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A Consistent Nationwide Data Matching Strategy Donna Roach & Nancy Walker
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A Consistent Nationwide Data Matching Strategy Donna Roach & Nancy Walker

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Connecting Michigan for Health 2013 http://mihin.org/

Connecting Michigan for Health 2013 http://mihin.org/

Published in: Health & Medicine, Education

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  • 1. Patient Matching – Provider Perspective June 6, 2013 Donna M. Roach, CHCIO, FHIMSS Ascension Health Information Services CIO – Borgess Health & Our Lady of Lourdes
  • 2. Background Borgess Health – 3 hospital system located in Southwest Michigan – Focus on Cardio and Ortho Our Lady of Lourdes – Hospital System located in Binghamton, New York – Focus on Ambulatory
  • 3. Ascension Health
  • 4. Two Approaches to Patient Identification Deterministic – Byte by byte comparison – No tolerance for errors Probabilistic – Data elements assigned a weight – Score the match
  • 5. Pros and Cons Deterministic  No room for error  Greater likelihood of rejection – False negatives  Less sophisticated method  Lower cost Probabilistic  Looks at the probability of a match  Greater control over level of certainty – Organization sets level  Highly customized  Greater cost
  • 6. Borgess Approach to Patient Matching Components:  Policy Driven  Probablistic EMPI – Netrics  95 % tolerance – Weighted factors  Manual Intervention  HIM/Registration Supported Outcomes:  High Complexity – Shared domain  Duplicate Rate – 400/month  Merge after discharge  Monthly record clean up – 1000/month
  • 7. Duplicate Patient Account Process Jack Brown John Brown Dup Record Report Inpatient Outpatient EMPI ? Automated Manual Merge
  • 8. Conclusion
  • 9. MiHIN 2013 – Connecting Michigan for Health Patient Matching – A Patient Safety Issue Nancy Walker, MHA, RHIA CHE-Trinity Health
  • 10. Technological Usual Suspects • Deterministic (rules based) matching • Probabilistic (statistical) matching • Biometrics (fingerprints or retinal scans) • Unique/Voluntary Patient Identifier • These provide technical and policy implications/concerns
  • 11. Identification – Patient Matching is a Patient Safety Issue • The Joint Comission (TJC) • First Patient Safety Goal • Department of Veterans Affairs National Center for Patient Safety • Patient identification issues found in root cause analysis of safety events • Thousands of preventable deaths and preventable adverse events in hospitals each year • Delayed diagnosis, Incorrect treatment, Non treatment • Also potential wrongful disclosure under HIPAA
  • 12. Experience of the Care Givers • Patients who lack identifiers as they appear at the front door • Patients who use another’s identity • Patients with similar names on the same unit • Lab specimens incorrectly labeled • Too many patients not enough staff • Incomplete handoffs at shift change • Recording errors • Error remediation; human review of the content
  • 13. Mitigating the Risk • Human Responsibility • Design quality • Technical implementation • Process for the selection of the correct patient • Clinical decision making to determine consistency with clinical content • Standardization of technology and process • Encourage patient involvement for validation