JP7355415B2 - CNA-guided care to improve clinical outcomes and reduce total treatment costs - Google Patents

Description

This application claims the benefit of priority to U.S. Application No. 15/351,099, filed Nov. 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 general population lives longer, most health care costs associated with the aging population are increasing. The costs associated with serious and chronic diseases such as cancer are growing in an unsustainable manner. For example, cancer costs are projected to be the highest growth area of health care spending without commensurate improvements in outcomes. In the United States alone, approximately $125 billion was spent on cancer treatment in 2010, and estimates suggest that approximately 15-30% of health care costs can be classified as a result of unnecessary or inappropriate treatment; , too little or too much treatment for individual patients, leading to suboptimal clinical outcomes and excessive costs (hereinafter "adverse variance").

Under the current system, for every 100 seriously ill people treated, there is an average unfavorable variance ratio of 20%, meaning that individual patients are being given too little or too much treatment. do. Therefore, suboptimal clinical outcomes occur in 20% of patients on treatment, causing an increase in total treatment costs at the current population level of 100%.

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

The current preauthorization infrastructure, which allows payers to determine that a medical service is a benefit covered by a patient's health insurance, is expensive, takes too long, and compromises clinical outcomes by delaying needed treatment. It may have a negative impact.

As technology and medicine continue to advance, the science and clinical practice for treating diseases (cancer, heart disease, lung disease, and behavioral health) is rapidly evolving. 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 major challenges to health insurance and healthcare providers. As a result, health plans may require additional tools and support to manage medical (eg, oncology) operations. Similarly, healthcare professionals (e.g., doctors) will need more decision support tools to provide the best care and stay in business.

The Clinical Outcomes Tracking and Analysis (COTA) module enables, for example, healthcare professionals and/or other users to improve clinical practice, better manage and retrieve specific information related to diseases and/or patients, and reduce costs. It is a tool that can facilitate improved management.

Clinical outcome tracking and analysis parameters include sorting, outcome tracking, East Coast Cancer Trials Group (ECOG) performance status, treatment toxicity, and cost of treatment. In one aspect, the method and system receive one or more parameters for sorting a plurality of data records from a client device operated by a user, and in response to receiving the parameters, the method and system Includes a COTA module that sorts data records based on: A nodal address indicating one or more variables is applied to a sorted set of patient records to identify one or more clinically relevant sets of patient records. is determined as a sorted set of patient medical records that satisfy the variables. The COTA module then analyzes the set of clinically relevant patient medical records and communicates the classified and sorted data records and at least a portion of the updated data records to the client device for display. do.

Each data record includes data related to a disease and data related to a patient who currently has or has had a disease. The COTA module may receive data from an electronic medical record (EMR), a user, a healthcare provider, a specialist, 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 enables comparison of data or follow-up outcomes between patients, can identify particular patients as candidates for particular treatments or drugs, and communicates analysis tools to the client device, e.g. Facilitates the analysis of categorized and sorted data records, or allows comparison of Kaplan-Meier curves, based on which a particular physician involved in a patient is compared to other physicians treating other (similar) patients. It can be determined whether the patient is being treated according to the treatment regimen.

The COTA module can also send alerts to client devices upon the occurrence of triggers. Triggers can be, for example, at diagnosis, at progression, at dosage change, at drug change, at toxicity, when there is a trend toward deviation from a desired outcome, and/or at a specific time.

The COTA module tracks the results of treatment selection and reports on outcomes related to the use of COTA Nodal Addresses (CNAs). This allows for proactive consideration of biological variation by grouping patients within a patient population, effectively removing biological variation as a factor in the value of treatment and treating variation as a dominant factor in treatment outcome. leave. The COTA module receives and collects personal health information from each patient within 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, types of patients within the patient population are grouped based on their personal health information. Each CNA is a discrete punctuation digit string containing a prefix, midsection, and suffix that represents a preselected set of variables that divide the sorted and categorized information into clinically relevant health information sets. (discrete punctured string of digits). Multiple CNAs reduce trillions of possible permutations to a reduced number of clinically meaningful permutations based on discrete punctuated digit strings representing each nodal address, which initially and then alerts to variations in the resulting clinical and cost outcomes from ideal values, expressed as the best clinical outcome at the lowest possible cost, with regard to necessary care and avoidance of unnecessary care. into discrete punctuated digit strings representing each nodal address. Reduce processing requirements and processing time to make real-time monitoring of healthcare provider performance efficient based on and permutations of key points in time where behavioral changes may occur; Interrupt the flow of care to prevent over/underutilization of care to enable predictability and avoid behavioral variation.

The invention described herein provides CNA guided care. CNA-guided care has two aspects that together enable the application of CNA to drive value-based healthcare delivery in the United States through the provider, payer, and diseased patient triad. enable.

A first aspect of the enabling tool includes: a health care provider; a computer including a processor with a first COTA module; and a first client device with a second COTA module communicatively linked to the first COTA via a network. and interactions with payers. Thereby, the payer may communicate with the patient over the network via a first client device having a second COTA module communicatively linked to the first COTA module of the processor, the payer is considering for a patient for whom the patient's health insurance benefits cover the service. identifying the medical service for; indicating that the service is for the purpose of diagnosing or treating a medical condition; and enabling transmission of a communication to the processor identifying variables selected by the payer. Upon receiving communications from the payer, the first COTA module sends information to the second COTA module including (i) clinical outcome data for each CNA, and (ii) medical care provision for the patient at each nodal address at key points in time during treatment. (iii) Adverse variance data for each provider in each CNA, including both non-existent necessary medical care and unnecessary medical care that contributes to disadvantageous variance for patients who treat each patient within the patient population assigned to the CNA; and (iv) cost reports containing real-time cost data for (1) treatment costs relative to clinical outcomes, (2) toxicity incidence and severity relative to treatment costs and clinical outcomes of treatment, and (3) treatment and quality of life outcomes. Information including graphical analysis correlating one or more of the information can be sent. Based on this information, the payer must, at key points in time, ensure that (a) the health care service is an appropriate provision or level of service, taking into account the potential benefits and harms to the patient; b) the health service is effective in improving health outcomes by improving clinical outcomes and reducing total treatment costs, compared to alternative health interventions or no intervention; With respect to this patient's health care services, the payer may be required to can approve payments to

From a payer's perspective, this enabling tool can be effective in eliminating prior authorization, eliminating prior authorization, and eliminating the need for referrals for testing, diagnosis, and treatment. . From a provider's perspective, this tool reduces the time needed to pay providers for care. Enabling tools can therefore enable practices to optimize reimbursement on a per-visit basis and move from per-visit payments to risk-based treatment reimbursement (e.g., by bundling services). .

A second aspect of CNA-guided medicine includes a computer including a processor with a first COTA module, a second client device with a third COTA module communicatively linked to the first COTA module via a network, and a patient. including interactions between Through a series of communications between the first COTA module and the patient, CNA-guided treatment is performed firstly for the patient to obtain a complete medical evaluation of his or her disease and for the first COTA module to assign a CNA to the patient. Provide patients with sufficient information and, secondly, use CNA to assess geography, clinical outcome, cost, and patient preferences to allow patients to choose optimized treatments to avoid adverse differences. thirdly, directs the patient to health care providers within the patient's area that follow CNA-guided care with the best outcomes at an acceptable cost of treatment;

U.S. Application No. 15/351,099

The invention described herein, when executed on a processor, provides a first COTA module with operations that use CNA guided care to improve clinical outcomes at a specific patient level and reduce total cost of care at a population level. Non-transitory computer readable media, methods and systems for storing computer program instructions for execution are provided.

According to one aspect, the invention described herein provides a method for improving clinical outcomes at a specific patient level and reducing total treatment costs at a population level, and includes: A. a health care provider; a computer including a processor with a first clinical outcome tracking and analysis module; and a second clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module via a network. an interaction between a first client device comprising a first client device and a payer; B. a second client comprising a computer including a processor with a first clinical outcome tracking and analysis module and a third clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module via a network; interaction between the device and the patient, the computer including a processor running the first clinical outcome tracking and analysis module on the processor with the first clinical outcome tracking and analysis module; , (a) proactively account for biological variation by grouping patients within a patient population, thereby effectively removing biological variation as a factor in the value of treatment and improving treatment outcome. leaving treatment variation as a governing factor, which is accomplished by: (i) receiving, collecting, and recording in a database personal health information from each patient in the patient population; , each parameter characterizing each patient in the patient population, and information that affects survival, prognosis, or treatment based on current scientific knowledge and medical guidelines; (ii) each patient in the patient population; sorting the personal health information using a sorting filter to yield a sorted set of personal health information for this population, identifying patients within the patient population that meet each parameter; and (iii) first clinical outcome tracking. and classify similar personal health information based on the personal health information associated with the patient population by generating and assigning multiple clinical outcome and analysis nodal addresses (CNAs) within the analysis module. , the steps of grouping patient types within a patient population and generating and assigning multiple nodal addresses include (1) representing each CNA as a discrete punctuated digit string including a prefix, a middle part, and a suffix; (2) represent a set of preselected variables that partition the sorted and categorized information into clinically relevant health information sets, and (2) trillions of possible permutations. to a reduced number of clinically meaningful permutations based on discrete punctuated digit strings representing each nodal address, and this improves, first the behavioral and second the resulting clinical and cost outcomes. variation from the ideal value, expressed as the best clinical outcome at the lowest possible cost, can be analyzed in the necessary time to alert regarding necessary care and avoidance of unnecessary care. (3) on the basis of a discrete punctuated digit string representing each CNA and on the basis of a reduction in permutations. (4) reduce processing requirements and processing times to make real-time monitoring of health care provider performance efficient; and (4) enable prediction of key points in time at which behavioral changes may occur to improve to avoid variability, interrupting the flow of care to prevent overutilization/underutilization of care, and (iv) for one or more patients within a subset of the patient population. Measure clinical outcomes for each CNA by analyzing a set of clinically relevant personal health information and (v) assess treatment, testing, follow-up, adherence to prescribed medications, and patients assigned to each CNA. For each provider for each patient in the patient population assigned to each CNA by comparing the difference between one provider and another in the cost for each patient in the population. Measuring behavioral changes,
The above steps further include
(b) transmitting a communication to a first client with a second clinical outcomes tracking and analysis module identifying the medical services under consideration for a patient whose health insurance benefits cover the medical services and variables selected by the payer; Upon receipt from the payer via the device, transmits information to the second clinical outcomes tracking and analysis module including: (1') (iv) each CNA's clinical outcome data; (2') (v) behavioral variation data for each healthcare provider in each CNA in (3') a cost report containing real-time cost data for treating each patient in the patient population assigned to the CNA in (iii), (4' ) one or more graphical analyzes correlating treatment costs to clinical outcomes, the information being used by the payer at key points in time to (a) consider potential benefits and harms to the patient; (b') the health care services are effective in improving health outcomes by improving clinical outcomes and reducing total costs of care; (c') the service is cost-effective for the condition treated and the clinical outcome compared to alternative health interventions or no intervention; (d') the service is generally recognized as The payer may authorize payment to the health care provider for this health care service if there is sufficient evidence that compliance with the medical practice is met; A series of communications with the patient via a second client device with an analysis module and an analysis module provide the CNA with sufficient information for a complete medical evaluation of the patient and to assign the CNA to the patient. determine the optimal level of care with low risk of adverse differences, select a healthcare provider and the desired level of CNA-guided care, and a series of communications (i'') with the first clinical outcome tracking and analysis module. in response to a first communication from a patient reporting a health concern to a processor of a computer server with a second client device with a third clinical outcome tracking and analysis module, reporting the health concern as a medical condition. from the patient, including the results of the tests necessary to diagnose the health concern of (ii'') (i''), by sending in response a second communication containing a list of tests necessary to diagnose; in response to the third communication, the second client device with the third clinical outcome tracking and analysis module receives a fourth communication including a diagnosis of the medical condition and a list of additional tests needed to further classify the medical condition. in response to a fifth communication from the patient, including the results of the additional tests (iii'') (i''), the results of the tests, the diagnosis of (ii''), and (ii') Assign one CNA to the patient from the multiple CNAs available in (a'')(iii) based on the list of additional tests in transmitting a sixth communication to a second client device comprising a set of health information and (b'') comprising a third clinical outcome tracking module, including: (1'') an assigned CNA, (2'') A geographically organized list of healthcare professionals who treat patients within an assigned CNA, where this list of healthcare professionals is categorized by one or more of the following: geography, clinical outcome, or cost. , (iv'') a seventh request from the patient to select an optimal care plan with a low risk of adverse variance and a health care provider that meets at least one or more of the patient's geographic, cost, and outcome needs; In response to the communication, the patient is communicatively linked to a computing device with a third clinical outcome tracking and analysis module located at the selected health care professional's office to communicate with the selected health care professional and the patient. facilitate the scheduling of medical appointments.

