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Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit

Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit
Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit
Variability in demand for staffed beds from existing patients and new referrals in intensive care units presents a substantial problem to managers. Short term fluctuations in the number of patients requiring a bed can result in demand for beds exceeding capacity, or alternatively, seemingly inefficient use of an expensive resource. While operational research methods can help in capacity planning, there are many barriers to implementing such methods in practice. In this paper we describe an entire operational research project cycle. This included: deriving exact expressions for the probability distribution for the time-varying bed demand on an intensive care unit taking account of occupancy at the point of forecast and future planned and emergency admissions; applying these expressions to a specific hospital’s intensive care unit using historical data; building software that the hospital staff can use daily to produce forecasts of short term bed demand; implementing the software within the hospital; and an evaluation of this implementation from both a technical and non-technical perspective.

The main contribution of this paper is in describing the process of implementing an abstract mathematical model in a busy intensive care unit and the independent qualitative evaluation of the work about how potential barriers to implementation were addressed as part of a “modellers in residence” programme that led to us building a software tool that is still being used by the hospital more than 4 years after initial implementation. In particular, we draw together lessons from our work that we think will benefit other operational researchers wanting to work effectively with health care organisations on similar problems.
Pagel, Christina
ec7bd51c-47d5-4e1a-99f4-c13599ce8d94
Banks, Victoria
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Pope, Catherine
21ae1290-0838-4245-adcf-6f901a0d4607
Whitmore, Pauline
f0eb107f-bb60-4f2e-bb27-5065a7805e32
Brown, Katherine
fce5e26b-3ccd-4cee-92b0-573b027eb8bc
Goldman, Allan
c1c65fa0-c4d0-4de6-a637-459ab0ecdc55
Utley, Martin
c7107f2e-7a18-41d3-92c2-cea48151dfbc
Pagel, Christina
ec7bd51c-47d5-4e1a-99f4-c13599ce8d94
Banks, Victoria
44eaf113-4c22-42d1-9c12-9c5f1ca850e8
Pope, Catherine
21ae1290-0838-4245-adcf-6f901a0d4607
Whitmore, Pauline
f0eb107f-bb60-4f2e-bb27-5065a7805e32
Brown, Katherine
fce5e26b-3ccd-4cee-92b0-573b027eb8bc
Goldman, Allan
c1c65fa0-c4d0-4de6-a637-459ab0ecdc55
Utley, Martin
c7107f2e-7a18-41d3-92c2-cea48151dfbc

Pagel, Christina, Banks, Victoria, Pope, Catherine, Whitmore, Pauline, Brown, Katherine, Goldman, Allan and Utley, Martin (2017) Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit. Operations Research for Health Care. (doi:10.1016/j.orhc.2017.08.003).

Record type: Article

Abstract

Variability in demand for staffed beds from existing patients and new referrals in intensive care units presents a substantial problem to managers. Short term fluctuations in the number of patients requiring a bed can result in demand for beds exceeding capacity, or alternatively, seemingly inefficient use of an expensive resource. While operational research methods can help in capacity planning, there are many barriers to implementing such methods in practice. In this paper we describe an entire operational research project cycle. This included: deriving exact expressions for the probability distribution for the time-varying bed demand on an intensive care unit taking account of occupancy at the point of forecast and future planned and emergency admissions; applying these expressions to a specific hospital’s intensive care unit using historical data; building software that the hospital staff can use daily to produce forecasts of short term bed demand; implementing the software within the hospital; and an evaluation of this implementation from both a technical and non-technical perspective.

The main contribution of this paper is in describing the process of implementing an abstract mathematical model in a busy intensive care unit and the independent qualitative evaluation of the work about how potential barriers to implementation were addressed as part of a “modellers in residence” programme that led to us building a software tool that is still being used by the hospital more than 4 years after initial implementation. In particular, we draw together lessons from our work that we think will benefit other operational researchers wanting to work effectively with health care organisations on similar problems.

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Accepted/In Press date: 15 August 2017
e-pub ahead of print date: 14 September 2017

Identifiers

Local EPrints ID: 414853
URI: http://eprints.soton.ac.uk/id/eprint/414853
PURE UUID: 2161c9f9-adc7-434a-b49b-65dff65f133e
ORCID for Catherine Pope: ORCID iD orcid.org/0000-0002-8935-6702

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Date deposited: 12 Oct 2017 16:31
Last modified: 16 Mar 2024 05:48

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Contributors

Author: Christina Pagel
Author: Victoria Banks
Author: Catherine Pope ORCID iD
Author: Pauline Whitmore
Author: Katherine Brown
Author: Allan Goldman
Author: Martin Utley

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