a consistent nationwide data matching strategy donna roach & nancy walker
DESCRIPTION
Connecting Michigan for Health 2013 http://mihin.org/TRANSCRIPT
Patient Matching – Provider PerspectiveJune 6, 2013
Donna M. Roach, CHCIO, FHIMSSAscension Health Information ServicesCIO – Borgess Health & Our Lady of Lourdes
BackgroundBorgess 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
Ascension Health
Two Approaches to Patient Identification
Deterministic– Byte by byte comparison– No tolerance for errors
Probabilistic– Data elements assigned a weight– Score the match
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
Borgess Approach to Patient MatchingComponents: 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
Duplicate Patient Account Process
Jack Brown
John Brown
Dup Record Report
Inpatient
Outpatient
EMPI
?
Automated
Manual Merge
Conclusion
MiHIN 2013 – Connecting Michigan for Health Patient Matching – A Patient Safety Issue
Nancy Walker, MHA, RHIACHE-Trinity Health
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
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
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
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