied to further filter the classified and sorted data. Nodes are represented as nodal addresses that indicate preselected variables. Variables can include, for example, diagnosis, demographics, outcomes, phenotypes, and the like. A phenotype is a compound of human observable characteristics or characteristics such as human morphology, development, biochemical or physiological characteristics, phenology, behavior, and the product of behavior. The phenotype derives from the expression of human genes, as well as the effects of environmental factors, and the interactions between the two. In one embodiment, node variables are selected by experts in the relevant discipline to divide the data into clinically relevant results.

The COTA node 455 is represented as a nodal address within the COTA module 220. In one embodiment, the nodal address is represented as a list of selected variables (as a function of letters representing variables and numbers representing selections within variables). For example, as shown in FIG. 4B, node 455 is A1-2 (A represents a gender or gender variable, 1-2 is represented by a block surrounding both female and male variables of gender variable A. Represents female and male patients). Node 455 also includes B1-4 as it contains racial variables with all of the subordinate variables selected (indicated by the box surrounding all of the racial 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 period-separated sequence of digits, each of which is one or more variables (eg, disease, phenotype, treatment type, progression / follow-up, gender). Etc.). For example, the first number string can represent a particular disease, the second number string can represent the type of disease, the third number string can represent the subtype of the disease, and the fourth. The sequence of numbers can represent a representational type. Therefore, in this example, the first number string can be 01 indicating cancer, the second number string can be 02 indicating breast oncology, and the third number string can be 02 indicating breast cancer. It can be 01, and the fourth digit string can be 1201, which represents a certain characteristic of the phenotype, and as a result, the nodal address is 01.02.01.1201. It should be understood that a nodal address can include any number of digits and is not limited to four sequences.

In one embodiment, a sequence of numbers representing a phenotype can be provided by representing the characteristics of the phenotype as a directed graph. FIG. 4C draws a directed graph 460 showing the characteristics of a phenotype in order to provide a sequence of numbers representing the phenotype according to one embodiment. The directed graph 460 includes nodes representing phenotypes and edges representing relationships between the nodes. The graph is traced from the root "start" node 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 sequence of digits for the selected phenotype indicating a Caucasian male would be represented as 11. Other types of combinations are also available. Advantageously, by representing the characteristics of the phenotype as a directed graph, it is possible to add other nodes without changing the entire structure. The appearance of the screen is a result of CNA and the appearance can be changed, but it is desirable to present information.

Node 455 provides COTA module 220 with the ability to match resources and alerts specific to each associated phenotype. Resources can be information, content, links to live support, and so on. 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 progress.

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 professionals, hospitals, groups, insurance companies, etc. to optimize patient care and / or costs. In one embodiment, the bundle can show many image scans, drug or drug selection, schedule of when to administer the drug, surgery or procedure, frequency and frequency of follow-up visits, and the like. Bundling of patient care services can be particularly useful with respect to risk contracts. For example, each bundle corresponding to a nodal address (related to 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 specific illnesses. It also reduces the cost of processing and retaining records. In addition, healthcare professionals will be informed in advance of a predetermined course of treatment, which motivates physicians to seek better outcomes at lower cost.

The International Classification of Diseases and Related health Problems 10th Revision (ICD-10), as classified by the World Health Organization (WHO), is a disease, sign, symptom, abnormality, abnormality, as classified by the World Health Organization (WHO). , Discomfort, social conditions, and encoding of external factors of injury or illness. These code sets are considered classification code sets and are of a higher standard than any other medical code set such as the Systemized Nomenclature of Medicine (SNOMED) used by the federal government system for the 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 for a more effective course of treatment than the traditional ICD-10 code, especially when it comes to treatment planning when the patient is misdiagnosed.

FIG. 5A is a flow chart showing the steps performed by the COTA module 220 according to one embodiment. At stage 505, the COTA module 220 collects data records. Each data record contains data related to the disease (eg, cancer). The data record may include patient data of a patient suffering from or previously suffering from the disease. For example, data records can include diagnostics, demographics, outcomes, costs, or other relevant information. Data records can be collected from electronic databases (eg, electronic medical records) provided by users (eg, healthcare professionals, professionals, specialists, etc.) or from any other source. In one embodiment, the COTA module 220 can store data records in the database 240.

At step 510, the COTA module 220 receives one or more parameters to sort the data recordings. One or more parameters can be received from the user computer 210 as user select input. One or more parameters can include, for example, diagnostics, demographics, outcomes, costs, or any other parameter.

At step 515, the COTA module 220 sorts the data recordings 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 (fidelity) based on the latest scientific and / or medical guidelines recognized in the relevant field. 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 variables of the nodal address are selected by experts in this field. Nodal addresses can be represented as multiple digits separated by periods. Each sequence of digits can represent one or more variables (eg, disease, disease type, disease subtype, phenotype, or any other related variable). Other representations of nodal addresses are also envisioned.

