Clinical outcome tracking and analysis

• US 9,378,531 B2
• Filed: 01/12/2015
• Issued: 06/28/2016
• Est. Priority Date: 10/08/2013
• Status: Active Grant

• Current Assignee: COTA, Inc.
• Original Assignee: COTA, Inc.
• Inventors: Pecora, Andrew L.
• Primary Examiner(s): Gilligan, Luke
• Application Number:
  US14/594,969
• Publication Number:
  US 20150127385A1
• Time in Patent Office: 533 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/20 relating to practices or guidelines

Abstract

The described invention provides a system and method for clinical outcome tracking and analysis. The clinical outcome tracking and analysis comprises sorting, outcome tracking, quality of life metrics, toxicity to therapy and cost of care. The system and method includes receiving one or more parameters. Exemplary parameters for sorting include sex, age, ethnicity, comorbidities, tobacco use, source of insurance, medical record number, primary care physician, referring physician, hospital, approved service vendors, disease-specific clinical molecular phenotype, therapy intent, stage of therapy, biomarkers, and cost of care. A plurality of patient medical records are sorted, by a clinical outcome tracking and analysis module executed by a processor, to provide a set of patient medical records satisfying the one or more parameters. A nodal address, indicating one or more variables, is applied to the sorted set of patient medical records to determine a clinically relevant set of patient medical records as the sorted set of patient medical records satisfying the one or more variables. The clinically relevant set of patient medical records is analyzed. A communication is transmitted based on the analyzing to a user to effect treatment, to monitor performance, or to reduce at least one of treatment variability, waste or inefficiency while delivering on intended outcome.

Claims

1. A method that enables a user to optimize value of care, comprising interactions between a medical care provider, a computer containing a processor comprising a first clinical outcome tracking and analysis module, a client device comprising a second clinical outcome tracking and analysis module that is communicatively linked to the first clinical outcome tracking and analysis module via a network, and the user, the method comprising:

(a) executing on the processor comprising the first clinical outcome tracking and analysis module the steps of;

(A) accounting for biological variance up front by grouping patients in a patient population, thereby effectively removing biological variance as a factor in value of care, and leaving treatment variance as a predominant factor in treatment outcome by;

(i) Receiving, collecting and recording in a database personal health information from each patient in the patient population, the personal health information comprising each parameter that characterizes each patient in the patient population;

(ii) sorting the personal health information for each patient in the patient population using a sorting filter, (i) to provide a sorted set of personal health information for that population, and (ii) to identify patients satisfying each parameter in the patient population;

(iii) classifying like personal health information, and grouping types of patients in the patient population, based on the personal health information associated with the patient population by generating and assigning a plurality of nodal addresses within the first clinical outcome tracking and analysis module, wherein said generating and assigning said plurality of nodal addresses comprises;

(1) Representing each nodal address as a discrete punctuated string of digits comprising a prefix, a middle, and a suffix that represent a set of preselected variables that partition the sorted and classified information into a clinically relevant set of health information;

(2) Reducing trillions of possible permutations to a reduced number of clinically meaningful permutations based on the discrete punctuated string of digits representing each nodal address that enable analysis of first behavioral and then consequent clinical and cost outcome variance from an ideal value, expressed as best clinical outcome at lowest possible cost, in a requisite time needed to alert for necessary care and avoidance of unnecessary care, thereby increasing the value of care, meaning better clinical outcomes at a lowest possible cost;

(3) allowing the user (a′

) to identify certain of the personal health information as a desired set of characteristics, and (b′

) to add one or more attribute(s) to the personal health information to identify the personal health information of each patient as being of on an equal level of importance to other health information in the patient population database,

(4) reducing processing requirements and time for processing to make real-time monitoring of medical provider performance efficient based on the discrete punctuated string of digits representing each nodal address and based on the reduction in permutations; and

(5) enabling prediction of key points in time at which behavioral variance is likely to occur and interrupting treatment flow to avoid over-/under-utilization of care to prevent the behavioral variance;

B. measuring clinical outcome for each nodal address by;

analyzing the clinically relevant set of personal health information for the subset of the patient population for one or more patients in the subset of the patient population;

C. measuring behavioral variance for each medical care provider of each patient in the patient population assigned to each nodal address by comparing differences between one medical care provider and another medical care provider(s), in treating, testing, following-up, complying with prescribed medicines, and cost for each patient in the patient population assigned to each nodal address; and

