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通过电子病历的改变促进实验室检测的更好利用:一所学术医疗中心的经验

Promoting improved utilization of laboratory testing through changes in an electronic medical record: experience at an academic medical center.

作者信息

Krasowski Matthew D, Chudzik Deborah, Dolezal Anna, Steussy Bryan, Gailey Michael P, Koch Benjamin, Kilborn Sara B, Darbro Benjamin W, Rysgaard Carolyn D, Klesney-Tait Julia A

机构信息

Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.

Department of Anesthesia, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.

出版信息

BMC Med Inform Decis Mak. 2015 Feb 22;15:11. doi: 10.1186/s12911-015-0137-7.

DOI:10.1186/s12911-015-0137-7
PMID:25880934
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4344785/
Abstract

This case study over time describes five years of experience with interventions to improve laboratory test utilization at an academic medical center. The high-frequency laboratory tests showing the biggest declines in order volume post intervention were serum albumin (36%) and erythrocyte sedimentation rate (17%). Introduction of restrictions for 170 high-cost send-out tests resulted in a 23% decline in order volume. Targeted interventions reduced mis-orders involving several "look-alike" tests: 1,25-dihydroxyvitamin D, 25-hydroxyvitamin D; manganese, magnesium; beta-2-glycoprotein, beta-2-microglobulin. Lastly, targeted alerts reduced duplicate orders of germline genetic testing and orders of hepatitis B surface antigen within 2 weeks of hepatitis B vaccination.

摘要

本案例研究历时五年,描述了在一家学术医疗中心为提高实验室检测利用率而采取干预措施的经验。干预后订单量下降幅度最大的高频实验室检测项目是血清白蛋白(36%)和红细胞沉降率(17%)。对170项高成本外送检测实施限制后,订单量下降了23%。针对性干预减少了涉及多项“相似”检测的错误订单:1,25-二羟基维生素D、25-羟基维生素D;锰、镁;β2糖蛋白、β2微球蛋白。最后,针对性警报减少了种系基因检测的重复订单以及乙肝疫苗接种后两周内乙肝表面抗原的订单。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7454/4344785/bd887c5304e1/12911_2015_137_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7454/4344785/306066ecc682/12911_2015_137_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7454/4344785/bd887c5304e1/12911_2015_137_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7454/4344785/306066ecc682/12911_2015_137_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7454/4344785/bd887c5304e1/12911_2015_137_Fig2_HTML.jpg

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Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts.使用机器学习来预测临床决策支持的依从性、减轻警报负担以及评估重复实验室检查医嘱警报。
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