In some embodiments of the method, the sorting parameters of (a):(a)(ii) include gender, age, ethnicity, comorbidity, smoking, health source, medical record number, primary care physician, referring physician. (b): (a)(iii) The preselected set of variables in (1) includes a disease-specific clinical molecular phenotype, and the string of numbers representing the phenotype is determined based on a directed graph, or (c): Step (a) (iii) The key points in time for (4) and steps (b) (1') and (4') are diagnosis, progression, dose change, drug change, toxicity, and variation from the desired outcome. or (d): (b) (1') (4') correlating the cost of treatment to clinical outcome, including one or more of the trends: incidence of toxicity, severity of toxicity, therapy, and or (e): receiving the transmission from the client device in (b) or (c) comprises correlating one or more of the quality of life to treatment costs and treatment outcomes; or through a user or technical process, or (f): (c) (iii") (b") (1"), the assigned CNA shall perform predefined patient care services for the treatment of the disease. The clinical outcomes associated with one or more bundles, or (g): (c) (iii") (b") (2"), include the therapeutic agent received, the dose intensity delivered, the dosing interval, the duration of dosing. , quality of life metrics, toxicity to treatment, progression-free survival, overall survival, response metrics, and death, or (h): stage (c) (iii”) (b The list of healthcare professionals in ``)(2'') is visually categorized by clinical outcome.

In some embodiments, a given bundle or bundles of patient care services provide a predetermined course of treatment, cost certainty for treatment of a disease, or both.

In some embodiments, the method determines the best clinical outcome at the lowest cost of treatment in a CNA by ranking both clinical outcome and cost of treatment in one or more bundles of predetermined patient care services. The method further includes identifying.

In some embodiments, the visual classification of the provider list by clinical outcome includes (a) green indicating an above-average clinical outcome, (b) yellow indicating an average clinical outcome, and (c) ) Red indicates below average clinical outcome.

In some embodiments, the method includes, with permission from the patient and the selected health care professional, transferring the data from the first clinical outcome tracking and analysis module to a fourth clinical outcome tracking at the selected health care professional's office. and transmitting the patient's personal health information to a computing device having an analysis module.

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

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

In some embodiments, the cost report includes a comparison of average cost per revenue between physicians, or a dollar and percentage comparison of a hospital's contribution margin, or a comparison of a hospital's average revenue per patient, or a comparison of a hospital's average cost per patient. Average cost, or average cost per case for each physician, or weight peer average, or average cost of imaging, laboratory tests, evaluation and administration, drugs, medical supplies, and other costs for each physician. or a combination thereof.

In some embodiments, the variables selected by the payer include the variables that identify particular attributes of the personal health information as a desired set of characteristics, the variables that are appended to the personal health information by the payer to identify each patient's personal health information. includes one or more attributes, or both, that identify the health information as having an equal level of importance as other health information in the patient population database.

According to another aspect, the invention described herein provides a system for improving clinical outcomes at a specific patient level and reducing total treatment costs at a population level, the system comprising: (A) a subject; a processor of a computer server comprising a database containing population personal health information and data; a first clinical outcome tracking and analysis module communicatively linked to the database and the network; and a 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 instructions of (A), when executed on the processor, cause the first clinical outcome tracking and analysis module to perform operations including: (a) grouping patients within a patient population; including the step of pre-accounting for biological variation, thereby effectively removing biological variation as a factor in the value of the treatment and leaving treatment variation as a dominant factor in treatment outcome; , performed by (i) receiving, collecting, and recording in a database personal health information from each patient in the patient population, where the personal health information includes each parameter characterizing each patient in the patient population; (ii) sorting the personal health information of each patient within the patient population using a sorting filter; yielding a sorted set of personal health information for this population, identifying patients meeting each parameter within the patient population; and (iii) creating a plurality of clinical outcome tracking and analysis nodes within the first clinical outcome tracking and analysis module. Classify similar personal health information based on personal health information associated with a patient population by generating and assigning Nodal Addresses (CNAs) to group types of patients within a patient population. , generating and assigning the plurality of CNAs includes (1) representing each CNA as a discrete punctuated digit string including a prefix, a middle part, and a suffix, which contain sorted and categorized information; , representing a pre-selected set of variables that is partitioned into a set of clinically relevant health information; (2) trillions of possible permutations based on a discrete punctuated digit string representing each CNA; Reduced to a reduced number of clinically meaningful permutations, this is the first behavioral and second resulting clinical and cost outcome, as the best clinical outcome at the lowest possible cost. Allows variations from the expressed ideal values to be analyzed in the necessary time to warn about necessary care and avoidance of unnecessary care, thereby providing better results at the lowest possible cost. (3) to streamline real-time monitoring of health care provider performance based on discrete punctuated digit strings representing each CNA and based on permutation reduction; (4) interrupting the flow of care to avoid behavioral changes by reducing processing requirements and processing times; and (4) enabling prediction of key points in time at which behavioral changes are likely to occur. (iv) analyze clinically relevant sets of personal health information of a subset of the patient population for one or more patients within the subset of the patient population; Measures clinical outcomes for CNAs and (v) compares one healthcare provider with another in treatment, testing, follow-up, adherence to prescribed medications, and costs for each patient within the patient population assigned to each CNA. Measure behavioral variation for each provider for each patient within the patient population assigned to each CNA by comparing the differences between providers and (b) the health care services selected by the payer and upon receiving a communication from a payer via a first client device with a second clinical outcomes tracking and analysis module identifying a medical service under consideration for a patient whose health insurance benefits cover the variable; Send information to the clinical outcomes tracking and analysis module, including (1') (iv) clinical outcome data for each CNA; (2') (v) behavioral variation data for each provider in each CNA; , (3') a cost report containing real-time cost data for treating each patient in the patient population assigned to the CNA in (iii); (4') correlating treatment costs to clinical outcomes 1 or 2; The above graphical analysis provides that this information allows the payer, at key points in time, to: (b') the health care service is effective in improving health outcomes by improving clinical outcomes and reducing total costs of care; (c') alternative health interventions or (d') Sufficient to establish that the service is cost-effective for the condition treated and the clinical outcome compared to no intervention; (d') the service is in accordance with generally accepted medical practice; (c) communicate with the patient via a second client device with a third clinical outcome tracking and analysis module; communication provides sufficient information for a complete medical evaluation of the patient and to assign the CNA to the patient, allowing the CNA to determine the optimal level of care with low risk of adverse variance. selects a health care provider and desired level of CNA-guided care, and a series of communications (i'') first reports the health concern to a computer server processor with a clinical outcome tracking and analysis module; In response to the first communication from the patient, respond to a second client device with a third clinical outcome tracking and analysis module a second communication including a list of tests necessary to diagnose the health concern as a medical condition. third clinical outcome tracking and in response to a third communication from the patient containing the results of the tests necessary to diagnose the health concern of (ii'') (i''); (iii'') (i'') by sending a fourth communication in response to a second client device comprising an analysis module, including a diagnosis of the medical condition and a list of additional tests needed to further classify the medical condition; In response to a fifth communication from the patient containing the results of additional tests, based on the results of the tests, the diagnosis of (ii''), and the list of additional tests of (ii''), (a'' ) (iii) allocate one CNA to the patient from the multiple CNAs available; the assigned CNA contains a clinically relevant set of health information for the patient; and (b'') a third clinical transmitting a sixth communication to a second client device with an outcome tracking module that includes: (1'') the assigned CNA; (2'') the healthcare worker treating the patient within the assigned CNA; a geographically organized list of medical professionals, categorized by one or more of the following: geography, clinical outcome, or cost; (iv'') optimal care with low risk of adverse differences; In response to a seventh communication from the patient selecting a health care provider that meets at least one or more of the plan and patient's geographic, cost, and outcome needs, the patient selects a health care provider that meets at least one or more of the patient's geographic, cost, and outcome needs. communicatively linked to a computing device with a third clinical outcome tracking and analysis module located in an office of the patient to facilitate scheduling of patient appointments with selected health care professionals.

In some embodiments, the sorting parameters of (a):(a)(ii) include gender, age, ethnicity, comorbidity, smoking, health source, medical record number, primary care physician, referring physician, hospital , an approved service vendor, a disease-specific clinical molecular phenotype, treatment objectives, treatment stages, biomarkers, and treatment costs; or (b): a preselected set of variables of (a)(iii) (1). contains a disease-specific clinical molecular phenotype, and the number string representing the phenotype is determined based on a directed graph, or (c): step (a) (iii) (4) and step (b) (1' ) (4') temporal key points include one or more of the following: at diagnosis, at progression, at dose change, at drug change, at toxicity, and trends in variation from desired outcome, or ( d): (b) (1') (4') The step of correlating treatment costs with clinical outcomes involves one or more of the following: incidence of toxicity, severity of toxicity, therapy, and quality of life. or (e): receiving the transmission from the client device in (b) or (c) via a human user or a technological process; f): The CNA assigned in (c)(iii'')(b'')(1'') is associated with one or more predetermined bundles of patient care services for the treatment of the disease. or (g): (c)(iii'') (b'') (2'') The clinical outcome is determined by the therapeutic agent received, dose intensity delivered, dosing interval, duration of dosing, quality of life metrics, including one or more of the following: toxicity to treatment, progression-free survival, overall survival, response metrics, and death, or (h): stage (c) (iii'') (b'') ( 2'') list of healthcare professionals is visually categorized by clinical outcome.

In some embodiments, a predetermined bundle or bundles of patient care services provide a predetermined course of treatment, cost certainty for treatment of a disease, or both.

In some embodiments, the system selects the best CNA with the lowest cost of care for an assigned CNA by ranking both clinical outcomes and cost of care for a given bundle or bundles of patient care services. The method further includes identifying a clinical outcome.

In some embodiments, the visual classification of the provider list by clinical outcome includes (a) green indicating an above-average clinical outcome, (b) yellow indicating an average clinical outcome, and (c) ) Red indicates below average clinical outcome.

In some embodiments, the system, with permission from the patient and the selected health care professional, transfers the data from the first clinical outcome tracking and analysis module to the fourth clinical outcome tracking located at the selected health care professional's office. and transmitting the patient's personal health information to a computing device having an analysis module.

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

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

In some embodiments, the cost report includes a comparison of average cost per revenue between physicians, or a dollar and percentage comparison of a hospital's contribution margin, or a comparison of a hospital's average revenue per patient, or a comparison of a hospital's average cost per patient. The average cost, or the average cost per case for each physician, or the weight peer average, or the average cost of imaging, laboratory tests, evaluation and administration, drugs, medical supplies, and other costs for each physician. or a combination thereof.

