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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Process Mining in Primary Care: A Literature Review.

Richard Williams1, Eric Rojas2, Niels Peek1

  • 1MRC Health eResearch Centre, Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, UK.

Studies in Health Technology and Informatics
|April 22, 2018
PubMed
Summary

Process mining in healthcare can improve patient care pathways. However, its application in primary care is limited, facing challenges like data quality and tool selection.

Keywords:
Process miningcare pathwaysprimary careworkflow

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Area of Science:

  • Health Informatics
  • Process Management
  • Data Science

Background:

  • Process mining, initially used for business processes, is increasingly applied to healthcare.
  • It offers insights into clinical pathways and service redesign.
  • Its potential in primary care settings remains underexplored.

Purpose of the Study:

  • To review the literature on process mining applications in primary care.
  • To identify the extent of its adoption in this specific healthcare domain.
  • To pinpoint challenges encountered when applying process mining to primary care.

Main Methods:

  • Systematic literature review of process mining in healthcare.
  • Identification and analysis of studies focusing on primary care settings.
  • Categorization of reported challenges in primary care applications.

Main Results:

  • A small fraction of reviewed papers (7 out of 143) focused on primary care.
  • Key challenges identified include data quality issues (consistency, completeness).
  • Other challenges involve algorithm/tool selection, results presentation, and real-world implementation.

Conclusions:

  • Process mining has significant potential for primary care improvement but is underutilized.
  • Addressing data quality and practical implementation challenges is crucial for broader adoption.
  • Further research is needed to facilitate process mining in primary care settings.