A Consistent Nationwide Data Matching Strategy Donna Roach & Nancy Walker

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This document discusses patient matching from the provider perspective. It describes two hospitals, Borgess Health and Our Lady of Lourdes, and their approaches to patient matching. Borgess Health uses a probabilistic enterprise master patient index from Netrix with a 95% tolerance threshold that weights different patient identifying factors. Their process involves policy, the probabilistic system, manual intervention from HIM and registration teams, and results in merging duplicate records after discharge and monthly record clean up. The conclusion emphasizes that patient matching is a patient safety issue according to organizations like The Joint Commission, and risks can be mitigated through human responsibility, design quality, technical implementation, standardized processes, and patient involvement.

Patient Matching – Provider Perspective
June 6, 2013
Donna M. Roach, CHCIO, FHIMSS
Ascension Health Information Services
CIO – Borgess Health & Our Lady of
Lourdes

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

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 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

Duplicate Patient Account Process
Jack
Brown
John
Brown
Dup
Record
Report
Inpatient
Outpatient
EMPI
?
Automated
Manual Merge

MiHIN 2013 – Connecting Michigan for
Health
Patient Matching – A Patient Safety Issue
Nancy Walker, MHA, RHIA
CHE-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

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Today more than ever, hepatocellular carcinoma therapy is experiencing profound and substantial changes. The association atezolizumab (ATEZO) plus bevacizumab (BEVA) has demonstrated its effectiveness in the post-operative treatment of patients, improving the results that can be achieved with liver resections. This after the failure of the use of sorafenib in the already historic STORM study. On the other hand, the prognostic classification of BCLC is now widely questioned. It is now well recognized that the indications for surgery for patients with hepatocellular carcinoma are certainly narrow in BCLC and no longer reflect what is common everyday clinical practice. Today, the concept of multiparametric therapeutic hierarchy, which makes the management of patients with hepatocellular carcinoma much more flexible and allows the best therapy for the individual patient to be identified based on their clinical characteristics, is gaining more and more importance. The presentation traces these profound changes that are taking place in recent years and offers a modern vision of the management of patients with hepatocellular carcinoma.

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Hepatocarcinoma today between guidelines and medical therapy. The role of sur...

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

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

A Consistent Nationwide Data Matching Strategy Donna Roach & Nancy Walker

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