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Using data to measure outcomes.

P Matthews1, N Carter, K Smith

  • 1Piedmont Medical Care Foundation, Atlanta, GA, USA.

Healthcare Information Management : Journal of the Healthcare Information and Management Systems Society of the American Hospital Association
|January 2, 1997
PubMed
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Healthcare providers face increasing demand for outcomes data. Implementing data collection requires careful planning, including defining outcomes and identifying data sources and users.

Area of Science:

  • Health Services Research
  • Healthcare Management
  • Data Science in Healthcare

Background:

  • The healthcare industry is shifting towards value-based care, necessitating the collection and reporting of patient outcomes.
  • Providers are increasingly pressured by the healthcare marketplace to demonstrate the effectiveness of treatments and services through data.

Purpose of the Study:

  • To outline the essential components for establishing a robust outcomes data collection process in healthcare settings.
  • To address the labor-intensive nature of implementing outcomes data collection and provide a strategic planning framework.

Main Methods:

  • This study involved a review of current healthcare market demands for outcomes data.
  • Key considerations for planning data collection were identified, including defining specific outcomes.

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  • The process of identifying data sources and end-users was analyzed.

    • Main Results:

    • Implementing outcomes data collection is a complex, labor-intensive undertaking.
    • Strategic planning is crucial, encompassing the definition of measurable outcomes.
    • Identifying appropriate data sources and understanding user needs are critical for successful implementation.

    Conclusions:

    • Effective outcomes data collection requires meticulous planning and a clear understanding of data utilization.
    • Providers must proactively develop strategies to meet the growing demand for outcomes data.
    • Successful implementation hinges on defining outcomes, identifying data sources, and recognizing user requirements.