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Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care.

Rebecca R Kitzmiller1, Ashley Vaughan1, Angela Skeeles-Worley2

  • 1School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States.

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This summary is machine-generated.

Clinician perceptions of predictive analytics technology in neonatal intensive care units (NICUs) influenced adoption. Integrating data into workflow and training improved its use as a vital sign for better patient care.

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Area of Science:

  • Medical Informatics
  • Clinical Technology Adoption
  • Neonatal Care

Background:

  • Integrating new technologies into clinical settings, particularly neonatal intensive care units (NICUs), presents significant challenges.
  • Clinician adoption of innovative tools is often slow, hindering the realization of expected benefits.

Purpose of the Study:

  • To explore neonatal intensive care unit (NICU) clinician perceptions of a continuous predictive analytics technology.
  • To understand how these perceptions influenced the adoption and integration of the technology into clinical practice.

Main Methods:

  • Qualitative descriptive analysis of semistructured interviews with 14 physicians and 8 nurses in a U.S. medical center.
  • Interviews were conducted 18 months post-randomized control trial of the predictive monitoring technology.
  • Thematic development was guided by innovation attributes from Diffusion of Innovation Theory.

Main Results:

  • Initial barriers to adoption included poor integration into workflow and lack of clear data utilization methods.
  • Routine data collection, documentation, and reporting during rounds and handoffs led clinicians to perceive the data as a vital sign.
  • Observational learning and dialogue facilitated clinician understanding and integration of data into care decisions, highlighting the technology's value.

Conclusions:

  • Implementation strategies for predictive technologies must consider the clinician's risk-benefit analysis in treatment decisions.
  • Leveraging existing clinician training methods and emphasizing data integration into daily workflow are crucial for successful adoption.
  • Predictive analytics, when integrated effectively, can become a valuable tool in neonatal care delivery.