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Collaborative patient care protocols: a development process.

J Blaufuss1, J Wynn, M Hujcs

  • 1LDS Hospital, Salt Lake City, UT.

Healthcare Information Management : Journal of the Healthcare Information and Management Systems Society of the American Hospital Association
|March 4, 1994
PubMed
Summary
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Computerizing patient care protocols allows for measuring nursing decision-making and improving patient outcomes. This framework supports future research and continuous quality improvement in critical care settings.

Area of Science:

  • Nursing Informatics
  • Health Services Research

Background:

  • Patient care protocols are essential in critical care settings.
  • Computerization of these protocols is an emerging area for research and development.
  • Measuring nursing decision-making and its impact on patient outcomes requires robust methodologies.

Purpose of the Study:

  • To assess the feasibility of developing and implementing computerized patient care protocols in critical care.
  • To establish a framework for measuring nursing decision-making through protocol utilization.
  • To explore the potential for improving patient outcomes, cost-effectiveness, and quality improvement.

Main Methods:

  • Developing computerized patient care protocols.
  • Testing relationships between physiologic parameters, interventions, and decision-making rules.

Related Experiment Videos

  • Implementing protocols in a critical care setting to measure feasibility.

    • Main Results:

    • The study provides a framework for further research into nursing decision-making.
    • Computerized protocols can be tailored to patient-specific needs and facilitate data management.
    • Feasibility of implementation in a critical care setting was a primary goal.

    Conclusions:

    • Computerized patient care protocols offer a feasible approach to measuring nursing decision-making.
    • This technology can enhance patient care, support continuous quality improvement, and be adapted to other healthcare sites.
    • Further research can build upon this framework to optimize clinical decision support systems.