Barry H C, Ebell M H
Department of Family Practice, Michigan State University, College of Human Medicine, East Lansing, USA.
Endocrinol Metab Clin North Am. 1997 Mar;26(1):45-65. doi: 10.1016/s0889-8529(05)70233-4.
We have demonstrated using several examples how different test characteristics can be used to assist clinicians in making better decisions for their patients. These probabilistic models may seem confusing and difficult to implement. Some general rules may help, such as SnNout and SpPin. Clinicians should know the test characteristics and decision rules for the acute problems they may face. For chronic conditions, advanced planning may be helpful. Electronic medical record systems may be able to incorporate these at the user interface. The improvements in hand-held computers may bring clinical decision-support systems directly to the point of service. We may also begin to see laboratories report test characteristics for important conditions as likelihood ratios (we already see estimates of the risk of heart disease corresponding to different lipid ratios). We also suspect that the medical literature will report likelihood ratios more frequently. As practice networks develop more sophisticated disease-tracking mechanisms, clinicians will be able to obtain estimates of disease prevalence more appropriate to their practice. Ultimately, for physicians to make better decisions, appropriate data are needed, including accurate estimates of test characteristics and of disease probability.
我们通过几个例子展示了如何利用不同的检验特征来帮助临床医生为患者做出更好的决策。这些概率模型可能看起来令人困惑且难以实施。一些通用规则可能会有所帮助，比如“真阴性排除”和“真阳性纳入”。临床医生应该了解他们可能面临的急性问题的检验特征和决策规则。对于慢性病，提前规划可能会有帮助。电子病历系统或许能够在用户界面纳入这些内容。手持电脑的改进可能会将临床决策支持系统直接带到服务点。我们或许还会开始看到实验室将重要病症的检验特征报告为似然比（我们已经看到了与不同血脂比值相对应的心脏病风险估计）。我们也怀疑医学文献会更频繁地报告似然比。随着实践网络发展出更复杂的疾病追踪机制，临床医生将能够获得更适合其实践的疾病患病率估计值。最终，为了让医生做出更好的决策，需要合适的数据，包括检验特征和疾病概率的准确估计值。
