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评估自动患者严重程度指数的经验教训。

Lessons from evaluating an automated patient severity index.

作者信息

Gibson R F, Haug P J, Horn S D

机构信息

Division of Information Services, Providence Health System, Portland, OR, USA.

出版信息

J Am Med Inform Assoc. 1996 Sep-Oct;3(5):349-57. doi: 10.1136/jamia.1996.97035026.

Abstract

OBJECTIVE

To report lessons learned from evaluation of an automated interface between a hospital clinical information system and a severity of illness index.

DESIGN

A system was developed to convert coded electronic patient findings from the HELP System at LDS Hospital into the attributes used by the Computerized Severity Index (CSI) to calculate a severity of illness score. Performance was assessed by comparing the automated CSI score with the manual CSI score (from paper chart review) and by evaluating changes introduced by augmenting the manual CSI score with verified patient data discovered by the automated CSI method.

MEASUREMENTS

The strengths and weaknesses of each method are presented.

RESULTS

The automated CSI score matched the manual CSI score in 61% of the cases. Sources of errors were analyzed. When the automated score was in error, two-thirds of the time it was due to the lack of codes in the HELP system representing CSI concepts; one-third of the time it was due to nurses not using established HELP system codes. Surprisingly, significant problems were also discovered in the manual system, making it difficult to define a "gold standard".

CONCLUSIONS

Automated computerized severity indices have great potential for future applicability once their performance exceeds that of the time-consuming manual chart review method. Neither automated nor manual methods are adequate at the present time. This area remains a fertile ground for future research.

摘要

目的

报告对医院临床信息系统与疾病严重程度指数之间的自动化接口进行评估所获得的经验教训。

设计

开发了一个系统,用于将LDS医院HELP系统中编码的电子患者检查结果转换为计算机化严重程度指数(CSI)用于计算疾病严重程度评分的属性。通过将自动化CSI评分与手动CSI评分(来自纸质病历审查)进行比较,并通过评估用自动化CSI方法发现的经核实的患者数据增强手动CSI评分所带来的变化来评估性能。

测量

介绍了每种方法的优缺点。

结果

在61%的病例中,自动化CSI评分与手动CSI评分相符。分析了误差来源。当自动化评分出现错误时,三分之二的情况是由于HELP系统中缺乏代表CSI概念的代码;三分之一的情况是由于护士未使用既定的HELP系统代码。令人惊讶的是,在手动系统中也发现了重大问题,这使得难以定义“金标准”。

结论

一旦自动化计算机化严重程度指数的性能超过耗时的手动病历审查方法,其在未来的适用性具有巨大潜力。目前,自动化方法和手动方法都不够完善。该领域仍是未来研究的沃土。

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Lessons from evaluating an automated patient severity index.评估自动患者严重程度指数的经验教训。
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