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The aim of this review is to identify and provide a structured overview of quantitative models in the pharmaceutical supply chain, a subject not exhaustively studied in the previous reviews on healthcare logistics related mostly to quantitative models in healthcare or logistics studies in hospitals. The models are classified into three categories of classification: network design, inventory models, and optimization of a pharmaceutical supply chain. A taxonomy for each category is shown describing the principal features of each echelon included in the review; this taxonomy allows the readers to identify easily a paper based on the actors of the pharmaceutical supply chain. The search process included research articles published in the databases between 1984 and November 2016. In total 46 studies were included. In the review process we found that in the three fields the most common source of uncertainty used is the demand in the 56% of the cases. Within the review process we can conclude that most of the articles in the literature are focused on the optimization of the pharmaceutical supply chain and inventory models but the field on supply chain network design is not deeply studied.

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Bed occupancy ratio reflects the state of the hospital at a given time. It is important for management to keep track of this figure to proactively avoid overcrowding and maintain a high level of quality of care. The objective of this work consists in proposing a decision-aid tool for hospital managers allowing to decide on the bed requirements for a given hospital or network of hospitals on a short-medium term horizon. To that extent we propose a new data-driven discrete-event simulation model based on data from a French university hospital to predict bed and staff requirements. We propose a case study to illustrate the tool's ability to monitor bed occupancy in the recovery unit given the admission rate of ED patients during the pandemic of Sars-Cov-2. These results give an interesting insight on the situation, providing decision makers with a powerful tool to establish an enlightened response to this situation.

Objective The objective of this study is twofold. First, we seek to understand the characteristics of the multimorbid population that needs hospital care by using all diagnoses information (ICD-10 codes) and two aggregated multimorbidity and frailty scores. Second, we use machine learning prediction models on these multimorbid patients characteristics to predict rehospitalization within 30 and 365 days and their length of stay. Methods This study was conducted on 8 882 anonymized patients hospitalized at the University Hospital of Saint-Étienne. A descriptive statistical analysis was performed to better understand the characteristics of the patient population. Multimorbidity was measured using raw diagnoses information and two specific scores based on clusters of diagnoses: the Hospital Frailty Risk Score and the Calderon-Larrañaga index. Based on these variables different machine learning models (Decision Tree, Random forest and k-nearest Neighbors) were used to predict near future rehospitalization and length of stay (LoS). Results The use of random forest algorithms yielded better performance to predict both 365 and 30 days rehospitalization and using the diagnoses ICD-10 codes directly was significantly more efficient. However, using the Calderon-Larrañaga’s clusters of diagnoses can be used as an efficient substitute for diagnoses information for predicting readmission. The predictive power of the algorithms is quite low on length of stay indicator. Conclusion Using machine learning techniques using patients’ diagnoses information and Calderon-Larrañaga’s score yielded efficient results to predict hospital readmission of multimorbid patients. These methods could help improve the management of care of multimorbid patients in hospitals.

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