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

Perez, Iker, Brown, Michael, Pinchin, James, Martindale, Sarah, Sharples, Sarah, Shaw, Dominick and Blakey, John ORCID: 0000-0003-2551-8984 (2016) Out of hours workload management: Bayesian inference for decision support in secondary care. ARTIFICIAL INTELLIGENCE IN MEDICINE, 73. pp. 34-44.

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Official URL: http://dx.doi.org/10.1016/j.artmed.2016.09.005

Abstract

<h4>Objective</h4>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.<h4>Methods and material</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.

Item Type:	Article
Uncontrolled Keywords:	Healthcare management, Multivariate time series, Count data, Out of hours, Graphical model
Depositing User:	Symplectic Admin
Date Deposited:	13 Feb 2020 16:40
Last Modified:	19 Jan 2023 00:03
DOI:	10.1016/j.artmed.2016.09.005
Open Access URL:	http://eprints.nottingham.ac.uk/37274/1/DraftTimSe...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3074891