Le nuove metodologie di valutazione del fabbisogno del personale infermieristico, medico e di supporto: Un esempio di applicazione

Dario Laquintana, Silvia Pazzaglia, Antonia Demarchi

Research output: Contribution to journalArticle

Abstract

Summary. The new methods to define the staffing requirements for doctors, nurses and nurses aides: an example of their implementation in an Italian hospital. The Italian government, after the transposition of European Union legislation on working hours, made a declaration of commitment to increase the number of staff of the National Health Service (NHS). The method for assessing the staffing needs innovates the old one that dated back a few decades. Aim. To implement the method proposed by the Ministry of Health to an Italian hospital and assess its impact on staffing and costs. Methods. The model was implemented on all the wards, multiplying the minutes of care expected in 2016, dividing the result by 60 to obtain the hours of care, and further dividing by the number of yearly hours of work of a nurse (1418). Same was done for nurses aides. The minutes of care were related to mean weight of the Diagnosis Related Groups of the ward and the results obtained compared to the actual staffing of nurses and nurses aides. The costs of the differences were calculated. Results. The implementation of the model produced an excess of 23 nurses and a scarcity of 95 nurses aides compared to the actual staffing, with an increase of the costs of € 1.828.562,00. Conclusions. The results obtained and the criticisms received so far show the need of major changes. The data from international studies that associate staffing and patients outcomes and the nurse/patient ratio are macro-indicators already available that may orient choices and investments on the health care professions.

Original languageItalian
Pages (from-to)123-134
Number of pages12
JournalAssistenza Infermieristica e Ricerca
Volume36
Issue number3
DOIs
Publication statusPublished - Jul 1 2017

ASJC Scopus subject areas

  • Nursing(all)

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