以数据为导向的方法识别潜在的实验室过度使用在一般内科 (GIM) 住院患者
Adina S Weinerman1,2, Yishan Guo3, Sudipta Saha3
1Department of Medicine, University of Toronto, Toronto, Ontario, Canada Adina.Weinerman@sunnybrook.ca.
BMJ open quality
|July 26, 2023
概括
一种新的数据驱动方法通过结合成本,异常结果率和医生变异来识别实验室测试过度使用. 这种方法有助于优先测试减少,改善患者护理质量和减少医疗保健浪费.
科学领域:
- 临床实验室科学 临床实验室科学
- 医疗服务研究 医疗服务研究
- 改善医疗保健质量 改善医疗保健质量
背景情况:
- 减少过度使用实验室测试对于高质量,以患者为中心的护理至关重要.
- 确定减少低价值检测的优先事项在医疗保健中是一个重大挑战.
研究的目的:
- 开发一种简单的,数据驱动的方法来识别实验室测试过度使用的潜在来源.
- 该方法结合了总成本,异常结果的比例和医生水平测试利用率的变化.
主要方法:
- 在加拿大多伦多的三个学术医院进行了一项多中心的回顾性研究.
- 分析了2010年4月至2017年10月期间106,813例通用内科住院病人的数据.
- 测试的排名是基于累积成本,异常结果的比例和医生级别的变化.
主要成果:
- 在所有地点,21次测试的累计成本超过了15,400美元.
- 血电解质是最昂贵的测试;红细胞叶酸具有最低的异常结果率 (0.2%);抗脂抗体显示了最高的医生水平变化 (CV 3.08).
- 对过度使用潜力的前五个测试是乳酸,抗脂抗体,,素和部分血栓形成时间.
结论:
- 综合成本,异常结果率和医生变异的多维,数据驱动的方法可以指导干预措施,以减少实验室测试的过度使用.
- 实施这些战略对于促进高价值,以患者为中心的护理和优化资源配置至关重要.
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