Nurse Staffing in Psychiatric Inpatient Care: A Multicentre Study Using Routine Patient and Structural Data

Nurse staffing in psychiatric inpatient care is difficult to plan because reliable measures of patient demand are lacking. Existing indicators are not well established across diverse patient groups. The Health of the Nation Outcome Scales (HoNOS), routinely collected in Switzerland, may provide a pragmatic proxy for patient acuity to inform staffing decisions.

We conducted a multicentre cross-sectional study (Match^RN Psychiatry) in adult, nonforensic psychiatric inpatient units in Switzerland, linking 2022 routine patient data with 2023/2024 unit-manager reports of typical structural staffing configurations. Patient mental health status and symptom severity were measured using the HoNOS. Associations between nurse staffing hours, HoNOS scores and unit characteristics were analysed using lasso-selected linear mixed-effects models. Model estimates were used to describe how RN staffing varied with patient acuity, and model-predicted staffing was compared with reported staffing to characterise between-unit variation.

Data from 107 psychiatric inpatient units in 13 hospitals (25,294 patient cases) showed wide variation in unit structures and patient mental health status but relatively little variation in staffing patterns. The median number of beds was 21, and the typical early shift included 2 registered nurses. Regression analyses indicated that higher HoNOS total scores and several single items were significantly associated with increased RN staffing, with the total score showing the strongest effect. A 20% increase in the HoNOS total score corresponded to 48.5 additional registered nurse hours per week or 1.3 full-time equivalents per year. Comparing predicted with reported staffing, smaller units tended to fall below and larger units above their predicted levels.

Routinely collected clinical data, particularly HoNOS scores, were linked to nurse staffing in psychiatric inpatient care. Patient acuity and unit size were both associated with staffing, indicating that routine data can provide a pragmatic basis for more transparent and sustainable workforce planning.