Accepted for/Published in: JMIR Formative Research
Date Submitted: Nov 11, 2022
Date Accepted: Oct 27, 2023
Date Submitted to PubMed: Oct 30, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Using a data entry tool results in higher fidelity to a COVID-19 hospitalization risk tier assessment algorithm than provider insight alone
ABSTRACT
Background:
A coronavirus disease 2019 (COVID-19) risk prediction algorithm can determine estimated risk for future hospitalization for outpatients with COVID-19. Utilization of a point-of-care data entry tool may enable healthcare providers to more appropriately assign patients a level of risk for future hospitalization.
Objective:
This study aims to compare fidelity to a risk of future hospitalization assessment algorithm using a data entry tool, “COVID-19 Risk Tier Assessment Tool,” or medical provider insight alone.
Methods:
We studied COVID-19 positive patients enrolled in a telemedicine monitoring program from May 27, 2020 through August 24, 2020 who were not hospitalized at the time of enrollment. The primary analysis used patients from this program who later experienced hospitalization from COVID-19. We created a matched group of non-hospitalized patients for comparison. We used descriptive statistics to show comparison of the risk tier assignment reported by algorithm trained healthcare providers versus the risk tier assignment produced by the data entry tool.
Results:
Of 42 matched patients (21 hospitalized and 21 not hospitalized), 26 had identical risk tier assignments and 16 different risk tier assignments. Elements that led to different risk tier assignments were identified to be: (1) provider “missed” comorbidity (n=6), (2) provider noted comorbidity but under-coded risk (n=10), and/or (3) provider mis-coded symptom severity and course (n=7).
Conclusions:
Clinical decision-making could be enhanced by using similar risk assessment data entry tools for other disease states, such as influenza and community acquired pneumonia. Clinical Trial: None
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.