A Consistent Nationwide Data Matching Strategy Donna Roach & Nancy Walker
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
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
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