Evidence map›Paper›PMID 35679119›Full record

ArticleJMIR formative research2022

Assessing the Usability of a Novel Wearable Remote Patient Monitoring Device for the Early Detection of In-Hospital Patient Deterioration: Observational Study.

Edward Itelman, Gadi Shlomai, Avshalom Leibowitz, Shiri Weinstein, Maya Yakir, Idan Tamir, Michal Sagiv, Aia Muhsen, Maxim Perelman, Daniella Kant and 2 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04220359 (Early Identification of Hospitalized Patients at High-risk for Clinical Deterioration During the First 72-hours in an Internal Medicine Department. A Prospective, Continuous, Physiologic Big-data-analysis Study), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% of its field
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.

NCT04220359 completednot on this map

Early Identification of Hospitalized Patients at High-risk for Clinical Deterioration During the First 72-hours in an Internal Medicine Department. A Prospective, Continuous, Physiologic Big-data-analysis Study

TypeobservationalSponsorBiobeat Technologies Ltd.Ran2020 to 2021Enrolled410ConditionsClinical Deterioration, Hemodynamic InstabilityArmsnon-invasive monitoring
3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. The Use of Remote Monitoring in Care Management for Bone Marrow Transplant Patients.Journal of the advanced practitioner in oncology · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. Mobile Diagnostic Clinics.ACS sensors · 2024
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Edward ItelmanChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0003-0142-0253
Gadi ShlomaiChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0002-8563-0858
Avshalom LeibowitzChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0003-2622-4750
Shiri WeinsteinChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0002-5269-8194
Maya YakirChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0001-8517-6176
Idan TamirChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0001-5032-7421
Michal SagivChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0002-7289-8767
Aia MuhsenChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0002-7382-3812
Maxim PerelmanChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0002-2344-9041
Daniella KantChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0002-1341-147X
Eyal ZilberChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0002-9368-6878
Gad SegalChaim Sheba Medical Center, Sheba Beyond, Virtual Hospital, Ramat Gan, Israel.ORCID https://orcid.org/0000-0002-3851-3245
Sheba Medical Center · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients admitted to general wards are inherently at risk of deterioration. Thus, tools that can provide early detection of deterioration may be lifesaving. Frequent remote patient monitoring (RPM) has the potential to allow such early detection, leading to a timely intervention by health care providers.

objectiveThis study aimed to assess the potential of a novel wearable RPM device to provide timely alerts in patients at high risk for deterioration.

methodsThis prospective observational study was conducted in two general wards of a large tertiary medical center. Patients determined to be at high risk to deteriorate upon admission and assigned to a telemetry bed were included. On top of the standard monitoring equipment, a wearable monitor was attached to each patient, and monitoring was conducted in parallel. The data gathered by the wearable monitors were analyzed retrospectively, with the medical staff being blinded to them in real time. Several early warning scores of the risk for deterioration were used, all calculated from frequent data collected by the wearable RPM device: these included (1) the National Early Warning Score (NEWS), (2) Airway, Breathing, Circulation, Neurology, and Other (ABCNO) score, and (3) deterioration criteria defined by the clinical team as a "wish list" score. In all three systems, the risk scores were calculated every 5 minutes using the data frequently collected by the wearable RPM device. Data generated by the early warning scores were compared with those obtained from the clinical records of actual deterioration among these patients.

resultsIn total, 410 patients were recruited and 217 were included in the final analysis. The median age was 71 (IQR 62-78) years and 130 (59.9%) of them were male. Actual clinical deterioration occurred in 24 patients. The NEWS indicated high alert in 16 of these 24 (67%) patients, preceding actual clinical deterioration by 29 hours on average. The ABCNO score indicated high alert in 18 (75%) of these patients, preceding actual clinical deterioration by 38 hours on average. Early warning based on wish list scoring criteria was observed for all 24 patients 40 hours on average before clinical deterioration was detected by the medical staff. Importantly, early warning based on the wish list scoring criteria was also observed among all other patients who did not deteriorate.

conclusionsFrequent remote patient monitoring has the potential for early detection of a high risk to deteriorate among hospitalized patients, using both grouped signal-based scores and algorithm-based prediction. In this study, we show the ability to formulate scores for early warning by using RPM. Nevertheless, early warning scores compiled on the basis of these data failed to deliver reasonable specificity. Further efforts should be directed at improving the specificity and sensitivity of such tools.

trial registrationClinicalTrials.gov NCT04220359; https://clinicaltrials.gov/ct2/show/NCT04220359.

Indexed as

clinical predictionearly warning score systemgeneral wardnoninvasive monitoringpatient deteriorationremote patient monitoringuHealthwearable devices

Identifiers

PMID35679119
PMCPMC9227660
OpenAlexW4281850960

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.