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Evaluating drug prescribing in a large ambulatory population: application of an embedded expert system.

机译:在庞大的非流动人群中评估药物处方:嵌入式专家系统的应用。

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摘要

DUR is a process of problem detection and intervention designed to improve the quality and economy of drug prescribing. Retrospective DUR attempts to detect and address patterns of prescribing that might be indicative of inappropriate therapy. When the process is extended to a largely ambulatory population such as Medicaid beneficiaries, a number of complications are introduced due to the large numbers of patients and sparsity of data. In order to examine the impact of implementing a Medicaid DUR program, we developed a system that would apply screening criteria to prescription claims. It has been used to screen prescribing of groups of two antihypertensive drugs in the 1990 Maryland Medicaid population for 177,409 Medicaid eligible individuals. Potentially significant problems were detected with respect to dosing, duplication of therapeutic agents and drug interactions. The system represents, we believe, a significant improvement in the ability to detect and report prescribing decisions by increasing the specificity of the detection system. By the application of this system to a set of real-world data, we have demonstrated that it is feasible to implement such a system and derive results that are potentially useful in reducing the incidence of inappropriate physician decision-making.
机译:DUR是一个问题检测和干预过程,旨在提高药物处方的质量和经济性。回顾性DUR尝试检测并解决可能指示不适当治疗的处方方式。当该过程扩展到诸如医疗补助受益人之类的非卧床活动人群时,由于大量患者和数据稀疏,引入了许多并发症。为了检查实施Medicaid DUR计划的影响,我们开发了一种将筛选标准应用于处方药索赔的系统。它已被用来筛选1990年马里兰医疗补助人群中针对177,409名医疗补助合格个人的两种降压药处方。在剂量,治疗剂的重复和药物相互作用方面检测到潜在的重大问题。我们认为,该系统通过提高检测系统的特异性,在检测和报告处方决策的能力方面取得了显着改善。通过将该系统应用于一组真实世界的数据,我们证明了实施这样的系统并获得可能对减少不适当的医师决策发生率有用的结果是可行的。

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