Clinical outcome tracking and analysis

• US 9,734,289 B2
• Filed: 08/10/2016
• Issued: 08/15/2017
• 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:
  US15/233,564
• Publication Number:
  US 20160350492A1
• Time in Patent Office: 370 Days
• 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 for identifying a patient as a candidate for a specific treatment, 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 a medical professional user in need of the patient as a candidate for the specific treatment, 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 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 the 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 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 for alerting a medical professional with respect to a specific patient that is a good candidate for the desired treatment;

B. identifying the specific patient as a candidate for the desired treatment 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. the first clinical outcome and tracking analysis module communicating an alert over the network to the client device comprising the second clinical outcome tracking and analysis module that enables the medical professional user to access the sorted nodal address data records to precisely identify the specific patient for which the medical professional is searching;

(b). the medical professional user of the client device being able to assess the benefits of the specific treatment for a defined patient population at the lowest possible cost.

2. The method as recited in claim 1, wherein the key points in time in (a)(A)(iii)(5) for alerting the medical professional include at new patient diagnosis, an update to a diagnosis;

a real-time scheduled event;

changes to group membership;

change in toxicity, change in dose intensity;

at disease progression, upon administration of a drug;

upon occurrence of a side effect;

a cycle-dependent alert;

or upon trending towards variance from a desired outcome.

3. The method as recited in claim 1, wherein

(a) the communicating in (C) is via a table, chart, a list or a link;

or

(b) the communicating in (C) comprises sending at least a portion of the clinically relevant personal health information in (B) to a client device for display.

4. The method as recited in claim 1, wherein the medical professional is a pharmaceutical company seeking specific patients for administering a new drug candidate.

5. The method as recited in claim 4, wherein the method is effective to match the patient at the nodal address to the new drug candidate for treating a disease of the patient at the key point in time.

6. The method as recited in claim 1, wherein the specific treatment or drug is in context of a clinical trial.

7. The method as recited in claim 1, wherein

(a) parameters of sorting step (a)(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;

or

(b) the set of variables in (a)(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.

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

(a) 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;

or

(b) 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 8, wherein the clinical outcome 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.

10. The method as recited in claim 1, wherein steps of the method occur in real time.

11. A system that enables a medical professional user to identify a patient as a candidate for a specific treatment, the system comprising:

(a) a processor of a computer server comprising;

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

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

a memory to store computer program instructions,

(b) a 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,

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 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 the 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 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 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 in (A)(iii)(2); and

(5) enabling prediction of key points in time for alerting a medical professional with respect to a specific patient that is a good candidate for the specific treatment;

B. identifying the specific patient as a candidate for the specific treatment for each nodal address by;

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

C. the first clinical outcome and tracking analysis module communicating an alert over the network to the client device comprising the second clinical outcome tracking and analysis module that enables the medical professional user to access the sorted nodal address data records to precisely identify the specific patient for which the medical professional is searching; and

(c) producing for the medical professional a better defined patient population for assessing benefits of the specific treatment at the lowest possible cost.

12. The system as recited in claim 11, wherein the key points in time in A(iii)(5) for alerting the medical professional include at new patient diagnosis, an update to a diagnosis;

a real-time scheduled event;

changes to group membership;

change in toxicity, change in dose intensity;

at disease progression, upon administration of a drug;

upon occurrence of a side effect;

a cycle-dependent alert;

or upon trending towards variance from a desired outcome.

13. The system as recited in claim 11, wherein

(a) the communicating in (C) is via a table, chart, a list or a link;

or

(b) the communicating in (C) comprises sending at least a portion of the clinically relevant personal health information in (B) to a client device for display.

14. The system as recited in claim 11, wherein the medical professional is a pharmaceutical company seeking specific patients for administering a new drug candidate.

15. The system as recited in claim 14, wherein the system is effective to match the patient at the nodal address to the new drug candidate for treating a disease of the patient at the key point in time.

16. The system as recited in claim 11, wherein the specific treatment or drug is in context of a clinical trial.

17. The system as recited in claim 11, wherein

(a) 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;

or

(b) the set of variables in A (iii)(2) include a disease-specific clinical molecular phenotype and the string of digits representing the phenotype is determined based on a directed graph.

18. The system as recited in claim 11, wherein analyzing in (B) comprises

(a) 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;

or

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

19. The system as recited in claim 18, wherein the clinical outcome 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.

20. The system according to claim 11, wherein steps of the operations occur in real time.

21. A non-transitory computer readable medium storing computer program instructions for identifying a patient as a candidate for a specific treatment, 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 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 the 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 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 in (a)(A)(iii)(2); and

(5) enabling prediction of key points in time for alerting a medical professional user with respect to a specific patient that is a good candidate for the specific treatment;

B. identifying the specific patient as a candidate for the specific treatment for each nodal address by;

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

C. the first clinical outcome and tracking analysis module communicating an alert over the network to a client device comprising a second clinical outcome tracking and analysis module that enables the medical professional user to access the sorted nodal address data records to precisely identify the specific patient for which the medical professional is searching; and

(b). producing for the medical professional a better defined patient population for assessing benefits of the specific treatment at the lowest possible cost.

22. The non-transitory computer readable medium as recited in claim 21, wherein the key points in time in (a)(A)(iii)(5) for alerting the medical professional include at new patient diagnosis, an update to a diagnosis;

a real-time scheduled event;

changes to group membership;

change in toxicity, change in dose intensity;

at disease progression, upon administration of a drug;

upon occurrence of a side effect;

a cycle-dependent alert;

or upon trending towards variance from a desired outcome.

23. The non-transitory computer readable medium as recited in claim 21, wherein

(a) the communicating in (C) is via a table, chart, a list or a link;

or

(b) the communicating in (C) comprises sending at least a portion of the clinically relevant personal health information in (B) to a client device for display.

24. The non-transitory computer readable medium as recited in claim 21, wherein the medical professional is a pharmaceutical company seeking specific patients for administering a new drug candidate.

25. The non-transitory computer readable medium as recited in claim 24, wherein the non-transitory computer readable medium is effective to match the patient at the nodal address to the new drug candidate for treating a disease of the patient at the key point in time.

26. The non-transitory computer readable medium as recited in claim 21, wherein the specific treatment or drug is in context of a clinical trial.

27. The non-transitory computer readable medium as recited in claim 21, wherein

(a) 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;

or

(b) the set of variables in A (iii)(2) include a disease-specific clinical molecular phenotype and the string of digits representing the phenotype is determined based on a directed graph.

28. The non-transitory computer readable medium as recited in claim 21, wherein analyzing in (B) comprises:

(a) 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;

or

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

29. The non-transitory computer readable medium as recited in claim 28, wherein the clinical outcome comprises at least one of progression free survival (PFS), overall survival (OS), 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, and death.

30. The non-transitory computer readable medium as recited in claim 21, wherein the steps occur in real time.