JMIR Publications

ABSTRACT

Background:

The coronavirus disease 2019 (COVID-19) pandemic resulted in shortages of diagnostic tests, personal protective equipment (PPE), hospital beds, and other critical resources.

Objective:

We sought to improve management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics.

Methods:

With complex eligibility criteria for COVID-19 tests and a challenging EHR implementation of associated testing orders, providers faced obstacles selecting the appropriate test modality. As test choice was dependent upon specific patient criteria, we built a decision tree within the EHR to automate test selection using a branching series of questions that linked clinical criteria to the appropriate SARS-CoV-2 test and triggered an EHR flag for patients who met our institutional persons under investigation (PUI) criteria.

Results:

The percentage of tests that had to be canceled and reordered due to errors in selecting the correct testing modality was 3.8% (23/608) pre-CDS implementation and 1.0% (262/26,643) post-CDS implementation (P < .0001). Patients who had multiple tests ordered during a 24-hour period accounted for 0.8% (5/608) and 0.3% (76/26,643) of orders pre- and post-CDS implementation, respectively (P = .035). Nasopharyngeal molecular assay results for patients classified as asymptomatic were positive in 3.4% (826/24,170) of patients compared to 10.9% (1,421/13,074) for symptomatic patients (P < .0001). Positive tests were more frequent among asymptomatic patients with a history of exposure to COVID-19 (12.7%, 36/283) than among asymptomatic patients without such history (3.3%, 790/23,887; P < .0001).

Conclusions:

Leveraging the EHR and our CDS algorithm decreased order entry errors and appropriately flagged PUI status. These interventions optimized reagent and PPE usage. Data collection in the decision tree regarding symptom and exposure status correlated with the likelihood of positive test results, suggesting that clinicians appropriately utilized questions in the decision tree algorithm.