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Bridging Terminology and Classification Gaps among Patient
Safety Information Systems
Andrew Chang, JD, MPH, Laurie Griesinger, MPH, Peter Pronovost, MD, PhD, Jerod Loeb, PhD
Joint Commission on Accreditation of Healthcare Organizations
A Centralized Patient Safety Information System?
JCAHO
STATEADVERSE
EVENTREPORTINGSYSTEM**
MedMARx(USP)
MEDICATIONERROR
REPORTINGPROGRAM
(ISMP & USP)
HOSPITALSAMBULATORY
CARESETTINGS
OTHERS*
* Other delivery settings include behavioral health, home care, nursing home, subacute care setting, rehabilitation setting, hospice and clinical laboratory.
** Only 20 states have some form of mandatory adverse event reporting system
*** Near misses are not currently reported in existing systems
FDA
SentinelEvents
MedicationErrors
MedicationErrors
DE-IDENTIFIEDDATA
CONGRESS
Agency for Healthcare Research andQuality (AHRQ)
Periodic Reporting
PatientInjuries/
Facility issues
Identifiable Data
Non-identifiable Data
NearMisses***
SentinelEvents
NearMisses***
NATIONALNOSOCOMIAL
INFECTIONSURVEY
(CDC)
Hospital-acquiredinfections
3
Background
1. Uniform formats and data standards for reporting adverse events and near-misses
2. Data standards applicable to the coding and classification of patient safety information
3. Data standards that are understandable to all
4. Data standards to enable interoperability within and across health care organizations
(2003 IOM Patient Safety: Achieving a New Standard for Care)
Challenge #1: Discordant Terminology
Adverse event/outcome Unintended consequence Unplanned clinical
occurrence Therapeutic misadventure Peri-therapeutic accident Iatrogenic complication/
injury Hospital-acquired
complication Near miss Close call
Incident Medical mishap Unexpected
occurrence Untoward incident Bad call Sentinel event Failure Mistake Lapse Slip
Errors
Iatrogenic AdverseEvents
PreventableErrors
NegligentAdverse Event
PreventableAdverseEvents
Sentinel Events
Accidents
Adapted from HoferTP, Kerr EA, Hayward RA (2000)
Challenge #2: Discordant Nomenclature
IV. Cause
III. Domain
Overuse,Underuse, Misuse(Chassin, 1998)
Legal definition(e.g., errors
resulting fromnegligence)
Active & LatentFailures
(Reason, 1990)
Severity of Harm(e.g., JCAHO
Sentinel EventsReporting,
NCC MERP)
II. Type
I. Impact
V. Prevention & Mitigation
Type of healthcare service
provided (e.g.,Einthoven Classification)
Type of individualinvolved (e.g.,
physician , nurse,patient
Type of setting(e.g., hospital,home health)
Interventions (e.g., JCAHO National
Patient Safety Goals
Challenge #3: Discordant Classification
Methods
Comparison of two independent patient safety terminology, nomenclature, and classification schemas
Patient Safety Event Taxonomy (PSET) Intensive Care Unit Safety Reporting System
(ICUsrs)
Patient Safety Event Taxonomy (PSET) Alpha version developed by JCAHO in January 2002,
refinement is ongoing
High-level taxonomy
Mapping and Classification Schema (“back-end”)
5 primary classifications: Impact; Type; Domain; Cause; Prevention &
Mitigation
Under the 5 primary classifications, there are: 16 secondary classifications 60 tertiary classifications 127 quaternary classifications ICD-9, SNOMED, Narrative fields
Intensive Care Unit Safety Reporting System (ICUsrs)
Developed by The Johns Hopkins University and funded by AHRQ starting in October, 2001
Over 1900 events collected to date (“front-end”)
31 ICUs in the U.S. participate
Web-based, confidential, non-punitive reporting tool that can be used by any hospital staff member
114 coded and narrative fields
Methods
1. Classification nodes of the PSET were mapped to the fields in the ICUsrs
2. The degree of match was assessed using a 5-point Likert Scale (match, synonymous, related, extrapolated, no match)
3. Overall similarity of the schemas was found by averaging the scores of the secondary classifications under each primary classification
Methods
Example: Classification of Causes
Cause (Primary) Human Factors (Secondary)
Practitioner (Tertiary)• Skilled-based (Quaternary)
Results
Of the 75 coded fields in ICUsrs containing event-related data
46 (61%) fields mapped to PSET 29 (39%) fields unmapped
Results
Of the the most frequently coded fields that mapped to PSET (n=34), ICUsrs fields mapped with the following degree of similarity:
4 (12%) match 10 (29%) synonymous 5 (15%) related 4 (12%) extrapolated 11 (32%) no match
Results
The average Likert Scale ranking of secondary, tertiary and quaternary nodes by PSET primary
classification
1
2
3
4
5
Likert Score
Impact Type Domain Cause Prevention
Primary Classification
5 Match
4 Synonymous
3 Related
2 Extrapolated
1 No match
Results
1
2
3
UK AUS USA Neth
Impact
Type
Domain
Cause
Prevention
The average Likert Scale ranking by PSET primary classification
3 match
2 extrapolated
1 no match
Map to a Standardized Taxonomy
Incident ReportsActive Surveillance
AnalysesFollow-up
ReportingSystem 1
HCO1 - Patient Safety in Surgery
Incident ReportsActive Surveillance
AnalysesFollow-up
ReportingSystem 2
HCO2 - Patient Safety in Pediatrics
Incident ReportsActive Surveillance
AnalysesFollow-up
ReportingSystem 3
HCO3 - Patient Safety in Select Area(s)
CAUSES
TYPES
IMPACTDOMAINS
MAP&
CLASSIFY
MAP&
CLASSIFY
MAP&
CLASSIFY
PREVENTION &MITIGATION
Conclusions
Results suggest that standardization of patient safety event data may not be as simple as presumed by the 2003 Institute of Medicine (IOM) report, Patient Safety: Achieving a New Standard of Care.
We believe that this overall approach of explicit linking of information via PSET provides a potentially powerful capability for common data exchange among non-common reporting systems.