Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study

Naveed Rabbani; Stephen P Ma; Ron C Li; Marcy Winget; Susan Weber; Srinivasan Boosi; Tho D Pham; David Svec; Lisa Shieh; Jonathan H Chen

doi:10.1016/j.clinbiochem.2023.01.002

Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study

Clin Biochem. 2023 Mar:113:70-77. doi: 10.1016/j.clinbiochem.2023.01.002. Epub 2023 Jan 7.

Authors

Naveed Rabbani¹, Stephen P Ma², Ron C Li², Marcy Winget³, Susan Weber⁴, Srinivasan Boosi⁴, Tho D Pham⁵, David Svec², Lisa Shieh², Jonathan H Chen⁶

Affiliations

¹ Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: nrabbani@stanford.edu.
² Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
³ Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
⁴ Technology and Digital Solutions, Stanford University School of Medicine, Stanford, CA, USA.
⁵ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
⁶ Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA; Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA; Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.

Abstract

Introduction: Unnecessary laboratory testing contributes to patient morbidity and healthcare waste. Despite prior attempts at curbing such overutilization, there remains opportunity for improvement using novel data-driven approaches. This study presents the development and early evaluation of a clinical decision support tool that uses a predictive model to help providers reduce low-yield, repetitive laboratory testing in hospitalized patients.

Methods: We developed an EHR-embedded SMART on FHIR application that utilizes a laboratory test result prediction model based on historical laboratory data. A combination of semi-structured physician interviews, usability testing, and quantitative analysis on retrospective laboratory data were used to inform the tool's development and evaluate its acceptability and potential clinical impact.

Key results: Physicians identified culture and lack of awareness of repeat orders as key drivers for overuse of inpatient blood testing. Users expressed an openness to a lab prediction model and 13/15 physicians believed the tool would alter their ordering practices. The application received a median System Usability Scale score of 75, corresponding to the 75th percentile of software tools. On average, physicians desired a prediction certainty of 85% before discontinuing a routine recurring laboratory order and a higher certainty of 90% before being alerted. Simulation on historical lab data indicates that filtering based on accepted thresholds could have reduced ∼22% of repeat chemistry panels.

Conclusions: The use of a predictive algorithm as a means to calculate the utility of a diagnostic test is a promising paradigm for curbing laboratory test overutilization. An EHR-embedded clinical decision support tool employing such a model is a novel and acceptable intervention with the potential to reduce low-yield, repetitive laboratory testing.

Keywords: Clinical decision support; Healthcare utilization; Laboratory information systems; Laboratory testing; Prediction algorithms.

MeSH terms

Computer Simulation
Decision Support Systems, Clinical*
Electronic Health Records
Humans
Physicians*
Retrospective Studies
Software

Abstract

MeSH terms

Grants and funding