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Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A
Naveed Rabbani1, Stephen P Ma2, Ron C Li2
1Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.
Clinical Biochemistry
|January 9, 2023
Summary
A new predictive model in an EHR tool can help reduce unnecessary lab tests in hospitals. This data-driven approach shows promise in decreasing low-yield, repetitive testing and improving healthcare efficiency.
Area of Science:
- * Clinical Informatics
- * Health Services Research
- * Predictive Analytics
Background:
- * Unnecessary laboratory testing leads to patient harm and significant healthcare waste.
- * Existing methods to reduce overutilization have limitations, necessitating innovative solutions.
- * Data-driven approaches offer a promising avenue for optimizing diagnostic test ordering.
Purpose of the Study:
- * To develop and evaluate a clinical decision support tool (CDST) using a predictive model.
- * To reduce low-yield and repetitive laboratory testing in hospitalized patients.
- * To assess the tool's acceptability and potential clinical impact.
Main Methods:
- * Developed an Electronic Health Record (EHR)-embedded SMART on FHIR application.
- * Utilized a laboratory test result prediction model trained on historical laboratory data.
- * Employed physician interviews, usability testing, and retrospective data analysis.
Main Results:
- * Physicians cited routine and lack of awareness as drivers of test overuse.
- * 13/15 physicians believed the tool would change their ordering habits.
- * Simulation suggested a potential reduction of ~22% in repeat chemistry panels.
Conclusions:
- * Predictive algorithms offer a novel paradigm for assessing diagnostic test utility.
- * An EHR-embedded CDST utilizing predictive modeling is a feasible and acceptable intervention.
- * The tool has the potential to significantly decrease low-yield, repetitive laboratory testing.
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