D. reporting the behavioral variance data for each nodal address in (C) to the client device comprising the second clinical outcome tracking analysis module communicatively linked to the first clinical outcome tracking and analysis module of the processor by transmitting a communication containing an alert over the network;

(b) notifying the medical care provider of both necessary care that is absent and of unnecessary care contributing to the medical care provider'"'"'s behavioral variance for patients at each nodal address at key points in time during treatment, and

(c) leading the medical care provider to change his/her behavior in time to make adjustments in care by reducing care unnecessary to clinical outcome, adding care necessary to clinical outcome, or both; and

(d) producing for the medical care provider better clinical outcomes at the lowest possible cost.

2. The method as recited in claim 1, wherein the clinical outcome in (B) comprises determining one or more of a therapeutic treatment received, delivered dose, dose interval, dose duration, quality of life metrics, toxicity to therapy and cost of care.

3. The method as recited in claim 2, wherein parameters of sorting step (A)(ii) comprise sex, age, ethnicity, comorbidities, tobacco use, source of insurance, medical record number, primary care physician, referring physician, hospital, approved service vendors, disease-specific clinical molecular phenotype, therapy intent, stage of therapy, biomarkers, and cost of care.

4. The method as recited in claim 1, wherein the set of variables in A (iii)(2) includes a disease-specific clinical molecular phenotype, and the string of digits representing the phenotype is determined based on a directed graph.

5. The method as recited in claim 1, wherein each nodal address is associated with one or more bundles of predetermined patient care services for treatment of the disease.

6. The method as recited in claim 1, wherein analyzing in (B) comprises:

updating the personal health information in the database of the one or more patients in the patient population based on the clinical outcomes of the one or more patients in the patient population.

7. The method as recited in claim 6, wherein the clinical outcome in (B) comprises at least one of progression free survival (PFS), overall survival, response metrics, quality of life metrics, incidence of drug toxicity, severity of drug toxicity, delivered dose intensity, drugs received, drug interval, drug duration, cost of care, resource utilization, and death.

8. The method as recited in claim 1, wherein analyzing in (B) comprises:

comparing clinical outcomes between patients in the set of personal health information for the patient population in the database.

9. The method as recited in claim 1, wherein analyzing in (B) comprises:

identifying a specific patient as a candidate for a specific drug.

10. The method as recited in claim 1, wherein measuring behavioral variance in (C) comprises:

comparing clinical outcomes between patients based on treatment, cost, or a combination thereof in the set of personal health information for the patient population in the database.

11. The method as recited in claim 1, wherein analyzing in (B) comprises:

comparing a therapy to a quality of life.

12. The method as recited in claim 1, wherein reporting step in (D) comprises:

sending at least a portion of the analyzed set of personal health information for patients in the patient population in the database to the client device for display.

13. The method as recited in claim 1, wherein reporting step in (D) comprises:

Transmitting the alert in response to a trigger comprising at least one of diagnosis, progression, dose change, drug change, toxicity, trending towards variance from a desired outcome, and a specific time.

14. The method as recited in claim 1, wherein measuring behavioral variance in (C) comprises:

comparing outcome performance of a medical professional treating a disease to outcome performance of an aggregate of medical professionals treating the disease.

15. The method as recited in claim 1, wherein the comparing clinical outcome of like patients to like patients in (B) is effective to minimize biological variability of clinical outcome.

16. The method according to claim 1, wherein the method tracks clinical outcomes and cost outcomes in real time.

17. The method as recited in claim 1, wherein the user is one or more of a medical professional, a payer, an expert, a pharmaceutical company, a health plan, a managed care consortium, an insurer or a patient.

18. The method as recited in claim 1, wherein the measuring behavioral variance in (C) comprises one or more of:

(i) comparing the clinical outcome in (B) and cost outcome for a medical professional that treats a disease to the clinical outcome in (B) and cost outcome for other medical professionals that treat the disease;

(ii) comparing the clinical outcome in (B) and cost outcome for a medical professional that treats a disease to the clinical outcome in (B) and cost outcome for a group of medical professionals that treats the disease;

(iii) comparing clinical outcome in (B) and cost outcome for a health system that treats the disease to the clinical outcome in (B) and cost outcome for another health system that treats the disease;

or

(iv) comparing clinical outcome in (B) and cost outcome for a first payer to the clinical outcome in (B) and cost outcome for a second payer.

