Modelling admission lengths within psychiatric intensive care units
Objectives To examine whether discharge destination is a useful predictor variable for the length of admission within psychiatric intensive care units (PICUs).Methods A clinician-led process separated PICU admissions by discharge destination into three types and suggested other possible variables as...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2023-12-01
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| Series: | BMJ Health & Care Informatics |
| Online Access: | https://informatics.bmj.com/content/30/1/e100685.full |
| _version_ | 1797370983567327232 |
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| author | Malcolm Campbell Elizabeth Rose Faisil Sethi Koravangattu Valsraj Stephen Dye Thomas Kearney Leia Penfold |
| author_facet | Malcolm Campbell Elizabeth Rose Faisil Sethi Koravangattu Valsraj Stephen Dye Thomas Kearney Leia Penfold |
| author_sort | Malcolm Campbell |
| collection | DOAJ |
| description | Objectives To examine whether discharge destination is a useful predictor variable for the length of admission within psychiatric intensive care units (PICUs).Methods A clinician-led process separated PICU admissions by discharge destination into three types and suggested other possible variables associated with length of stay. Subsequently, a retrospective study gathered proposed predictor variable data from a total of 368 admissions from four PICUs. Bayesian models were developed and analysed.Results Clinical patient-type grouping by discharge destination displayed better intraclass correlation (0.37) than any other predictor variable (next highest was the specific PICU to which a patient was admitted (0.0585)). Patients who were transferred to further secure care had the longest PICU admission length. The best model included both patient type (discharge destination) and unit as well as an interaction between those variables.Discussion Patient typing based on clinical pathways shows better predictive ability of admission length than clinical diagnosis or a specific tool that was developed to identify patient needs. Modelling admission lengths in a Bayesian fashion could be expanded and be useful within service planning and monitoring for groups of patients.Conclusion Variables previously proposed to be associated with patient need did not predict PICU admission length. Of the proposed predictor variables, grouping patients by discharge destination contributed the most to length of stay in four different PICUs. |
| first_indexed | 2024-03-08T18:12:15Z |
| format | Article |
| id | doaj.art-058ee349e0244cf9bcee616aeccf291d |
| institution | Directory Open Access Journal |
| issn | 2632-1009 |
| language | English |
| last_indexed | 2024-03-08T18:12:15Z |
| publishDate | 2023-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Health & Care Informatics |
| spelling | doaj.art-058ee349e0244cf9bcee616aeccf291d2023-12-31T23:55:08ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092023-12-0130110.1136/bmjhci-2022-100685Modelling admission lengths within psychiatric intensive care unitsMalcolm Campbell0Elizabeth Rose1Faisil Sethi2Koravangattu Valsraj3Stephen Dye4Thomas Kearney5Leia Penfold6Central and North West London NHS Foundation Trust, London, UKCentral and North West London NHS Foundation Trust, London, UKDorset HealthCare University NHS Foundation Trust, Poole, UKSouth London and Maudsley NHS Foundation Trust, London, UKSuffolk Access Team, Norfolk and Suffolk NHS Foundation Trust, Norwich, UKNHS Improvement, London, UKSuffolk Central IDT, Norfolk and Suffolk NHS Foundation Trust, Norwich, UKObjectives To examine whether discharge destination is a useful predictor variable for the length of admission within psychiatric intensive care units (PICUs).Methods A clinician-led process separated PICU admissions by discharge destination into three types and suggested other possible variables associated with length of stay. Subsequently, a retrospective study gathered proposed predictor variable data from a total of 368 admissions from four PICUs. Bayesian models were developed and analysed.Results Clinical patient-type grouping by discharge destination displayed better intraclass correlation (0.37) than any other predictor variable (next highest was the specific PICU to which a patient was admitted (0.0585)). Patients who were transferred to further secure care had the longest PICU admission length. The best model included both patient type (discharge destination) and unit as well as an interaction between those variables.Discussion Patient typing based on clinical pathways shows better predictive ability of admission length than clinical diagnosis or a specific tool that was developed to identify patient needs. Modelling admission lengths in a Bayesian fashion could be expanded and be useful within service planning and monitoring for groups of patients.Conclusion Variables previously proposed to be associated with patient need did not predict PICU admission length. Of the proposed predictor variables, grouping patients by discharge destination contributed the most to length of stay in four different PICUs.https://informatics.bmj.com/content/30/1/e100685.full |
| spellingShingle | Malcolm Campbell Elizabeth Rose Faisil Sethi Koravangattu Valsraj Stephen Dye Thomas Kearney Leia Penfold Modelling admission lengths within psychiatric intensive care units BMJ Health & Care Informatics |
| title | Modelling admission lengths within psychiatric intensive care units |
| title_full | Modelling admission lengths within psychiatric intensive care units |
| title_fullStr | Modelling admission lengths within psychiatric intensive care units |
| title_full_unstemmed | Modelling admission lengths within psychiatric intensive care units |
| title_short | Modelling admission lengths within psychiatric intensive care units |
| title_sort | modelling admission lengths within psychiatric intensive care units |
| url | https://informatics.bmj.com/content/30/1/e100685.full |
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