Systems and Methods for Modeling Healthcare Costs, Predicting Same, and Targeting Improved Healthcare Quality and Profitability

• US 20110166883A1
• Filed: 09/01/2010
• Published: 07/07/2011
• Est. Priority Date: 09/01/2009
• Status: Abandoned Application

• Current Assignee: Amit Bhagat, Arthur Bertolero, David Theodoro, Maxwell Bertolero, Robert D. Palmer
• Original Assignee: Amit Bhagat, Arthur Bertolero, David Theodoro, Maxwell Bertolero, Robert D. Palmer
• Inventors: Bertolero, Maxwell, Palmer, Robert D., Bhagat, Amit, Theodoro, David, Bertolero, Arthur
• Application Number:
  US12/874,033
• Publication Number:
  US 20110166883A1
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• 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: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A comprehensive healthcare analytic and predicative modeling system that tracks costs for patients on a long term basis (greater than 6 months, one-year, or more) to assess the long-term effectiveness of various treatment options. Based upon the evaluation of the long-term effectiveness of various treatment options, the system then delivers a predictive model, which is based on data extracted and aggregated from dissimilar databases, that analyzes up-to-date economic and clinical outcomes, and then, using this data, can estimate long-term future treatment results from an economic and clinical perspective. Also disclosed herein is a personal electronic medical record on a computer network created by a medical provider on the authorization of the patient and controlled by the patient. Lastly, disclosed herein is a computer system for the consolidation of medical and financial data from disparate databases into a unitary data format.

Claims

1. A computer-readable memory storing computer-executable instructions for storing and accessing a patient'"'"'s personal medical record on a computer network, the memory comprising:

computer-executable instructions for storing a dataset comprising a personal medical record of a patient created by a medical provider on said patient'"'"'s authorization, said personal medical record being controlled by said patient;

computer-executable instructions for identifying a user trying to access said personal medical record as said patient;

computer-executable instructions for allowing said patient to review, add to and modify said personal medical record after access to said personal medical record is granted to said patient via a network;

computer-executable instructions for allowing an interested party to request access to said personal medical record from said patient;

computer-executable instructions for allowing said patient to accept said request for access to said personal medical record, wherein if said request is accepted, then said interested party can access, add to, and modify said personal medical record;

computer-executable instructions for allowing said patient to revoke access to said interested party after said request has been accepted, wherein if said access is revoked, then said interested party can no longer access, add to, and modify said patient'"'"'s personal medical record.

2. The memory of claim 1, wherein said interested party is a physician, a healthcare practitioner, a health insurance company, a hospital or a healthcare facility providing medical care to said patient.

3. The memory of claim 2, wherein said dataset comprising said patient'"'"'s personal medical record is created when said patient first visits said interested party, said personal medical record being created by said interested party at a point of care.

4. The memory of claim 3, the memory further comprising computer-executable instructions for automatically updating said personal electronic medical record with information relevant to said patient from a database associated with said interested party.

5. The memory of claim 1, the memory further comprising computer-executable instructions for transmitting non-personalized information from said patient'"'"'s personal medical record to a third-party database.

6. The memory of claim 4, wherein said personal electronic medical record is passively updated with medical information relevant to said patient from said third-party database without an action being taken by said patient or said interested party.

7. The memory of claim 1, the memory further comprising computer-executable instructions for providing said patient access to a premium level of services, wherein if said patient pays a monetary fee, then said patient can access said premium level of services.

8. The memory of claim 1, the memory further comprising:

computer-executable instructions for receiving an invoice from said interested party;

computer-executable instructions for determining which portion of said invoice is the responsibility of said patient and which portion of said invoice is the responsibility of a third party payer;

computer-executable instructions for storing payment information of said patient, said payment information of said patient selected from the group consisting of;

a credit card, a bank account, a money order, and an e-commerce payment account; and

computer-executable instructions for automatic payment of said patient'"'"'s portion of said invoice with said payment information of said patient upon said receipt of said invoice.

9. A computer system for the consolidation of medical and financial data, said computer system comprising:

a medical database accessed by a first computer;

a financial database accessed by a second computer;

a third computer connected to said first computer and said second computer by a network;

a data warehouse accessed by said third computer;

a data set stored in said medical database, said data set comprising medical data; and

a data set stored in said financial database, said data set comprising financial data;

wherein said third computer requests said medical data set from said first computer over said network and said financial data set from said second computer over said network;

wherein said first computer retrieves said medical data set from said medical database and said second computer retrieves said financial data set from said financial database;

wherein said first computer and said second computer transmits said medical data set and said financial data set to said third computer;

wherein said third computer receives said medical data set and said financial data set;

wherein said third computer automatically transforms said requested data sets into a unitary data format and said second computer automatically associates data contained in said requested data sets with a classification corresponding to a specific medical procedure to create a final data set;

wherein said third computer stores said final data set in said data warehouse; and

wherein said final data set is retrievable by said classification.

10. The computer system of claim 9, wherein said classification corresponds to a medical service line.

11. The computer system of claim 9, wherein said classification corresponds to a Medicare Severity Diagnosis Related Group (MSDRG)

12. The computer system of claim 9, wherein said third computer requests a plurality of data sets from a plurality of databases, a plurality of data sets from said plurality of databases are transmitted to said third computer, said third computer receives said plurality of data sets and aggregates said plurality of data sets after said third computer transforms said plurality of data sets into a unitary data format.

13. The computer system of claim 12, wherein said plurality of databases contain clinical data, financial data, diagnostic images, published medical evidence and historical data.

15. The memory of claim 13, wherein said plurality of databases contain clinical data, financial data, A level data and historical data.

16. The memory of claim 13, wherein said decision tree is comprised of benchmarks, actual output and target output.

17. The memory of claim 13, wherein Monte Carlo simulation is used to produce said decision tree.

18. The memory of claim 13, wherein factors considered in said step of analyzing are selected from the group consisting of:

expected patient population, frequency at which a procedure is performed, cost of a procedure, service line, population yield, average length of stay, volume of procedures, revenue of provider per procedure, direct costs to provider, and contribution profit of the provider.

19. The memory of claim 13, wherein said step of analyzing said data set is performed to produce a predictive model for average and best practices for an individual clinician and patient at the point of care, with recommendations for treatment.

20. The memory of claim 18, wherein said decision tree is comprised of benchmarks, actual value and target values in the preoperative, intraoperative and post operative stages of a specific medical procedure for said individual clinician.

14. A computer-readable memory storing computer-executable instructions for an analytical and predictive modeling system on a computer network, the memory comprising:

computer-executable instructions for capturing data sets from a plurality of databases;

computer-executable instructions for transforming said captured data sets into a unitary data format, said unitary data format associating said captured data sets with a classification corresponding to a specific medical procedure;

computer-executable instructions for collecting multiple data sets associated with classifications corresponding to a service line into an amalgamated data set;

computer-executable instructions for analyzing said single data set to produce a predictive model for average and best practices for said service line;

computer-executable instructions for receiving an individual provider'"'"'s input variables in said unitary format for said service line; and

computer-executable instructions for comparing said predictive model to said individual provider'"'"'s input variables to produce a decision tree for said service line for said individual provider.