At stage 525, data records of clinically relevant patients are analyzed. Analysis of data records can include (eg, real-time) tracking of clinical outcomes of patients associated with the disease. Outcomes are, for example, delivery dose intensity, received therapeutic agent, dose, dosing interval and duration, toxicity incidence and severity, cost, progression-free survival (PFS), overall survival (OS), response. Efficiency and the like can be included. The COTA module 220 can compare the tracked outcomes between patients. The COTA module 220 also determines, on the basis of follow-up, whether a particular physician associated with the tracked patient is treating a patient according to the treatment of another physician who treats another (similar) patient. You can also. In one embodiment, the COTA module 220 determines this based on the outcome of many patients.

In another embodiment, the analysis of the data record may include updating at least a portion (eg, in real time) 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 the drug XYZ and has been taking it for 2 years, and is currently in remission for the past 3 years. If the COTA module 220 determines this information from the patient ABC tracking, the module 220 can update the data records associated with the patient ABC with this information.

In another embodiment, the analysis of the data recording comprises performing an analysis to determine the survival rate of the patient, for example by creating a Kaplan-Meier curve. The Kaplan-Meier curve is a curve showing the 5-year survival rate that can occur, for example, with respect to one doctor (or health care worker) or with respect to a group of doctors (or health care workers). Kaplan-Meier curves can be created for overall survival and / or progression-free survival. Other types of analysis are also envisioned.

To facilitate the analysis, the COTA module 220 may also include an analysis tool for the user computer 210 and / or the user computer 230. The analysis tool can be a user interface accessible via a web page, tabs on an existing web page, software applications, apps, etc. The user interface as shown in the figures of this specification 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. Communication can be in the form of alerts to users. In one embodiment, the COTA module 220 communicates classified and sorted data records and / or updated data records to the user computer 210 and / or the user computer 230. For example, the COTA module 220 communicates tables, charts, lists, links, etc. that allow users to access sorted or updated data records. In another embodiment, the COTA module 220 can send a notification (eg, associated with it) to the user computer 210 and / or the user computer 230 along with the data recording. 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, to pharmaceutical companies, health insurance, managed medical associations, insurance companies, and the like. The COTA module 220 can transmit communications to the user computer 210, the user computer 230, or any other entity (eg, via network 215).

The COTA module 220 is used by many people, workers, and / or companies and can benefit these people. For example and as mentioned above, the highly specialized pipeline of pharmaceutical companies has many aspects (eg, development / post-marketing surveillance including Phase 4 trials, marketing, sales, pricing, and contracts). It seems that a new business model is needed. In one embodiment, pharmaceutical company experts 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 drug. The COTA module 220 can enable accurate patient identification through its own sorting and nodal addressing functions. In one embodiment, the COTA module 220 users (eg, pharmaceuticals) one or more patients who are or will be good candidates for a particular drug released or under development by a pharmaceutical company. Provides matching capabilities that allow companies to search (in real time).

In addition, the COTA module 220 can benefit health insurance. As mentioned above, cancer treatment 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 COTA module 220 (similar to what health insurance once did with drug benefits). It reduces costs, such as by reducing total treatment costs, and provides cost offsets by replacing pathways, reducing expensive pre-licensed infrastructure, and reducing other people who “manage cancer”. In addition, the US Affordable Care Act stipulates that 85% of insurance premiums must be used for clinical practice-related activities in contrast 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 oncologists.

In one embodiment, the COTA module 220 can benefit organizations engaged in diagnostic methods or tools. Organizations engaged in diagnostic methods or tools, such as those involved in next-generation gene sequencing, will likely require efficient education, marketing, and sales / distribution channels. The use of the COTA module 220 benefits such organizations as it can accurately sort and identify patients and send time-based alerts to doctors (or other healthcare professionals). be able to.

FIG. 5B is a flowchart according to an embodiment showing steps for allowing a patient diagnosed with a disease to optimize his or her treatment options. The step 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 can interact with the user (eg, patient) through 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 medical worker) through network 215 via the running COTA module 220. The COTA module 220 can include a server computer 205, a user computer 210, and a plurality of instances of the COTA module 220 for execution on the user computer 230.

At step 550, the COTA module 220 running on the server computer 205 receives communication from the patient user computer 210. For example, the communication can include indications that the patient is looking for treatment options, healthcare professionals, or any other indication. The COTA module 220 running on the server computer 205 is communicably linked to a database 240 containing personal health information for a patient population that may or may not contain 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 step before receiving the communication from the patient. For example, COTA module 220 running on 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, personal health information can be analyzed as described herein by measuring the behavioral variability of each healthcare provider assigned to each nodal address.

At stage 555, the patient's 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 illness the patient has been diagnosed with. In one embodiment, the disease is selected with specificity by the patient via the COTA module 220 running on the user computer 210 from the list and sublist of diseases available in database 240 of computer server 205. .. The COTA module 220 running on the server computer 205 determines a list of tests for further classification of the disease based on the received patient health information.

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

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

At stage 570, a nodal address is assigned to the patient based on the results of the test. The assigned nodal addresses can be assigned from a plurality of nodal addresses generated for the patient population during the preprocessing stage 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 health information sets. In one embodiment, the assigned nodal address is associated with one or more bundles of predetermined patient care services (eg, treatment plans) for the treatment of the disease. Predetermined patient care services can be determined by one or more of a healthcare professional, hospital, medical group, or insurance company. A bundle of pre-determined patient care services can provide a pre-determined course of treatment. A pre-determined bundle of patient care services can provide cost certainty for illness.

