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监测监测器:临床事件监测器的自动统计跟踪

Monitoring the monitor: automated statistical tracking of a clinical event monitor.

作者信息

Hripcsak G

机构信息

Center for Medical Informatics, Columbia-Presbyterian Medical Center, New York, NY 10032.

出版信息

Comput Biomed Res. 1993 Oct;26(5):449-66. doi: 10.1006/cbmr.1993.1032.

Abstract

At Columbia-Presbyterian Medical Center, a clinical event monitor processes a set of rules called Medical Logic Modules (MLMs), which generate messages (interpretations, warnings, suggestions) for health care providers. The automated statistical tracker (AST) monitors the operation of the clinical event monitor for the purpose of detecting malfunctions in MLMs or in the clinical event monitor itself. The AST follows the number of messages generated by each MLM each day and issues an alert to a system administrator if the current count of messages seems unusual compared to the MLM's past activity. The AST is based upon a combination of Poisson and normal distributions. The AST was implemented using Unix shell scripts and put into operation. Of two malfunctions that occurred during a prospective study of 12 MLMs over 85 days, the AST automatically detected one that might otherwise have gone undetected, and the system administrator detected the other during routine review of the AST's daily report. The AST's performance was compared to that of five human subjects, and it was found to rank third among the six total subjects. The AST generated eight false-positive alerts during the study period (false-positive rate = 0.009 alerts/MLM day); seven of these were also picked by the human subjects. Subsequent experience has proven the AST to be useful and efficient for a system with 20 to 60 MLMs.

摘要

在哥伦比亚长老会医学中心,临床事件监测器会处理一组称为医学逻辑模块(MLM)的规则,这些规则会为医疗保健提供者生成消息(解释、警告、建议)。自动统计跟踪器(AST)监测临床事件监测器的运行情况,以检测医学逻辑模块或临床事件监测器本身是否出现故障。AST会跟踪每个医学逻辑模块每天生成的消息数量,如果与该医学逻辑模块过去的活动相比,当前消息计数显得异常,就会向系统管理员发出警报。AST基于泊松分布和正态分布的组合。AST是使用Unix shell脚本实现并投入运行的。在对12个医学逻辑模块进行85天的前瞻性研究期间发生的两次故障中,AST自动检测到一次,否则可能无法被发现,而系统管理员在对AST每日报告的例行审查中发现了另一次。将AST的性能与五名人类受试者的性能进行了比较,结果发现它在六个受试者中排名第三。在研究期间,AST产生了8次误报警报(误报率 = 0.009次警报/医学逻辑模块·天);其中7次也被人类受试者检测到。后续经验证明,对于一个有20到60个医学逻辑模块的系统,AST是有用且高效的。

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