Determinants of per diem Hospital Costs in Mental Health

Abstract

IntroductionAn 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.MethodsThe 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.ResultsThe 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.ConclusionAlthough 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.

Work-Time Distribution of Physicians at a German University Hospital

“…The category of other activities embraced all activities that did not directly serve to deliver hospital care, such as training, waiting, research, teaching, moving from place to place, and breaks. The detailed results of the individual studies in the Department of Ophthalmology and the Department of Psychiatry and Psychotherapy can be found elsewhere (21,22).…”

“…Specifically, we wanted to investigate what proportion of working time was dedicated directly to patients and how much time was spent on other activities. Some of the results from individual departments have already been published in scientific journals (20)(21)(22), but this is the first presentation of the overall findings. No study of this type and size has previously been carried out in the Germanspeaking countries.…”

The interRAI Suite of Mental Health Assessment Instruments: An Integrated System for the Continuum of Care

“…These systems tend to have two main components: a) a classification system, based on clinical characteristics, to group service users into categories with similar levels of resource use; and b) a weighting system (case-mix indexes or CMIs) assigns ratiolevel numeric values to these groups that can, among other functions, be applied to funding formulas in payment systems. interRAI-based case-mix systems are available for nursing homes (307)(308)(309), home care (310), intellectual disability services (311,312), and intervener/interpreter services for dual sensory loss (313).…”

“…The Diagnosis Related Groups (314) system is widely used in acute general hospitals, but a consensus was reached over three decades ago that the system was inadequate for describing resource use in psychiatry (315). A number of studies pointed to the potential to use per-diem based case-mix systems that estimate costs of care per day of stay, rather than episode-based systems that attempt to predict length of stay (316)(317)(318). Most research of this type in mental health has been in hospital settings with only modest progress in community mental health services (319).…”

Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach

“…It has gained increasing attention in health care by achieving impressive results, for instance, in early prediction and diagnosis of breast cancer [12], acute kidney injury [13], skin cancer [14], prostate cancer [15], diabetic retinopathy [16] and depression [17]. Other studies applied machine learning to aspects relevant to the organisation of hospital care, such as predicting patient volume in emergency departments [18][19][20], the management of acute sepsis [21][22][23] and the daily costs per psychiatric inpatient [24].…”