At stage 575, COTA module 220 running on server computer 205 provides a geographically organized list of assigned nodal addresses and medical personnel treating patients within the assigned nodal addresses. It is transmitted to the COTA module 220 running on the user computer 210. The geographically organized list of healthcare professionals can be visually categorized by clinical outcome. In one embodiment, the geographically organized list of healthcare professionals can be visually categorized by clinical outcome by color. For example, in a geographically organized list of healthcare professionals, green has a better than average clinical outcome, yellow has an average clinical outcome, and red has a worse than average clinical outcome. , Can be visually classified.

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

In one embodiment, the patient user computer 210 is communicably linked to the health care worker user computer 230 (eg, in the office of the selected health care worker candidate) to select the health care worker candidate. Smooth the schedule setting of medical appointments with. In another embodiment, the COTA module 220 running on the server computer 205 transmits personal health information of the patient to the COTA module 220 running on the user computer 230 of the healthcare professional.

Advantageously, the stage in FIG. 5B allows 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 an alert provided by the COTA module 220 according to an embodiment. In one embodiment, physicians or other healthcare professionals are alerted based on their preferences. These preferences can be set by the healthcare professional / physician and may include, for example, a trigger 610 for an alert and / or a technique used to provide an alert. Triggers for alerts are, for example, new patient diagnosis 615, updates to diagnosis, real-time scheduled events, group member changes (eg, new genes identified as possibly changing grouping, and new genes. / Or someone leaves the group), toxicity and / or dose intensity change 620, disease progression 625, administration of a particular drug, trend of variation from the desired outcome 630, and / or expected time or cycle Dependent alerts 635 (eg, side effect alerts and / or diagnostic test reminders) can be included. The alert can 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, addition of content to software libraries or web pages, and / or any other message or communication sent to or accessed by the user computer 210 can also be envisioned. Although described above as providing an alert, the trigger can be any action that triggers the execution of any other action by the COTA module 220.

FIG. 7 is a graph representation of a mobile device 705 (eg, a user computer 210) that summarizes the alerts received by the device 705 according to one embodiment. As shown in FIG. 7, the COTA alerts received are Neocolon CA (New Colon CA) 710, New Renal Cell CA 715, Dose Adjustment 720, and Drug Discontinuation. Titles or themes, such as 725, New Progression 730, New Breast CA 735, CHOP Third Cycle Alert 740, neutrophil hypoplasia risk alert 745, and available clinical trials 750. Listed for each. CHOP is an abbreviation for a combination of drugs used in chemotherapy, including cyclophosphamide (Cytoxan / Neosar), doxorubicin (or adriamycin), vincristine (oncovin), and prednisolone, for example, to treat non-Hodgkin's lymphoma. Used to do.

The COTA module 220 provides a specific disease data set (eg, on-demand, in real time), including, for example, disease morbidity (eg, by COTA sort), progression-free survival by progression, and / or overall survival. ) Can be provided. In one embodiment, the COTA module 220 can provide drug utilization datasets, such as data related to total or partial treatment, toxicity, and / or treatment changes.

FIG. 8 shows a graph representation 800 of disease prevalence by subtype of cancer that can be provided by the COTA module 220, according to one embodiment. Here, COTA Graph 800 relates to lymphoma from 2010 to 2013. The user can use the graph search input section 810 to narrow down the information to be graphed. The graph 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). Edition) Codes, comorbidities, disease progression, gender, age, date range, race, diabetes, smoking history, past chemotherapy and radiotherapy history, etc. can be included.

FIG. 9 is a graph representation 900 of a sort based on variable input to the COTA module 220, which can be provided by the COTA module 220 according to one embodiment. Graph representation 900 shows a COTA graph of Hodgkin lymphoma 2010-2013, divided by gender. Graph representation 900 shows statistics 910 of various patients suffering from this disease, graphed in representation 900. FIG. 10 shows an exemplary list of variables 1005 (variables shown herein relating to lymphoma) associated with a particular disease, according to one embodiment.

FIG. 11 is a graph representation 1100 comprising a real-time Kaplan-Meier curve with confidence intervals for pancreatic cancer, which can be provided by the COTA module 220, according to one embodiment. As mentioned above, the Kaplan-Meier curve is a curve indicating the 5-year survival rate that can be developed, for example, with respect to one physician (or healthcare professional) or with respect to a group of physicians (or healthcare professionals). Kaplan-Meier curves can be created for overall survival and / or progression-free survival. The user indicates his / her graph search variables in the graph search input section 1110.

FIG. 12 is a graph representation 1200 showing a Kaplan-Meier curve for each disease progression, which can be provided by the COTA module 220, according to one embodiment. Line 1205 is for all pancreatic cancers and thick line 1210 is for pancreatic cancer with initial exacerbations.

FIG. 13 is a graphical representation of real-time benchmarking of bilateral outcomes 1300, which can be provided by the COTA module 220, according to one embodiment. Graph 1300 includes a curve 1305 for the outcome of John Doe doctor treating pancreatic cancer and a curve 1310 for the outcome of the remaining doctors treating pancreatic cancer. FIG. 13 also includes a meter 1320 to measure whether the outcome of Dr. John Doe passes positively or negatively.

