Health status assessment using reverse supply chain data
2010 (English)In: Management Research Review, ISSN 2040-8269, E-ISSN 2040-8277, Vol. 33, no 2, 111-122 p.Article in journal (Refereed) Published
Purpose – The purpose of this paper is to suggest the use of reverse medical supply chain data to infer changes of a population's health status with regard to a focal disease. It includes a detailed illustration of how health status information can be obtained from drug reverse chains.
Design/methodology/approach – A Bayesian dynamical model linking drug reverse supply chain data to relevant health status indicators with regard to a focal disease is developed. A detailed implementation of the model on computer‐simulated data is considered. The predictive ability of the methodology is also assessed using out‐of‐sample Monte Carlo‐based predictive analysis.
Findings – The results substantiate the good fit of the model to the empirical data.
Research limitations/implications – Difficulty in obtaining actual return data and in selecting appropriate health status indicators. The correspondence disease‐drug is typically not one‐to‐one. Experts' opinion is required in setting up suitable mixing weights as many drugs may inform the health status relative to a given disease and vice versa.
Practical implications – Reverse logistics data may contain potential information, and this is not exclusive to medical chains.
Originality/value – The paper's suggestions tend to reinforce the notion that supply chain data may be used in many unsuspected settings. Solutions to issues of immediate concern in public health require multidisciplinary cooperation, and this paper shows how supply chain management can contribute. It is believed that the potential of reverse chain data in the health status prospect has previously hardly ever been pointed out.
Place, publisher, year, edition, pages
2010. Vol. 33, no 2, 111-122 p.
Monte Carlo methods, Predictive process, Supply chain management, Health services, Drugs, Medical management
IdentifiersURN: urn:nbn:se:hj:diva-29865DOI: 10.1108/01409171011015801OAI: oai:DiVA.org:hj-29865DiVA: diva2:926087