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Determinants of per diem Hospital Costs in Mental Health

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article numbere0152669
JournalPLOS One
Issue number3
Accepted/In press17 Mar 2016
Published31 Mar 2016


King's Authors


INTRODUCTION: An understanding of differences in hospital costs between patient groups is relevant for the efficient organisation of inpatient care. The main aim of this study was to confirm the hypothesis that eight a priori identified cost drivers influence per diem hospital costs. A second aim was to explore further variables that might influence hospital costs.

METHODS: The study included 667 inpatient episodes consecutively discharged in 2014 at the psychiatric hospital of the Medical Centre- University of Freiburg. Fifty-one patient characteristics were analysed. Per diem costs were calculated from the hospital perspective based on a detailed documentation of resource use. Mixed-effects maximum likelihood regression and an ensemble of conditional inference trees were used to analyse data.

RESULTS: The study confirmed the a priori hypothesis that not being of middle age (33-64 years), danger to self, involuntary admission, problems in the activities of daily living, the presence of delusional symptoms, the presence of affective symptoms, short length of stay and the discharging ward affect per diem hospital costs. A patient classification system for prospective per diem payment was suggested with the highest per diem hospital costs in episodes having both delusional symptoms and involuntary admissions and the lowest hospital costs in episodes having neither delusional symptoms nor somatic comorbidities.

CONCLUSION: Although reliable cost drivers were identified, idiosyncrasies of mental health care complicated the identification of clear and consistent differences in hospital costs between patient groups. Further research could greatly inform current discussions about inpatient mental health reimbursement, in particular with multicentre studies that might find algorithms to split patients in more resource-homogeneous groups.

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