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

  • Systems-Level Quality Improvement
  • Published:
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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.

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Notes

  1. A simplified code framework is hosted at https://github.com/shimingyoung/MoMs.

  2. It is estimated by assuming the data are of 2 s resolution. 1800 point/h × 24 h × 7 days × 13 units × 4 variables =15.7 million data points.

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Authors and Affiliations

  1. Department of Anesthesiology and Program in Trauma, University of Maryland School of Medicine, 11 S. Paca St. Suite LL01, Baltimore, MD, 21201, USA

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

  2. US Air Force Center for the Sustainment of Trauma and Readiness Skills, Baltimore, MD, USA

    Catriona Miller

Authors
  1. Peter F. Hu
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  2. Shiming Yang
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  3. Hsiao-Chi Li
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  4. Lynn G. Stansbury
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  5. Fan Yang
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  6. George Hagegeorge
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  8. Peter Rock
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  9. Deborah M. Stein
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  10. Colin F. Mackenzie
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Corresponding author

Correspondence to Shiming Yang.

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Funding

This study was supported by FA8650–11-2-6D01(ONPOINT1), FA8650–12-2-6D09(ONPOINT4)& FA8650–12-2-6D09 (Fit to Fly).

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The authors declare that they have no conflict of interest.

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This article is part of the Topical Collection on Systems-Level Quality Improvement.

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Hu, P.F., Yang, S., Li, HC. et al. Reliable Collection of Real-Time Patient Physiologic Data from less Reliable Networks: a “Monitor of Monitors” System (MoMs). J Med Syst 41, 3 (2017). https://doi.org/10.1007/s10916-016-0648-5

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  • DOI: https://doi.org/10.1007/s10916-016-0648-5

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