19. The method according to claim 1, wherein in step (A(ii)) of the method, the sorting filter comprises latest scientific knowledge, medical guidelines or both.

20. A system that enables a user to optimize value of care, the system comprising:

(a) a processor of a computer server comprising;

a first clinical outcome tracking and analysis module communicatively linked to the database and a network;

a database comprising personal health information data for a population of human subjects;

and

a memory to store computer program instructions,

the computer program instructions when executed on the processor causing the first clinical outcome tracking and analysis module to perform operations comprising;

(A) accounting for biological variance up front by grouping patients in a patient population, thereby effectively removing biological variance as a factor in value of care, and leaving treatment variance as a predominant factor in treatment outcome in a patient population by;

(i) Receiving, collecting and recording in a database personal health information from each patient in the patient population, the personal health information comprising each parameter that characterizes each patient in the patient population;

(ii) sorting the personal health information for each patient in the patient population using a sorting filter, (i) to provide a sorted set of personal health information for that population, and (ii) to identify patients satisfying each parameter in the patient population;

(iii) classifying like personal health information, and grouping types of patients in the patient population, based on the personal health information associated with the patient population by generating and assigning a plurality of nodal addresses within the first clinical outcome tracking and analysis module, wherein said generating and assigning said plurality of nodal addresses comprises;

(1) Representing each nodal address as a discrete punctuated string of digits comprising a prefix, a middle, and a suffix that represent a set of preselected variables that partition the sorted and classified information into a clinically relevant set of health information;

(2) Reducing trillions of possible permutations to a reduced number of clinically meaningful permutations based on the discrete punctuated string of digits representing each nodal address that enable analysis of first behavioral and then consequent clinical and cost outcome variance from an ideal value expressed as best clinical outcome at lowest possible cost, in a requisite time needed to alert for necessary care and avoidance of unnecessary care, thereby increasing increase the value of care meaning better clinical outcomes at a lowest possible cost;

(3) allowing the user (a′

) to identify certain of the personal health information as a desired set of characteristics, and (b′

) to add one or more attribute(s) to the personal health information to identify the personal health information of each patient as being on an equal level of importance to other health information in the patient population database,

(4) reducing processing requirements and time for processing to make real-time monitoring of medical provider performance efficient, based on the discrete punctuated string of digits representing each nodal address and based on the reduction in permutations; and

(5) enabling prediction of key points in time at which behavioral variance is likely to occur and interrupting treatment flow to avoid over-/under-utilization of care to prevent the behavioral variance;

B. measuring clinical outcome for each nodal address by;

analyzing the clinically relevant set of personal health information for the subset of the patient population for one or more patients in the subset of the patient population;

C. measuring behavioral variance for each medical care provider of each patient in the patient population assigned to each nodal address by comparing differences between one medical care provider and another medical care provider(s) in treating, testing, following-up, complying with prescribed medicines, and cost for each patient in the patient population assigned to each nodal address; and

D. reporting the behavioral variance data for each nodal address in (C) to a client device by transmitting a communication containing an alert over a network;

(b) the 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, which second clinical outcome tracking and analysis module receives from the first clinical outcome tracking and analysis module of the processor of the computer server in (D) the communication comprising the alert,

(b) notifying the medical care provider of both necessary care that is absent and of unnecessary care contributing to the medical care provider'"'"'s behavioral variance for patients at each nodal address at key points in time during treatment, and

(c) leading the medical care provider to change his/her behavior in time to make adjustments in care by reducing care unnecessary to clinical outcome, adding care necessary to clinical outcome, or both; and

(d) producing for the medical care provider better clinical outcomes at the lowest possible cost.

21. The system as recited in claim 20, wherein analyzing in (B) comprises determining one or more of progression-free survival (PFS), overall survival (OS), response metrics, quality of life metrics, incidence of drug toxicity to therapy, severity of drug toxicity, delivered drug intensity, drugs received, drug interval, drug duration, resource utilization, cost of care, and death.

22. The system as recited in claim 20, wherein parameters of sorting step (A)(ii) comprise sex, age, ethnicity, comorbidities, tobacco use, source of insurance, medical record number, primary care physician, referring physician, hospital, approved service vendors, disease-specific clinical molecular phenotype, therapy intent, stage of therapy, biomarkers and cost of care.

