Machine learning for healthcare behavioural OR: Addressing waiting time perceptions in emergency care

Abstract Recent research has discovered links between patient satisfaction and waiting time perceptions. We examine factors associated with waiting time estimation behaviour and how it can be linked to patient flow modelling. Using data from more than 250 patients, we evaluate machine learning (ML) methods to understand waiting time estimation behaviour in two emergency department areas. Our attribute ranking and selection methods reveal that actual waiting time, clinical attributes, and the service environment are among the top ranked and selected attributes. The classification precision for the true outcome of overestimating waiting times reaches almost 70% and 78% in the waiting area and the treatment room, respectively. We linked the ML results with a discrete-event simulation model. Our scenario analysis reveals that changing staffing patterns can lead to a substantial drop-off in overestimation of waiting times. These insights can be employed to control waiting time perceptions and, potentially, increase patient satisfaction.

[1]  Michael D Witting,et al.  Support for a waiting room time tracker: a survey of patients waiting in an urban ED. , 2013, The Journal of emergency medicine.

[2]  Jonathan Karnon,et al.  Modeling Using Discrete Event Simulation , 2012 .

[3]  Nor Hayati Othman,et al.  A review of feature selection techniques via gene expression profiles , 2008, 2008 International Symposium on Information Technology.

[4]  O. Soremekun,et al.  Framework for analyzing wait times and other factors that impact patient satisfaction in the emergency department. , 2011, The Journal of emergency medicine.

[5]  Rema Padman,et al.  Machine Learning Approaches for Early DRG Classification and Resource Allocation , 2015, INFORMS J. Comput..

[6]  Geoff Holmes,et al.  Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..

[7]  S. Landi,et al.  Socioeconomic status and waiting times for health services: An international literature review and evidence from the Italian National Health System. , 2018, Health policy.

[8]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[9]  Leroy White,et al.  Behavioural operational research: Towards a framework for understanding behaviour in OR interventions , 2016, Eur. J. Oper. Res..

[10]  J. Hartung,et al.  Statistik: Lehr- und Handbuch der angewandten Statistik , 2009 .

[11]  Rainer Kolisch,et al.  Scheduling the hospital-wide flow of elective patients , 2014, Eur. J. Oper. Res..

[12]  Andrew M. Hardin,et al.  When Filling the Wait Makes it Feel Longer: A Paradigm Shift Perspective for Managing Online Delay , 2013, MIS Q..

[13]  Walter Daelemans,et al.  Machine Learning Approaches , 1999 .

[14]  Alain Guinet,et al.  Modeling and Simulation of emergency services with ARIS and Arena , 2007 .

[15]  Shari J. Welch.  Twenty Years of Patient Satisfaction Research Applied to the Emergency Department: A Qualitative Review , 2010, American journal of medical quality : the official journal of the American College of Medical Quality.

[16]  P R Yarnold,et al.  How accurate are waiting time perceptions of patients in the emergency department? , 1996, Annals of emergency medicine.

[17]  P R Yarnold,et al.  Effects of actual waiting time, perceived waiting time, information delivery, and expressive quality on patient satisfaction in the emergency department. , 1996, Annals of emergency medicine.

[18]  Toni Anwar,et al.  A survey on application of artificial intelligence for bus arrival time prediction , 2012 .

[19]  Pratik J. Parikh,et al.  Analyzing Discharge Strategies during Acute Care , 2014, Medical decision making : an international journal of the Society for Medical Decision Making.

[20]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[21]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[22]  Peter C. Verhoef,et al.  Erim Report Series Research in Management Consumer Perception and Evaluation of Waiting Time: a Field Experiment Bibliographic Data and Classifications , 2022 .

[23]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[24]  Masha Shunko,et al.  Humans Are Not Machines: The Behavioral Impact of Queueing Design on Service Time , 2017, Manag. Sci..

[25]  LarrañagaPedro,et al.  A review of feature selection techniques in bioinformatics , 2007 .

[26]  Samina Khalid,et al.  A survey of feature selection and feature extraction techniques in machine learning , 2014, 2014 Science and Information Conference.

[27]  Wenhong Luo,et al.  Impact of process change on customer perception of waiting time: a field study , 2004 .

[28]  J. Olsen,et al.  Patient satisfaction in the emergency department and the use of business cards by physicians. , 2012, The Journal of emergency medicine.

[29]  Upali Nanda,et al.  Impact of visual art on patient behavior in the emergency department waiting room. , 2012, The Journal of emergency medicine.

[30]  R. Kolisch,et al.  Overutilization and underutilization of operating rooms - insights from behavioral health care operations management , 2017, Health care management science.

[31]  J. Caro,et al.  Modeling Using Discrete Event Simulation , 2012, Medical decision making : an international journal of the Society for Medical Decision Making.

[32]  Perica Strbac,et al.  Toward optimal feature selection using ranking methods and classification algorithms , 2011 .

[33]  Edwin D Boudreaux,et al.  Patient satisfaction in the Emergency Department: a review of the literature and implications for practice. , 2004, The Journal of emergency medicine.

[34]  J. Hedges,et al.  Satisfied Patients Exiting the Emergency Department (SPEED) Study. , 2002, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[35]  Sally C. Brailsford,et al.  Towards incorporating human behaviour in models of health care systems: An approach using discrete event simulation , 2003, Eur. J. Oper. Res..

[36]  Alain Guinet,et al.  Modelling and simulation of emergency services with ARIS and Arena. Case study: the emergency department of Saint Joseph and Saint Luc Hospital , 2009 .