Through a comparative study of six different Italian hospitals, the paper develops and tests a framework to analyze hospital-wide patient flow performance. The framework adopts a system-wide approach to patient flow management and is structured around three different levels: 1) the hospital, 2) the pipelines (possible patient journeys within the hospital) and 3) the production units (physical spaces, such as operating rooms, where hospital production takes places). The focus groups and the data analysis conducted within the study support that the model is an appropriate tool to investigate hospital-wide implications of patient flow problems. Finally, the paper provides evidence supporting some explanations of other authors about the causes of hospital patient flow problems. Particularly, while shortage of capacity does not seem to be a relevant driver, our data shows that patient flow variability caused by a bad allocation of capacity does represent a key problem. Another aspect that is important to address is represented by the lack of coordination between different pipelines and production units. Finally, the problem of overlapping between elective and unscheduled cases can be solved by setting aside a certain level of capacity to meet the unscheduled demand since urgent/unscheduled cases turned out to be less variable at aggregate level and much more predictable compared with elective cases.
A framework to analyze hospital-wide patient flows logistics: evidence from an Italian comparative study
Villa S.;Prenestini A.;Giusepi I.
2014
Abstract
Through a comparative study of six different Italian hospitals, the paper develops and tests a framework to analyze hospital-wide patient flow performance. The framework adopts a system-wide approach to patient flow management and is structured around three different levels: 1) the hospital, 2) the pipelines (possible patient journeys within the hospital) and 3) the production units (physical spaces, such as operating rooms, where hospital production takes places). The focus groups and the data analysis conducted within the study support that the model is an appropriate tool to investigate hospital-wide implications of patient flow problems. Finally, the paper provides evidence supporting some explanations of other authors about the causes of hospital patient flow problems. Particularly, while shortage of capacity does not seem to be a relevant driver, our data shows that patient flow variability caused by a bad allocation of capacity does represent a key problem. Another aspect that is important to address is represented by the lack of coordination between different pipelines and production units. Finally, the problem of overlapping between elective and unscheduled cases can be solved by setting aside a certain level of capacity to meet the unscheduled demand since urgent/unscheduled cases turned out to be less variable at aggregate level and much more predictable compared with elective cases.
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