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Patient Identifiers: The Link with New Medical Knowledge

Patient Identifiers: The Link with New Medical Knowledge. Christopher G. Chute, M.D., Dr.P.H. Professor of Medical Informatics Associate Professor of Epidemiology Mayo Foundation Rochester, Minnesota NCVHS Subcommittee on Standards and Security.

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Patient Identifiers: The Link with New Medical Knowledge

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  1. Patient Identifiers:The Link with New Medical Knowledge Christopher G. Chute, M.D., Dr.P.H. Professor of Medical Informatics Associate Professor of Epidemiology Mayo Foundation Rochester, Minnesota NCVHS Subcommittee on Standards and Security 1

  2. Health Care Is An Information Intensive Industry • Control of Health Care Costs ... • Improved Quality of Care ... • Improved Health Outcomes ... • Appropriate Use of Health Technology... • Compassionate Resource Management... • ...depend upon information • … Ultimately Integrated Patient Data 2

  3. New Healthcare KnowledgeThe Driving Force • What Data Linkage • How Patient Identifier • Why To Learn about: • Disease Natural History • Treatment Responses • Functional Outcomes • Effectiveness, Efficiency, and Satisfaction 3

  4. Premises • Absence of a common patient identifier compromises the efficient and efficacious delivery of care to an individual. • New Insights which Expand our Knowledge about Health and Disease Require Data Linkage (Identifiers) • Data Security is Possible and Required 4

  5. Perspective • What do you (we) want to know when we visit a healthcare facility: • Anything wrong? • What does it mean? • What can we do about it? • Derives from (Unbiased) Analyses of Patient Experiences. 5

  6. Episodic vs. Longitudinal Data:Anecdote vs. Information • American Health Care is Highly Fragmented • Decentralization of Specialties and Services • Highly Mobile Workforce and Corresponding Coverages • Ineffective Information Transfer • Clinical Decisions and Research Premised upon Incomplete Information 6

  7. Clinical Episodes:Missing or Erroneous Information • Results from Labs, X-Rays, Studies, … • Generated by Free Standing Contractors With Idiosyncratic Enumeration (Local Ids) • May Not Be Merged With Clinical Record Used As Basis of Diagnostic and Treatment Decisions (Omission) • May Be Inadvertently Merged With Wrong Record (Comission) 7

  8. Outcomes Research: • Papilloma Virus and Cervical Cancer • Events separated by 20-30 years • Multiple Providers, Geographic History • Detail Rarely Transferred • Natural History of Chronic Conditions • Incidence and Prevalence of Disease • Under-reported by Hospitals and Public Health 8

  9. Population Based Research:Complete, Unbiased Experience • Biased Hospital Based Studies • Immediate, Episode related Information • Skewed Representation of patients • Socio-economic Referrals • Non-representative Health Profiles (sick) • Population Based Alternative • Rochester Epidemiology Project 9

  10. Rochester Epidemiology Project • NIH Funded Study of Health and Disease in Free Living Population • Begun 1966 • Over 1,000 Peer Reviewed Publications • Integrates Health Experience Across All Providers for Persons in Olmsted County, MN. 10

  11. Rochester Epidemiology Project:Logistics • De facto Master Patient Index Approach • Know Well its Limitations and Inaccuracies • Cooperation of all Providers in Region • Master Diagnostic and Procedure Index • Highly Secure Data Format • Restricted Access • Encrypted Communication of DATA 11

  12. Rochester Epidemiology Project:Benefits • First Resource to Unequivocally Demonstrate Hospital Biases • Natural History of Disease Differs in Hospital Series from that in Community • Hospital Series Patients Tend to Be Sicker • First Example: Multiple Sclerosis 1950 • Double Prevalence cited at time • Vastly Better Prognosis 12

  13. Rochester Epidemiology Project:Benefits • First Resource to Unequivocally Demonstrate Distance Referral Biases • Patients Referred from Distant Sites fare Much Better [Able to Travel] • Local Patients Present with More Advanced, Less Treatable Disease 13

  14. Mayo LegacyPatient Data as Valued Resource Why did Mayo emerge from MN cornfields? • Heritage of Organizing, Preserving, and Linking Patient Data • Medical Record Structures: 1907 • Common Identifier to Facilitate Linkage • Commitment and Resource to Indexing • 5x7 Cards by Disease and Procedure: 1909 14

  15. Mayo Record During • 91 years of continuous use • over 5.5 million patients • conducting over 4,000 studies per year Mayo has no record of confidentiality breached by research projects accessing patient data. 15

  16. Special Concern Confidentiality Overriding Concern: Welfare of the patient 16

  17. Whither Anonymous? • Adequate for final analyses of linked data • Linkage process per se precludes complete anonymity. • Computer only knowledge (encryption?) • Distinctions surround when the key is broken - application specific. • Immediately for Epidemiology • Never for clinical care 17

  18. It’s the Data, Folks(not the Obscurity of an Identifier) • Encryption • Exclusive Health Application • Shielding • Incorporation of Personal Characteristics • Public Access (but private application) 18

  19. On Check Digits • Useful Service • Data integrity when humanly keyed • Adequate Algorithm • Sufficient to detect adds, deletes, inversions • Typically factored by discreet prime numbers • Length • Function of how sure (1 in 10; 1 in 342) 19

  20. Why Have a Unique Identifier? • To Facilitate Data Linkage, in Direct Support of an Individuals Care, and Indirectly by Acquiring New Healthcare Knowledge From Aggregate Patient Experiences. 20

  21. How to Improve Upon IDs in Use Today? • By Making Them Standardized, Consistent, Comparable, and Interchangeable. There Are Grave and Conceivably Fatal Implications to Multiple Identifiers for a Single Individual. 21

  22. Viable Alternatives? • Business As Usual. • Huge Opportunity Cost to Society and Persons • Engage Master Patient Indices • Substantial Room for Error and Mistreatment • Very Large Cost to Create and Maintain • Virtually Impossible for Data Interchange 22

  23. Impact on Privacy? • The Identifier Would Have No Impact on Privacy. It Is the Health Data Which Is Sensitive. Implementing an Identifier Must Be in Tandem With Policies and Legislation Ensuring Privacy. 23

  24. How should the Government be Involved? • Logical Broker for Establishing, Issuing, Maintaining, and Overseeing Health Identifiers. • Monitor Appropriate Patient Data Use Via Policy and Audit. 24

  25. 2nd to the Last Word • A Common Health Identifier Would Ultimately Serve the Best Interests of Individual Patients Through Better Integration of Their Care and Enabling Observational Research on Health Outcomes. 25

  26. Last Word • If Fairly and Intelligently Implemented, Electronic Patient Records Using Common Patient Identifiers Might Actually Reduce Risks to Patient Confidentiality Relative to What Exists in Today's In-auditable Paper Environment. 26

  27. Christopher G. Chute MD, DrPH Professor of Medical Informatics Associate Professor of Epidemiology Head, Section Medical Information Resources Mayo Clinic/Foundation Rochester, MN 55905 Ph: 507 284 5541 Fx: 507 284 1516 Em: chute@Mayo.edu

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