In some embodiments, the variables selected by the payer include the variables that identify particular attributes of the personal health information as a desired set of characteristics, the variables that are appended to the personal health information by the payer to identify each patient's personal health information. includes one or more attributes, or both, that identify the health information as having an equal level of importance as other health information in the patient population database.

According to another aspect, the invention described herein provides for: (A) a health care provider, a computer comprising a processor with a first clinical outcome tracking and analysis module, a first clinical outcome tracking and analysis module via a network; a first client device with a second clinical outcomes tracking and analysis module communicatively linked to the payer; and (B) a computer network including a processor with the first clinical outcomes tracking and analysis module. a second client device comprising a third clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module via the computer program instructions for facilitating interaction between the patient and the patient; providing a non-transitory computer-readable medium storing a first clinical outcome tracking and analysis module, when executed on a processor with a first clinical outcome tracking and analysis module, causing the first clinical outcome tracking and analysis module to perform operations including: (a) Pre-account for biological variation by grouping patients within patient populations, thereby removing biological variation as a factor in the value of treatment and as a dominant factor in treatment outcome. retaining treatment variation, which is performed by (i) receiving, collecting, and recording in a database personal health information from each patient within the patient population; (ii) personal health information for each patient in the patient population, including information that affects survival, prognosis, or treatment, based on each parameter characterizing each patient in the patient population, and current scientific knowledge and medical guidelines; , sorting using a sorting filter to yield a sorted set of personal health information for this population, identifying patients within the patient population that meet each parameter; and (iii) within the first clinical outcome tracking and analysis module. By generating and assigning multiple clinical outcome and analysis Nodal Addresses (CNAs) in the Grouping patient types to generate and assign a plurality of nodal addresses includes (1) representing each CNA as a discrete punctuated digit string including a prefix, a middle part, and a suffix; , these represent a set of preselected variables that partition the sorted and categorized information into clinically relevant health information sets, and (2) each of the trillions of possible permutations. Based on discrete punctuated digit strings representing CNA and reduced to a reduced number of clinically meaningful permutations, this reduces the likelihood of, firstly behavioral and secondly resulting clinical and cost outcomes. variations from the ideal value, expressed as the best clinical outcome at the lowest cost, can be analyzed at the necessary time to alert on necessary care and avoidance of unnecessary care, thereby (3) increasing the value of care, meaning better clinical outcomes at the lowest possible cost; (4) reduce processing requirements and processing times to make real-time monitoring of performance efficient; and (4) avoid behavioral fluctuations by enabling prediction of key points in time at which behavioral fluctuations are likely to occur. (iv) interrupting the flow of care to prevent overutilization/underutilization of care; and (iv) providing clinically relevant information for one or more patients within a subset of the patient population. By analyzing a set of personal health information, each CNA's clinical outcomes are measured and (v) treatment, testing, follow-up, adherence to prescribed medications, and each patient within the patient population assigned to each CNA are measured. measuring the behavioral variation of each health care provider for each patient within the patient population assigned to each CNA by comparing the difference in cost between one health care provider and another health care provider; and (b) transmitting a communication identifying the medical services under consideration for a patient whose health insurance benefits cover the medical services and variables selected by the payer to a second clinical outcomes tracking and analysis module. 1 receives from the payer via the client device, transmits information to the second clinical outcome tracking and analysis module including: (1') (iv) clinical outcome data for each CNA; (2') ( v) behavioral variation data for each healthcare provider in each CNA in (3') a cost report containing real-time cost data for treating each patient in the patient population assigned to the CNA in (iii); 4') one or more graphical analyzes correlating treatment costs to clinical outcomes, the information being available to the payer at key points in time to determine (a') potential benefits and harms to the patient; (b') the medical service is effective in improving health outcomes by improving clinical outcomes and reducing total costs of care; (c') this service is cost-effective for the condition treated and clinical outcomes compared to alternative health interventions or no interventions; (d') this service is generally The payer may authorize payment to the health care provider for this health care service if sufficient to establish compliance with accepted medical practice; and (c) tertiary clinical outcome tracking. A series of communications with the patient via a second client device with an analysis module and an analysis module provide the CNA with sufficient information for a complete medical evaluation of the patient and to assign the CNA to the patient. determine the optimal level of care with low risk of adverse differences, select a healthcare provider and the desired level of CNA-guided care, and a series of communications (i'') with the first clinical outcome tracking and analysis module. in response to a first communication from a patient reporting a health concern to a processor of a computer server with a second client device with a third clinical outcome tracking and analysis module, reporting the health concern as a medical condition. from the patient, including the results of the tests necessary to diagnose the health concern of (ii'') (i''), by sending in response a second communication containing a list of tests necessary to diagnose; in response to the third communication, the second client device with the third clinical outcome tracking and analysis module receives a fourth communication including a diagnosis of the medical condition and a list of additional tests needed to further classify the medical condition. in response to a fifth communication from the patient, including the results of the additional tests (iii'') (i''), the results of the tests, the diagnosis of (ii''), and (ii') Assign one CNA to the patient from the multiple CNAs available in (a'')(iii) based on the list of additional tests in transmitting a sixth communication to a second client device comprising a set of health information and (b'') comprising a third clinical outcome tracking module, including: (1'') an assigned CNA, (2' ') A geographically organized list of healthcare professionals who treat patients within an assigned nodal address, where this list of healthcare professionals is organized by one or more of the following: geography, clinical outcome, or cost. and (iv'') selecting a health care provider that meets at least one or more of the patient's geographic, cost, and outcome needs. In response to the seventh communication, the patient is communicatively linked to a computing device with a third clinical outcome tracking and analysis module located at the selected health care professional's office, and the patient is sent to the selected health care professional's office. and facilitate scheduling of patient appointments.

In some embodiments, the sorting parameters of (a):(a)(ii) include gender, age, ethnicity, comorbidity, smoking, health source, medical record number, primary care physician, referring physician, hospital , an approved service vendor, a disease-specific clinical molecular phenotype, treatment objectives, treatment stages, biomarkers, and treatment costs; or (b): a preselected set of variables of (a)(iii) (1). contains a disease-specific clinical molecular phenotype, and the number string representing the phenotype is determined based on a directed graph, or (c): step (a) (iii) (4) and step (b) (1' ) (4') temporal key points include one or more of the following: at diagnosis, at progression, at dose change, at drug change, at toxicity, and trends in variation from desired outcome, or ( d): (b) (1') (4') The step of correlating treatment costs with clinical outcomes involves one or more of the following: incidence of toxicity, severity of toxicity, therapy, and quality of life. or (e): receiving the transmission from the client device in (b) or (c) via a human user or a technological process; f): The CNA assigned in (c)(iii'')(b'')(1'') is associated with one or more predetermined bundles of patient care services for the treatment of the disease. or (g): (c)(iii'') (b'') (2'') The clinical outcome is determined by the therapeutic agent received, dose intensity delivered, dosing interval, duration of dosing, quality of life metrics, including one or more of the following: toxicity to treatment, progression-free survival, overall survival, response metrics, and death, or (h): stage (c) (iii'') (b'') ( 2'') list of healthcare professionals is visually categorized by clinical outcome.

In some embodiments, a predetermined bundle or bundles of patient care services provide a predetermined course of treatment, cost certainty for treatment of a disease, or both.

In some embodiments, the non-transitory computer-readable medium determines the lowest cost of care in a CNA by ranking both clinical outcomes and cost of care in one or more bundles of predetermined patient care services. The method further includes identifying the best clinical outcome.

In some embodiments, the visual classification of the provider list by clinical outcome includes (a) green indicating an above-average clinical outcome, (b) yellow indicating an average clinical outcome, and (c) ) Red indicates below average clinical outcome.

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

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

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

In some embodiments, the cost report includes a comparison of average cost per revenue between physicians, or a dollar and percentage comparison of a hospital's contribution margin, or a comparison of a hospital's average revenue per patient, or a comparison of a hospital's average cost per patient. The average cost, or the average cost per case for each physician, or the weight peer average, or the average cost of imaging, laboratory tests, evaluation and administration, drugs, medical supplies, and other costs for each physician. or a combination thereof.

In some embodiments, the variables selected by the payer include the variables that identify particular attributes of the personal health information as a desired set of characteristics, the variables that are appended to the personal health information by the payer to identify each patient's personal health information. includes one or more attributes, or both, that identify the health information as having an equal level of importance as other health information in the patient population database.

These and other aspects and embodiments will become apparent to those skilled in the art upon reference to the following detailed description and accompanying drawings.

In the following figures, which are not to scale, like reference numerals indicate like elements across the figures.

FIG. 2 is a block diagram of some examples of pressures in the cancer market and some potential solutions. 1 is a block diagram illustrating a server computer communicating with one or more computers over a network to provide a clinical outcome tracking and analysis (COTA) module to one or more user computers, according to an embodiment of the present disclosure. FIG. FIG. 2 is a block diagram illustrating some functionality provided by a COTA module, according to embodiments of the present disclosure. FIG. 2 is a block diagram illustrating the use of a COTA module to sort data related to colon cancer patients, according to an embodiment 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. 2 is a block diagram illustrating a directed graph for determining strings of numbers representing phenotypic characteristics for nodal addressing, according to embodiments of the present disclosure. 3 is a flowchart illustrating steps performed by a COTA module, according to an embodiment of the present disclosure. 1 is a flowchart illustrating steps for enabling a patient diagnosed with a disease to optimize treatment options, according to embodiments of the present disclosure. FIG. 2 is a flow diagram of a COTA module that sends an alert in response to a trigger, according to an embodiment of the present disclosure. 2 is a graphical representation of a mobile device summarizing received alerts, according to an embodiment of the present disclosure. 2 is a graphical representation of disease prevalence by cancer subtype, according to an embodiment of the present disclosure. 2 is a graphical representation of a search refined by variable input to a COTA module, according to an embodiment of the present disclosure. FIG. 3 is a diagram illustrating a list display of multiple variables associated with a particular disease, according to an embodiment of the present disclosure. 2 is a graphical representation including a real-time Kaplan-Meier curve with confidence intervals for pancreatic cancer, according to an embodiment of the present disclosure. 1 is a graphical representation showing Kaplan-Meier curves per disease progression, according to embodiments of the present disclosure. 2 is a real-time benchmarking graphical representation of bilateral outcomes in accordance with an embodiment of the present disclosure. 3 is a graphical representation of a cost report, according to an embodiment of the disclosure. 2 is a graphical representation of a therapy interface, according to an embodiment of the present disclosure. 2 is a graphical representation of a therapy interface, according to an embodiment of the present disclosure. 3 is a graphical representation of an outcome screen, according to an embodiment of the present disclosure. 3 is a graphical representation of a treatment detail report screen, according to an embodiment of the present disclosure. 2 is a graphical representation of an analysis screen comparing toxicity and cost, according to an embodiment of the present disclosure. 2 is a graphical representation of an analysis screen comparing treatment and quality of life, according to an embodiment of the present disclosure. FIG. 3 is a flow diagram of feedback support provided to a healthcare professional according to an embodiment of the present disclosure. FIG. 3 is a diagram displaying an embodiment of a graphical representation of different diagnostic types, according to an embodiment of the present disclosure. FIG. 3 is a diagram displaying an embodiment of a graphical representation of different diagnostic types, according to an embodiment of the present disclosure. FIG. 3 is a diagram displaying an embodiment of a graphical representation of different diagnostic types, according to an embodiment of the present disclosure. 2 is a graphical representation illustrating data generation and sorting of a COTA module for breast oncology - breast cancer with invasive ductal carcinoma histology from 2008 to 2013, according to an embodiment of the present disclosure. 2 is a graphical representation illustrating data generation and sorting of the COTA module for breast oncology - breast cancer tumor grade and stage from 2008 to 2013, according to embodiments of the present disclosure. 2 is a graphical representation illustrating data generation and sorting of a COTA module for breast cancer - Stage IIB from 2008 to 2013, according to an embodiment of the present disclosure. 1 is a graphical representation showing overall survival outcomes for breast cancer patients according to embodiments of the present disclosure. 1 is a graphical representation showing breast cancer outcomes - a two-way comparison, according to embodiments of the present disclosure. 1 is a diagram illustrating one example of a conceptual diagram of a client device according to an embodiment of the present disclosure. FIG. 1 is a block diagram illustrating the internal architecture of a computer according to an embodiment of the present disclosure. FIG. 2 is a flowchart illustrating patient aspects of CNA guided care according to embodiments of the present disclosure for breast cancer patients. FIG. 2 is a block diagram illustrating the benefits of two aspects of CNA guided care. The provider-payer aspect includes eliminating prior authorization for testing, diagnosis, and treatment selection; eliminating prior authorization and the need for recommendations to optimize reimbursement on a per-attendance basis; and payment on a per-attendance basis. to enable a transition from risk-based refunds to risk-based refunds. The patient aspects of patients using CNA-guided care are threefold: First, the patient must have a complete assessment of their disease (e.g., physical evaluation, laboratory evaluation, contrast imaging) so that a CNA can be assigned. secondly, to present patient treatment options for their medical conditions that optimize treatment and reduce the risk of adverse differences; and thirdly, to provide CNA-guided care. Accordingly, we direct patients to physicians in their area who have the best outcomes at an acceptable cost of care.

