Development and implementation of an operating room scheduling tool: an action research study

Abstract

In this study, we report the results of an action research project whose aim was to develop and implement an operating room scheduler at the Meyer University Children’s Hospital. The study offers insights into the features that make an MSS optimisation model and scheduler effective and easy to implement, and shed light on those actions facilitating their introduction and use. Specifically, it suggests that creating an effective operating room scheduler requires clustering patients in homogeneous surgery groups and developing a flexible tool that allows: scheduling surgery groups instead of actual patients, easily adding/removing constraints, changing the objective function(s) and adjusting the planning horizon. In addition, it posits that gaining the commitment of top management by showing credible preliminary results, inferring stakeholder preferences by letting them comment on tentative schedules, introducing changes gradually and involving staff at lower levels of the hospital hierarchy can significantly facilitate the scheduler development and implementation.

Keywords:

• Healthcare
• operating room
• master surgical scheduling
• action research
• optimisation

Acknowledgements

We are grateful to Meyer University Children’s Hospital, and in particular to General Director Dr. Alberto Zanobini, and to the Medical Director Dr. Francesca Bellini for supporting the research project that has inspired this study and for authorising us to disclose the Meyer Hospital name in the manuscript. We are also grateful to IBIS Lab for authorising us to disclose the lab name in the manuscript.

Notes

1. The International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) is the official system for assigning codes to diagnoses and procedures associated with hospital utilisation in the United States and other countries, including Italy (see http://www.cdc.gov/nchs/icd/icd9cm.htm).

2. In mathematical optimisation, constraints can be either hard or soft. Hard constraints set conditions for the variables that must be strictly satisfied. Soft constraints include some variable values that are penalised in the objective function if and to the extent that the conditions for the variables are not satisfied.

3. For explanatory purposes, the example was created setting the beds’ target utilisation equal to 100%.

4. Please notice that some sessions are totally (e.g. OR1/Sess2) or partially (e.g. OR4/Sess2) used for urgent cases or for specialties (e.g. Neuro Surgery) that are not scheduled using the model.