Transcript Impact of Laboratory Services on Diagnostic Errors

INTERVENTIONS TO REDUCE
INAPPROPRIATE TEST UTILIZATION
Diagnostic Error in Medicine
12 November 2012
Paul L Epner
TRENDS
SUGGEST INCREASED DIAGNOSTIC ERRORS
 Aging
population means more diagnoses
 Increasing chronic comorbidities mean
increased diagnostic complexity
 Decreasing number of primary care physicians
combined with emphasis on “cost
effectiveness” means less time with patients
 Anecdotal evidence of reduced skills in taking
history and conducting physical
 Diagnosis is an evolving term
2
DEFINITION
OF DIAGNOSIS IS EXPANDING
The cause of symptoms (traditional)
 The condition’s subtype (for best treatment)

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Antimicrobial susceptibility testing
 Tumor typing

The body’s likely response to treatments
 The stratification of risk

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THE ROLE OF LABORATORY TESTING IN DIAGNOSIS IS
LIMITED BUT IMPORTANT AND LIKELY INCREASING
In a study of 248 hospitalized patients, 246 had
definitive diagnosis within 3 months of
hospitalization.
 The primary determinant of diagnosis for 215 with
“exact” in-hospital diagnosis was:

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History and Physical – 48.4%
 Radiologic exam – 33.5%
 Blood test or culture – 9.8%


Study limitations
did not examine diagnostic error
 did not examine time to diagnosis
 did not examine appropriate use of diagnostic tools

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Source: Wahner-Roedler, D. L.et al. (2007). Who makes the diagnosis? The role of clinical skills and
diagnostic test results. Journal of evaluation in clinical practice, 13(3)
OLDER
STUDIES YIELD COMPARABLE RESULTS
80 prospective outpatient cases
 Final diagnosis made

Following history - 61 (76%)
 Following physical – 10 (12%)
 Following laboratory – 9 (11%)
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
Confidence in diagnosis rose with more information
Following history – 7.1 (scale of 1 to 10)
 Following physical – 8.2
 Following laboratory – 9.3


Some evidence that skill in conducting history and
physical is decreasing while reliance on data is
increasing
5
Source: M.C. Peterson, J.H. Holbrook, D. Von Hales, N.L. Smith, and L.V. Staker, “Contributions of the history, physical
examination, and laboratory investigation in making medical diagnoses.,” The Western journal of medicine, vol. 156, Feb. 1992.
THE
ROLE OF TESTING IN DIAGNOSTIC
ERRORS
IS
SIGNIFICANT
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N= 583 Cases
6
G. D. Schiff et al., “Diagnostic error in medicine: analysis of 583 physician-reported errors.,” Archives of
internal medicine, vol. 169, no. 20, pp. 1881-7, Nov. 2009.
U.S.
MALPRACTICE CASES CONFIRM SIGNIFICANCE
Of 307 closed cases (ambulatory) studied because they alleged missed or
delayed diagnosis, 181 did involve diagnostic errors that harmed patients
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Source: T. GANDHI, A. KACHALIA, E.J. Thomas, A.L. Puopolo, C. Yoon, T. Brennan, and D. Studdert, “Missed and delayed
diagnoses in the ambulatory setting: A study of closed malpractice claims.,” Annals of internal medicine, vol. 145, 2006.
TRADITIONAL
LABORATORY QUALITY MEASURES ARE NOT
SPECIFIC FOR PATIENT HARM OR DIAGNOSTIC ERRORS
Prolonged turn-around time
 Error logs
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Missing ID, Hemolysis, Short fills, Interface error logs,
Incomplete requisitions, uncollected samples, order entry
errors, lost specimens, contaminated specimens
Incident reports
 Corrected result reports

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A FRAMEWORK FOR LABORATORY-RELATED DIAGNOSTIC
ERRORS HAS BEEN DEFINED*
Inappropriate test is ordered
 Appropriate test is not ordered
 Appropriate test result utilization is delayed
 Appropriate test result is not properly utilized


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Knowledge deficit
 Failure of synthesis
 Misleading result
 Systematic failure

Appropriate test result is wrong
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*Adapted from P Epner and M Astion, “Focusing on Test Ordering Practices to Cut
Diagnostic Errors,” Clinical Laboratory News, vol. 38, no. 7, July 2012
THE

FRAMEWORK GUIDES INTERVENTIONS
Inappropriate test ordered or appropriate test not
ordered




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CPOE design and monitoring
Algorithms, clinical pathways, guidelines
Reflex testing
Data mining
Inter-physician variance analysis
Resource utilization committee
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THE

Test result not utilized properly or fully


Interpretive comments
EMR interface
Real-time triggers
Test result delayed or not retrieved
Process monitor
 Discharge monitor


Appropriate test result is wrong

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
FRAMEWORK GUIDES INTERVENTIONS
Delta checks
Controls/Calibrations
Autoverification
Second read (AP)
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REQUISITION
DESIGN
Design changes
focused on
medical necessity,
reduction in
panels, test
groupings linked
to specialty, etc.
 Reduction in tests
per visit occurred
 No assessment of
impact on Dx
errors was made

