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Predicting turnaround time reductions of the diagnostic track in the histopathology laboratory using mathematical modelling
  1. A G Leeftink1,2,
  2. R J Boucherie1,
  3. E W Hans1,
  4. M A M Verdaasdonk3,
  5. I M H Vliegen1,
  6. P J van Diest3
  1. 1Centre for Healthcare Operations Improvement & Research (CHOIR), University of Twente, Enschede, The Netherlands
  2. 2UMC Utrecht Cancer Center, University Medical Center Utrecht, Utrecht, The Netherlands
  3. 3Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
  1. Correspondence to Anne G Leeftink, Centre for Healthcare Operations Improvement & Research, University of Twente, P. O. Box 217, Enschede 7500 AE, The Netherlands; a.g.leeftink{at}utwente.nl

Abstract

Background Pathology departments face a growing volume of more and more complex testing in an era where healthcare costs tend to explode and short turnaround times (TATs) are expected. In contrast, the histopathology workforce tends to shrink, so histopathology employees experience high workload during their shifts. This points to the need for efficient planning of activities in the histopathology laboratory, to ensure an equal division of workload and low TATs, at minimum costs.

Methods The histopathology laboratory of a large academic hospital in The Netherlands was analysed using mathematical modelling. Data were collected from the Laboratory Management System to determine laboratory TATs and workload performance during regular working hours. A mixed integer linear programme (MILP) was developed to model the histopathology processes and to measure the expected performance of possible interventions in terms of TATs and spread of workload.

Results The MILP model predicted that tissue processing at specific moments during the day, combined with earlier starting shifts, can result in up to 25% decrease of TATs, and a more equally spread workload over the day.

Conclusions Mathematical modelling can help to optimally organise the workload in the histopathology laboratory by predicting the performance of possible interventions before actual implementation. The interventions that were predicted by the model to have the highest performance have been implemented in the histopathology laboratory of University Medical Center Utrecht. Further research should be executed to collect empirical evidence and evaluate the actual impact on TAT, quality of work and employee stress levels.

  • HISTOPATHOLOGY
  • LABORATORY MANAGEMENT
  • QUALITY CONTROL

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