The RAFAELA system: a workforce planning tool for nurse staffing and human resource management

Fagerström, Lisbeth; Lønning, Kjersti; Andersen, Marit Helen

doi:10.7748/nm2014.04.21.2.30.e1199

Cited by 38 publications

(63 citation statements)

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“…A study conducted in Finland more than a decade ago is highlighted, that proposed a method to estimate the best level of nursing care intensity, contributing to the allocation of resources to meet the needs of patients ( 23 ) . More recently, this assessment has been made up of a system implemented in almost every hospital in this country along with the daily patient classification, the number of available nursing staff and financial information ( 24 ) . …”

Section: Discussionmentioning

confidence: 99%

The qualitative dimension of Nursing workload: a measurement proposal

Cucolo

Perroca

2019

Rev. Latino-Am. Enfermagem

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Objective:construct and test a proposal to measure the qualitative dimension of nursing workload; identify the workload cut-off point and its indicator as predictors of the good and optimal nursing care product score.Method:this is a descriptive study conducted in four inpatient units and four intensive care units of a Brazilian teaching hospital, considering 308 evaluations performed by 19 nurses. Four measurement instruments were used: three to assess the care demand in relation to nursing and the other to classify the care product delivered at the end of the shift. The workload was calculated and its indicator was constructed.Results:a weak and inverse correlation was found between the care product score, workload and the workload indicator and the workload indicator in the units and moderate and inverse between Nursing care planning and Care needs assistance with the number of hospitalized patients.Conclusion:it is possible to associate workload and its indicator with the care product. Nursing workload ≤ 173 hours (24 hours) and indicator ≤ 12.3 hours / professional were associated with a higher probability of obtaining a “good” and “optimal” score in the care product in the inpatient units.

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Section: Discussionmentioning

confidence: 99%

The qualitative dimension of Nursing workload: a measurement proposal

Cucolo

Perroca

2019

Rev. Latino-Am. Enfermagem

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“…Some models (e.g., Jaffry & Capon, 2005;Reeves & Reid, 1999;Sing, Love, & Tam, 2014;Williford & Chang, 1999) were just tested based on feedback from professionals and qualitative data collected from senior managers. However, some models have been precisely tested based on actual patterns (e.g., Choudhury, Sarkar, & Mukherjee, 2002b;Dawson, Barrett, & Ross, 1990;Fagerstrom, Lonning, & Andersen, 2014;Lin et al, 2001), models' ability to replicate the past (e.g., Jorssen, 1989;Kinstler et al, 2008;Song & Rathwell, 1993), historic censuses over time (e.g., the work of Freed, Nahra, & Wheeler, 2003), fit tests, and residual error tests (e.g., Lin & Juan, 2010;Wang & Lin, 2007;Zhihong, Zhiguang, & Qi, 2003). Some articles merely mentioned that the validity of the model has been conducted, but the test approach has not been specified (e.g., Grobler & Zock, 2010;Masnick, 2009).…”

Section: Discussionmentioning

confidence: 99%

Workforce forecasting models: A systematic review

Safarishahrbijari

2018

Journal of Forecasting

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Workforce analytics involves using models that integrate internal and external data to predict future workforce and help organizations in any industry examine factors that have a prognostic effect. This paper assesses workforce modeling and prediction methods by examining their rationale, strengths, and constraints. It aims to identify enhancements for further development of workforce forecasting models and compares the capacity and reliability of different forecasting methods. Past and present modeling trends are described and critiqued based on their relevance to current requirements. Several approaches are reviewed, such as time series modeling and system dynamics simulation. Sensitivity analysis in models is assessed. The models are decomposed into three modes: supply‐based, demand‐based, and need‐based, which in some cases provide substantially different estimates of future workforce need. The chronological progression of models' development is analyzed. The articles are also classified based on the countries and the sectors that have paid great attention to workforce prediction research. Consideration of the use of workforce models and the inputs into such models is not within the scope of this paper.

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“…The RAFAELA™ Patient Classification System, which is widely used in Finland and to some degree in other Scandinavian countries, that is, Norway, Sweden, and Iceland, consists of three components: instruments for measuring the NI in different nursing environments (e.g., OPCq [Oulu Patient Classification] instrument for somatic wards), daily registration of actual nursing staff resources, and the PAONCIL (Professional Assessment of Optimal Nursing Care Intensity Level) instrument to determine the optimal NI level for each unit (Andersen, Lønning, & Fagerström, ; Fagerström, ; Fagerström, Lønning, & Andersen, ; Fagerström & Vainikainen, ). Recently, there has been interest to extend the use of the RAFAELA™ system also to other European countries (Van Oostveen, Ubbink, Mens, Pompe, & Vermeulen, ).…”

Section: Introductionmentioning

confidence: 99%