FIG. 14 is a graphical representation of the cost report 1400 associated with (eg, provided by) COTA module 220, according to one embodiment. The appearance of the screen is the result of CNA and its appearance is mutable, 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 specific practices. In one embodiment, the COTA module 220 tracks the costs of various treatments, doctors, hospitals, etc. in real time. As shown in FIG. 14, Cost Report 1400 shows a comparison of average costs per revenue among physicians. The Cost Report 1400 also includes contribution benefits from hospital dollars and percentages, average hospital revenue and costs (eg, average revenue per patient, average cost per patient), average cost per physician case (eg, for each physician). Average cost per case, weight average, average cost per physician's income (eg, contrast, clinical examination, assessment and management, pharmaceuticals, medical supplies, and other costs for each physician) Other comparisons such as may also be included.

15A and 15B are graphical representations of the treatment interface 1500 associated with (eg, provided by) COTA module 220 for facilitating the link between outcome and treatment according to one embodiment. .. As shown in FIG. 15A, the treatment interface 1500 is a list of different types of treatments administered (or declined by a patient) to a patient suffering from breast cancer, such as surgery, antitumor agents, cell therapy, radiation therapy. Can be included. Treatment can be prepared 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 the initial diagnosis and select a drug within or from a plurality of categories. can do.

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

FIG. 16 is a graphical representation of the outcome screen 1600 for facilitating outcome tracking according to one embodiment. The outcome screen 1600 can facilitate, for example, diagnosis (ie, zero exacerbations), first exacerbations, and outcome tracking from second to fourth exacerbations, each exacerbation being considered a different disease. The outcome screen tabs are the diagnosis date (eg, in one or more drop-down menus or other fields), the start and end dates of treatment, the response to treatment (eg, complete, incomplete, stable), and the response. It can include fields for notes about the day, reaction (eg, incomplete fields, CR-RA-Pet Negative fields, CR fields, etc.), and end-of-track data that can include final contact and death. The outcome screen 1600 can also enter the toxicity of the drug treatment, what has occurred (eg, discontinuation, continuation, no change, drug dose change, and number of times), number of delays, number of drug changes, and / or reduced number of times. It can also include an input area to do so. In one embodiment, the user of the COTA module 220 can flag the patient.

FIG. 17 is a graphical representation of the detailed treatment report screen 1700 showing a comparison between cost and outcome according to one embodiment. The treatment detail report screen 1700 correlates treatment costs with clinical outcomes to optimize the value of treatment. Cost and financial data can be collected and analyzed by the hospital and by the physician over a given period of time (eg, 5 years). Cost and financial data can be represented in one or more cost ranges. In one embodiment, the cost range includes a cost range of over $ 25,000, a cost range of 1705, a cost range of $ 10,000 to $ 25,000, and a cost range of less than $ 10,000, 1715. 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 the analysis screen 1800 provided by the COTA module 220 showing a comparison between toxicity and cost according to one embodiment. The appearance of the screen is a result of CNA and the appearance can be changed, but it is desirable to present information. Analysis screen 1800 correlates the incidence and severity of toxicity with treatment costs and outcomes. Toxicity can be expressed numerically (eg, in a range), by criteria (eg, grade), and the like. For example, as shown in FIG. 18, toxicity is represented as toxicity grade 1-4 based on the Terminology Criteria for Harmful Events (CTCAE) classification. Toxicity grades are compared graphically with costs. The analysis screen 1800 can be used to optimize the value and effectiveness of the treatment, the value being efficacy / cost. In one embodiment, the COTA module 220 attempts to obtain high efficacy and low cost.

FIG. 19 is a graphical representation of the analysis screen 1900 provided by the COTA module 220 showing a comparison between treatment and quality of life according to one embodiment. The treatment can be represented on the analysis screen 1900 for each therapeutic agent. However, other forms of treatment such as surgery, procedures are also envisioned. In one embodiment, treatment comprises the incidence, severity, and toxicity of treatment. Quality of life can be measured based on the average ECOG (Eastern Cooperative Oncology Group) scale, ranging from 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 the degree of progression of the patient's disease, how the disease affects the patient's daily living ability, and appropriate treatment and prognosis assessment.

FIG. 20 is a graph representation 2000 of an alert provided to a healthcare professional according to one embodiment. The appearance of the screen is a result of CNA and the appearance can be changed, but it is desirable to present information. In one embodiment, the information in the alert helps the user make future decisions. In one embodiment, the information in the alert provides a set of attributes that have occurred in the past period. In one embodiment, this proactively influences user decisions and provides a reactive digest report of how healthcare professionals / physicians have done in the last week, last month, and previous quarter. In one embodiment, there are different alerts for different users, each of which can influence the decisions made by the user. Alerts can be used for real-time orbit correction to drive the highest value, for example, if the treatment administered is prone to variation from the desired outcome. In block 2005, the definition is triggered based on clinical data. Definitions are triggered using any criterion, such as new disease diagnosis, disease progression, patient response, patient characteristic changes, dose changes / drug toxicity changes, or trends in variation from the desired outcome. can do. Criteria can be adjusted based on the disease and its parameters. Alerts 2010-A, 2010-B, 2010-C (collectively referred to as Alert 2010) are transmitted based on the triggered definition. It should be understood that Alert 2010 can include any number of alerts. Alert 2010 can include content or links to content. Alert 2010 may be sent to the attending physician, other healthcare professional, hospital, pharmaceutical company, or any other person or entity.

