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

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