Skip to main navigation menu Skip to main content Skip to site footer

Original article

Vol. 143 No. 3536 (2013)

Measurement of resident workload in paediatric intensive care

  • Bernhard Frey
  • Johann Peter Hossle
  • Monika Seiler Sigrist
  • Vincenzo Cannizzaro
Cite this as:
Swiss Med Wkly. 2013;143:w13844


OBJECTIVE: To measure the workload of residents in a paediatric intensive care unit (PICU) and to compare this value with the possible explanatory variables “nine equivalents of nursing manpower use score” (NEMS), length of stay (LOS), patient age and severity of illness at admission.

METHODS: This was a prospective study in a tertiary, interdisciplinary neonatal-paediatric intensive care unit. In 2010 and 2011, residents estimated their workload for each patient they looked after at admission and then twice a day (morning and night shift) (minor workload 0–30 minutes, medium >30–90 minutes, high >90 minutes). The following demographic and illness severity parameters were also collected prospectively: age, LOS, NEMS, Paediatric Index of Mortality (PIM2), and main diagnosis at admission.

RESULTS: There were 2,513 admissions to PICU. Independent predictors of residents’ workload were LOS (coefficient in multiple regression 8.9, p <0.0001) and NEMS (coefficient 1.4, p <0.0001). R2 of 0.928 indicated a strong overall relationship. Severity of illness at admission and patient age did not explain overall workload for the whole patient stay in PICU.

CONCLUSIONS: NEMS, a therapeutic intervention score, and LOS are both independent predictors of clinical workload of residents in PICU. The correlation with LOS means that workload depends mainly on routine procedures (rounds, discussions with parents, administrative tasks) unrelated to the severity of illness. After calibration, LOS or NEMS, two widely used measures, may be used to calculate resident workload.


  1. Valentin A, Ferdinande P. Recommendations on basic requirements for intensive care units: structural and organizational aspects. Intensive Care Med. 2011;37(10):1575–87.
  2. Peets A, Ayas NT. Restricting resident work hours: the good, the bad, and the ugly. Crit Care Med. 2012;40(3):960–6.
  3. Hämmig O, Brauchli R, Bauer GF. Effort-reward and work-life imbalance, general stress and burnout among employees of a large public hospital in Switzerland. Swiss Med Wkly. 2012;142:w13577.
  4. Reis Miranda D, Moreno R, Iapichino G. Nine equivalents of nursing manpower use score (NEMS). Intensive Care Med. 1997;23(7):760–5.
  5. Rothen HU, Küng V, Ryser DH, Zürcher R, Regli B. Validation of “nine equivalents of nursing manpower use score” on an independent data sample. Intensive Care Med. 1999;25(6):606–11.
  6. Perren A, Previsdomini M, Perren I, Merlani P. High accuracy of the nine equivalents of nursing manpower use score assessed b critical care nurses. Swiss Med Wkly. 2012;142:w13555.
  7. De Keizer NF, Bonsel GJ, Al MJ, Gemke RJ. The relation between TISS and real paediatric ICU costs: a case study with generalizable methodology. Intensive Care Med. 1998;24(10):1062–9.
  8. Minimal dataset of the Swiss Society of Intensive care Medicine. -> Qualität -> MDSi (1 march 2013)
  9. Slater A, Shann F, Pearson G. PIM2: a revised version of the Paediatric Index of Mortality. Intensive Care Med. 2003;29(2):278–85.
  10. Slater A, Shann F, McEniery J. The ANZPIC Registry diagnostic codes: a system for coding reasons for admitting children to intensive care. Intensive Care Med. 2003;29(2):271–7.
  11. Vagts DA. Ärztliche Personalbedarfsermittlung in der Intensivmedizin. Anaesthesiol Intensivmed Notfallmed Schmerzther. 2007;42(4):306–11.
  12. Zupancic JA, Richardson DK. Characterization of neonatal personnel time inputs and prediction from clinical variables – a time and motion study. J Perinatol. 2002;22(8):658–63.

Most read articles by the same author(s)