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Stephen T. Parente, Ph.D. Associate Professor, UM sparente@csom.umn

University of Minnesota Information Technology in Healthcare Course: MILI/PUBH 6562 Fall Semester B, 2009. Stephen T. Parente, Ph.D. Associate Professor, UM sparente@csom.umn.edu. Class # 3 Health IT Data Structure: The Insurer. Basic data structures Units of analysis Health data over time

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Stephen T. Parente, Ph.D. Associate Professor, UM sparente@csom.umn

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  1. University of MinnesotaInformation Technology inHealthcare Course: MILI/PUBH 6562Fall Semester B, 2009 Stephen T. Parente, Ph.D. Associate Professor, UM sparente@csom.umn.edu

  2. Class # 3Health IT Data Structure:The Insurer • Basic data structures • Units of analysis • Health data over time • Health insurance data architecture • Small group exercise

  3. Data Structure Fundamentals • Simple text/column data • Pre-packaged data • Relational databases

  4. Simple Column Text Data Patient DOS Procedure Allowed Joe 02/03/09 MD Visit $100 Joe 06/05/09 Lab test $60 Joe 02/03/09 Lab test $60 Joe 06/05/09 MD Visit $100 Mary 01/06/09 MD Visit $100 Penny 04/23/09 Eye Exam $80

  5. Pre-packaged databases • MS-Access databases • SQL databases • SAS databases • SPSS databases • STATA databases • Excel spreadsheets

  6. Relational Databases • The database stores data in individual files or tables with data items arranged in rows and columns. • AT LEAST one data item (the key) is common to each table and is used to LINK two or more tables for ad hoc queries. • Common method to use relational databases is through SQL (structured query language).

  7. Health Data Display by Different Units of Analysis • By provider • By person • By episode/incident • By transactions • Over fixed units of time

  8. By Person

  9. By Episode

  10. By Transaction

  11. Health Data Representation: Over Time? Jan Apr July Oct Dec =Pysch consult Mental Health Cumulative Cost Well $50K Normal Sick $0

  12. Insurers’ Role in Health Information Technology • They are the ‘links’ that connect to everything about a patient in an electronic form. • Employers • Providers • Patients • Government agencies • Researchers

  13. Insurer’s IT Paradox • They are being held accountable for an insured patient’s total care. • Best breadth of data • Most all places of service • ‘Standardized’ data • Worst detail • No clinical info on patient health status and outcomes.

  14. IS Management Structure CEO COO CIO CFO UR MCO MIS Claims Systems Actuarial Accounting Working Group Backbone

  15. HEALTH INSURANCE CLAIM FORM

  16. The Health MIS Pyramid Decision Support Software Life Support Software Clinical & Financial Data Hardware

  17. Insurer Hardware - Mainframe

  18. Insurance Database Architecture • Claims • Membership • Provider files • Case management • Utilization review / Demand Management • Decision-support databases • Analytic / Financial data

  19. Claims Data • Entered manually (20%), submitted electronically (80%) - on average. • Key items: • Claim ID and date or service • Member / Subscriber ID • Provider of service • Diagnosis & procedure • Charges, reimbursements & copays • Administrative information

  20. Claims Data Example

  21. Claims Data Example

  22. Membership / Subscriber Data • Member / Subscriber ID (sometimes not person specific!) • If managed care, assigned gatekeeper • Dates of enrollment • Age, gender, case-mix, health risks • Address • Type of policy, employer • Status of benefits used during enrollment

  23. Subscriber Data Example

  24. Provider Files • Used to pay bills and identify providers to be included in ‘Panels’ for new products. • Key Data Items: • Provider ID • Specialty, Board Certification, Education • Malpractice history & insurance • Address • Profiling summary

  25. Provider File Example

  26. Case Management • Patient tracking systems • Check to see if recommended ‘process of care’ is occurring as part of good quality care. • Patient reminder systems (mail) • Provider reminder systems (phone, mail & electronic) • Outcomes and cost assessment

