gms | German Medical Science

20. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin e. V.

Deutsches Netzwerk Evidenzbasierte Medizin e. V.

21. - 23.03.2019, Berlin

Variation of patients’ flow and patient-to-nurse ratio on a 30-minutes basis for 3 years: analysis of routine data of a Swiss university hospital

Meeting Abstract

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  • Sarah N. Musy - University of Basel, Institute of Nursing Science, Basel, Schweiz; Inselspital Bern University Hospital, Nursing & Midwifery Research Unit, Bern, Schweiz
  • Michael Simon - University of Basel, Institute of Nursing Science, Basel, Schweiz; Inselspital Bern University Hospital, Nursing & Midwifery Research Unit, Bern, Schweiz

EbM und Digitale Transformation in der Medizin. 20. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin. Berlin, 21.-23.03.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. Doc19ebmS2-V3-03

doi: 10.3205/19ebm013, urn:nbn:de:0183-19ebm0138

Veröffentlicht: 20. März 2019

© 2019 Musy et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Background/research question: Determination of safe nurse staffing is a key challenge for hospitals. Care demands on each shift vary, since the number of patients is changing through admissions, discharges and transfers. Those fluctuations may result in a reduction or an increase of the nurses’ work. Most research in this field is based on data aggregated over time (e.g. year) and/or the hospital level. Thus, the results do not reflect daily or shift variability, e.g. under- or overstaffing is not recognized due to aggregation.

The objective of the current study was to analyze the patient flow and patient-to-nurse ratio every 30 minutes for all inpatients at one Swiss University Hospital.

Methods: Routine data for the years 2015 to 2017 were used containing information about nurses and patients. The datasets allowed the calculation of the patients’ admissions, discharges, and transfers. Patient-to-nurse ratio was calculated by dividing the number of patients by the number of nurses for each unit and department. Those calculations were computed every 30 minutes in order to get a detailed overview.

Results: Ten departments and 77 units were analyzed with 85,706 patients and 5,721 staffing observations. The final data computed every 30 minutes has more than 58 million data points. Patient flow varies considerably. While for some units entries (admission/transfer in) and exits (discharge/transfer out) occur at the same time; other units have volatile periods with different times of the day where entries and exits occur leading to substantially more or less patients. For the patient-to-nurse ratio, three key time points are used. Three departments have lower ratios (more nursing staff) than the others (shown in brackets): pediatrics, cardiology, and ICU. The ratio for the weekdays range from 1) at 00:00 between 7.3 to 10.1 (1.2 to 5); 2) at 08:00 between 2.3 to 2.9 (0.8 to 1.6); and 3) at 16:00 between 1.4 to 2.2 (0.4 to 1). On the weekends, the ratio is generally higher with the only exception of the time point 00:00, which has lower ratios.

Conclusions: It is the first analysis of patient flow and patient-to-nurse ratio conducted on this level of granularity. The descriptive analysis shows that in order to predict nurse staffing, each unit has a different time-varying load profile during the day. Thus, staffing analysis should be conducted on the unit level, considering different times of the day and not on aggregated data.