Embodiments will now 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 like reference numerals.

Although various embodiments are disclosed herein, the disclosed embodiments and user interfaces as shown herein are merely illustrative of the present disclosure and may be implemented in various forms. Additionally, each of the examples provided in connection with the various embodiments are illustrative and not intended to be limiting. Additionally, the drawings are not necessarily to scale and some features may be exaggerated to show details of particular elements (and any size, materials, and similar details shown in the drawings may be exaggerated). illustrative and not intended to be limiting). Accordingly, the specific structural and functional details disclosed herein should not be construed as limitations, but rather as exemplary basis for teaching those skilled in the art how to variously utilize the disclosed embodiments. It's nothing more than that.

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

In some alternative implementations, the functions/acts depicted within the blocks may be performed differently from the order depicted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially simultaneously or the blocks may sometimes be executed in the reverse order, depending on the functions/acts involved. In some cases, it may be done. Additionally, the method embodiments presented and described as flowcharts in this disclosure are shown by way of example in order to provide a more thorough understanding of the technology. The disclosed method is not limited to the operations and logic flows presented herein. Alternate embodiments are contemplated in which the order of the various operations is changed and sub-operations described as part of a larger operation are performed separately.

Although the clinical outcome treatment analysis described here is described in relation to cancer conditions, it is important to note that any clinical condition (e.g., cardiovascular disease, metabolic disease (diabetes), immune disease (e.g., lupus, rheumatoid arthritis)) , organ transplantation, neurodegenerative disorders, pulmonary diseases, infectious diseases, liver disorders, and behavioral health). The practitioner will be aware of the parameters of each such condition.

Throughout this specification and claims, terms may be given meanings beyond those expressly indicated or implied by the context. 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 different embodiments. It does not refer to the form. For example, claimed subject matter is intended to include, in whole or in part, combinations of example embodiments.

Generally, terms can be understood at least in part from their use in context. For example, the words "and," "or," or "and/or," as used herein, depend at least in part on the context in which such words are used. It can have various meanings depending on the Generally, when "or" is used to relate a list such as A, B, or C, it is used in an inclusive sense with A, B, and C; Or used in an exclusive sense with C. Additionally, as used herein, the term "one or more" may be used to refer to any feature, structure, or property in the singular, depending at least in part on the context. or can be used to express a feature, structure, or combination of characteristics in more than one sense. Similarly, words such as "a," "an," or "the" can also be understood to convey singular or plural usage, depending at least in part on the context. In addition, it can be understood that the term "based on" is not necessarily intended to convey an exclusive set of factors, but instead, again, at least partially dependent on the context. Accordingly, the presence of additional factors that are not necessarily explicitly indicated may be allowed.

The pharmaceutical industry has devoted much of its research and development (R&D) investment to specialty compounds, with oncology being the main category. For example, approximately 30-35% of the Phase 3 pipeline is in oncology. These compounds are likely to be targeted and specialized treatments based on the latest scientific advances and require commercial and development models different from those that exist today. Pharmaceutical companies' current systems are generally inefficient and likely cannot be supported by future products.

Diagnostic device companies developing new companion diagnostic tests for a new generation of treatments will need new ways to cultivate physicians and efficient sales and distribution channels.

The U.S. reimbursement model is likely to change from a fee-for-service payment model to a value-based payment model. This constant element (for example, ACCOUNTABLE CARE CARE ORGANIZATIONS) (ACO) & patient -centered Medical Home (ACCOUNTABLE CARE CARE CARE ORGANIZATIONS) (for example, AFFORDUBLE CARE ACT). PATIENT CENTERED MEDICAL HOME ) (PCMHs) model) is accelerating, and there is a move by payers towards bundling payments within specialties (eg, orthopedics). Current fee-for-service payment models are likely not sustainable for governments, employees, other payers, and/or physicians. Many physicians are also realizing that the economics of the fee-for-service model are not sustainable. As mentioned above, governments appear to be moving towards value-based payment models.

FIG. 1, for example, shows a block diagram of some examples of pressures in the cancer market and some potential solutions. Oncologists 105 are facing financial pressures and many are unable to continue operating under current models and are attempting to find leverage in new ways. Possible oncologist solutions 110 include bundle-like aggregation and novel payment models. Pharmaceutical companies (illustrated as "Pharma") 115 typically have much or all of their cancer pipeline as targeted therapies. Additionally, the era of blockbuster drugs is ending. Furthermore, "outdated" business models are no longer suitable and cost too much. Potential pharmaceutical solutions 120 include patient identification and changes in their business models. Health insurance 125 in general is increasingly required to "manage" oncology. Also, there is no internal reliable tool or functionality to perform this management. Additionally, medical professionals such as doctors cannot endorse health insurance. Potential health insurance solutions 130 include new payment models (eg, bundling) and cost controls.

FIG. 2 illustrates clinical outcome tracking in communication with a user computer (also referred to herein as a client device) 210 and a user computer (also referred to herein as a computing device) 230 over a network 215, according to one embodiment. 2 shows a block diagram of a server computer 205 (hereinafter also referred to as server 205) that provides a COTA module 220 to a user computer 210 and/or a user computer 230. Server 205 can generate and/or service web pages that are displayed by a browser (not shown) on user computer 210 and/or user computer 230 over network 215, such as the Internet, for example. In one embodiment, COTA module 220 is a web page (or a portion of a web page) and thus is accessed by a user of user computer 210 and/or user computer 230 via a web browser. In another embodiment, COTA module 220 is a software application, such as a mobile “app,” that can be downloaded from server computer 205 to user computer 210 and/or user computer 230. In further embodiments, COTA module 220 provides a user interface to enable the functionality described herein.

Computing devices, such as server computer 205, user computer 210, and user computer 230, are capable of sending and receiving signals, such as over a wired or wireless network, or process or store signals in memory, such as physical memory states. have the ability to Devices capable of operating as servers include, by way of example, dedicated rack-mounted servers, desktop computers, laptop computers, set-top boxes, and integrated devices that combine functions of two or more of the above-mentioned devices. Devices and the like can be included. Servers can vary widely in configuration or functionality, but typically a server can include one or more central processing units and memory. A server may also include one or more mass storage devices, one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, or a Windows server, a Mac One or more operating systems such as OS X®, Unix®, Linux®, FreeBSD®, etc. may also be included.

Server 205 may include a device that includes settings for providing content to other devices over a network. Server 205 may host sites such as, for example, social networking sites, examples of these sites include, but are not limited to, Flickr, Twitter, Facebook, This can include LinkdIn® or personal user sites (blogs, video blogs, etc.). Server 205 may 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 can host, 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 may further be provided, including calendar services, photo services, etc. Examples of content may include text, images, audio, video, etc., which are processed in the form of physical signals, e.g. electrical signals, or stored in memory, e.g. physical states. Can be stored. Examples of devices that can act as servers include desktop computers, multiprocessor systems, microprocessor-type or programmable consumer electronics, and the like.

In one embodiment, server 205 hosts or is in communication with database 240. Database 240 may be stored locally or remotely from server 205. In one embodiment, COTA module 220 accesses or searches or sorts data stored within database 240. COTA module 220 can also read information over network 215 (eg, from the Internet). Database 240 may store patient data or other patient medical information. For example, the data entered into the database or COTA module 220 may be from experts in their respective fields (e.g., oncologists with 5, 10, 15, 20, 25, 30 or more years of experience). I can do it. Data may be entered into database 240 and/or COTA module 220 manually or automatically.

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

The communication link or channel can be, for example, an analog telephone line such as a twisted pair line, a coaxial cable, a full or fractional digital line including a T1, T2, T3 or T4 type line, an integrated services digital network (ISDN), a digital subscriber line. (DSL), wireless links including satellite links, or other communication links or channels as known to those skilled in the art. Additionally, computing devices or other related electronic devices may be coupled remotely to the network, such as via a telephone line or link.

Wireless networks can couple client devices with the network. The wireless network can be a standalone ad hoc network, a mesh network, a wireless LAN (WLAN) network, a cellular network, or the like. A wireless network may further include systems such as terminals, gateways, routers, etc. coupled by wireless radio links etc., which are free to move randomly or organize themselves arbitrarily, so that the network topology is , can sometimes change quite rapidly. Wireless networks may employ multiple network access technologies, including Long Term Evolution (LTE), WLAN, Wireless Router (WR) mesh, or second, third, or fourth generation (2G, 3G, or 4G) cellular technologies. It can be used further. Network access technologies can enable wide area coverage for devices such as client devices with varying degrees of mobility, for example.

For example, the networks include Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), General Packet Radio Service (GPRS), and Enhanced Data GSM Environment (EDGE). , 3GGP Long Term Evolution (LTE), LTE Advanced, Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n, etc. may enable RF or wireless type communications. A wireless network can include virtually any type of wireless communication mechanism that can communicate signals between devices, such as client devices or computing devices, between networks, or within networks.

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

The COTA module 220 can establish an effective method to manage patients, resulting in better outcomes at controlled costs. In one embodiment, COTA module 220 is a connector or interface between a third party and a medical professional (eg, an oncologist). In one embodiment, COTA module 220 is an analysis tool that sorts cancers to the highest level of clinical and molecular fidelity. The COTA module 220 then tracks outcomes such as overall survival (OS), progression free survival (PFS), and costs in real time.

Overall survival can be a study endpoint that is usually expressed in terms of time (survival time), eg, months. Often, an intermediate value is used so that a study endpoint can be calculated once 50% of the subjects have reached the endpoint. An example is disease-free survival, which is typically used to analyze the results of treatments for localized disease that leave the patient apparently disease-free, such as surgery or surgery and adjuvant therapy. Ru. In disease-free survival, the event is recurrence rather than death. People who relapse are still alive but are no longer considered disease-free.

Progression-free survival is the period of time during and after medication or treatment during which the disease being treated (eg, cancer) does not worsen. This may be used as a metric to study the health of people with diseases to determine how well new treatments work.

