Lenert L A, Cher D J
Health Services Research and Development, San Diego Veterans Administration Medical Center and the University of California-San Diego, 92161, USA.
J Am Med Inform Assoc. 1999 Sep-Oct;6(5):412-9. doi: 10.1136/jamia.1999.0060412.
Describe and evaluate an Internet-based approach to patient decision support using mathematical models that predict the probability of successful treatment on the basis of meta-analytic summaries of the mean and standard deviation of symptom response.
An Internet-based decision support tool was developed to help patients with benign prostatic hypertrophy (BPH) determine whether they wanted to use alpha blockers. The Internet site incorporates a meta-analytic model of the results of randomized trials of the alpha blocker terazosin. The site describes alternative treatments for BPH and potential adverse effects of alpha blockers. The site then measures patients' current symptoms and desired level of symptom reduction. In response, the site computes and displays the probability of a patient's achieving his objective by means of terazosin or placebo treatment.
Self-identified BPH patients accessing the site over the Internet.
Patients' perceptions of the usefulness of information.
Over a three-month period, 191 patients who were over 50 years of age and who reported that they have BPH used the decision support tool. Respondents had a mean American Urological Association (AUA) score of 18.8 and a desired drop in symptoms of 10.1 AUA points. Patients had a 40 percent chance of achieving treatment goals with terazosin and a 20 percent chance with placebo. Patients found the information useful (93 percent), and most (71 percent) believed this type of information should be discussed before prescribing medications.
Interactive meta-analytic summary models of the effects of pharmacologic treatments can help patients determine whether a treatment offers sufficient benefits to offset its risks.
描述并评估一种基于互联网的患者决策支持方法,该方法使用数学模型,根据症状反应的均值和标准差的荟萃分析总结来预测成功治疗的概率。
开发了一种基于互联网的决策支持工具,以帮助良性前列腺增生(BPH)患者确定他们是否想使用α受体阻滞剂。该网站纳入了α受体阻滞剂特拉唑嗪随机试验结果的荟萃分析模型。该网站描述了BPH的替代治疗方法以及α受体阻滞剂的潜在不良反应。然后,该网站测量患者当前的症状以及期望的症状减轻程度。作为回应,该网站计算并显示患者通过特拉唑嗪或安慰剂治疗实现其目标的概率。
通过互联网访问该网站的自我认定为BPH的患者。
患者对信息有用性的看法。
在三个月的时间里,191名年龄超过50岁且报告患有BPH的患者使用了该决策支持工具。受访者的美国泌尿外科学会(AUA)平均评分为18.8分,期望的症状减轻幅度为10.1个AUA评分点。患者使用特拉唑嗪实现治疗目标的概率为40%,使用安慰剂的概率为20%。患者认为这些信息有用(93%),并且大多数人(71%)认为在开药前应讨论此类信息。
药物治疗效果的交互式荟萃分析总结模型可以帮助患者确定一种治疗是否能提供足够的益处以抵消其风险。