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

In one embodiment, the content 2015 can include feedback support to a healthcare professional 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, and yellow is good enough, but you need to be careful. It can mean that there is, and red can mean that the user needs to pay attention to what is about the health care worker's approach to this disease. Other implementations of the feedback indicator are also available.

21-24 show graph representations of different diagnostic types according to one or more embodiments. FIG. 21 shows a diagnostic screen 2100 for gastroenterological 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 chest oncology (eg, lung cancer). The diagnostic screens 2100, 2200, 2300 include many different parameters such as disease test or aspect. Parameters can be represented as simple indicators, numerically based parameters, standard based parameters and the like.

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

FIG. 25 shows a graphical representation of the report screen 2500 showing data generation and sorting of the COTA module 220 for breast oncology. Report screen 2500 shows tumors for all stages of grade 2 breast cancer from 2008 to 2013 according to one embodiment.

FIG. 26 shows a graphical representation of the reporting screen 2600 showing data generation and sorting of the 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 the report screen 2600 shows all stage IIB breast cancers by progesterone receptor status.

FIG. 27 shows a graphical representation of the analysis screen 2700 showing the overall survival outcome of a breast cancer patient according to one embodiment. FIG. 28 shows a graph representation 2800 showing the survival outcome of breast cancer according to one embodiment as a comparison between Dr. John Doe (thick line) and total (non-thick line).

In one embodiment, the "node" described above represents any possible sequence of variables shown in one or more of the graph representations (eg, one or more of FIGS. 21-27).

As shown in the example of FIG. 29, the 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, a user computer 210. The memory 2930 can be a permanent storage and interface with the computer bus 2925. Memory 2930 includes RAM 2932 and ROM 2934. ROM2934 includes BIOS2940. The memory 2930 incorporates an operating system 2941, an application program 2942, a device driver, and a program code, and / or a function described herein, eg, one or more of the processing flows described herein. It interfaces with the computer bus 2925 to provide the information stored in the memory 2930 to the CPU 2922 during execution of a software program such as software modules 2943, 2945 including possible processing steps. The CPU 2922 first loads a computer executable processing stage 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 in order to execute the loaded computer executable processing steps. The stored data, for example, the data stored by the storage device, can be accessed by the CPU 2922 during the execution of the computer executable processing stage.

One or more persistent storage media 2944 is a computer-readable storage medium 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 device drivers, web pages, content files, plays such as one or more of digital camera drivers, monitor drivers, printer drivers, scanner drivers, or other device drivers. It can also be used to store lists and other files. One or more permanent storage media 2206s may further include program modules and data files used to implement one or more embodiments of the present disclosure.

For the purposes of the present disclosure, computer readable media may store computer data, which may include computer program code executable by the computer in machine readable form. By way of example, but not limited to, a computer-readable medium can include a computer-readable storage medium for storing tangible or fixed data, or a communication medium for the temporary interpretation of a signal containing a 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. Includes 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 are, 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 that can be used to tangibly store the desired information or data or instructions and is accessible by a computer or processor.

The client device 2905 also has a power supply 2926, a network interface 2950, an audio interface 2952, a display 2954 (eg, a monitor or screen), a keypad 2965, 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®, optical link), digital I / O port ( RS232, USB, DisplayWire, Modem), expansion slots (PCMCIA, ExpressCard, PCI, PCIe) can also be included.

For the purposes of the present disclosure, a module may include software, hardware, or firmware (or a combination thereof) system, processing, or function, or the processing, features, and / or functions described herein (mutually by humans). These components that perform or facilitate (with or without action or augmentation). Modules can include submodules. The software components of the module can be stored on computer-readable media. Modules can be integrated with one or more servers, or loaded and run by one or more servers. One or more modules can be grouped into one engine or one application.

FIG. 30 is a block diagram showing the internal architecture of an embodiment of a computer such as a server computer 205, a user computer 210, and / or a user computer 230 according to one or more embodiments of the present disclosure. Computer as referred to herein refers to any device with a processor capable of executing logical or coded instructions, to name a few, servers, personal computers, set-top boxes. , Tablets, smartphones, pad computers, or media devices. As shown in the example of FIG. 30, the 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. Interfaces to the computer bus 302 are further such as one or more persistent storage media 3006, network interface 3014, memory technology 3004, such as random access memory (RAM), runtime temporary memory, read-only memory (ROM), floppy. Media disk drive interface 2308, an interface for drives that can read and write to and from media including removable media such as CD-ROM, DVD, display, an interface for monitors or other display devices. Interface 3010, keyboard interface 3016, which is an interface for keyboards, pointing device interface 3018, which is an interface for mice or other pointing devices, and various other interfaces not individually shown, such as parallel and serial port interfaces. There is a universal serial bus (USB) interface and so on.

