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Out of hours workload management: Bayesian inference for decision support in secondary care.
- Source :
-
Artificial Intelligence in Medicine . Oct2016, Vol. 73, p34-44. 11p. - Publication Year :
- 2016
-
Abstract
- <bold>Objective: </bold>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.<bold>Methods and Material: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09333657
- Volume :
- 73
- Database :
- Academic Search Index
- Journal :
- Artificial Intelligence in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 119156110
- Full Text :
- https://doi.org/10.1016/j.artmed.2016.09.005