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Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
Published on: January 15, 2017
Rationale and design of individualized quality improvement based on the Computer Analysing system to improve Stroke
Yi Chen1, Wansi Zhong1, Xiaoxian Gong1
1Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88# Jiefang Road, Hangzhou, 310009, China.
Background:
Guideline-based medical care has been identified to improve outcomes in stroke. However, data acquisition and medical quality management during hospital stay still need to be improved in China. We have developed a computer-based medical data collecting system, together with automated calculation of key performance indicators (KPIs) and regular individualized education, and thus aim to explore whether it can improve the medical care quality of acute ischemic stroke (AIS) during hospital stay in stroke centers.
Methods:
The individualized quality improvement based on the Computer Analysing system to improve Stroke management quality Evaluation (CASE) trial is a prospective, multicenter, historical control study among 30 stroke centers in China. In this trial, the data is directly extracted from the saved original medical record of each AIS patient during hospital stay, regardless of different Electronic Medical Record System (EMRS) in each center. Then, the automated calculation of KPIs and the regular education via teleconference per month allow the clinicians to examine the causes of non-compliance of guideline-based care and develop programs to decrease their frequency.
Discussion:
We compare KPIs between pre-intervention stage and post-intervention stage (without or with education) among stroke centers. If proved effective, this approach might be generalized around China and even worldwide, where a unified EMRS is difficult to be applied and in-patient care needs to be improved.
Trial Registration:
ClinicalTrials.gov NCT03684629 . Registered on 9 December 2018. Retrospectively registered.
