Suppr超能文献

Relationship between tort claims and patient incident reports in the Veterans Health Administration.

作者信息

Schmidek J M, Weeks W B

机构信息

Field Office of VA's National Center for Patient Safety, White River Junction, VT 05009, USA.

出版信息

Qual Saf Health Care. 2005 Apr;14(2):117-22. doi: 10.1136/qshc.2004.010835.

Abstract

OBJECTIVE

The Veterans Health Administration's patient incident reporting system was established to obtain comprehensive data on adverse events that affect patients and to act as a harbinger for risk management. It maintains a dataset of tort claims that are made against Veterans Administration's employees acting within the scope of employment. In an effort to understand the thoroughness of reporting, we examined the relationship between tort claims and patient incident reports (PIRs).

METHODS

Using social security and record numbers, we matched 8260 tort claims and 32 207 PIRs from fiscal years 1993-2000. Tort claims and PIRs were considered to be related if the recorded dates of incident were within 1 month of each other. Descriptive statistics, odds ratios, and two sample t tests with unequal variances were used to determine the relationship between PIRs and tort claims.

RESULTS

4.15% of claims had a related PIR. Claim payment (either settlement or judgment for plaintiff) was more likely when associated with a PIR (OR 3.62; 95% CI 2.87 to 4.60). Payment was most likely for medication errors (OR 8.37; 95% CI 2.05 to 73.25) and least likely for suicides (OR 0.25; 95% CI 0.11 to 0.55).

CONCLUSIONS

Although few tort claims had a related PIR, if a PIR was present the tort claim was more likely to result in a payment; moreover, the payment was likely to be higher. Underreporting of patient incidents that developed into tort claims was evident. Our findings suggest that, in the Veterans Health Administration, there is a higher propensity to both report and settle PIRs with bad outcomes.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验