Clinical outcome tracking and analysis

• US 9,734,288 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, Christopher L
• Application Number:
  US15/233,548
• Publication Number:
  US 20160350491A1
• 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 bundling of patient care services to provide clinical outcome certainty and cost certainty, 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 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 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 the 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 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 efficient based on the discrete punctuated string of digits representing each nodal address and based on the reduction in permutations;

(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; and

(6) associating each nodal address with one or more bundles of predetermined patient care services at a predetermined cost for treatment of the disease associated with the nodal address;

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; and

comparing clinical outcome of like patients to like patients in (B) to minimize biological variability of clinical outcome;

C. measuring behavioral variance for each health 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;

D. reporting to the user via 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, the alert containing;

(i) the clinical outcome data in (B);

(ii) the behavioral variance in (C); and

(iii) a cost report comprising cost data for treating each patient in the patient population assigned to each nodal address; and

(b) enabling the user to select a bundle of patient care services comprising a predetermined course of treatment at a predetermined cost for the particular disease.

2. The method as recited in claim 1, wherein

(a) the services included in each bundle of patient care services are determined by one or more of a medical professional, a medical group, a hospital, an insurance company;

or

(b) the bundle of patient care services indicates one or more of a number of imaging scans, a drug or choice of drugs;

a schedule of when to administer the drug(s);

an operation or procedure;

number and frequency of follow-up visits.

3. 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.

4. 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.

5. The method as recited in claim 4, whereinthe 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, resource utilization, and death.

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

(a) sending at least a portion of the clinically relevant personal health information in (B) to a client device for display;

or

(b) transmitting the alert in response to a trigger comprising one or more of new patient diagnosis, an update to a diagnosis, real-time scheduled event, changes to group membership, toxicity change;

dose intensity change, disease progression, administration of a particular drug, trending towards variance from desired outcome, a prospective time dependent alert;

a cycle dependent alert.

7. The method as recited in claim 1, wherein

(a) the method tracks clinical outcome and cost outcome in real time;

or

(b) the alert in D is set by user preference.

8. The method as recited in claim 1, wherein

(a) the cost report provided by the first clinical outcome tracking and analysis module in D (iii) tracks cost of treatment, cost of physicians, and cost of hospital stay in real time;

or

(b) the cost report in D (iii) contains one or more comparisons selected from the group consisting of between physician and average cost per revenue, hospital contribution margin in dollars and percent, hospital average revenue and cost;

average revenue per patient;

average cost per patient;

physician average cost per case;

physician average cost per revenue;

average cost of imaging, lab work, evaluation and management, pharmaceuticals, medical supplies and expenses for each physician.

9. The method as recited in claim 1, wherein

(a) the user is one or more of a medical professional, a payer, an expert, a pharmaceutical company, a health plan, a managed care consortium, a health system, an insurer or a patient;

or

(b) the predetermined cost in 1(b) is determined based on historical data associated with the nodal address;

or

(c) the predetermined course of treatment in 1(b) is effective to incentivize medical professionals to obtain better clinical outcomes at lower cost.

10. A system for bundling patient care services to provide clinical outcome certainty and cost certainty, 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, and

(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 the 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 a 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 step A(iii)(2); and

(5) associating each nodal address with one or more bundles of predetermined patient care services at a predetermined cost for treatment of a disease associated with the nodal address;

B. measuring clinical outcome 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; and

comparing clinical outcome of like patients to like patients in (B) to minimize biological variability of clinical outcome;

C. measuring behavioral variance for each health 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;

D. reporting to the user via 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, the alert containing;

(i) the clinical outcome data in (B);

(ii) the behavioral variance in (C); and

(iii) a cost report comprising cost data for treating each patient in the patient population assigned to each nodal address;

(c) enabling the user to select a bundle of patient care services comprising a predetermined course of treatment at a predetermined cost for the disease associated with each nodal address.

11. The system as recited in claim 10, wherein

(a) the services included in each bundle of predetermined patient services are determined by one or more of a medical professional, a medical group, a hospital, an insurance company;

or

(b) the bundle of patient services indicates one or more of a number of imaging scans, a drug or choice of drugs;

a schedule of when to administer the drug(s);

an operation or procedure;

number and frequency of follow-up visits.

12. The system as recited in claim 10, 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) includes a disease-specific clinical molecular phenotype and the string of digits representing the phenotype is determined based on a directed graph.

13. The system as recited in claim 10, 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.

14. The system as recited in claim 13, 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, resource utilization, and death.

