Evidence map›Paper›PMID 27817131›Full record

ArticleJournal of medical systems2017

Reliable Collection of Real-Time Patient Physiologic Data from less Reliable Networks: a "Monitor of Monitors" System (MoMs).

Peter F Hu, Shiming Yang, Hsiao-Chi Li, Lynn G Stansbury, Fan Yang, George Hagegeorge, Catriona Miller, Peter Rock, Deborah M Stein, Colin F Mackenzie

Abstract read
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Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. A telediagnosis assistance system for multiple-lead electrocardiography.Physical and engineering sciences in medicine · 2021
    Article
  8. Article
  9. Article
  10. Review
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

10 authors.

Peter F HuDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA.
Shiming YangDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA. syang@anes.umm.edu.ORCID http://orcid.org/0000-0003-0338-4268
Hsiao-Chi LiDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA.
Lynn G StansburyDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA.
Fan YangDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA.
George HagegeorgeDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA.
Catriona MillerUS Air Force Center for the Sustainment of Trauma and Readiness Skills, Baltimore, MD, USA.
Peter RockDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA.
Deborah M SteinDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA.
Colin F MackenzieDepartment of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research and practice based on automated electronic patient monitoring and data collection systems is significantly limited by system down time. We asked whether a triple-redundant Monitor of Monitors System (MoMs) to collect and summarize key information from system-wide data sources could achieve high fault tolerance, early diagnosis of system failure, and improve data collection rates. In our Level I trauma center, patient vital signs(VS) monitors were networked to collect real time patient physiologic data streams from 94 bed units in our various resuscitation, operating, and critical care units. To minimize the impact of server collection failure, three BedMaster® VS servers were used in parallel to collect data from all bed units. To locate and diagnose system failures, we summarized critical information from high throughput datastreams in real-time in a dashboard viewer and compared the before and post MoMs phases to evaluate data collection performance as availability time, active collection rates, and gap duration, occurrence, and categories. Single-server collection rates in the 3-month period before MoMs deployment ranged from 27.8 % to 40.5 % with combined 79.1 % collection rate. Reasons for gaps included collection server failure, software instability, individual bed setting inconsistency, and monitor servicing. In the 6-month post MoMs deployment period, average collection rates were 99.9 %. A triple redundant patient data collection system with real-time diagnostic information summarization and representation improved the reliability of massive clinical data collection to nearly 100 % in a Level I trauma center. Such data collection framework may also increase the automation level of hospital-wise information aggregation for optimal allocation of health care resources.

Indexed as

Trauma CentersData CollectionEquipment DesignEquipment FailureHumansMonitoring, PhysiologicReproducibility of ResultsSoftwareVital SignsData collectionDatastreamDiagnosisReal-timeTriple redundantVital signs

Identifiers

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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.