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2023 HSR&D/QUERI National Conference Abstract

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1172 — Using the Electronic Health Record to Measure Missed Opportunities in Diagnosis in Emergency Departments

Lead/Presenter: Viralkumar Vaghani,  COIN - Houston
All Authors: Vaghani V (Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine), Gupta A (Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas) Murphy D (Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas) Mushtaq U (Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas) Mir U (Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas) Li W (Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas) Sittig DF (University of Texas – Memorial Hermann Center for Healthcare Quality & Safety, School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX ) Singh H (Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas)

Objectives:
Missed opportunities in diagnosis (MODs) are a significant safety concern and lead to patient harm and malpractice claims. Methods to identify and understand MODs in emergency departments (ED) are under-developed but essential to inform improvement efforts. We developed and tested EHR-based algorithms to help measure MODs in ED settings.

Methods:
We developed EHR-based algorithms called electronic triggers, henceforth defined as ‘e-trigger’, to identify and flag medical records with potential missed opportunities in diagnosis. We used an established framework, the Safer Dx Trigger Tools Framework, which outlines a step-wise approach to algorithm development. Using this logic, records flagged by e-triggers are ‘trigger-positive’ and need manual confirmatory reviews to determine true MODs. We developed three types of e-triggers: 1) Missed test results: Algorithms identified three test results at high risk of being missed after an ED visit: abnormal urine cultures, abnormal blood cultures, and abnormal Thyroid Stimulating Hormone (TSH) test results. Algorithms used structured EHR data to identify tests ordered during treat and release ED visits that had not been followed-up as determined through certain expected follow-up actions. 2) Missed stroke: Algorithm identified stroke hospitalizations within 30 days of treat and release ED visits for dizziness in patients with 2 or more stroke risk factors. 3) Missed high-risk abdominal pain: Algorithm flagged hospitalizations within 10 days after treat and release ED visit for abdominal pain with high (T>99. F or >37.5 C) or low body temperature (T < 96.8 F or < 36.3 C). We excluded patients enrolled in hospice or palliative care and those terminally ill with cancer. We pilot-tested the chart review process using operational definitions and standardized procedures. Physician-reviewers were trained to ensure standardized data collection and minimize data entry errors.

Results:
E-triggers were applied to >9 million patient records in VA’s corporate data warehouse in 2018-19. For E-trigger 1, we randomly selected 105 trigger-positive cases including 35 Urine cultures, 35 Blood cultures, and 35 TSHs. Of 100 trigger-positive cases reviewed thus far, 52 (52%) cases had MODs and 48 (48%) cases had no MODs. MODs had the following distribution: 9 (25.7%) of Blood cultures, 22 (62.9%) of TSH, and 21 (70.0%) of Urine cultures. Overall, the positive predictive value (PPV) of E-trigger 1 is 52.0% which is far superior to any manual efforts to identify missed test results. Of 91 cases for E-trigger 2 reviewed thus far, 48 (52.6%) cases had MODs, 39 (42.8%) had no MODs, and 4 (4.6%) had coding errors. Of 86 cases reviewed for E-trigger 3 thus far, 26 (30.2%) had MODs and 60 (69.8%) had no MODs.

Implications:
E-triggers using EHR data mining and confirmatory record reviews identified patients with missed opportunities in diagnosis with high PPVs.

Impacts:
E-triggers can efficiently identify missed opportunities in diagnosis. E-trigger-based strategy for measurement of diagnostic errors and delays could serve as a useful tool to inform safety improvement efforts. Such e-triggers could also be used to proactively identify and track patients who need follow-up in order to prevent delays in diagnosis and treatment.