15. The system as recited in claim 10, wherein reporting in (D) comprises:

(a) sending at least a portion of the clinically relevant personal health information in (B) to a client device for display;

or

(b) transmitting the alert in response to a trigger comprising one or more of new patient diagnosis, an update to a diagnosis, real-time scheduled event, changes to group membership, toxicity change;

dose intensity change, disease progression, administration of a particular drug, trending towards variance from desired outcome, a prospective time dependent alert;

a cycle dependent alert.

16. The system as recited in claim 10, wherein

(a) the system tracks clinical outcome and cost outcome in real time;

or

(b) the alert in D is set by user preference.

17. The system as recited in claim 10, wherein

(a) the cost report in D(iii) tracks cost of treatment, cost of physicians, and cost of hospital stay in real time;

or

(b) the cost report in D(iii) contains one or more comparisons selected from the group consisting of between physician and average cost per revenue, hospital contribution margin in dollars and percent, hospital average revenue and cost;

average revenue per patient;

average cost per patient;

physician average cost per case;

physician average cost per revenue;

average cost of imaging, lab work, evaluation and management, pharmaceuticals, medical supplies and expenses for each physician.

18. The system as recited in claim 10, wherein

(a) the user is one or more of a medical professional, a payer, an expert, a pharmaceutical company, a health plan, a managed care consortium, a health system, an insurer or a patient;

or

(b) the predetermined cost is determined based on historical data associated with each nodal address;

or

(c) the predetermined course of treatment is effective to incentivize medical professionals to obtain better clinical outcomes at lower cost.

19. A non-transitory computer readable medium storing computer program instructions for bundling of patient care services to provide clinical outcome certainty and cost certainty 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 value of care, meaning better clinical outcomes at a lowest possible cost;

(3) allowing a 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 a 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 step (a)(A)(iii)(2); and

(5) associating each nodal address with one or more bundles of predetermined patient care services at a predetermined cost for treatment of a disease associated with each nodal address;

B. measuring clinical outcome 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; and

comparing clinical outcome of like patients to like patients in (B) to minimize biological variability of clinical outcome;

C. measuring behavioral variance for each health 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;

D. reporting to the user via a client device comprising a 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, the alert containing;

(i) the clinical outcome data in (B);

(ii) the behavioral variance in (C); and

(iii) a cost report comprising cost data for treating each patient in the patient population assigned to each nodal address; and

(b) enabling the user to select a bundle of patient care services comprising a predetermined course of treatment at a predetermined cost for the disease associated with each nodal address.

20. The non-transitory computer readable medium as recited in claim 19, wherein

(a) the services included in each bundle of predetermined patient services are determined by one or more of a medical professional, a medical group, a hospital, an insurance company;

or

(b) the bundle of predetermined patient services indicates one or more of a number of imaging scans, a drug or choice of drugs;

a schedule of when to administer the drug(s);

an operation or procedure;

number and frequency of follow-up visits.

21. The non-transitory computer readable medium as recited in claim 19, 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.

22. The non-transitory computer readable medium as recited in claim 19, 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.

23. The non-transitory computer readable medium as recited in claim 22, wherein the clinical outcome in (B) 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, resource utilization, and death.

24. The non-transitory computer readable medium as recited in claim 19, wherein reporting (D) comprises:

(a) sending at least a portion of the clinically relevant personal health information in (B) to a client device for display;

or

(b) transmitting the alert in response to a trigger comprising one or more of new patient diagnosis, an update to a diagnosis, real-time scheduled event, changes to group membership, toxicity change;

dose intensity change, disease progression, administration of a particular drug, trending towards variance from desired outcome, a prospective time dependent alert;

a cycle dependent alert.

25. The non-transitory computer readable medium as recited in claim 19, wherein

(a) the non-transitory computer readable medium tracks clinical outcome and cost outcome in real time;

or

(b) the alert in (D) is set by user preference.

26. The non-transitory computer readable medium as recited in claim 19, wherein

(a) the cost report in D(iii) tracks cost of treatment, cost of physicians, and cost of hospital stay in real time;

or

(b) the cost report in D(iii) contains one or more comparisons selected from the group consisting of between physician and average cost per revenue, hospital contribution margin in dollars and percent, hospital average revenue and cost;

average revenue per patient;

average cost per patient;

physician average cost per case;

physician average cost per revenue;

average cost of imaging, lab work, evaluation and management, pharmaceuticals, medical supplies and expenses for each physician.

27. The non-transitory computer readable medium as recited in claim 19, wherein

(a) the user is one or more of a medical professional, a payer, an expert, a pharmaceutical company, a health plan, a managed care consortium, a health non-transitory computer readable medium, an insurer or a patient;

or

(b) the predetermined cost is determined based on historical data associated with each nodal address;

or

(c) the predetermined course of treatment is effective to incentivize medical professionals to obtain better clinical outcomes at lower cost.