Download Repurposing Decision Support Data to Detect Prescribing Errors—An

Repurposing Decision Support Data to Detect Prescribing Errors—An Application for Quality
Measurement
David L. Chin, Ashley S. Trask, Victoria T. Johnson, Michelle H. Wilson, Brittanie Neaves, Andrea Gojova, Heejung Bang, Patrick S. Romano
Center for Healthcare Policy and Research, (Chin and Romano) and Dept. of Pharmacy (Gojova and Trask) and Div. of General Internal Medicine–Dept. of Internal Medicine (Johnson, Neaves and Romano) and Div. of Biostatistics–
Dept. of Public Health Sciences (Bang) University of California, Davis; Dept. of Internal Medicine, Santa Clara Valley Medical Center; San Jose, CA (Wilson)
Why:
• Clinical Decision Support (CDS) data can be used to
measure prescribing errors in the hospital.
•
“Real-time” quality measures have not been described.
Applications:
•
•
Measure physician-level quality of care.
(future) Provide real-time monitoring, dashboard reporting
for QI.
Generalizability:
•
This approach could be generalized in any hospital
equipped with a computerized physician order entry (CPOE)
and CDS.
Setting:
• One 619-bed academic medical center
hospital.
Algorithm Development*:
(n=147,420) were generated by a knowledgebased CDS (Epic) beginning Mar 2008 for 45
months.
• Constructed the prescribing error detection
algorithm (PEDA) by identifying prescribing
error candidates that had high potential to
cause harm.
•
•
•
Medication errors harm 1.5 million patients per year, yet EHRbased method to detect prescribing errors have not been
reported.
Despite significant improvements in quality of care, medication
prescribing errors are still common in hospitals.
Figure 1: Example of prescribing alert
•
•
No methods exist to measure real-time hospital processes or
outcomes.
Figure 2: Data stream and development processes
•
•
Medication Orders (n = 6.0M)
CDS
Inpatient Alerts (n = 147,420)
•
Exclusion Criteria by Alert
Characteristic:
•
•
•
•
Not physician
Pediatric order
Removed (order withdrawn)
Other category (Pregnancy,
Drug-Drug, etc.)
Dose
(n=19,616)
Drug-Drug
Interaction
(n-21,403)
Error
Decision
Error
Decision
Exclusion Criteria
by Order
Characteristic:
• Opioids
• Benzodiazepines
Errors
(n=9,965)
Although repurposed EHR data can provide many opportunities
to conduct research, multiple gaps exist between the acquisition
and interpretation of raw data, and operationalizing data for
research.
Despite these limitations, alert data taken from a CDS can be
used to generate useful information:
•
Nonerrors
(n=1,223)
Errors
(n=2,243)
Physician-level real-time quality measurement.
Medication order safety profiling.
Hospital quality surveillance, monitoring in real-time.
Conclusions:
Non-errors
(n=19,160)
PEDA Performance:
•
•
CDS systems are highly customized, reducing the
generalizability of source code and potential for interoperability.
•
•
•
•
We present a systematic and reproducible tool to measure
quality real-time in the hospital.
Further work is needed to expand this tool to detect other
prescribing errors from CDS data and validation in multiple
clinical settings.
Dose: PPV= 96%; NPV = 32%
Drug-drug interaction: a blinded validation was
not feasible.
Hospital Setting
• ICU: 19.8 errors/100 alerts
• Internal Medicine: 11.7 errors/100 alerts
• Emergency Dept: 12.9 errors/ 100 alerts
• Surgery: 12.2 errors/100 alerts
Repurpose clinical data to separate true prescribing errors
from non-error noise.
Develop a method to measure real-time quality events that is
systematic and reproducible.
Create a new method to simultaneously measure patient and
provider-level elements related to quality of care from EHR
data.
Drug-drug interaction: if two drugs ordered were
duplicative therapy or from the same class.
Algorithm development and validation required multiple
iterations to understand data structure and limitations. For
example, upon chart review we found allergy alerts did not
correlate with errors.
REFERENCES & ACKNOWLEDGMENTS
Prescribing Error Rates:
Goals of Project:
•
•
• Dose and drug-drug interaction alerts were
included in algorithm. Allergy alerts were not
associated with prescribing errors (false
positive rate >80%).
•
benzodiazepine-class and inhaled steroid medications were excluded).
** Exceptions: NSAID threshold was defined as 13%.
• 6,079,783 medication orders prescribing alerts
independently manually reviewed a stratified
random sample of events from dose, allergy,
drug-drug interaction categories.
Background:
Error definitions:
• Dose: if the order exceeded the maximum daily
or the maximum single dose, by ≥ 20%**. (Opioids,
Data Sources and Analysis:
• Medication Prescribing Alerts, Medication
orders—Clarity/SQL.
• Analyses— SAS 9.3.
• One pharmacist and one physician
BACKGROUND and MOTIVATION
LESSONS LEARNED
METHODS and RESULTS
TOPICS FOR DISCUSSION
* Exclusions include pediatric and outpatient orders. Alert
criteria are defined by First Databank.
Physician Type:
• Intern: 14.2 errors/100 alerts
• Resident: 10.7 errors/ 100 alerts
• Fellow: 33.2 errors/ 100 alerts
• Faculty: 16.6 errors/ 100 alerts
1. Preventing Medication Errors: Quality Chasm Series. The National Academies
Press; 2007.
Acknowledgments: The project was supported by the NIH National Center for
Advancing Translational Sciences, through grant number UL1 TR000002 and
linked award TL1 TR000133. Support was also provided by grant number
1T32HS022236-01 from the Agency for Healthcare Research and Quality (AHRQ)
through the Quality Safety Comparative Effectiveness Research Training
(QSCERT) Program.
AcademyHealth acknowledges the Agency for Healthcare Research and Quality
(AHRQ) for its support: Grant U13 HS19564-01.The authors acknowledge
Rebecca Davis for her assistance in reviewing the literature, Terry Vierra and
Michael Fong for interpreting and making use of the raw data.
For more information contact [email protected] or visit www.edm-forum.org