Evidence map›Paper›PMID 34781715›Full record

ArticleAnnals of internal medicine2022

Comparative Effectiveness of an Automated Text Messaging Service for Monitoring COVID-19 at Home.

M Kit Delgado, Anna U Morgan, David A Asch, Ruiying Xiong, Austin S Kilaru, Kathleen C Lee, David Do, Ari B Friedman, Zachary F Meisel, Christopher K Snider and 15 more

Abstract read
In one paragraph

Article in Annals of internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

25 authors.

M Kit DelgadoCenter for Emergency Care Policy and Research, Department of Emergency Medicine, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, and Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (M.K.D.).
Anna U MorganDivision of General Internal Medicine, Department of Medicine, Perelman School of Medicine and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania (A.U.M.).
David A AschDivision of General Internal Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Wharton School, and Leonard Davis Institute of Health Economics, University of Pennsylvania, and Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (D.A.A.).ORCID 0000-0002-7970-286X
Ruiying XiongCenter for Emergency Care Policy and Research, Department of Emergency Medicine, and Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania (R.X.).ORCID 0000-0002-7524-6764
Austin S KilaruCenter for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania (A.S.K., A.B.F., Z.F.M.).
Kathleen C LeeCenter for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, and Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (K.C.L.).
David DoDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania, and Center for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (D.D.).ORCID 0000-0003-3604-5609
Ari B FriedmanCenter for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania (A.S.K., A.B.F., Z.F.M.).
Zachary F MeiselCenter for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania (A.S.K., A.B.F., Z.F.M.).ORCID 0000-0001-8474-7993
Christopher K SniderCenter for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (C.K.S., D.L., A.P., M.P., C.J.O., M.B., N.K.).ORCID 0000-0001-7064-4352
Doreen LamCenter for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (C.K.S., D.L., A.P., M.P., C.J.O., M.B., N.K.).
Andrew ParambathCenter for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (C.K.S., D.L., A.P., M.P., C.J.O., M.B., N.K.).
Christian WoodCenter for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (C.W., C.M.W., D.L.C.).
Chidinma M WilsonCenter for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (C.W., C.M.W., D.L.C.).ORCID 0000-0002-0343-095X
Michael PerezCenter for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (C.K.S., D.L., A.P., M.P., C.J.O., M.B., N.K.).
Deena L ChisholmCenter for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (C.W., C.M.W., D.L.C.).
Sheila KellyDivision of General Internal Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (S.K.).
Christina J O'MalleyCenter for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (C.K.S., D.L., A.P., M.P., C.J.O., M.B., N.K.).ORCID 0000-0002-2081-922X
Nancy MannionCenter for Connected Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania (N.M., A.M.H., S.M.).
Ann Marie HuffenbergerCenter for Connected Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania (N.M., A.M.H., S.M.).ORCID 0000-0002-6134-550X
Susan McGinleyCenter for Connected Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania (N.M., A.M.H., S.M.).ORCID 0000-0002-5789-5023
Mohan BalachandranCenter for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (C.K.S., D.L., A.P., M.P., C.J.O., M.B., N.K.).
Neda KhanCenter for Health Care Innovation, University of Pennsylvania Health System, Philadelphia, Pennsylvania (C.K.S., D.L., A.P., M.P., C.J.O., M.B., N.K.).
Nandita MitraDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania (N.M.).ORCID 0000-0002-7714-3910
Krisda H ChaiyachatiDivision of General Internal Medicine, Department of Medicine, Perelman School of Medicine, Leonard Davis Institute of Health Economics, University of Pennsylvania, and Center for Health Care Innovation and Center for Connected Care, University of Pennsylvania Health System, Philadelphia, Pennsylvania (K.H.C.).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough most patients with SARS-CoV-2 infection can be safely managed at home, the need for hospitalization can arise suddenly.

objectiveTo determine whether enrollment in an automated remote monitoring service for community-dwelling adults with COVID-19 at home ("COVID Watch") was associated with improved mortality.

designRetrospective cohort analysis.

settingMid-Atlantic academic health system in the United States.

participantsOutpatients who tested positive for SARS-CoV-2 between 23 March and 30 November 2020.

interventionThe COVID Watch service consists of twice-daily, automated text message check-ins with an option to report worsening symptoms at any time. All escalations were managed 24 hours a day, 7 days a week by dedicated telemedicine clinicians. MEASUREMENTS: Thirty- and 60-day outcomes of patients enrolled in COVID Watch were compared with those of patients who were eligible to enroll but received usual care. The primary outcome was death at 30 days. Secondary outcomes included emergency department (ED) visits and hospitalizations. Treatment effects were estimated with propensity score-weighted risk adjustment models.

resultsA total of 3488 patients enrolled in COVID Watch and 4377 usual care control participants were compared with propensity score weighted models. At 30 days, COVID Watch patients had an odds ratio for death of 0.32 (95% CI, 0.12 to 0.72), with 1.8 fewer deaths per 1000 patients (CI, 0.5 to 3.1) ( LIMITATION: Observational study with the potential for unobserved confounding.

conclusionEnrollment of outpatients with COVID-19 in an automated remote monitoring service was associated with reduced mortality, potentially explained by more frequent telemedicine encounters and more frequent and earlier presentation to the ED. PRIMARY FUNDING SOURCE: Patient-Centered Outcomes Research Institute.

Indexed as

Text MessagingAdultAgedComparative Effectiveness ResearchCOVID-19Emergency Service, HospitalFemaleHome Care ServicesHospitalizationHumansMaleMiddle AgedRemote ConsultationRetrospective StudiesUnited States

Identifiers

PMID34781715
PMCPMC8722738

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.