Source: J.F. Emerson and S.S. Emerson, “The impact of
requisition design on laboratory utilization,” American Journal
of Clinical Pathology, vol. 116, Dec. 2001.
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CLINICAL DECISION SUPPORT/BEST PRACTICE ALERTS
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13
Source: Jones, Jay, “Lab Enterprise Analytics,” Executive War College 2009
DIAGNOSTIC ALGORITHMS
Clinical variables drive six distinct but potentially
overlapping algorithms for prolonged PTT




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Evaluation preoperatively of an asymptomatic prolonged PTT
Evaluation of a persistently prolonged PTT with bleeding
Evaluation of a persistently prolonged PTT without bleeding
Evaluation of an elderly patient without bleeding history
accompanied by sudden development of soft tissue
hematomas and/or persistent and significant gastrointestinal
or genitourinary hemorrhage
Evaluation of hospitalized newborn with prolonged PTT
Evaluation of a unexplained prolonged PTT following
multiple, appropriate workups; searching for rare diagnoses
Source: Tcherniantchouk, O., Laposata, M., & Marques, M. B. (2012). The isolated
prolonged PTT. American journal of hematology.
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
14
15
Developed by the Centers for Disease Control with the support of the
Algorithm Subgroup of CLIHC™
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REFLEX
AND REFLECTIVE TESTING
Creating protocols for the sequential addition of
tests based on earlier results reduces diagnostic
delays and patient inconvenience while reducing
test volume
 Reflex testing can improve diagnostic accuracy
 The improvement in diagnostic accuracy is linked to
the threshold criteria and varies with the clinical
scenario

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Source: R. Srivastava, W. a Bartlett, I.M. Kennedy, A. Hiney, C. Fletcher, and M.J.
Murphy, “Reflex and reflective testing: efficiency and effectiveness of adding on
laboratory tests.,” Annals of clinical biochemistry, vol. 47, May. 2010.
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DATA
MINING
Data mining is the process of nontrivial extraction of
implicit, previously unknown and potentially useful
information from data stored in repositories.1
 Strategies can be driven by published guidelines
 Retrospective study2 of more than 450,000 HPV tests
against new guideline published in 2004

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HPV testing is contraindicated in women under age 21
 HPV testing is contraindicated without positive cytology.

Study showed multi-year improvements in compliance
 Data mining is a tool that identifies opportunities for
education or other interventions

S.J. and Siau,K., “A review of data mining techniques,” Industrial Management & Data
Systems, Vol. 101, January 2001.
2B.H. Shirts and B.R. Jackson, “Informatics methods for laboratory evaluation of HPV ordering
patterns with an example from a nationwide sample in the United States, 2003-2009.,” Journal
of pathology informatics, vol. 1, Jan. 2010.
1Lee,
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PHYSICIAN-LEVEL


When physicians are given feedback on their test
ordering patterns compared to colleagues or guidelines,
test ordering behavior changes.
In one study1, clinicians were educated about the
laboratory tests needed to monitor patients on
antihypertensive medication. Additionally, they were
given feedback on their testing patterns. Appropriate
testing improved.
In another study2, quarterly feedback of practice
requesting rates for nine laboratory tests, enhanced with
educational messages were provided to primary care
physicians which proved to be an effective strategy for
reducing inappropriate testing
1Lafata,
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
PERFORMANCE FEEDBACK
J.E. et al, “Academic detailing to improve laboratory testing among outpatient medication 20
users.,” Medical care, vol. 45, Oct. 2007.
2Thomas, R.E. et al, “Effect of enhanced feedback and brief educational reminder messages on
laboratory test requesting in primary care: a cluster randomised trial.,” Lancet, vol. 367, Jun. 2006.
RESOURCE
UTILIZATION COMMITTEE
Typically involves locally driven consensus
 One study is noteworthy for assessment of patient
impact.*

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*Neilson, E. G., Johnson, K. B., Rosenbloom, S. T., Dupont, W. D., Talbert, D., Giuse, D. A., Kaiser, A., et al. (2004).
The impact of peer management on test-ordering behavior. Annals of internal medicine, 141(3), 196–204.
FOCUS

ON SYSTEMATIC ERROR REDUCTION
Many laboratory professionals routinely drive
initiatives to reduce systematic errors.
Tools in use
Lean
 6 Sigma
 Root Cause Analysis
 Failure Mode & Effect Analysis

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Bias in problem selection may exist
Within the laboratory walls
 Within the control or shared control of the laboratory


Evidence for the use of these tools to eliminate
diagnostic errors is difficult to find
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INTERPRETIVE COMMENTS

