Evidence map›Paper›PMID 36464463›Full record

ArticleMayo Clinic proceedings2022

Impact of a High-Risk, Ambulatory COVID-19 Remote Patient Monitoring Program on Utilization, Cost, and Mortality.

Tufia C Haddad, Jordan D Coffey, Yihong Deng, Amy E Glasgow, Laura A Christopherson, Lindsey R Sangaralingham, Sarah J Bell, Vishal P Shah, Joshua C Pritchett, Robert Orenstein and 4 more

Abstract read
In one paragraph

Article in Mayo Clinic proceedings, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–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

19 citing papers in PubMed.

  1. Review
  2. Article
  3. Observational
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  5. Article
  6. Article
  7. Article
  8. Article
  9. Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024
    Article
  10. Article
  11. Article
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  16. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Tufia C HaddadCenter for Digital Health, Mayo Clinic, Rochester, MN, USA; Department of Oncology, Mayo Clinic, Rochester, MN, USA. Electronic address: haddad.tufia@mayo.edu.
Jordan D CoffeyCenter for Digital Health, Mayo Clinic, Rochester, MN, USA.
Yihong DengKern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
Amy E GlasgowKern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
Laura A ChristophersonCenter for Digital Health, Mayo Clinic, Rochester, MN, USA.
Lindsey R SangaralinghamKern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
Sarah J BellCenter for Digital Health, Mayo Clinic, Rochester, MN, USA; Department of Nursing, Mayo Clinic, Rochester, MN, USA.
Vishal P ShahKern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA; Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Joshua C PritchettDepartment of Oncology, Mayo Clinic, Rochester, MN, USA; Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
Robert OrensteinDepartment of Medicine, Mayo Clinic, Scottsdale, AZ, USA.
Leigh L SpeicherDepartment of Medicine, Mayo Clinic, Jacksonville, FL, USA.
Michael J ManiaciCenter for Digital Health, Mayo Clinic, Rochester, MN, USA; Department of Medicine, Mayo Clinic, Jacksonville, FL, USA.
Ravindra GaneshDepartment of Medicine, Mayo Clinic, Rochester, MN, USA.
Bijan J BorahKern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA; Division of Health Care Delivery Science, Mayo Clinic, Rochester, MN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate care utilization, cost, and mortality among high-risk patients enrolled in a coronavirus disease 2019 (COVID-19) remote patient monitoring (RPM) program.

methodsThis retrospective analysis included patients diagnosed with COVID-19 at risk for severe disease who enrolled in the RPM program between March 2020 and October 2021. The program included in-home technology for symptom and physiologic data monitoring with centralized care management. Propensity score matching established matched cohorts of RPM-engaged (defined as ≥1 RPM technology interactions) and non-engaged patients using a logistic regression model of 59 baseline characteristics. Billing codes and the electronic death certificate system were used for data abstraction from the electronic health record and reporting of care utilization and mortality endpoints.

resultsAmong 5796 RPM-enrolled patients, 80.0% engaged with the technology. Following matching, 1128 pairs of RPM-engaged and non-engaged patients comprised the analysis cohorts. Mean patient age was 63.3 years, 50.9% of patients were female, and 81.9% were non-Hispanic White. Patients who were RPM-engaged experienced significantly lower rates of 30-day, all-cause hospitalization (13.7% vs 18.0%, P=.01), prolonged hospitalization (3.5% vs 6.7%, P=.001), intensive care unit admission (2.3% vs 4.2%, P=.01), and mortality (0.5% vs 1.7%; odds ratio, 0.31; 95% CI, 0.12 to 0.78; P=.01), as well as cost of care ($2306.33 USD vs $3565.97 USD, P=0.04), than those enrolled in RPM but non-engaged.

conclusionHigh-risk COVID-19 patients enrolled and engaged in an RPM program experienced lower rates of hospitalization, intensive care unit admission, mortality, and cost than those enrolled and non-engaged. These findings translate to improved hospital bed access and patient outcomes.

Indexed as

COVID-19Ambulatory Care FacilitiesFemaleHospitalizationHumansMaleMiddle AgedMonitoring, PhysiologicRetrospective Studies

Identifiers

PMID36464463
PMCPMC9444887

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.