The memory 3004 is a computer executable process incorporating an operating system, an application program, a device driver, and a program code, and / or a function described herein, for example, one or more of the processing flows described herein. It interfaces with the computer bus 3002 to provide the information stored in the memory 3004 to the CPU 3012 during the execution of a software program such as a software module including steps. The CPU 3012 first loads a computer executable processing stage 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 in order to execute the loaded computer executable processing steps. The stored data, for example, the data stored by the storage device, can be accessed by the CPU 3012 during the execution of the computer executable processing stage.

As mentioned above, one or more persistent storage media 3006 is a computer readable storage medium 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 digital camera drivers, monitor drivers, printer drivers, scanner drivers, or other device drivers, web pages, content files, playlists, and other files. It can also be used to store the above device drivers. One or more permanent 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 (as described above) includes microphones, video cameras, TV / radio tuners, audio / video capture cards, sound cards, analog audio inputs with A / D converters, modems, digital media inputs (HDMI (Registration). (Trademark), optical link), digital I / O ports (RS232, USB, FireWire, Thunderbolt), expansion slots (PCMCIA, ExpressCard, PCI, PCIe) can be included.

CNA Guided Care has two dimensions, which together enable the provision of value-based long-term care in the United States through the application of CNA through a triad of healthcare providers, payers, and patients suffering from illness. To.

The activation tool, which is the first aspect, is a medical provider, a computer including a processor equipped with a first COTA module, and a first client device equipped with a second COTA module communicably linked to the first COTA over a network. And the interaction with the payer. This allows the payer to consider the patient whose health insurance benefits guarantee service over the network via a first client device with a second COTA module communicatively linked to the first COTA module of the processor. Identify medical services; indicate that this service is for diagnostic or therapeutic purposes; allow communications to be transmitted to the processor to identify variables 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 to patients at each nodal address at key points in time during treatment. Disadvantageous difference data of each healthcare provider in each CNA, including both non-existent necessary and unnecessary medical care, which contributes to the disadvantageous difference of the person, (iii) Treat each patient in the patient population assigned to the CNA. Cost reports containing real-time cost data for, and (iv) (1) treatment costs for clinical outcomes, (2) toxicity incidence and severity for treatment costs and clinical outcomes, (3) therapy and quality of life. Information can be transmitted, including graph analysis that correlates one or more of them. Based on this information, the payer at a key point in time, (a) considers the potential benefits and harms to the patient, that the medical service is an appropriate provision or level of service, ( b) Health 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 interventions. The payer can ensure that it is cost-effective for the medical condition and clinical outcome to be treated, and that this service follows generally accepted medical practices, and the payer will provide medical care for this patient's medical service. Can approve payment to a person.

As shown in the embodiment of FIG. 31, a second aspect of CNA-guided medical care according to an embodiment of the present disclosure for a breast cancer patient is to a computer comprising a processor with the first COTA module and to the first COTA module via a network. Includes patient interaction with a second client device with a communicably linked third COTA module. When communicating with the 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 ( For example, physical assessment, laboratory assessment, contrast, etc.) are obtained to inform the patient so that they can be assigned a CNA; the assigned CNA is used to optimize care and risk disadvantageous differences. Presenting the patient with treatment options for mitigation, illness; the patient provides medical care by following CNA guided care and showing the patient a doctor within the patient's geographic area with the best outcome at an acceptable cost of treatment. Allows the patient and the desired level of CNA guided care to be selected.

As shown in FIG. 32, from the payer's point of view, this activation tool eliminates prior permission for testing, diagnosis, and treatment, eliminates pre-approval, and eliminates the need for referrals. Can be effective. From a healthcare provider's perspective, this tool reduces the time required to pay a provider for healthcare. Thus, activation tools allow healthcare to optimize per-medical payment-based refunds and shift from per-medical payments to risk-based treatment refunds (eg, by bundling services). .. From the patient's point of view, CNA guided care has three things: first, enough for the patient to get a complete medical assessment of his or her disease and for the first COTA module to assign CNA to the patient. Patient-configured to provide patients with information and, secondly, to use CNA to allow patients to choose optimized treatments to avoid disadvantages in terms of geography, clinical outcomes, costs, and disadvantages. Based on other criteria, the patient is presented with treatment options and, thirdly, the patient's local care provider who follows CNA-guided care with the best outcome at an acceptable cost of treatment.

Those skilled in the art will appreciate that the methods and systems of the present disclosure can be implemented in many 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, the functional elements performed by a single or multiple components, and the individual functions are in either the user computing device and / or the server. Can be distributed among software applications. In this regard, any number of features of the various embodiments described herein can be combined into one or more embodiments, with less than or less than all of the features described herein. Alternative embodiments with many are possible. Functions can also be totally or partially dispersed among multiple components in a manner known now or in the future. Therefore, innumerable combinations of software / hardware / firmware are possible to realize the functions, features, interfaces, and preferences described herein. Further, the scope of this disclosure is to be added to the features, 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 in the future. It ranges from conventionally known methods for carrying out modifications and modifications that can be made.

Although the systems and methods have been described for one or more embodiments, it should be understood that the present disclosure need not be limited to the embodiments disclosed herein. It covers the various modifications and similar settings contained within the spirit and scope of the claims, and this scope should allow the broadest interpretation to include all such modifications and similar structures. .. The present disclosure includes all embodiments of the following claims.