Criteria for providing interpretive comments have been
described*




Areas where Interpretive reports are most relevant
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
a decision on treatment is indicated by the results in
combination with the clinical details provided
a result is unexpected
a specific question has been posed but it is not obvious
whether the results provide the answer
a clinician has requested a test with which he/she is not
likely to be familiar
23
Piva and M. Plebani, “Interpretative reports and critical values.,” Clinica chimica acta;
international journal of clinical chemistry, vol. 404, 2009.
*E.
DIAGNOSTIC MANAGEMENT TEAMS AT VANDERBILT
ENSURE APPROPRIATE CONSULTATIVE SERVICES
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PENDING
LAB RESULTS: PROCESS MONITORING
Shifts the focus from catching failures e.g., clinical
event monitors to workflow process control
 Some efforts are ongoing: MSTART (Multi-Step Task
Alerting, Reminding, and Tracking)

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*Tarkan, S., Plaisant, C., Shneiderman, B., & Hettinger, A. (2010). Improving Timely Clinical
Lab Test Result Management: A Generative XML Process Model to Support Medical Care.
PENDING
LAB RESULTS: DISCHARGE MONITOR
Several attempts to create automated tools have been
tried with limited success
 Positive results were obtained with a system of email
notifications1
 A computer-based antimicrobial monitoring (CBAM)
system has been used to ensure positive microbiology
cultures receive attention with improved outcomes2
 Discharge systems need to alert both hospital-based
and primary care physician

A. K., Schnipper, J. L., Poon, E. G., Williams, D. H., Rossi-Roh, K., Macleay, A., Liang, C. L., et al.
(2012). Design and implementation of an automated email notification system for results of tests pending at
discharge. Journal of the American Medical Informatics Association : JAMIA, 19(4), 523–8.
2Wilson, J. W., Marshall, W. F., & Estes, L. L. (2011). Detecting delayed microbiology results after hospital
discharge: improving patient safety through an automated medical informatics tool. Mayo Clinic proceedings.
Mayo Clinic, 86(12), 1181–5. doi:10.4065/mcp.2011.0415
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1Dalal,
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TOOLS

Robust research on the role of laboratory services
does not exist
Research on the effectiveness of available tools is
limited
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
EXIST; PROVING VALUE IS MORE DIFFICULT
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IMPROVEMENTS
IN TEST SELECTION AND RESULTS
INTERPRETATION (ITSRI) – A RESEARCH AGENDA
Appropriate testing
 Appropriate interpretation


©2012 Paul Epner LLC
Strategic Intent
 Establish empirically the optimum role for
laboratory medicine’s physicians and scientists to
maximize positive patient outcomes
Identify evidence-based interventions that support
the optimum role
28
ITSRI STATUS
 Narrowed
scope to diagnostic errors
 Seeking to catalyze research
Diagnostic Process Variation
Chief complaint specific
 Diagnosis specific
 Test domain specific


Intervention effectiveness
©2012 Paul Epner LLC

 Building
awareness
 Recruiting collaborators



NorthShore University HealthSystem
Virginia Commonwealth University
Kaiser Permanente
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OTHER
EFFORTS ONGOING
Diagnostic errors and the clinical laboratory
 AHRQ ACTION II
 CLIHC™
 Significant challenges remain
 Lack of funding and resources
 Shifting the focus from laboratory costs

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AHRQ FUNDED
RESEARCH




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Awarded to RTI in
August, 2011; 18
month effort
Developing risk
assessment tools
which will be tested
in three sites:
Vanderbilt
Emory
Seattle Children’s
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REFERRAL LABORATORY RISK ASSESSMENT
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IDENTIFICATION
AND
PRIORITIZATION
OF
RISK
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CLINICAL LABORATORY INTEGRATION INTO
HEALTHCARE COLLABORATIVE – CLIHC™







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A survey of medical schools to understand curricular changes
since 1992 involving laboratory medicine
A survey of pathology residency programs quantifying time
spent teaching consultation
A survey of primary care clinicians to quantify the barriers to
appropriate laboratory utilization
An initiative to define nomenclature issues and investigate
technology strategies for addressing them
An initiative that will develop and publish algorithms to guide
clinicians in the use of complex tests (with iPhone app)
An initiative that seeks to experimentally determine the
effectiveness of laboratory interventions on diagnostic error
reduction (ITSRI)
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
CDC sponsored
Seeking to break down the barriers between care
providers and laboratory professionals
Key initiatives are moving forward
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KEY
MESSAGES
Diagnostic error is a major patient safety problem
 The total testing process is a significant source of
diagnostic errors
 Laboratory-directed interventions are available and
can be effective in reducing errors
 Laboratory physicians and scientists will realize
other benefits from leading collaborative efforts

©2012 Paul Epner LLC
Improve patient outcomes
 Strengthen relationships with clinicians
 Reduce the level of risk in the health system
 Become indispensable stewards of clinical data

35
FINAL THOUGHT: SHIFTING THE GOAL
THE
CLINICAL LAB’S MISSION SHOULD NOT BE:
ALTHOUGH
THE
NECESSARY, IT IS NOT SUFFICIENT
CLINICAL LAB’S MISSION SHOULD BE:
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To provide accurate, timely, low cost test results
To rapidly and efficiently enable the accurate
diagnosis of conditions, the selection of
appropriate treatments and the effective
monitoring of health status*
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* Epner, Paul, “Impact of Laboratory Services on Diagnostic Errors,” ThinkLab ‘11