Out of hours workload management: Bayesian inference for decision support in secondary care

P�rez L�pez, Iker; Brown, Michael; Pinchin, James; Martindale, Sarah; Sharples, Sarah; Shaw, Dominick E.; Blakey, John

Abstract

Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures.

Methods and Material: We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data.

Results: Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation.

Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives.

Citation

Pérez López, I., Brown, M., Pinchin, J., Martindale, S., Sharples, S., Shaw, D. E., & Blakey, J. (in press). Out of hours workload management: Bayesian inference for decision support in secondary care. Artificial Intelligence in Medicine, 73, 34-44. https://doi.org/10.1016/j.artmed.2016.09.005

• Journal Article Type: Article
• Acceptance Date: Sep 29, 2016
• Online Publication Date: Oct 1, 2016
• Deposit Date: Sep 30, 2016
• Publicly Available Date: Oct 1, 2016
• Journal: Artificial Intelligence in Medicine
• Print ISSN: 0933-3657
• Electronic ISSN: 1873-2860
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 73
• Pages: 34-44
• DOI: https://doi.org/10.1016/j.artmed.2016.09.005
• Keywords: Healthcare Management, Multivariate Time Series, Count Data,; Out of Hours, Graphical Model
• Public URL: https://nottingham-repository.worktribe.com/output/808199
• Publisher URL: http://authors.elsevier.com/sd/article/S0933365716301555
• Contract Date: Sep 30, 2016