Implementation of a multisite, interdisciplinary remote patient monitoring program for ambulatory management of patients with COVID-19
Open accessAbstract
Established technology, operational infrastructure, and nursing resources were leveraged to develop a remote patient monitoring (RPM) program for ambulatory management of patients with COVID-19. The program included two care-delivery models with different monitoring capabilities supporting variable levels of patient risk for severe illness. The primary objective of this study was to determine the feasibility and safety of a multisite RPM program for management of acute COVID-19 illness. We report an evaluation of 7074 patients served by the program across 41 US states. Among all patients, the RPM technology engagement rate was 78.9%. Rates of emergency department visit and hospitalization within 30 days of enrollment were 11.4% and 9.4%, respectively, and the 30-day mortality rate was 0.4%. A multisite RPM program for management of acute COVID-19 illness is feasible, safe, and associated with a low mortality rate. Further research and expansion of RPM programs for ambulatory management of other acute illnesses are warranted.
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Existing studies have explored RPM implementation challenges in a single program or health system.
“Existing studies have explored RPM implementation challenges in a single program or health system.10–14”
From Challenges and Facilitators in Implementing Remote Patient Monitoring Programs in Primary Care (2024) · cites this paper as [10]
Previous studies have highlighted how workflow changes are needed to integrate RPM.
“Previous studies have also highlighted how workflow changes are needed to integrate RPM10”
From Challenges and Facilitators in Implementing Remote Patient Monitoring Programs in Primary Care (2024) · cites this paper as [10]
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- Authors
- Coffey, J. D., Christopherson, L. A., Glasgow, A. E., Pearson, K. K., Brown, J. K., Gathje, S. R., Sangaralingham, L. R., Carmona Porquera, E. M., Virk, A., Orenstein, R., Speicher, L. L., Bierle, D. M., Ganesh, R., Cox, D. L., Blegen, R. N., & Haddad, T. C.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Haddad, Tufia C.
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2021-08-13
- Volume
- 4
- Issue
- 1
- Pages
- 123-123
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC8363637
- OpenAlex
- W3193041130
- Cited by
- 61
- References
- 21
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 9 · 2023: 15 · 2022: 21 · 2021: 7
- FWCI
- 11.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Mayo Clinic
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
- Keywords
- remote patient monitoring, COVID-19 pandemic, outpatient care, telemedicine, feasibility and safety, 30-day hospitalization, emergency department visits, 30-day mortality, models of care, risk stratification, technology engagement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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