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Towards effective clinical decision support systems: A systematic review.

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Clinical Decision Support Systems (CDSS) analysis reveals rule-based systems and recommendations are common. Most CDSS remain at maturity level 2, indicating underrepresentation in choice and implementation phases.

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Area of Science:

  • Health Informatics
  • Computer Science
  • Decision Science

Background:

  • Clinical Decision Support Systems (CDSS) aid healthcare decision-making.
  • Developing effective CDSS requires understanding current theories, techniques, and methods.
  • This study assesses CDSS features and effectiveness.

Purpose of the Study:

  • Identify common characteristics and trends in CDSS.
  • Analyze CDSS maturity using Simon's decision-making theory.
  • Determine the effectiveness of current CDSS.

Main Methods:

  • Systematic literature review of studies from 2000-2020.
  • Searched databases: AIS e-library, Decision Support Systems journal, Nature, PlosOne, PubMed.
  • Utilized PRISMA statements for reporting.

Main Results:

  • Rule-based modules dominate knowledge representation in CDSS.
  • Recommendations and suggestions are the most frequent technological features.
  • Most CDSS are standalone (51.92%) or web-based (19.23%), with most not exceeding maturity level 2.

Conclusions:

  • Identified key CDSS trend characteristics and assessed maturity levels.
  • Developed a CDSS Maturity Staging Model based on Simon's theory.
  • Underrepresentation in choice and implementation phases is a significant gap for effective CDSS development.