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

In one embodiment, COTA module 220 can alert a user (eg, a medical professional) of user computer 210 and/or user computer 230 to provide relevant information at key moments. COTA module 220 may also enable communication and collaboration between healthcare professionals and content publication (eg, by healthcare professionals). In one embodiment, the COTA module 220 allows healthcare professionals to execute at-risk agreements (eg, bundled payments) with payers.

Although COTA module 220 is described in the context of cancer, 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, treatment toxicity, and treatment cost.

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

In one embodiment, COTA module 220 performs COTA sorting 310 to identify patients who meet one or more parameters. Parameters may include, for example, demographic parameters, such as gender, age, ethnicity, comorbidities, smoking, medical record number, source of health care, primary care physician, referring physician, hospital, authorized service vendor (e.g., pharmacy), disease. It can include unique clinical molecular phenotypes, therapeutic objectives, treatment stages relative to disease progression, and biomarkers. Parameters can be simple indicators (eg, positive, negative, no access), numerically based parameters (eg, tumor size), standards-based parameters (eg, tumor grade), etc. The parameters may be received by COTA module 220 as user-selected inputs. Since each patient has different mortality, morbidity, treatment, and costs, it can be sorted to the highest level of clinical and molecular fidelity at diagnosis 310. The term "highest level of clinical and molecular fidelity" refers to the highest level of patient information available in accordance with current scientific and/or medical guidelines as accepted in the relevant field. 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. COTA module 220 can sort lung cancer patients by any combination of these ten results. COTA module 220 may include additional scientific and/or medical guidelines as they become recognized in the relevant field. In one embodiment, 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.

Additionally, COTA module 220 performs 320 outcome tracking and analysis. COTA module 220 tracks outcomes in real time. In one embodiment, the Outcome Tracking elements include progression-free survival, overall survival, performance status/quality of life metrics, incidence/severity of toxicity (e.g., whether the drug or drug harmed the individual). mortality rates, and drug utilization (e.g., delivered dose strength, dosing interval, and treatment duration). Other types of outcomes are also envisioned.

The ECOG performance status/quality of life metrics component refers to the way a patient's quality of life can be tracked over time. This is part of the disease-specific clinical molecular phenotype of demographic parameters, ie, the patient's stage of health at the start of treatment, and is included within the sorting. For example, a comparison of the ECOG at the start of treatment (eg, ECOG3) to the ECOG after treatment (eg, ECOG2) reflects the effectiveness of the treatment.

In one embodiment, exemplary parameters for the Toxicity to Therapy component are incidence and severity. In one embodiment, the COTA module 220 enables at-risk financial contracting between payers and suppliers, so that both reduce diversity, waste, and inefficiency, but still Able to achieve desired outcomes.

COTA module 220 also sends communications, such as alerts 330, at key points, e.g., upon diagnosis, upon progression, upon dose change/drug change/toxicity, and/or when there is a trend toward variation from a desired outcome. It may 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 entity). In one embodiment, the COTA module 220 provides an alert to the health care professional that identifies the particular patient that the health care professional is searching for. For example, the COTA module 220 can provide real-time alerts to a pharmaceutical company seeking a particular patient to administer a particular (eg, new) drug or drug candidate. Alerts can identify particular patients who are suitable candidates for particular drugs.

FIG. 4A is a block diagram illustrating the stages of sorting data related to colon cancer patients, according to one embodiment. Although described in the context of cancer, such as colon cancer, the description and illustrations may apply to any type of cancer, or in another embodiment, any type of disease for which patient-related data exists. It can be applied to

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

Typically, patient information is stored within an electronic medical record (EMR). However, the information contained in an EMR can be overwhelming, and thus it is difficult for medical personnel to locate specific information of interest from the vast amount of information stored within the EMR. Furthermore, most of the information in the EMR is not relevant to the information that medical personnel are searching for. Unlike an EMR, whose purpose is to capture all or most of the data related to patients entering and exiting the clinic, the COTA module 220 allows the user to search for specific data related to a particular patient; Modules are targeted. Accordingly, COTA module 220 can sort the data to search for certain specialized information. The data that COTA module 220 receives is typically via a web page and is discrete (e.g., typically when a user selects one or more selections within a drop-down menu or via one or more checkboxes). (provided by your choice).

The COTA module classifies, sorts, and smoothes groupings of patient types based on these variable results and embodies these classification variables in the designation of unique COTA nodal addresses (CNAs). In one embodiment, data is introduced into the system via a human user or a technical process, such as an API, a layer within an application (referring to a part of an application that performs a specific function), For example, whether the information is correct, whether the information is corrupt, what information is present, what information is missing/holes in the information, how the information is Look at the formatting and spelling, evaluate it, correct any problems with the information the application has detected so far, and assign a CNA to that information set. In one embodiment, a CNA is an address for classifying similar data. In one embodiment, the COTA module identifies relationships between different characteristics within a group, allowing COTA to categorize information about any patient within the group. In one embodiment, the information set contained within the database is pre-assigned with CNAs. In one embodiment, the COTA module captures a large amount of information encompassing many different attributes, allowing the user to identify some of the attributes as a characteristic set, and allowing this information to be combined with other information in the database. Attributes are added to information to indicate that it is similar, ie, this information is of the same type/value as another piece of information. Thus, a nodal address is a numerical value that allows a user to specifically compare similar patients to similar patients. This allows minimizing biological variability in outcome, resulting in greater precision regarding the effect of therapeutic agents on outcome.

In one embodiment, a user wants to validate personal health information (PHI) from a patient to ensure that the information is correct in all respects and then assign the appropriate CNA. As used herein, personal health information (PHI) is information that can be used to identify an individual patient and that is created, used, or disclosed in the course of providing health care services, such as diagnosis or treatment. , refers to any information within a medical record or specified record set. Examples of personal identifiers in PHI include, but are not limited to, name, street address, city, county, province, geographic subdivision finer than state, including zip code, date of birth, date of admission, and discharge date directly associated with the individual. day, date of death, and all elements of a date (excluding year) that include all ages greater than 89, and all elements of a date (including year) that indicate such age, telephone number, fax number, email addresses, social security numbers, medical record numbers, health insurance beneficiary numbers, account numbers, deed/license numbers, vehicle identifiers and serial numbers, including license plate numbers, device identifiers and serial numbers, and web Universal Resource Locators (URLs). , 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 not encoding 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 to be assigned a CNA, which defines the attributes of patient A's record, which is then put into the database, i.e. the set of patient attributes classified into this CNA type is assigned to the CNA. tied to. Once this is complete, the next time the user logs into the application and accesses the database, the database will return all of Patient A's information and the assigned CNA. Therefore, the user immediately understands how to handle the symptoms/attributes of this patient, i.e. the user has an overview of how this type of patient is related to other patients whose information is included in the database. obtain.

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

COTA module 220 analyzes classified and sorted data 430 with respect to these variables (eg, variables 440, 445, 450) to generate unique COTA nodes 455. COTA module 220 applies these nodes to the categorized and sorted data to yield results that are more clinically relevant. 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 indicating preselected variables. Variables can include, for example, diagnosis, demographics, outcome, phenotype, and the like. A phenotype is a compound of observable characteristics or characteristics of a person, such as the person's morphology, development, biochemical or physiological characteristics, phenology, behavior, and behavioral products. Phenotypes result from the expression of a person's genes as well as the influence of environmental factors and the interaction between the two. In one embodiment, the variables of the nodes are selected by experts in the relevant field to partition the data into clinically relevant outcomes.

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

In another embodiment, the node address is represented as multiple number strings separated by periods, where each number string represents one or more variables (e.g., disease, phenotype, treatment type, progression/tracking, gender). etc.). For example, a first string of digits can represent a particular disease, a second string of digits can represent a type of disease, a third string of digits can represent a subtype of a disease, and a fourth string of digits can represent a disease subtype. A string of numbers can represent a phenotype. Thus, in this example, the first string of digits may be 01 to indicate cancer, the second string of digits may be 02 to indicate breast oncology, and the third string of digits may be 02 to indicate breast cancer. 01 and the fourth digit string can be 1201 representing a certain characteristic of the phenotype, so the nodal address is 01.02.01.1201. It should be understood that nodal addresses can include any number of digit strings and are not limited to four digit strings.

In one embodiment, a string of numbers representing a phenotype can be provided by representing characteristics of the phenotype as a directed graph. FIG. 4C depicts a directed graph 460 illustrating characteristics of a phenotype to provide a string of numbers representing the phenotype, according to one embodiment. Directed graph 460 includes nodes representing phenotypes and edges representing relationships between nodes. The graph is started at the root "start" node and traced to the node of the selected phenotype. Each edge is associated with a number. The expressive number string for the node address is provided as a combination of numbers. For example, the number string for the selected phenotype indicating a white male would be represented as 11. Other types of combinations can also be used. Advantageously, representing the phenotypic properties as a directed graph allows the addition of other nodes without changing the entire structure. The appearance of the screen is the result of the CNA, and although the appearance can be changed, it is desirable to present information.

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

Each nodal address can be associated with one or more bundles of a given patient care service (eg, treatment plan). Each bundle may also be associated with one or more nodes. The services included in each bundle may be determined by one or more healthcare providers, hospitals, groups, insurance companies, etc. to optimize patient care and/or costs. In one example, a bundle may indicate a number of image scans, a drug or drug selection, a schedule for when to administer the drug, a surgery or procedure, a number 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 related to this particular disease or nodal address. Advantageously, bundling of services provides cost certainty to insurance companies and/or hospitals for specific diseases. This also reduces the cost of processing and maintaining records. Additionally, the medical professional will know in advance the predetermined course of treatment, which motivates the physician to obtain better outcomes at lower costs.

The International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) is a standard published by the World Health Organization (WH). Diseases, signs, symptoms, and abnormal findings as classified by , the encoding of discomfort, social situations, and external causes of injury or illness. These code sets are considered classification code sets and have a higher level of information than some other medical code sets, such as the Systematized Nomenclature of Medicine (SNOMED) used by federal systems for the electronic exchange of clinical health information. It is. In one advantageous embodiment, nodal addresses are used to identify treatment courses instead of using ICD-10 (International Classification of Diseases, 10th Edition) codes to identify and treat diseases. For example, this allows for a more effective treatment course than traditional ICD-10 codes, especially with regard to treatment planning if a patient is misdiagnosed.

FIG. 5A is a flowchart illustrating the steps performed by COTA module 220, according to one embodiment. At step 505, COTA module 220 collects data records. Each data record includes data related to a disease (eg, cancer). The data records may include patient data for patients who suffer from or have previously suffered from the disease. For example, data records may include diagnosis, demographics, outcomes, costs, or other relevant information. Data records can be collected from electronic databases (eg, electronic medical records) provided by users (eg, medical professionals, professionals, specialists, etc.) or from any other source. In one embodiment, COTA module 220 may store data records in database 240.

At step 510, COTA module 220 receives one or more parameters to sort the data records. One or more parameters may be received from user computer 210 as user selection input. The one or more parameters can include, for example, diagnostic, demographic, outcome, cost, or any other parameter.

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

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

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

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

In another embodiment, analyzing the data records includes performing an analysis to determine patient survival, such as by constructing a Kaplan-Meier curve. A Kaplan-Meier curve is a curve that shows the possible 5-year survival rate, for example, for one doctor (or health care worker) or for a group of doctors (or health care workers). Kaplan-Meier curves can be constructed for overall survival and/or progression-free survival. Other types of analysis are also envisioned.

