five

Using Computerized Data to Identify Adverse Drug Events in Outpatients

收藏
PubMed Central2026-05-16 收录
下载链接:
https://pmc.ncbi.nlm.nih.gov/articles/PMC131033/
下载链接
链接失效反馈
官方服务:
资源简介:
Objective: To evaluate the use of a computer program to identify adverse drug events (ADEs) in the ambulatory setting and to evaluate the relative contribution of four computer search methods for identifying ADEs, including diagnosis codes, allergy rules, computer event monitoring rules, and text searching. Design: Retrospective analysis of one year of data from an electronic medical record, including records for 23,064 patients with a primary care physician, of whom 15,665 actually came for care. Measurement: Presence of an ADE; sensitivity and specificity of computer searches for ADE. Results: The computer program identified 25,056 incidents, which were associated with an estimated 864 (95 percent confidence interval [CI], 750–978) ADEs. Thus, the ADE rate was 5.5 (CI, 5.2–5.9) per 100 patients coming for care. Furthermore, in 79 (CI, 68–89) ADEs, the patient required hospitalization, resulting in an estimated rate of 3.4 (CI, 2.7–4.3) admissions per 1,000 patients. The sensitivity of the search methods for identifying ADEs was estimated to be 58 (CI, 18–98) percent, and the estimated specificity was 88 (CI, 87–88) percent. The positive predictive value was 7.5 (CI, 6.5–8.5) percent, and the negative predictive value was 99.2 (CI, 95.5–99.98) percent. Compared with age and gender-matched controls with no positive screen, patients with ADEs had twice as many outpatient visits and were taking nearly three times as many drugs. Antihypertensives, ACE-inhibitors, antibiotics, and diuretics were associated with 56 (CI, 47–65) percent of ADEs. Among ADEs, 23 (CI, 16–32) percent were lifethreatening or serious, and 38 (CI, 29–47) percent were judged preventable. Conclusion: Computerized search programs can detect ADEs, and free-text searches were especially useful. Adverse drug events were frequent, and admissions were not rare, although most hospitals today do not identify them. Thus, such detection programs demonstrate “value-added” for the electronic record and may be useful for directing and assessing the impact of quality improvement efforts.
提供机构:
Oxford University Press
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作