23. The system as recited in claim 20, wherein the set of variables in A(iii)(1) includes a disease-specific clinical molecular phenotype, the string of digits representing the phenotype is determined based on a directed graph.

24. The system as recited in claim 20, wherein each nodal address is associated with one or more bundles of predetermined patient care services for treatment of a disease.

25. The system as recited in claim 20, wherein the steps occur in real time.

26. The system as recited in claim 20, wherein the user is one or more of a medical professional, a payer, an expert, a pharmaceutical company, a health plan, a managed care consortium, an insurer, or a patient.

27. The system as recited in claim 20, wherein the measuring behavioral variance in (C) comprises one or more of:

(i) comparing the clinical outcome in (B) and cost outcome for a medical professional that treats a disease to the clinical outcome in (B) and cost outcome for other medical professionals that treat the disease;

(ii) comparing the clinical outcome in (B) and cost outcome for a medical professional that treats a disease to the clinical outcome in (B) and cost outcome for a group of medical professionals that treats the disease;

(iii) comparing clinical outcome in (B) and cost outcome for a health system that treats the disease to the clinical outcome in (B) and cost outcome for another health system that treats the disease;

or

(iv) comparing clinical outcome in (B) and cost outcome for a first payer to the clinical outcome in (B) and cost outcome for a second payer.

28. The system according to claim 20, wherein in step (A(ii)), the sorting filter comprises latest scientific knowledge, medical guidelines or both.

29. The system as recited in claim 20, wherein the clinical outcome in B comprises determining one or more of a therapeutic treatment received, delivered dose, dose interval, dose duration, quality of life metrics, toxicity to therapy and cost of care.

30. The system as recited in claim 20, wherein analyzing in (B) comprises Updating the personal health information in the database of the one or more patients in the patient population based on the clinical outcomes of the one or more patients in the patient population.

31. The system as recited in claim 20, wherein analyzing in (B) comprises:

Comparing clinical outcomes between patients in the set of personal health information for the patient population in the database.

32. The system as recited in claim 20, wherein analyzing in (B) comprises:

Identifying a specific patient as a candidate for a specific drug.

33. The system as recited in claim 20, wherein analyuzing in (B) comprises:

Comparing a therapy to a quality of life.

34. The system as recited in claim 20, wherein the reporting in (D) comprises:

sending at least a portion of the analyzed set of personal health information for patients in the patient population in the database to the client device for display.

35. The system as recited in claim 20, wherein the reporting in (D) comprises:

Transmitting the alert in response to a trigger comprising at least one of diagnosis, progression, dose change, drug change, toxicity, trending towards variance from a desired outcome, and a specific time.

36. The system as recited in claim 20, wherein measuring behavioral variance in (C) comprises:

comparing outcome performance of a medical professional treating a disease to outcome performance of an aggregate of medical professionals treating the disease.

37. The system as recited in claim 20, wherein the comparing clinical outcome of like patients to like patients in (B) is effective to minimize biological variability of clinical outcome.

38. A non-transitory computer readable medium storing computer program instructions for optimizing value of care, which, when executed on a processor comprising a first clinical outcome tracking and analysis module, causes the first clinical outcome and tracking module to perform operations comprising:

(a) executing on the processor comprising the first clinical outcome tracking and analysis module the steps of;

(A) accounting for biological variance up front by grouping patients in a patient population, thereby effectively removing biological variance as a factor in value of care, and leaving treatment variance as a predominant factor in treatment outcome by;

(i) Receiving, collecting and recording in a database personal health information from each patient in the patient population, the personal health information comprising each parameter that characterizes each patient in the patient population;

(ii) sorting the personal health information for each patient in the patient population using a sorting filter, (i) to provide a sorted set of personal health information for that population, and (ii) to identify patients satisfying each parameter in the patient population;

(iii) classifying like personal health information, and grouping types of patients in the patient population, based on the personal health information associated with the patient population by generating and assigning a plurality of nodal addresses within the first clinical outcome tracking and analysis module, wherein said generating and assigning said plurality of nodal addresses comprises;

(1) Representing each nodal address as a discrete punctuated string of digits comprising a prefix, a middle, and a suffix that represent a set of preselected variables that partition the sorted and classified information into a clinically relevant set of health information;