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

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

COTA module 220 can be used by and benefit many people, workers, and/or businesses. For example, and as discussed above, a pharmaceutical company's highly specialized pipeline includes many aspects (e.g., development/post-market surveillance, including Phase 4 trials, marketing, sales, pricing, and contracts). A new business model seems necessary. In one embodiment, pharmaceutical company professionals can use the COTA module 220 to facilitate this new business model. For example, COTA module 220 can match the correct patient to the correct medication. The COTA module 220 can enable accurate patient identification through its own sorting and nodal addressing capabilities. In one embodiment, the COTA module 220 identifies one or more patients who are or will be good candidates for a particular drug released or in development by a pharmaceutical company (e.g., a pharmaceutical company). company) provides a matching function that allows searches (in real time).

Additionally, COTA module 220 can benefit health insurance. As mentioned above, cancer treatment can become more complex and it is not efficient for health insurance to continue to manage it directly. In one embodiment, the health plan outsources cancer treatment to the COTA module 220 (similar to what health plans used to do with drug benefits). This reduces costs, such as by reducing total treatment costs, and provides cost offsets, such as by replacing pathways, reducing expensive pre-authorization infrastructure, and reducing other human resources to "manage the cancer." Additionally, the U.S. Affordable Care Act stipulates that 85% of insurance premiums must be earmarked for clinical practice-related activities, as opposed to administrative costs. There is. In one embodiment, COTA module 220 provides an analytics interface with connections to insurance claims data to support health plans in managing oncologists.

In one embodiment, 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. Use of the COTA module 220 would benefit such organizations since it can accurately sort and identify patients and send time-based alerts to physicians (or other healthcare professionals). be able to.

FIG. 5B is a flowchart illustrating steps for enabling a patient diagnosed with a disease to optimize his or her treatment options, according to an embodiment. The steps of FIG. 5B may be performed by COTA module 220 running on server computer 205. COTA module 220 running on server computer 205 can interact with a user (e.g., a patient) via COTA module 220 running on user computer 210 and/or Users (eg, medical personnel) may interact through the network 215 via the running COTA module 220 . COTA module 220 may include multiple instances of COTA module 220 for execution on server computer 205, user computer 210, and user computer 230.

At step 550, COTA module 220 running on server computer 205 receives a communication from patient user computer 210. For example, the communication may include an indication that the patient is seeking treatment options, a health care professional, or any other indication. COTA module 220 running on server computer 205 is communicatively linked to a database 240 containing personal health information for a population of patients, which may or may not include health information for this patient. COTA module 220 running on server computer 205 is configured to analyze personal health information stored within database 240. This can be performed as a pre-processing step before receiving communications from the patient. For example, COTA module 220 running on server computer 205 sorts and categorizes personal health information, assigns multiple nodal addresses to each patient in a patient population, and measures clinical outcomes for each nodal address. However, by measuring behavioral variation for each health care provider assigned to each nodal address, personal health information can be analyzed as described herein.

At step 555, patient health information is received. Health information may be received from COTA module 220 running on user computer 210. The patient's health information may include the disease for which the patient has been diagnosed. In one embodiment, diseases are selected by the patient from the list and sublists of diseases available in the database 240 of the computer server 205, along with their specificity, via the COTA module 220 running on the user computer 210. . 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 the list of tests needed to further classify the disease to the COTA module 220 running on the user computer 210.

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

At step 570, a nodal address is assigned to the patient based on the results of the test. The assigned nodal address may be assigned from a plurality of nodal addresses generated for the patient population during a pre-processing phase by COTA module 220 running on server computer 205. The assigned nodal addresses include a preselected set of variables effective to divide 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 treatment of a disease. Predetermined patient care services may be determined by one or more of a health care professional, hospital, medical group, or insurance company. A predetermined bundle of patient care services can provide a predetermined course of treatment. Predetermined bundles of patient care services can provide cost certainty for illnesses.

At step 575, the COTA module 220 running on the server computer 205 determines the assigned nodal address and a geographically organized list of healthcare professionals who treat patients within the assigned nodal address. to the COTA module 220 running on the user computer 210. A geographically organized list of healthcare professionals can be visually categorized by clinical outcome. In one embodiment, a geographically organized list of healthcare professionals can be visually sorted by clinical outcome by color. For example, a geographically organized list of health care workers may be displayed such that green indicates better-than-average clinical outcomes, yellow indicates average clinical outcomes, and red indicates worse-than-average clinical outcomes. , can be visually classified.

At step 580, selections of potential health care workers are received. The selected health care worker candidates may be selected by the patient to meet one or more of geographic limitations, clinical outcome criteria, and cost criteria set by the patient. In one embodiment, at the patient's option, after receiving the selection of potential health care workers, each health care worker is further classified within the group of potential health care workers based on the cost of treatment. For example, each health care worker can be further classified as superior, inferior, or substantially equal to the selected health care worker candidate.

In one embodiment, the patient's user computer 210 is communicatively linked to a health care worker's user computer 230 (e.g., located at the selected health care worker candidate's office) to communicate with the selected health care worker candidate. Facilitate scheduling of medical appointments with. In another embodiment, the COTA module 220 running on the server computer 205 sends the patient's personal health information to the COTA module 220 running on the healthcare worker's user computer 230.

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

FIG. 6 is a flow diagram 600 of alerts provided by COTA module 220, according to an embodiment. In one embodiment, physicians or other health care professionals are alerted based on their preferences. These preferences can be set by a healthcare professional/physician and can include, for example, the trigger 610 for the alert and/or the technology used to provide the alert. Triggers for alerts may include, for example, new patient diagnoses 615, updates to diagnoses, real-time scheduled events, changes in group membership (e.g., new genes identified that may change grouping), and (someone leaving the group), changes in toxicity and/or dose intensity 620, upon disease progression 625, administration of a particular drug, trends in variation from desired outcome 630, and/or expected time or periodicity. Dependency alerts 635 (eg, side effect alerts and/or diagnostic test reminders) can be included. The alert may include a text message 640 or email 645 sent to the user computer 210. Other types of alerts may be sent, such as telephone calls to user computer 210, updates on web pages, social media updates, Twitter, Facebook, or other social media sites. Adding content to a software library or web page, and/or any other message or communication sent to or accessed by user computer 210 may also be envisioned. Although the trigger is described above as providing an alert, it can be any action that causes the COTA module 220 to perform any other action.

FIG. 7 is a graphical representation of a mobile device 705 (eg, user computer 210) summarizing alerts received by the device 705, according to one embodiment. As shown in FIG. 7, the received COTA alerts are New Colon CA 710, New Renal Cell CA 715, Dose Adjustment 720, and Drug Discontinuation. 725, New Progression 730, New Breast CA 735, CHOP Third Cycle Alert 740, Neutropenia Risk Alert 745, and Available Clinical Trials 750. A list will be displayed 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, to treat, for example, non-Hodgkin's lymphoma. used for

The COTA module 220 can process specific disease datasets (e.g., on-demand and in real-time), including, e.g., disease prevalence (e.g., by COTA sorting), progression-free survival by stage, and/or overall survival. ) can be provided. In one embodiment, COTA module 220 can provide drug utilization data sets, such as data related to total or partial treatment, toxicity, and/or therapy changes.

FIG. 8 illustrates a graphical representation 800 of disease prevalence by cancer subtype that may be provided by COTA module 220, according to one embodiment. Here, COTA graph 800 relates to lymphoma from 2010 to 2013. A user can utilize the graph search input section 810 to narrow down the information to be graphed. Graph search input section 810 includes, for example, selection of report subject (e.g., minimal diagnosis, complete diagnosis, and/or audited patient), diagnosis type, cancer site/subtype, ICD9 (International Classification of Diseases, Ninth This can include information such as version) code, comorbidities, disease progression, gender, age, date range, race, diabetes, smoking history, past chemotherapy and radiation treatment history, etc.

FIG. 9 is a graphical representation 900 of sorting based on variable inputs to COTA module 220 that may be provided by COTA module 220, according to one embodiment. Graphical representation 900 shows a COTA graph for Hodgkin's lymphoma 2010-2013, segmented by gender. Graphical representation 900 shows statistics 910 of various patients who have suffered from the disease that are graphed in representation 900. FIG. 10 illustrates an exemplary list display of a plurality of variables 1005 related to a particular disease (the variables shown here relate to lymphoma), according to one embodiment.

FIG. 11 is a graphical representation 1100 that includes a real-time Kaplan-Meier curve with confidence intervals for pancreatic cancer that can be provided by the COTA module 220, according to one embodiment. As mentioned above, a Kaplan-Meier curve is a curve showing the five-year survival rate that can be expressed, for example, for a single physician (or health care professional) or for a group of physicians (or health care professionals). Kaplan-Meier curves can be constructed for overall survival and/or progression-free survival. The user indicates the variables of his graph search in the graph search input section 1110.

FIG. 12 is a graphical representation 1200 illustrating Kaplan-Meier curves per disease progression that may be provided by the COTA module 220, according to one embodiment. Line 1205 relates to all pancreatic cancers, and thick line 1210 relates to pancreatic cancers with first progression.

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

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

15A and 15B are graphical representations of a treatment interface 1500 associated with (e.g., provided by) COTA module 220 for facilitating the link between outcomes and treatment, according to one embodiment. . As shown in FIG. 15A, a treatment interface 1500 includes a list of different types of treatments to be administered to (or declined by) a patient with breast cancer, such as surgery, antineoplastic drugs, cell therapy, radiation therapy, etc. can include. Treatment can be tailored according to disease progression. For example, drugs in oncology are typically given in cycles, and any number of drugs can be given in any one cycle. In one embodiment, the user can select an exacerbation (e.g., expressed as exacerbation 0 to exacerbation 4), where exacerbation 0 is after initial diagnosis, and select a drug within or from multiple categories. can do.

In FIG. 15B, in another embodiment, treatment interface 1510 can include treatment plans for one or more treatments, graphically represented as tabs 1515 on treatment interface 151. Treatment interface 1510 can include fields to indicate start and end data for the regimen, dosage intensity, treatment description, particular drug brand, etc. The treatment plan may be graphically represented as a treatment list in table 1520. Table 1520 can include action icons 1505 for each treatment. Action icons 1505 can facilitate actions such as editing, closing, displaying elements, etc., for example. In one embodiment, action icon 1505 can be a shortcut to perform a complex task (eg, requiring multiple clicks or selections) with a single selection. For example, an icon in the Diagnosis column can move the user to a diagnosis screen.

FIG. 16 is a graphical representation of an outcomes screen 1600 to facilitate outcome tracking, according to one embodiment. The outcome screen 1600 can, for example, facilitate outcome tracking from diagnosis (ie, zero exacerbations), first exacerbation, second exacerbation to fourth exacerbation, with each exacerbation considered a different disease. The outcome screen tabs include (e.g., in one or more drop-down menus or other fields) diagnosis date, treatment start and end dates, response to treatment (e.g., complete, incomplete, stable), and response. Date, fields for notes about the response (eg, Incomplete field, CR-RA-Pet Negative field, CR field, etc.), and end-of-trace data that can include last contact and death. The outcome screen 1600 also allows for entry of drug therapy toxicity, what occurred (e.g., discontinued, continued, no change, drug dose change, and number of times), number of delays, number of drug changes, and/or number of reductions. It may also include an input area for . In one embodiment, a user of COTA module 220 can flag a patient.

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

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

FIG. 19 is a graphical representation of an analysis screen 1900 provided by COTA module 220 showing a comparison between treatment and quality of life, according to one embodiment. Treatments can be represented by therapeutic agent in analysis screen 1900. However, other forms of treatment are also envisaged, such as, for example, surgery, manipulation. In one embodiment, treatment includes incidence, severity, and toxicity of treatment. Quality of life can be measured based on the average ECOG (East Coast Cancer Clinical Trials 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 assessment of how advanced the patient's disease is, how the disease affects the patient's daily living abilities, and appropriate treatment and prognosis.

