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SMART COVID Navigator, a Clinical Decision Support Tool for COVID-19 Treatment: Design and Development Study.
Suraj, Varun; Del Vecchio Fitz, Catherine; Kleiman, Laura B; Bhavnani, Suresh K; Jani, Chinmay; Shah, Surbhi; McKay, Rana R; Warner, Jeremy; Alterovitz, Gil.
  • Suraj V; Biomedical Cybernetics Laboratory, Brigham and Women's Hospital, Boston, MA, United States.
  • Del Vecchio Fitz C; Reboot Rx, Boston, MA, United States.
  • Kleiman LB; Reboot Rx, Boston, MA, United States.
  • Bhavnani SK; Preventive Medicine and Population Health Institute for Translational Sciences, University of Texas Medical Branch, University of Texas Health Science Center in Houston, Houston, TX, United States.
  • Jani C; Department of Internal Medicine, Mount Auburn Hospital, Harvard Medical School, Cambridge, MA, United States.
  • Shah S; Hematology, Oncology and Bone Marrow Transplantation, Mayo Clinic, Phoenix, AZ, United States.
  • McKay RR; Department of Medicine and Urology, University of California, San Diego, CA, United States.
  • Warner J; Medicine and Biomedical Informatics, Vanderbilt University, Nashville, TN, United States.
  • Alterovitz G; Biomedical Cybernetics Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
J Med Internet Res ; 24(2): e29279, 2022 02 18.
Article in English | MEDLINE | ID: covidwho-1700633
ABSTRACT

BACKGROUND:

COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19's effect on patients with certain pre-existing conditions.

OBJECTIVE:

In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient's electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19.

METHODS:

The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient's medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient's medical history will affect their COVID-19 outcomes.

RESULTS:

The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources-enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined.

CONCLUSIONS:

The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient's medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Decision Support Systems, Clinical / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 29279

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Decision Support Systems, Clinical / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: 29279