提高全科医生骨质疏松症治疗率的质量改进项目。

Patrick Bolton, Markus Seibel, Daniel Moses, Michael Moore, Brendan Goodger
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引用次数: 0

摘要

本研究测试了一种改善骨质疏松性骨折患者管理的模式。方法通过计算机化的近自然语言处理技术,从影像报告中识别出可能因骨质疏松导致骨折的患者。协调人员将发现的情况通知转诊的全科医生,并提供后续服务,提醒全科医生需要进行管理。这为评估全科医生采取的行动提供了机会。结果近自然语言处理技术有效地检测出了有骨质疏松症风险的患者的骨折情况。全科医生表示,他们正在对超过 40% 的已识别患者进行骨质疏松症管理。在通知全科医生的同时,骨质疏松症的管理也略有增加。结论信息技术可以识别具有骨质疏松症等重要临床风险的患者群体。让全科医生参与以最佳方式应对这一风险的方法尚待开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A quality improvement project to increase treatment rates of osteoporosis in general practice.

Objective This study tests a model to improve the management of patients with an osteoporotic fracture. Methods Patients with fractures potentially due to osteoporosis were identified from imaging reports using computerised near natural language processing. A coordinator notified the referring GP about the finding and provided follow-up to remind GPs of the need for management. This provided an opportunity to assess action taken by the GP. Results Near natural language processing efficiently detected fractures in patients at risk of osteoporosis. GPs reported that they are managing osteoporosis in over 40% of patients identified. Notification of GPs coincided with a small increase in osteoporosis management. Conclusion Information technology can identify patient populations with clinically important risks such as osteoporosis. Methods to engage GPs to optimally address this risk have yet to be developed.

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