Claims (12)

A way to improve clinical outcomes at the specific patient level and reduce total treatment costs at the population level.
A. A second clinical outcome tracking and analysis module communicably linked to the first clinical outcome tracking and analysis module over a network with a healthcare provider and a computer including a processor with a first clinical outcome tracking and analysis module. The interaction between the first client device and the payer,
B. A computer including the processor equipped with a first clinical outcome tracking and analysis module and a third clinical outcome tracking and analysis module communicably linked to the first clinical outcome tracking and analysis module via the network. Including the interaction between the second client device and the patient,
The computer is configured to perform the following (a)-(c) on the processor equipped with the first clinical outcome tracking and analysis module.
(A) By grouping patients within a patient population, biological variability can be effectively eliminated as a factor in the value of treatment and as a dominant factor in treatment outcome by considering biological variability in advance. A process that leaves treatment fluctuations is executed, and the process is
(I) Receiving, collecting and recording personal health information from each patient in the patient population in a database, wherein the personal health information is a parameter that characterizes each patient in the patient population. And based on the latest scientific knowledge and medical guidelines, including information that affects survival, prognosis, or treatment.
(Ii) The personal health information of each patient in the patient population is sorted using a sorting filter to provide a sorted personal health information set for this population, and the patients satisfying each parameter in the patient population. To identify,
(Iii) Generating and assigning multiple clinical outcomes and analysis nodal addresses (Nodal Addresses (CNA)) within the first clinical outcome tracking and analysis module.
(1) Each CNA is represented as a set of preselected variables that divide the sorted and classified information into clinically relevant health information sets.
(2) The trillions of possible sequences are reduced to a reduced number of clinically meaningful sequences based on the preselected set of variables representing each nodal address. Changes from ideal values, first expressed as behavioral and then clinical and cost outcomes, as the best clinical outcome at the lowest possible cost, necessary care and unnecessary. Allows analysis at the inevitable time required to warn about avoidance of care, thereby increasing the value of treatment, which means better clinical outcome at the lowest possible cost.
(3) Processing requirements and processing times to streamline real-time monitoring of the healthcare provider's capabilities based on the set of preselected variables representing each CNA and on the basis of the reduction in sequence. Reduced,
(4) To prevent the overuse / underuse of care by interrupting the flow of treatment in order to enable the prediction of key points in time when behavioral fluctuations may occur and to avoid the behavioral fluctuations.
By generating and assigning the plurality of clinical outcomes and analytical nodal addresses, including the above, similar personal health information is classified within the patient population based on the personal health information associated with the patient population. Grouping patient types,
(Iv) The clinical outcome of each CNA by analyzing the clinically relevant personal health information set of the subset of the patient population for one or more patients within the subset of the patient population. To measure,
(V) Between one healthcare provider and another healthcare provider in terms of treatment, testing, follow-up, adherence to prescribed medications, and the cost of each patient within said patient population assigned to each CNA. Measuring behavioral variability of each healthcare provider of each patient within said patient population assigned to each CNA by comparing the differences,
(B) Provided with the Second Clinical Outcome Tracking and Analysis Module, a communication identifying the medical service under consideration for the patient whose health insurance benefits guarantee the medical services and variables selected by the payer. Upon receipt from the payer via the first client device, the second clinical outcome tracking and analysis module is sent information including:
(1') Clinical outcome data of each CNA in (iv) above,
(2') Behavioral fluctuation data of each medical provider in each CNA in (v) above,
(3') Cost report, including real-time cost data for treating each patient in said patient population, assigned to said CNA in said (iii).
(4') A graph analysis of one or more that correlates treatment costs to clinical outcomes, wherein the payer is at the key point in time.
That the medical service is an appropriate provision or level of service, taking into account the potential benefits and harms to the patient.
That the medical service is effective in improving health outcomes by improving clinical outcomes and reducing total treatment costs.
The service is cost-effective for the medical condition being treated and the clinical outcome as compared to the absence of alternative health interventions or interventions.
If sufficient to ensure that the service complies with generally accepted medical practices, the payer may approve payment to the medical provider for the medical service.
(C) A series of communications with the patient via the second client device equipped with the third clinical outcome tracking and analysis module.
The third clinical outcome tracking and analysis module with the third clinical outcome tracking and analysis module in response to a first communication from the patient reporting a health concern to the processor of the computer server with the first clinical outcome tracking and analysis module. In response, a second communication containing a list of tests necessary to diagnose a health concern as a medical condition is sent to the second client device.
To the second client device equipped with the third clinical outcome tracking and analysis module in response to a third communication from the patient, including the results of the test necessary to diagnose the health concern. A fourth communication containing a list of the diagnosis of the medical condition and additional tests necessary to further classify the medical condition is sent in response.
The said available in (iii), based on the results of the test, the diagnosis of the medical condition, and the list of the additional tests, in response to a fifth communication from the patient, including the results of the additional test. Assigning the patient from multiple CNAs one CNA containing the clinically relevant health information set to one patient.
