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Templates as a method for implementing data provenance in decision support systems.

Vasa Curcin1, Elliot Fairweather1, Roxana Danger2

  • 1Division of Health and Social Care Research, King's College London, London, United Kingdom.

Journal of Biomedical Informatics
|November 19, 2016
PubMed
Summary
This summary is machine-generated.

Decision support systems (DSS) can improve clinical reasoning, but lack of transparency hinders adoption. Provenance templates offer a solution by capturing data and task traces, enhancing trust in evidence-based medicine and supporting Learning Health Systems.

Keywords:
D2.1 (Software Engineering) Requirements/specification J.3 (Life and Medical Sciences): Health data provenanceDecision support systemsModel-driven architectures

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Area of Science:

  • Medical Informatics
  • Computer Science

Background:

  • Decision support systems (DSS) aim to standardize guideline-based diagnosis and clinical reasoning.
  • Widespread adoption of DSS is limited by concerns regarding diagnostic performance and lack of transparency.
  • The Learning Health System (LHS) paradigm emphasizes data-driven medicine and the need for trust in evidence-based systems.

Purpose of the Study:

  • To introduce provenance templates as a method to enhance transparency and trust in DSS.
  • To address the challenges of adopting data provenance in traditionally non-computational fields like medical research.
  • To specify requirements for an LHS-based DSS and present a theoretical model for provenance templates.

Main Methods:

  • Development of provenance templates as abstract fragments representing domain actions.
  • Generation of a model-driven service interface for software tools to capture data and task provenance.
  • Testing and validation of the provenance infrastructure within a Diagnostic Decision Support System (DDSS) developed for the EU FP7 TRANSFoRm project.

Main Results:

  • Demonstration of a novel architecture for LHS-based DSS utilizing provenance templates.
  • Successful validation of the provenance infrastructure for capturing research task traces.
  • Facilitation of trust and reproducibility in medical research through automated provenance capture.

Conclusions:

  • Provenance templates provide a viable solution for increasing transparency and trust in DSS.
  • The proposed approach supports the integration of data provenance in medical research, aligning with LHS principles.
  • This work contributes to advancing data-driven medicine and reproducible research practices in healthcare.