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This case study is concerned with analysing policies for managing the blood inventory system in a typical UK hospital supplied by a regional blood centre. The objective of the project is to improve procedures and outcomes by modelling the entire supply chain for that hospital, from donor to recipient. The supply chain of blood products is broken down into material flows and information flows. Discrete-event simulation is used to determine ordering policies leading to reductions in shortages and wastage, increased service levels, improved safety procedures and reduced costs, by employing better system coordination. In this paper we describe the model and present results for a representative medium-sized hospital. The model can be used by both the National Blood Service and by hospital managers as a decision support tool to investigate different procedures and policies.

This case study is concerned with analysing policies for managing the blood inventory system in a typical UK hospital supplied by a regional blood centre. The objective of the project is to improve procedures and outcomes by modelling the entire supply chain for that hospital, from donor to recipient. The supply chain of blood products is broken down into material flows and information flows. Discrete-event simulation is used to determine ordering policies leading to reductions in shortages and wastage, increased service levels, improved safety procedures and reduced costs, by employing better system coordination. In this paper we describe the model and present results for a representative medium-sized hospital. The model can be used by both the National Blood Service and by hospital managers as a decision support tool to investigate different procedures and policies.

Introduction frailty is common in older adults and is associated with increased health and social care use. Longitudinal information is needed on population-level incidence, prevalence and frailty progression to plan services to meet future population needs. Methods retrospective open cohort study using electronic health records of adults aged ≥50 from primary care in England, 2006–2017. Frailty was calculated annually using the electronic Frailty Index (eFI). Multistate models estimated transition rates between each frailty category, adjusting for sociodemographic characteristics. Prevalence overall for each eFI category (fit, mild, moderate and severe) was calculated. Results the cohort included 2,171,497 patients and 15,514,734 person-years. Frailty prevalence increased from 26.5 (2006) to 38.9% (2017). The average age of frailty onset was 69; however, 10.8% of people aged 50–64 were already frail in 2006. Estimated transitions from fit to any level of frailty were 48/1,000 person-years aged 50–64, 130/1,000 person-years aged 65–74, 214/1,000 person-years aged 75–84 and 380/1,000 person-years aged ≥ 85. Transitions were independently associated with older age, higher deprivation, female sex, Asian ethnicity and urban dwelling. Mean time spent in each frailty category decreased with age, with the longest period spent in severe frailty at all ages. Conclusions frailty is prevalent in adults aged ≥50 and time spent in successive frailty states is longer as frailty progresses, resulting in extended healthcare burden. Larger population numbers and fewer transitions in adults aged 50–64 present an opportunity for earlier identification and intervention. A large increase in frailty over 12 years highlights the urgency of informed service planning in ageing populations.

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