Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital WorkplaceFunding This study was supported by the U.S. Agency for Healthcare Research and Quality (K12HS026370) and U.S. National Library of Medicine (T15-LM007088). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Library of Medicine or Agency for Healthcare Research and Quality. The sponsors had no role in the study design, data collection, analysis, writing of the report, or decision to submit the article for publication.
Background Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace activity, but little is known about the ability of these data to predict RN strain.
Objectives The purpose of this study was to assess the utility of ambient workplace data, defined as time-stamped transaction records and log file data produced by non-electronic health record care delivery tools (e.g., nurse call systems, communication devices), as an information channel for automated sensing of RN strain.
Methods In this exploratory retrospective study, ambient data for a 1-year time period were exported from electronic nurse call, medication dispensing, time and attendance, and staff communication systems. Feature sets were derived from these data for supervised machine learning models that classified work shifts by unplanned overtime. Models for three timeframes —8, 10, and 12 hours—were created to assess each model's ability to predict unplanned overtime at various points across the work shift.
Results Classification accuracy ranged from 57 to 64% across three analysis timeframes. Accuracy was lowest at 10 hours and highest at shift end. Features with the highest importance include minutes spent using a communication device and percent of medications delivered via a syringe.
Conclusion Ambient data streams can serve as information channels that contain signals related to unplanned overtime as a proxy indicator of RN strain as early as 8 hours into a work shift. This study represents an initial step toward enhanced detection of RN strain and proactive prevention of missed or delayed patient care.
Keywordsmachine learning - workplace - nursing - informatics - secondary analysis - stress - real time - health system
Protection of Human and Animal Subjects
The study protocol was reviewed and approved by the Oregon Health and Science University Institutional Review Board.
Received: 21 May 2020
Accepted: 20 July 2020
16 September 2020 (online)
© 2020. Thieme. All rights reserved.
Georg Thieme Verlag KG
Stuttgart · New York
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