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Related Experiment Video

Updated: Apr 12, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Using String Metrics to Identify Patient Journeys through Care Pathways.

Richard Williams1, Iain E Buchan1, Mattia Prosperi2

  • 1Centre for Health Informatics, University of Manchester, Manchester, UK ; Greater Manchester Primary Care Patient Safety Translational Research Centre, University of Manchester, Manchester, UK.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|May 9, 2015
PubMed
Summary
This summary is machine-generated.

Matching patient journeys to care pathways can be accurately done using string matching techniques. Normalized string metrics provide more unique pathway matches than non-normalized ones, making them preferable for this application.

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Health Services Research

Background:

  • Electronic health records (EHRs) capture patient clinical journeys.
  • Care pathways provide standardized treatment routes.
  • Variations in EHR data (omissions, reordering) complicate pathway matching.

Purpose of the Study:

  • To develop a method for accurately matching patient clinical journeys to care pathways.
  • To evaluate the effectiveness of string matching techniques for this purpose.
  • To determine the optimal string metrics for pathway matching.

Main Methods:

  • Representing care pathways and patient journeys as strings.
  • Applying string matching algorithms to compare pathway routes and patient journeys.
  • Comparing normalized and non-normalized string metrics for matching accuracy.

Main Results:

  • The string matching approach accurately matches patient journeys to care pathway routes.
  • Normalized string metrics yield more unique and precise pathway matches compared to non-normalized metrics.
  • This method effectively handles discrepancies in electronic health record data.

Conclusions:

  • String matching is a viable and accurate method for aligning patient journeys with care pathways.
  • Normalized string metrics are recommended for improved accuracy and uniqueness in pathway matching.
  • This technique enhances the utility of EHR data for clinical pathway analysis.