1

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.