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Area of Science:

  • Health Informatics
  • Nursing Research
  • Data Science

Background:

  • Secondary use of clinical routine data, termed big data, can support quality management and research.
  • Clinical data is crucial for evaluating health interventions and measuring care quality using indicators like nursing sensitive outcomes.
  • A standardized methodology for leveraging clinical routine data to measure nursing care quality is currently lacking.

Purpose of the Study:

  • To present initial concepts for a model to derive nursing outcome indicators from clinical routine data.
  • To investigate which care indicators can be extracted from big data to predict patient outcomes.
  • To establish a framework for utilizing secondary clinical data for nursing quality assessment.

Main Methods:

  • Conceptual model development for deriving nursing outcome indicators.
  • Analysis of clinical routine data for potential quality metrics.
  • Exploration of indicator extraction for outcome prediction.

Main Results:

  • The study outlines a foundational model for translating clinical routine data into measurable nursing quality indicators.
  • Identified potential care indicators that can be extracted for predicting nursing care outcomes.
  • Demonstrated the feasibility of using big data for nursing quality assessment.

Conclusions:

  • The proposed model offers a novel approach to measuring nursing care quality using readily available clinical data.
  • Further development is needed to refine the methodology for deriving and validating nursing outcome indicators.
  • Secondary data analysis holds significant potential for advancing nursing research and improving patient care quality.