Analytical Techniques to Support Hospital Case Mix Planning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929656088756224 |
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| author | Burdett, Robert L corry, Paul Cook, David Yarlagadda, Prasad |
| author_facet | Burdett, Robert L corry, Paul Cook, David Yarlagadda, Prasad |
| contents | This article introduces analytical techniques and a decision support tool to support capacity assessment and case mix planning (CMP) approaches previously created for hospitals. First, an optimization model is proposed to analyse the impact of making a change to an existing case mix. This model identifies how other patient types should be altered proportionately to the changing levels of hospital resource availability. Then we propose multi-objective decision-making techniques to compare and critique competing case mix solutions obtained. The proposed techniques are embedded seamlessly within an Excel Visual Basic for Applications (VBA) personal decision support tool (PDST), for performing informative quantitative assessments of hospital capacity. The PDST reports informative metrics of difference and reports the impact of case mix modifications on the other types of patient present. The techniques developed in this article provide a bridge between theory and practice that is currently missing and provides further situational awareness around hospital capacity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_07323 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Analytical Techniques to Support Hospital Case Mix Planning Burdett, Robert L corry, Paul Cook, David Yarlagadda, Prasad Artificial Intelligence Computers and Society This article introduces analytical techniques and a decision support tool to support capacity assessment and case mix planning (CMP) approaches previously created for hospitals. First, an optimization model is proposed to analyse the impact of making a change to an existing case mix. This model identifies how other patient types should be altered proportionately to the changing levels of hospital resource availability. Then we propose multi-objective decision-making techniques to compare and critique competing case mix solutions obtained. The proposed techniques are embedded seamlessly within an Excel Visual Basic for Applications (VBA) personal decision support tool (PDST), for performing informative quantitative assessments of hospital capacity. The PDST reports informative metrics of difference and reports the impact of case mix modifications on the other types of patient present. The techniques developed in this article provide a bridge between theory and practice that is currently missing and provides further situational awareness around hospital capacity. |
| title | Analytical Techniques to Support Hospital Case Mix Planning |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2308.07323 |