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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

Authors

Iker P�rez L�pez

Michael Brown

SARAH MARTINDALE Sarah.Martindale@nottingham.ac.uk
Assistant Professor of Digital Innovation in The Creative Industries

SARAH SHARPLES SARAH.SHARPLES@NOTTINGHAM.AC.UK
Professor of Human Factors

Dominick E. Shaw

John Blakey



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 Mar 29, 2024
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

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