Coronavirus Disease 2019 (COVID-19) Diagnostic Clinical Decision Support: A Pre-Post Implementation Study of CORAL (COvid Risk cALculator).

BACKGROUND: Isolation of hospitalized persons under investigation (PUIs) for coronavirus disease 2019 (COVID-19) reduces nosocomial transmission risk. Efficient evaluation of PUIs is needed to preserve scarce healthcare resources. We describe the development, implementation, and outcomes of an inpatient diagnostic algorithm and clinical decision support system (CDSS) to evaluate PUIs. METHODS: We conducted a pre-post study of CORAL (COvid Risk cALculator), a CDSS that guides frontline clinicians through a risk-stratified COVID-19 diagnostic workup, removes transmission-based precautions when workup is complete and negative, and triages complex cases to infectious diseases (ID) physician review. Before CORAL, ID physicians reviewed all PUI records to guide workup and precautions. After CORAL, frontline clinicians evaluated PUIs directly using CORAL. We compared pre- and post-CORAL frequency of repeated severe acute respiratory syndrome coronavirus 2 nucleic acid amplification tests (NAATs), time from NAAT result to PUI status discontinuation, total duration of PUI status, and ID physician work hours, using linear and logistic regression, adjusted for COVID-19 incidence. RESULTS: Fewer PUIs underwent repeated testing after an initial negative NAAT after CORAL than before CORAL (54% vs 67%, respectively; adjusted odd ratio, 0.53 [95% confidence interval, .44-.63]; P < .01). CORAL significantly reduced average time to PUI status discontinuation (adjusted difference [standard error], -7.4 [0.8] hours per patient), total duration of PUI status (-19.5 [1.9] hours per patient), and average ID physician work-hours (-57.4 [2.0] hours per day) (all P < .01). No patients had a positive NAAT result within 7 days after discontinuation of precautions via CORAL. CONCLUSIONS: CORAL is an efficient and effective CDSS to guide frontline clinicians through the diagnostic evaluation of PUIs and safe discontinuation of precautions.

Separating the rash from the chaff: novel clinical decision support deployed during the mpox outbreak.

A clinical decision support system, EvalMpox, was developed to apply person under investigation (PUI) criteria for patients presenting with rash and to recommend testing for PUIs. Of 668 patients evaluated, an EvalMpox recommendation for testing had a positive predictive value of 35% and a negative predictive value of 99% for a positive mpox test.

Development and implementation of a clinical decision support system tool for the evaluation of suspected monkeypox infection.

Monkeypox virus was historically rare outside of West and Central Africa until the current 2022 global outbreak, which has required clinicians to be alert to identify individuals with possible monkeypox, institute isolation, and take appropriate next steps in evaluation and management. Clinical decision support systems (CDSS), which have been shown to improve adherence to clinical guidelines, can support frontline clinicians in applying the most current evaluation and management guidance in the setting of an emerging infectious disease outbreak when those guidelines are evolving over time. Here, we describe the rapid development and implementation of a CDSS tool embedded in the electronic health record to guide frontline clinicians in the diagnostic evaluation of monkeypox infection and triage patients with potential monkeypox infection to individualized infectious disease physician review. We also present data on the initial performance of this tool in a large integrated healthcare system.

Preventing Infectious Complications of Immunomodulation in COVID-19 in Foreign-Born Patients.

Immunomodulating therapies for COVID-19 may carry risks of reactivating latent infections in foreign-born people. We conducted a rapid review of infection-related complications of immunomodulatory therapies for COVID-19. We convened a committee of specialists to formulate a screening and management strategy for latent infections in our setting. Dexamethasone, used in severe COVID-19, is associated with reactivation of latent tuberculosis, hepatitis B, and dissemination/hyperinfection of Strongyloides species and should prompt screening and/ or empiric treatment in appropriate epidemiologic contexts. Other immunomodulators used in COVID-19 may also increase risk, including interleukin-6 receptor antagonist (e.g., tocilizumab) and kinase inhibitors. People with specific risk factors should also be screened for HIV, Chagas disease, and endemic mycoses. Racial and ethnic minorities in North America, including foreign-born persons, who receive immunomodulating agents for COVID-19 may be at risk for reactivation of latent infections. We develop a screening and management pathway for such patients.