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.
journal_name
Artif Intell Medjournal_title
Artificial intelligence in medicineauthors
Perez I,Brown M,Pinchin J,Martindale S,Sharples S,Shaw D,Blakey Jdoi
10.1016/j.artmed.2016.09.005subject
Has Abstractpub_date
2016-10-01 00:00:00pages
34-44eissn
0933-3657issn
1873-2860pii
S0933-3657(16)30155-5journal_volume
73pub_type
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journal_title:Artificial intelligence in medicine
pub_type: 杂志文章
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journal_title:Artificial intelligence in medicine
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