  27. Utilization Review / Demand Management • Either run directly or contracted to 4th party acting as Insurer’s agent. • Have decision-support systems based on clinical algorithms (and possibly patient’s claims) to manage a patient’s care. • Common conditions reviewed/managed: • Schizophrenia, depression • Heart disease • Diabetes, Asthma, Glaucoma • AIDS

  28. Support Databases • Procedure fee schedules • Diagnosis codes • Institutional arrangements for managed care payment • Pharmacy fee schedules and formularies • ‘Grouper’ algorithms • DRGs, MDCs, • Case-mix and severity

  29. Life Support Systems • Accounts Receivable • Employers • Consumers • Government • Claims payment • Error checking • Provider payment • Fee schedules & payment algorithms • Benefits/eligibility

  30. Analytic / Financial Data • ‘Cleaned’ versions of claim, provider and membership files designed to: • Generate premium estimates • Adjust provider fee schedules • Profiling of: • population (e.g., all patients with diabetes) • practices • employer groups • patients

  31. Small Group Exercises(Part 1 of a 2 Part Exercise) • What ‘information’ can health insurance data provide? • Name 2 major strengths and weaknesses of claims data as a management tool. • Poof: You’re a Blue Cross Blue Shield CIO. • You have $50M to spend to upgrade your claims system. • It costs $1M per text/character to enhance your data. • What data fields would you add? • Are there any data fields you would consider deleting or optimizing?

  32. Intermission

  33. Medical Provider Data • Patient Diagnosis Information • Treatment plan • Referrals • Outcomes • Explanations for treatment

  34. The Operation • The Hospital submits lots of bills • Lab work • Blood • Anesthesia • ER room time • Supplies • Surgeon John submits a claim for surgery. • Dr. Bob submits a claim for IP consultation. • Internal hospital systems affected: • Inventory • Payroll • Accounts receivable • Medical records • PPO reimburses hospital. • PPO reimburses Dr. Bob • PPO reimburses Dr. John

  35. Medical Center Data Systems Life Support Data Hardware

  36. Medical Data Collection - 1 • Operational data: Transaction-oriented • Hospital pharmacies • Laboratories • Radiology departments • Critical care units • Order-processing units

  37. Medical Data Collection - 2 • Analytic data • Carry all variables of interest • Single record • Data is stored horizontally

  38. Code Systems Standards - 1 • HL7 - American National Standards Institute Health Level • Patient registration data • Patient orders • Clinical information (e.g., vital signs) • Referral information • Clinical trial data • Other operational transactions

  39. Code Systems Standards - 2 • X12 - Data Interchange Standards Association’s Accredited Standards Committee • Insurance enrollment & payment • Administrative messages

  40. Code Systems Standards - 3 • Diagnoses: International Classification of Diseases, Version 9 (ICD9) • Procedures: Current Procedural Terminology, Version 4 (CPT4) • Drugs: Food and Drug Administration’s Nation Drug Code (NDC) directory

  41. Code Systems Standards - 4 • LOINC - Logical Observations Identifier Names and Codes (LOINC) database: The Missing Link • Codes, names and synonyms for more than 12,000 observations: • laboratory tests • vital signs • electrocardiograph measurement • input & output measures • clinical impressions • discharge summary

  42. Code Systems Standards Examples

  43. Integrated Delivery SystemIT Network Decision Support Life Support Data Hardware

  44. Insurer Only Data 1/31/09 PCP visit Laboratory test Specialist visit Biopsy Surgery Sub-Acute Care 2/6/09

  45. Medical Data Available to a U.S. Fee-for-Service Insurer Decision Support Life Support Data Hardware

  46. Medical Data Available to a U.S. Staff Model HMO Decision Support Life Support Data Hardware

  47. Provider Only Data 1/31/09 Referral to specialist White blood cell count high Cancer metastasized Malignant cancer remains 2/6/09

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