This paper proposes a drugs inventory policy at point-of-use level, tailored for the Intensive CareUnit (ICU) case study and aimed at relieving nurses of the time-wasting task of drugs ordering and refilling. The policy aims at jointly reducing order occurrences and imposing service regularity, while keeping stock value as low as possible. An optimization model is proposed and solved on a one-month period real instance and on a set of realistic ones derived from drugs consumption data collection at the ward. The potentially conflicting priorities of three stakeholders (nurses, administration and clinicians) have been successfully incorporated and their impact on order occurrences and stock value has been discussed. Computational results suggest that it is possible to optimize the time-consuming order process currently adopted at the ICU case study. This study is part of a more comprehensive project in which the optimization block will be integrated with a demand forecasting tool and deployed in a rolling horizon framework.

Empirical Data Driven Intensive Care Unit Drugs Inventory Policies / Cappanera, Paola; Nonato, Maddalena; Rossi, Roberta. - STAMPA. - 210:(2017), pp. 155-166. (Intervento presentato al convegno Third International Conference on Health Care System Engineering) [10.1007/978-3-319-66146-9_14].

Empirical Data Driven Intensive Care Unit Drugs Inventory Policies

Cappanera, Paola;ROSSI, ROBERTA
2017

Abstract

This paper proposes a drugs inventory policy at point-of-use level, tailored for the Intensive CareUnit (ICU) case study and aimed at relieving nurses of the time-wasting task of drugs ordering and refilling. The policy aims at jointly reducing order occurrences and imposing service regularity, while keeping stock value as low as possible. An optimization model is proposed and solved on a one-month period real instance and on a set of realistic ones derived from drugs consumption data collection at the ward. The potentially conflicting priorities of three stakeholders (nurses, administration and clinicians) have been successfully incorporated and their impact on order occurrences and stock value has been discussed. Computational results suggest that it is possible to optimize the time-consuming order process currently adopted at the ICU case study. This study is part of a more comprehensive project in which the optimization block will be integrated with a demand forecasting tool and deployed in a rolling horizon framework.
2017
Health Care Systems Engineering
Third International Conference on Health Care System Engineering
Cappanera, Paola; Nonato, Maddalena; Rossi, Roberta
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1113455
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact