Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit
<br/>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, seeming...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
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Elsevier
2017
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| _version_ | 1797063411962478592 |
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| author | Pagel, C Banks, V Pope, C Whitmore, P Brown, K Goldman, A Utley, M |
| author_facet | Pagel, C Banks, V Pope, C Whitmore, P Brown, K Goldman, A Utley, M |
| author_sort | Pagel, C |
| collection | OXFORD |
| description | <br/>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.<br/>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. |
| first_indexed | 2024-03-06T20:59:28Z |
| format | Journal article |
| id | oxford-uuid:3a6263d9-2028-4c03-ba79-a68193824c6c |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:59:28Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:3a6263d9-2028-4c03-ba79-a68193824c6c2022-03-26T14:01:19ZDevelopment, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unitJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3a6263d9-2028-4c03-ba79-a68193824c6cSymplectic Elements at OxfordElsevier2017Pagel, CBanks, VPope, CWhitmore, PBrown, KGoldman, AUtley, M<br/>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.<br/>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. |
| spellingShingle | Pagel, C Banks, V Pope, C Whitmore, P Brown, K Goldman, A Utley, M Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit |
| title | Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit |
| title_full | Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit |
| title_fullStr | Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit |
| title_full_unstemmed | Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit |
| title_short | Development, implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit |
| title_sort | development implementation and evaluation of a tool for forecasting short term demand for beds in an intensive care unit |
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