Article Text

Download PDFPDF
Control-flow analysis of procedural skills competencies in medical training through process mining
  1. Rene de la Fuente1,
  2. Ricardo Fuentes1,
  3. Jorge Munoz-Gama2,
  4. Arnoldo Riquelme3,
  5. Fernando R. Altermatt1,
  6. Juan Pedemonte1,
  7. Marcia Corvetto1,
  8. Marcos Sepúlveda2
  1. 1Anaesthesiology, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
  2. 2Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile
  3. 3Gastroenterology, Centre for Medical Education and Health Sciences. Department of Health Sciences. Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
  1. Correspondence to Dr Ricardo Fuentes, Anaesthesiology, Pontificia Universidad Católica de Chile, Santiago, Chile; rfuente{at}med.puc.cl

Abstract

Background Procedural skills are key to good clinical results, and training in them involves a significant amount of resources. Control-flow analysis (ie, the order in which a process is performed) can provide new information for those who train and plan procedural training. This study outlines the steps required for control-flow analysis using process mining techniques in training in an ultrasound-guided internal jugular central venous catheter placement using a simulation.

Methods A reference process model was defined through a Delphi study, and execution data (event logs) were collected from video recordings from pretraining (PRE), post-training (POST) and expert (EXP) procedure executions. The analysis was performed to outline differences between the model and executions. We analysed rework (activity repetition), alignment-based fitness (conformance with the ideal model) and trace alignment analysis (visual ordering pattern similarities).

Results Expert executions do not present repetition of activities (rework). The POST rework is lower than the PRE, concentrated in the steps of the venous puncture and guidewire placement. The adjustment to the ideal model measure as alignment-based fitness, expressed as a median (25th–75th percentile) of PRE 0.74 (0.68–0.78) is less than POST 0.82 (0.76–0.86) and EXP 0.87 (0.82–0.87). There are no significant differences between POST and EXP. The graphic analysis of alignment and executions shows a progressive increase in order from PRE to EXP executions.

Conclusion Process mining analysis is able to pinpoint more difficult steps, assess the concordance between reference mode and executions, and identify control-flow patterns in procedural training courses.

  • education &amp
  • training
  • quality in health care
  • anaesthetics
View Full Text

Statistics from Altmetric.com

Footnotes

  • Contributors RDlF, RF, JM-G and MS participated in the design of this study, conducted data analysis and writing of the manuscript. RDlF, RF, FA, MC and JP conducted data collection. AR, FA and JP revised the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.