Modelling admission lengths within psychiatric intensive care units. Issue 1 (24th March 2023)

• Record Type: Journal Article
• Authors: Dye, Stephen; Sethi, Faisil; Kearney, Thomas; Rose, Elizabeth; Penfold, Leia; Campbell, Malcolm; Valsraj, Koravangattu
• Is Part Of: BMJ health & care informatics. Volume 30:Issue 1(2023)
• Publication Date: 2023-03-24
• Subjects: outcome and process assessment, health care -- health services research -- computer simulation -- data science; Medical informatics -- Great Britain -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; Primary care (Medicine) -- Great Britain -- Data processing -- Periodicals; 362.10285
• DOI: 10.1136/bmjhci-2022-100685
• Languages: English
• ISSNs: 2632-1009

Abstract

Abstract : 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.