FIG. 20 is a graphical representation 2000 of alerts provided to medical personnel, according to one embodiment. The appearance of the screen is the result of the CNA, and although the appearance can be changed, it is desirable to present information. In one embodiment, the information in the alert helps the user make decisions in the future. In one embodiment, the information within the alert provides a set of attributes that occurred in a past period. In one embodiment, this proactively influences the user's decisions and reactively provides a digest report of how the medical personnel/physician has performed over the last week, last month, and last quarter. In one embodiment, there are different alerts for different users, each of which may influence the decisions the users make. Alerts can be utilized for real-time course correction to drive the best value, for example, if there is a trend of variation in the administered therapy from the desired outcome. At block 2005, a definition is triggered based on clinical data. Definitions can be triggered using any criteria, e.g., new disease diagnosis, disease progression, patient response, changes in patient characteristics, changes in dosage/drug toxicity, trends in variation from desired outcome. can do. Criteria can be adjusted based on the disease and its parameters. Based on the triggered definition, alerts 2010-A, 2010-B, 2010-C (collectively referred to as alerts 2010) are sent. It should be appreciated 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 health care provider, hospital, pharmaceutical company, or any other person or entity.

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

In one embodiment, the content 2015 may include feedback support to a medical professional with a traffic light feedback indicator (not shown) on the display. For example, blue can mean very good performance (i.e., above average), green can mean standard performance, and yellow can mean good performance but care needs to be taken. Red can mean that the user needs to pay attention to things related to the medical professional's approach to this disease. Other implementations of feedback indicators are also available.

21-24 illustrate graphical representations of different diagnostic types in accordance with one or more embodiments. FIG. 21 shows a diagnostic screen 2100 for gastrointestinal oncology (eg, colon cancer). FIG. 22 shows a breast oncology (eg, breast cancer) diagnostic screen 2200. FIG. 23 shows a diagnostic screen 2300 for thoracic oncology (eg, lung cancer). Diagnostic screens 2100, 2200, 2300 include many different parameters such as the test or modality of the disease. Parameters can be expressed as simple indicators, numeric-based parameters, standards-based parameters, etc.

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

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

FIG. 26 shows a graphical representation of a report screen 2600 illustrating 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 report screen 2600 shows all stage IIB breast cancers by progesterone receptor status.

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

In one embodiment, the "nodes" described above represent all possible permutations of the variables shown in one or more of the graphical representations (eg, one or more of FIGS. 21-27).

As shown in the example of FIG. 29, client device 2905 can include one or more processing units (also referred to herein as CPUs) 2922, which interface with at least one computer bus 2925. Client device 2905 can be, for example, user computer 210. Memory 2930 can be persistent storage and interfaces with computer bus 2925. Memory 2930 includes RAM 2932 and ROM 2934. ROM2934 includes BIOS2940. Memory 2930 may include an operating system 2941, application programs 2942, device drivers, and program code and/or computer-implemented computer programs incorporating one or more of the functions described herein, such as the process flows described herein. Software programs such as software modules 2943 , 2945 including possible processing steps interface with computer bus 2925 to provide information stored in memory 2930 to CPU 2922 . CPU 2922 initially loads computer-executable process steps from storage, eg, memory 2932, one or more data storage media 2944, a removable media drive, and/or other storage device. CPU 2922 can then execute the stored process steps to execute the loaded computer-executable process steps. Stored data, eg, data stored by a storage device, may be accessed by CPU 2922 during execution of computer-executable processing steps.

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

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

Client device 2905 also includes a power source 2926, a network interface 2950, an audio interface 2952, a display 2954 (e.g., a monitor or screen), a keypad 2956, an illuminator 2958, an I/O interface 2960, a haptic interface 2962, a GPS 2964, and 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(R), optical link), digital I/O port ( RS232, USB, FireWire, Thunderbolt), and expansion slots (PCMCIA, ExpressCard, PCI, PCIe).

For the purposes of this disclosure, a module refers to a software, hardware, or firmware (or combination thereof) system, process, or functionality, or a process, feature, and/or functionality described herein. These components perform or facilitate (with or without action or augmentation). A module can include submodules. Software components of a module may be stored on a computer-readable medium. A module may be integrated with, or loaded and executed by, one or more servers. One or more modules can be grouped into one engine or one application.

FIG. 30 is a block diagram illustrating the internal architecture of an example computer, such as server computer 205, user computer 210, and/or user computer 230, in accordance with one or more embodiments of the present disclosure. A computer as referred to herein refers to any device equipped with a processor capable of executing logical or coded instructions, to name a few: servers, personal computers, set-top boxes, etc. , a tablet, a smartphone, a pad computer, or a media device. As shown in the example of FIG. 30, internal architecture 3000 includes one or more processing units (also referred to herein as CPUs) 3012 that interface with at least one computer bus 3002. Also interfacing with computer bus 302 are 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), floppies, etc. Media disk drive interface 2308, which is an interface for a drive that can read from and write to media, including removable media such as CD-ROMs, DVDs, etc., and a display, which is an interface for a monitor or other display device. an interface 3010, a keyboard interface 3016 that is an interface for a keyboard, a pointing device interface 3018 that is an interface for a mouse or other pointing device, and various other interfaces not individually shown, such as parallel and serial port interfaces; There are universal serial bus (USB) interfaces, etc.

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

As mentioned above, one or more persistent storage media 3006 are computer readable storage media that can be used to store software and data, such as an operating system and one or more application programs. The one or more persistent storage media 3006 may also store one or more 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 device drivers such as those described above. One or more persistent storage media 3006 can further include program modules and data files used to implement one or more embodiments of the present disclosure.

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

CNA-guided care has two aspects that together enable the application of CNA to deliver value-based medical care in the United States through the provider, payer, and diseased patient triad. Make it.

A first aspect of the enabling tool includes: a health care provider; a computer including a processor with a first COTA module; and a first client device with a second COTA module communicatively linked to the first COTA via a network. and interactions with payers. Thereby, the payer may communicate with the patient over the network via a first client device having a second COTA module communicatively linked to the first COTA module of the processor, the payer is considering for a patient for whom the patient's health insurance benefits cover the service. identifying the medical service for; indicating that the service is for the purpose of diagnosing or treating a medical condition; and enabling transmission of a communication to the processor identifying variables selected by the payer. Upon receiving communications from the payer, the first COTA module sends information to the second COTA module including (i) clinical outcome data for each CNA, and (ii) medical care provision for the patient at each nodal address at key points in time during treatment. (iii) Adverse variance data for each provider in each CNA, including both non-existent necessary medical care and unnecessary medical care that contributes to disadvantageous variance for patients who treat each patient within the patient population assigned to the CNA; and (iv) cost reports containing real-time cost data for (1) treatment costs relative to clinical outcomes, (2) toxicity incidence and severity relative to treatment costs and clinical outcomes of treatment, and (3) treatment and quality of life outcomes. Information including graphical analysis correlating one or more of the information can be sent. Based on this information, the payer must, at key points in time, ensure that (a) the health care service is an appropriate provision or level of service, taking into account the potential benefits and harms to the patient; b) the health service is effective in improving health outcomes by improving clinical outcomes and reducing total treatment costs, compared to alternative health interventions or no intervention; With respect to this patient's health care services, the payer may be required to can approve payments to

As shown in the example of FIG. 31, a second aspect of CNA-guided medical care according to an embodiment of the present disclosure for a breast cancer patient includes a computer including a processor with a first COTA module and a first COTA module connected to the first COTA module via a network. an interaction between a second client device with a communicatively linked third COTA module and a patient. Upon exchanging a series of communications with the patient via a second client device with a third clinical outcome tracking and analysis module, the first COTA module performs three things: (e.g., physical evaluation, laboratory evaluation, contrast imaging, etc.) and provide information to the patient so that a CNA can be assigned; use the assigned CNA to optimize care and reduce the risk of adverse differences. Showing patients treatment options for their disease that will alleviate the disease; directing patients to physicians within their geographic area who follow CNA-guided care and have the best outcomes at an acceptable cost of care, so patients can The patient and desired level of CNA-guided care can be selected.

As shown in Figure 32, from a payer's perspective, this enabling tool can help eliminate prior authorization for testing, diagnosis, and treatment, eliminate prior authorization, and eliminate the need for referrals. It can be effective. From a provider's perspective, this tool reduces the time needed to pay providers for care. Enabling tools can therefore enable practices to optimize reimbursement on a per-visit basis and move from per-visit payments to risk-based treatment reimbursement (e.g., by bundling services). . From the patient's perspective, CNA-guided care has three things: First, it is sufficient for the patient to obtain a complete medical evaluation of his illness and for the first COTA module to assign a CNA to the patient. Second, we use CNA to provide patients with information on geography, clinical outcomes, costs, and patient preferences that allow patients to choose optimized treatments to avoid adverse differences. Based on other criteria, patients are shown treatment options, and thirdly, they are shown health care providers within their region who follow CNA-guided care with the best outcomes 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, the functional elements and individual functions performed by single or multiple components in various combinations of hardware and software or firmware, either on the user computing device or on the server or both, Can be distributed between software applications. In this regard, any number of features of the various embodiments described herein may be combined into one or more embodiments, with fewer or more features described herein. Many alternative implementation configurations are possible. Functionality may also be distributed, in whole or in part, among multiple components in any manner now known or hereafter known. Accordingly, countless software/hardware/firmware combinations are possible in implementing the functions, features, interfaces, and preferences described herein. Further, the scope of this disclosure extends to the features and functions and interfaces described herein, as well as to the hardware or software or firmware components described herein, as is and hereafter understood by those of ordinary skill in the art. ranges from conventionally known methods for carrying out possible variations and modifications.

Although systems and methods have been described in terms of one or more embodiments, it should be understood that this disclosure is not necessarily limited to the embodiments disclosed herein. It is intended to cover various modifications and similar arrangements that fall within the spirit and scope of the claims, and this scope should be accorded the broadest interpretation to include all such modifications and similar constructions. . This disclosure includes any and all embodiments of the claims below.

