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Formal-Transfer In and Out of Stroke Care Units: An Analysis Using Bayesian Networks
Abstract
The positive impact of stroke care units (SCUs) on patient outcome has been previously reported. In this study, long-term stroke patients that are formally admitted to teaching-hospitals are compared with and without SCUs. The authors focus on the patients’ experience with ongoing care or formal transfers following current care as this cohort is often high users of the system with associated high costs. Bayesian Networks were employed to analyze routinely collected public-hospital administrative data. The results illustrate that the teaching-hospitals with SCUs, while achieving shorter length of stay, in fact deal with younger patients with lower overall patient complexity than non-SCU teaching-hospitals. Other differences include SCUs predominantly treating subarachnoid hemorrhages whereas the non-SCUs treat more cerebral infarctions. This study illustrates the power of Bayesian Networks to expose the nature of caseload and outcomes recorded in hospital-administrative data as a means to gain insight on current practice and create opportunities for benchmarking and improving care.
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