a consistent nationwide data matching strategy donna roach & nancy walker

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