Claims (15)

A method for improving clinical outcomes at a specific patient level, the method comprising:
(a) a computing system comprising one or more processors implementing a first clinical outcome tracking and analysis module, pre-selected for relevance to survival, prognosis, or treatment of one or more diseases; Account for biological variation in a patient population by grouping patients within the patient population into clinically relevant groups with the same preselected classification variable values, thereby effectively removing biological variation as a factor in treatment outcome within each patient group, and leaving treatment variation as a dominant factor in treatment outcome within each patient group, the step comprising:
(i) receiving or accessing personal health information about each patient in the patient population and identifying from the personal health information a value of the preselected classification variable for each patient in the patient population;
(ii) for each patient, said preselected classification variables selected by an expert in the relevant field of said one or more diseases to divide the patient's personal health information into clinically relevant groups; assigning one Clinical Outcome and Analysis Nodal Address (CNA) of a plurality of Clinical Outcome and Analysis Nodal Addresses (CNA) each representing a different combination of values to the personal health information of the patient; classifying similar personal health information based on the personal health information associated with the patient population to group types of patients within the patient population;
(iii) for each CNA, measuring the clinical outcome of the CNA by analyzing the clinical outcome in the personal health information of patients in the patient population to which the CNA has been assigned personal health information;
(iv) treatment, testing, and follow-up in the medical records of said patients in said patient population assigned to said CNA for each CNA and all healthcare providers of patients in said patient population assigned to said CNA; and by comparing differences in compliance with prescribed medications between the patient's health care provider and one or more other health care providers within the patient population assigned to the CNA. Measuring behavioral changes in health care providers;
The method further comprises:
(b) obtaining information sufficient for a complete medical evaluation of a new patient not included in the plurality of patients and sufficient to assign a CNA to said new patient; determining an optimal level of care with a low risk of adverse differences using the CNA and providing corresponding options for selecting a healthcare professional and a desired level of CNA-guided care, the step comprising: the computing system implementing a first clinical outcome tracking and analysis module;
(i') a first client device that implements a second clinical outcome tracking and analysis module from a health care provider for the new patient, or a health care provider for the new patient, who reports a health concern regarding the new patient; 1 communication, and in response to the first communication, send a second communication to the first client device including a list of tests necessary to diagnose the health concern as a medical condition;
(ii'') receive a second request from the new patient, or the health care provider for the new patient, via the first client device, containing the results of the test necessary to diagnose the health concern; 3 communication, and in response to the third communication, transmitting a fourth communication to the first client device including a diagnosis of the medical condition and a list of additional tests needed to further classify the medical condition;
(iii'') receiving, via the first client device, a fifth communication from the new patient or the health care provider for the new patient including the results of the additional test, and responding to the fifth communication; and based on the results of the test, the diagnosis and the results of the additional test,
(a'') assigning to the new patient a CNA corresponding to the combination of values of the pre-selected classification variables of the new patient from a plurality of CNAs;
(b'') the first client device;
(1'') the CNA assigned to the new patient;
(2'') a geographically organized list of healthcare professionals who treat patients within the CNA assigned to the new patient;
and wherein the list of health care workers is categorized by geography, clinical outcome, or both, and each of the health care workers is assigned to the new patient's CNA. a past or current health care provider for one or more patients within a patient population, and the clinical outcome of each of the health care workers is the measurement of the health care worker in step (a)(iv); Based on behavioral changes,
(iv'') an optimal care plan with a low risk of adverse variance from the new patient, or a health care provider for the new patient, and at least one or more of the patient's geographic and outcome needs; receiving a seventh communication selecting a health care worker from the list of health care workers that satisfies the above, and in response to the seventh communication, a third clinical outcome at the selected health care worker's office; a second client device implementing a tracking and analysis module to facilitate scheduling of the new patient appointment with the selected healthcare professional;
A method that is done by. (c) said computing system implementing said first clinical outcome tracking and analysis module;
from the approver via a third client device implementing a fourth clinical outcome tracking and analysis module; receiving one or more communications identifying a variable selected by an approver;
In response to the one or more communications, the third client device:
(1') clinical outcome data for the CNA assigned to the new patient;
(2') each medical provider's unfavorable variance data for the CNA assigned to the new patient;
further comprising the step of transmitting information including;
The information may be used by the approver at key points in time.
(a') whether the medical service is an appropriate provision or level of service, taking into account the potential benefits and harms to the new patient;
(b') whether the medical service is effective in improving health outcomes by improving clinical outcomes compared to alternative health interventions or no interventions; and
(c') whether such services are in accordance with generally accepted medical practice;
Confirm that
sufficient for the approver to approve the medical service for the new patient;
The method according to claim 1. The pre-selected classification variables include disease-specific clinical molecular phenotypes, or
Each CNA is represented as a discrete punctuated string of digits that includes a prefix, midpoint, and suffix representing preselected classification variables that divide the health information into clinically relevant health information sets, or
Key points in time include diagnosis, progression, dose changes, drug changes, toxicity, and trends in variation from desired outcome; or
the assigned CNA for the new patient is associated with one or more bundles of predetermined patient care services for treatment of the medical condition; or
The clinical outcome includes one or more of the following: therapeutic agent received, dose intensity delivered, dosing interval, dosing duration, quality of life metrics, toxicity to treatment, progression-free survival, overall survival, response metrics, and death. 2 or more, or
the list of healthcare professionals is visually categorized by clinical outcome;
The method according to claim 1 or 2. the predetermined one or more bundles of patient care services provide a predetermined course of treatment;
The method according to claim 3. The visual classification by clinical outcome of the list of healthcare professionals is:
(a) using the color green to indicate an above average clinical outcome;
(b) using yellow color to indicate average clinical outcome;
(c) using the color red to indicate a below-average clinical outcome;
The method according to claim 4. obtaining permission from said new patient and said selected healthcare professional;
transmitting the personal health information of the new patient from the first clinical outcomes tracking and analysis module to the second client device implementing the third clinical outcomes tracking and analysis module located at the selected healthcare professional's office; further comprising the step of
The method according to claim 1. The behavior variation data of each health care provider for the CNA assigned to the new patient is non-existent, contributing to disadvantageous variance of the health care provider relative to the CNA's patients at key points in time during treatment. including both necessary and unnecessary medical care;
The method according to claim 1. further comprising the step of indicating that the medical service is for the purpose of diagnosing or treating a medical condition;
The method according to claim 2. The variables selected by the approver include variables that identify particular attributes of the personal health information as a desired set of characteristics and are added to the personal health information by the approver to define the personal health information for each patient. one or more attributes, or both, that identify the health information as having an equal level of importance as other health information within the patient population;
The method according to claim 2. A system for improving clinical outcomes at a specific patient level, the system comprising:
(A) a computing system including one or more processors implementing a first clinical outcome tracking and analysis module;
a database containing personal health information and data of a patient population;
a memory storing computer program instructions executable by the one or more processors to implement the first clinical outcome tracking and analysis module;
wherein the first clinical outcome tracking and analysis module is communicatively linked to the database and network;
(B) a first client configured to implement a second clinical outcome tracking and analysis module including a processing unit and memory and communicatively linked to the first clinical outcome tracking and analysis module of the computing system; Equipped with more devices,
The computer program instructions, when executed on the one or more processors of the computing system, cause the first clinical outcome tracking and analysis module to:
(a) within said patient population into clinically relevant groups with the same value of a preselected classification variable preselected for relevance to survival, prognosis, or treatment of one or more diseases; considers biological variation in said patient population by grouping patients, thereby effectively removing biological variation as a factor in treatment outcomes within each patient group and improving treatment outcome within each patient group. A stage in which treatment variation remains as a controlling factor in
(i) identifying the value of the preselected classification variable for each patient in the patient population from the personal health information in the database;
(ii) for each patient, said preselected classification variables selected by an expert in the relevant field of said one or more diseases to divide the patient's personal health information into clinically relevant groups; the patient by assigning one clinical outcome and analysis nodal address (CNA) to the personal health information of the patient of a plurality of clinical outcome and analysis nodal addresses (CNAs) each representing a different combination of values; classifying similar personal health information based on the personal health information associated with a population to group types of patients within the patient population;
(iii) for each CNA, measuring the clinical outcome of the CNA by analyzing the clinical outcome in the personal health information of patients in the patient population to which the CNA has been assigned personal health information;
(iv) treatment, testing, and follow-up in the medical records of said patients in said patient population assigned to said CNA for each CNA and all healthcare providers of patients in said patient population assigned to said CNA; and measuring behavioral variation for each health care provider by comparing differences between said health care provider and one or more other health care providers in adherence to prescribed medications;
the steps carried out by
(b) obtaining information sufficient for a complete medical evaluation of a new patient not included in the plurality of patients and sufficient to assign a CNA to said new patient; determining an optimal level of care with a low risk of adverse differences using a CNA and providing corresponding options for selecting a healthcare professional and a desired level of CNA-guided care, said computing system implementing an outcome tracking and analysis module;
(i'') receiving, via the first client device, a first communication from the new patient or a health care provider for the new patient reporting a health concern regarding the new patient; In response to the communication, transmitting a second communication including a list of tests necessary to diagnose the health concern as a medical condition to the first client device including the second clinical outcome tracking and analysis module;
(ii'') receive a second request from the new patient, or the health care provider for the new patient, via the first client device, containing the results of the test necessary to diagnose the health concern; 3 communication, and in response to the third communication, transmitting a fourth communication to the first client device including a diagnosis of the medical condition and a list of additional tests needed to further classify the medical condition;
(iii'') receiving, via the first client device, a fifth communication from the new patient or the health care provider for the new patient including the results of the additional test, and responding to the fifth communication; and based on the results of the test, the diagnosis and the results of the additional test,
(a'') assigning to the new patient a CNA corresponding to the combination of values of the pre-selected classification variables of the new patient from a plurality of CNAs;
(b'') the first client device;
(1'') the CNA assigned to the new patient;
(2'') a geographically organized list of healthcare professionals who treat patients within the CNA assigned to the new patient;
and wherein the list of health care workers is categorized by geography, clinical outcome, or both, and each of the health care workers is assigned to the new patient's CNA. a past or current health care provider for one or more patients within a patient population, the clinical outcome of each of said health care workers being determined by said health care provider in step (a)(iv); Based on behavioral changes,
(iv'') an optimal care plan with a low risk of adverse variance from the new patient, or the health care provider for the new patient, and at least one or more of the patient's geographic and outcome needs; receiving a seventh communication selecting a health care worker from the list of health care workers satisfying one or more of the health care workers, and in response to the seventh communication, a third clinical communicatively linked to a second client device implementing an outcome tracking and analysis module to facilitate scheduling of the new patient appointment with the selected health care professional;
the steps carried out by
A system that performs operations including: (C) a third clinical outcome tracking and analysis module including a processing unit and memory and configured to implement a fourth clinical outcome tracking and analysis module communicatively linked to the first clinical outcome tracking and analysis module of the computing system; further comprising a client device, the operations performed by the first clinical outcome tracking and analysis module comprising:
from the approver, via the third client device implementing a fourth clinical outcome tracking and analysis module, the new patient and the medical service under consideration for the new patient whose health insurance benefits cover the medical service; receiving one or more communications identifying a variable selected by the approver;
In response, the third client device:
(1') clinical outcome data for the CNA assigned to the new patient;
(2') each medical provider's unfavorable variance data for the CNA assigned to the new patient;
further comprising the step of transmitting information including;
The information may be used by the approver at key points in time.
(a') whether the medical service is an appropriate provision or level of service, having regard to the potential benefits and harms to said new patient;
(b') whether the health service is effective in improving health outcomes by improving clinical outcomes compared to alternative health interventions or no interventions; and
(c') whether such services are in accordance with generally accepted medical practice;
to confirm that the
sufficient for the approver to approve the medical service for the new patient;
The system according to claim 10. The pre-selected classification variables include disease-specific clinical molecular phenotypes, or
Each CNA is represented as a discrete punctuated string of digits that includes a prefix, midpoint, and suffix representing preselected classification variables that divide the health information into clinically relevant health information sets, or
Key points in time include diagnosis, progression, dose changes, drug changes, toxicity, and trends in variation from desired outcome; or
the assigned CNA for the new patient is associated with one or more bundles of predetermined patient care services for treatment of the medical condition; or
The clinical outcome includes one or more of the following: therapeutic agent received, dose intensity delivered, dosing interval, dosing duration, quality of life metrics, toxicity to treatment, progression-free survival, overall survival, response metrics, and death. 2 or more, or
the list of healthcare professionals is visually categorized by clinical outcome;
The system according to claim 10 or 11. the predetermined one or more bundles of patient care services provide a predetermined course of treatment;
13. The system of claim 12. The visual classification by clinical outcome of the list of healthcare professionals is:
(a) Green indicates an above-average clinical outcome;
(b) yellow indicates average clinical outcome;
(c) red indicates below average clinical outcome;
13. The system of claim 12. with permission from said patient and said selected healthcare professional;
transmitting the personal health information of the new patient from the first clinical outcomes tracking and analysis module to the second client device implementing the third clinical outcomes tracking and analysis module located at the selected healthcare professional's office; further including:
The system according to claim 10.

Images