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Open Access Highly Accessed Study protocol

Nurse forecasting in Europe (RN4CAST): Rationale, design and methodology

Walter Sermeus1, Linda H Aiken2, Koen Van den Heede1*, Anne Marie Rafferty3, Peter Griffiths4, Maria Teresa Moreno-Casbas5, Reinhard Busse6, Rikard Lindqvist7, Anne P Scott8, Luk Bruyneel1, Tomasz Brzostek9, Juha Kinnunen10, Maria Schubert11, Lisette Schoonhoven12, Dimitrios Zikos13 and RN4CAST consortium13

Author Affiliations

1 Center for Health Services and Nursing Research, Katholieke Universiteit Leuven, Kapucijnenvoer 35/4, 3000 Leuven, Belgium

2 Center for Health Outcomes and Policy Research, University of Pennsylvania, 418 Curie Blvd. Claire M. Fagin Hall, 387R, Philadelphia, PA 19104-4217, USA

3 Florence Nightingale School of Nursing & Midwifery, King's College London, James Clerk Maxwell Building, 57 Waterloo Road, London SE1 8WA, UK

4 School of Health Sciences, University of Southampton, Building 67, Highfield Campus, Southampton 17 1BJ, UK

5 National Spanish Research Unit, Instituto de Salud Carlos III. Ministry of Science and Innovation, C/Monforte de Lemos, 5. Pabellón 13, 28029 Madrid, Spain

6 Department of Health Care Management, WHO Collaborating Centre for Health Systems Research and Management, Technische Universität Berlin, H 80, Strasse des 17. Juni 135, 10623 Berlin, Germany

7 Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, 171 77 Stockholm, Sweden

8 School of Nursing, Dublin City University, Dublin 9, Ireland

9 Department of Internal Diseases and Community Nursing, Jagiellonian University Medical College, Kopernika 25, 31-501 Krakow, Poland

10 Department of Health Policy and Management, University of Eastern Finland, POB 1627, 70211 Kuopio, Finland

11 Institute of Nursing Science, University of Basel, Bernoullistrasse 28, 4056 Basel, Switzerland

12 Scientific Institute for Quality of Healthcare, UMC St Radboud, Postbus 9101, 114 IQ healthcare, 6500 HB Nijmegen, The Netherlands

13 Laboratory of Health Informatics, Faculty of Nursing, National and Kapodistrian University of Athens, Papadiamantopoulou 123, 11527 Athens, Greece

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BMC Nursing 2011, 10:6  doi:10.1186/1472-6955-10-6

Published: 18 April 2011

Abstract

Background

Current human resources planning models in nursing are unreliable and ineffective as they consider volumes, but ignore effects on quality in patient care. The project RN4CAST aims innovative forecasting methods by addressing not only volumes, but quality of nursing staff as well as quality of patient care.

Methods/Design

A multi-country, multilevel cross-sectional design is used to obtain important unmeasured factors in forecasting models including how features of hospital work environments impact on nurse recruitment, retention and patient outcomes. In each of the 12 participating European countries, at least 30 general acute hospitals were sampled. Data are gathered via four data sources (nurse, patient and organizational surveys and via routinely collected hospital discharge data). All staff nurses of a random selection of medical and surgical units (at least 2 per hospital) were surveyed. The nurse survey has the purpose to measure the experiences of nurses on their job (e.g. job satisfaction, burnout) as well as to allow the creation of aggregated hospital level measures of staffing and working conditions. The patient survey is organized in a sub-sample of countries and hospitals using a one-day census approach to measure the patient experiences with medical and nursing care. In addition to conducting a patient survey, hospital discharge abstract datasets will be used to calculate additional patient outcomes like in-hospital mortality and failure-to-rescue. Via the organizational survey, information about the organizational profile (e.g. bed size, types of technology available, teaching status) is collected to control the analyses for institutional differences.

This information will be linked via common identifiers and the relationships between different aspects of the nursing work environment and patient and nurse outcomes will be studied by using multilevel regression type analyses. These results will be used to simulate the impact of changing different aspects of the nursing work environment on quality of care and satisfaction of the nursing workforce.

Discussion

RN4CAST is one of the largest nurse workforce studies ever conducted in Europe, will add to accuracy of forecasting models and generate new approaches to more effective management of nursing resources in Europe.