(2) Reducing trillions of possible permutations to a reduced number of clinically meaningful permutations based on the discrete punctuated string of digits representing each nodal address that enable analysis of first behavioral and then consequent clinical and cost outcome variance from an ideal value expressed as best clinical outcome at lowest possible cost, in a requisite time needed to alert for necessary care and avoidance of unnecessary care, thereby increasing increase the value of care meaning better clinical outcomes at a lowest possible cost;

(3) allowing the user (a′

) to identify certain of the personal health information as a desired set of characteristics, and (b′

) to add one or more attribute(s) to the personal health information to identify the personal health information of each patient as being of on an equal level of importance to other health information in the patient population database,

(4) reducing processing requirements and time for processing to make real-time monitoring of medical provider performance efficient based on the discrete punctuated string of digits representing each nodal address and based on the reduction in permutations; and

(5) enabling prediction of key points in time at which behavioral variance is likely to occur and interrupting treatment flow to avoid over-/under-utilization of care to prevent the behavioral variance;

B. measuring clinical outcome for each nodal address by;

analyzing the clinically relevant set of personal health information for the subset of the patient population for one or more patients in the subset of the patient population;

C. measuring behavioral variance for each medical care provider of each patient in the patient population assigned to each nodal address by comparing differences between one medical care provider and another medical care provider(s), in treating, testing, following-up, complying with prescribed medicines, and cost for each patient in the patient population assigned to each nodal address; and

D. reporting the behavioral variance data for each nodal address in (C) to the client device comprising the second clinical outcome tracking analysis module communicatively linked to the first clinical outcome tracking and analysis module of the processor by transmitting a communication containing an alert over the network;

(b) notifying the medical care provider of both necessary care that is absent and of unnecessary care contributing to the medical care provider'"'"'s behavioral variance for patients at each nodal address at key points in time during treatment, and

(c) leading the medical care provider to change his/her behavior in time to make adjustments and affect treatment outcome by reducing care unnecessary to clinical outcome, adding care necessary to clinical outcome, or both; and

(d) producing for the medical care provider better clinical outcomes at the lowest possible cost.

39. The non-transitory computer readable medium as recited in claim 38, wherein the clinical outcome in (B) comprises-progression-free survival (PFS), overall survival (OS), response metrics, quality of life metrics, incidence of drug toxicity to therapy, severity of drug toxicity, delivered drug intensity, drugs received, drug interval, drug duration, resource utilization, cost of care, and death.

40. The non-transitory computer readable medium as recited in claim 38, wherein the parameters in (A)(i) comprise sex, age, ethnicity, comorbidities, tobacco use, source of insurance, medical record number, primary care physician, referring physician, hospital, approved service vendors, disease-specific clinical molecular phenotype, therapy intent, stage of therapy, biomarkers and cost of care.

41. The non-transitory computer readable medium as recited in claim 38, wherein reporting in (D) comprises:

sending at least a portion of the analyzed set of personal health information for patients in the patient population to the client device for display.

42. The non-transitory computer readable medium as recited in claim 38, wherein reporting in (D) comprises:

Transmitting the alert in response to a trigger comprising at least one of diagnosis, progression, dose change, drug change, toxicity, trending towards variance from a desired outcome, and a specific time.

43. The non-transitory computer readable medium as recited in claim 38, wherein the steps occur in real time.

44. The non-transitory computer readable medium as recited in claim 38, wherein the user is one or more of a medical professional, a payer, an expert, a pharmaceutical company, a health plan, a managed care consortium, or an insurer.

45. The non-transitory computer readable medium as recited in claim 38, wherein the measuring behavioral variance in (C) comprises one or more of:

(i) comparing the clinical outcome in (B) and cost outcome for a medical professional that treats a disease to the clinical outcome in (B) and cost outcome for other medical professionals that treat the disease;

(ii) comparing the clinical outcome in (B) and cost outcome for a medical professional that treats a disease to the clinical outcome in (B) and cost outcome for a group of medical professionals that treats the disease;

(iii) comparing clinical outcome in (B) and cost outcome for a health system that treats the disease to the clinical outcome in (B) and cost outcome for another health system that treats the disease;

or

(iv) comparing clinical outcome in (B) and cost outcome for a first payer to the clinical outcome in (B) and cost outcome for a second payer.

46. The non-transitory computer readable medium storing computer program instructions according to claim 38, wherein in step (A(ii)), the sorting filter comprises latest scientific knowledge, medical guidelines or both.