ArticleJournal of medical Internet research2020
Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Review
- Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship.Frontiers in veterinary science · 2024Article
- Simultaneous Quantitative SARS-CoV-2 Antigen and Host Antibody Detection and Pre-Screening Strategy at the Point of Care.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- A Novel COVID-19 Severity Score is Associated With Survival in Patients Undergoing Percutaneous Dilational Tracheostomy.The Journal of surgical research · 2023Article
- Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19.Interactive journal of medical research · 2023Article
- Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics.Sensors (Basel, Switzerland) · 2022Article
- Article
- A rapid review of machine learning approaches for telemedicine in the scope of COVID-19.Artificial intelligence in medicine · 2022Review
- Diagnosing, Managing, and Controlling COVID-19 using Clinical Decision Support Systems: A Study to Introduce CDSS Applications.Journal of biomedical physics & engineering · 2022Article
- Biodiagnostics in an era of global pandemics-From biosensing materials to data management.View (Beijing, China) · 2022Review
- Clinical informatics solutions in COVID-19 pandemic: Scoping literature review.Informatics in medicine unlocked · 2022Article
- Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review.Robotics and autonomous systems · 2021Review
- A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems.JAMIA open · 2021Article
- Pandemic information support lifecycle: Evidence from the evolution of mobile apps during COVID-19.Journal of business research · 2021Article
- Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.Pathogens (Basel, Switzerland) · 2021Review
- Managing Pandemics with Health Informatics.Yearbook of medical informatics · 2021Review
- Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation.Journal of medical Internet research · 2021Article
- Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications.medRxiv : the preprint server for health sciences · 2021Article
- Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe coronavirus disease (COVID-19) pandemic has resulted in significant morbidity and mortality; large numbers of patients require intensive care, which is placing strain on health care systems worldwide. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease.
objectiveThe goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care.
methodsModel training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, nonlaboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts the probability of mortality using biomarker measurements (C-reactive protein, procalcitonin, D-dimer) and age. Both the Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China, comprising 160 and 375 patients, respectively.
resultsAll biomarkers were measured at significantly higher levels in patients who died vs those who were not hospitalized or discharged (P<.001). The Tier 1 and Tier 2 internal validations had areas under the curve (AUCs) of 0.79 (95% CI 0.74-0.84) and 0.95 (95% CI 0.92-0.98), respectively. The Tier 1 and Tier 2 external validations had AUCs of 0.79 (95% CI 0.74-0.84) and 0.97 (95% CI 0.95-0.99), respectively.
conclusionsOur results demonstrate the validity of the clinical decision support system and mobile app, which are now ready to assist health care providers in making evidence-based decisions when managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics and sites, where application of these tools could lead to improvements in patient outcomes and cost containment.
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