The second client device with the third clinical outcome tracking module is geographically integrated with the assigned CNA and the healthcare professional treating the patient within the assigned CNA, as well as the geographic and clinical outcomes. , Or send a sixth communication containing a list of health care workers categorized by one or two or more of costs.
Sending a seventh communication from the patient, selecting an optimal care plan with a low risk of disadvantage and a healthcare professional who meets at least one or more of the patient's geography, cost, and outcome needs. When you make the series of communications,
Provide sufficient information for the complete medical evaluation of the patient and for assigning the CNA to the patient.
The CNA is used to determine the optimal level of care with a low risk of adverse differences, to select the healthcare provider and the desired level of CNA-guided care.
A consultation appointment between the selected healthcare professional and the patient, communicably linked to a computing device with the third clinical outcome tracking and analysis module in the selected healthcare professional's office. A method that facilitates scheduling. The sorting parameters in (ii) above are gender, age, ethnicity, coexistence, smoking, health source, medical record number, family doctor, contract doctor, hospital, licensed service vendor, disease-specific clinical molecular phenotype. , Therapeutic objectives, stages of treatment, biomarkers, and one or more of the cost of treatment, or the set of preselected variables in (1) above comprises disease-specific clinical molecular phenotypes. , The sequence of numbers representing the disease-specific clinical molecular phenotype is determined based on a directed graph, or the key points in time of (4) and (4') are at diagnosis, progression, dose. The step of including one or more of the trends of variation from change, drug change, toxicity, and desired outcome, or correlating the treatment cost of (4') with clinical outcome, is the incidence of toxicity. Includes a step that correlates one or more of the severity of toxicity, therapy, and quality of life to treatment costs and outcomes, or the first client device or said in (b) or (c) above. Receiving transmissions from a second client device is via a human user or technical process, or the assigned CNA in (c) above is a predetermined patient care service for the treatment of a disease. The clinical outcome associated with one or more bundles, or in (c) above, is the therapeutic agent received, delivery dose intensity, dosing interval, duration of administration, quality metrics, toxicity to treatment, progression-free survival. , Includes one or more of total survival, response metrics, and death, or the list of said medical personnel in said (c) is visually categorized by clinical outcome.
The method according to claim 1, wherein the method is characterized by the above. The method of claim 2, wherein one or more bundles of the patient care services provide a predetermined course of treatment, cost certainty for the treatment of the disease, or both. Claims further include identifying the best clinical outcome at the lowest treatment cost in the CNA by ranking both clinical outcomes and treatment costs in one or more bundles of said patient care services. The method according to 2. The visual classification of the healthcare professional list by clinical outcome is:
(A) Green shows above-average clinical outcome,
(B) Yellow indicates average clinical outcome, and (c) Red indicates subaverage clinical outcome.
The method according to claim 2. With permission from the patient and selected healthcare professionals,
Sending the patient's personal health information from the first clinical outcome tracking and analysis module to a computing device equipped with a fourth clinical outcome tracking and analysis module in the office of the selected healthcare professional. The method according to claim 1, further comprising. The behavioral variability data for each healthcare provider in each CNA in (c) is present, contributing to the disadvantageous difference of the healthcare provider to each CNA patient at the key point in time during treatment. The method of claim 1, comprising both necessary and unnecessary medical care. The method of claim 1, further comprising indicating that the service is for the purpose of diagnosing or treating a medical condition. The cost report in (3') above is
Comparison of average cost per income between doctors, or comparison of hospital contribution profits in dollars and percentages, or average income per hospital patient, or average cost per hospital patient, or average per case for each doctor Includes cost, weight peer average, or average cost of imaging, clinical testing, evaluation and management, or other costs for medicines, medical supplies, and each doctor, or a combination thereof.
The method according to claim 1. The variable selected by the payer in (b) is a variable that identifies a particular attribute of the personal health information as a desired set of characteristics, added to the personal health information by the payer and for each patient. The method of claim 1, comprising one or more attributes that identify the personal health information as having the same level of importance as other health information in the patient population database, or both. A system for improving clinical outcomes at the specific patient level and reducing total treatment costs at the population level.
A database containing personal health information and data of the subject population,
A first clinical outcome tracking and analysis module communicatively linked to the database and network.
Memory for storing computer program instructions and
With a computer server processor and
A first client device comprising a processing unit, memory, and a second clinical outcome tracking and analysis module communicably linked to said first clinical outcome tracking and analysis module of said processor of said computer server.
A second client device comprising a processing unit, memory, and a third clinical outcome tracking and analysis module communicably linked to said first clinical outcome tracking and analysis module of said processor of said computer server. Prepare,
A system in which the processor is configured to perform the method according to any one of claims 1 to 10 by the computer program instruction. A medical provider, a computer including a processor equipped with a first clinical outcome tracking and analysis module, and a second clinical outcome tracking and analysis module communicably linked to the first clinical outcome tracking and analysis module via a network. A second client device with a first client device, and interactions between payers, as well as a third clinical outcome tracking and analysis module communicably linked to the computer, the first clinical outcome tracking and analysis module. , And a non-temporary computer-readable medium that stores computer program instructions to facilitate interaction between patients.
A non-temporary computer-readable medium in which a computer program instruction instructing the processor to perform the method according to any one of claims 1 to 10 is recorded.

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