A Data-mining Approach to CAM Medication Reconciliation

描述(由申请人提供)：由于其复杂的、多种草药的性质，许多使用补充和替代医学(CAM)的患者并不完全了解他们的药物中所含的成分。缺乏对CAM的披露可能会将患者置于危险之中，因为一些草药产品可能与药物产生不良反应或导致有害的副作用。这项提案试图通过开发一种计算机辅助专家系统来解决这一挑战，该系统可以通过算法确定患者的CAM草药的内容。在没有可靠的患者回忆的情况下，设想的系统使用经过验证的分析技术和一组已知的数据-包括历史患者记录和患者报告的数据，如患者人口统计和症状详细信息-以统计方式生成处方列表。设想的分析引擎将是一个强大的临床决策支持工具，可增强患者安全和围绕CAM使用的患者与患者之间的沟通。第一阶段的活动将生成一个原型，用于评估一种常见的疾病：普通感冒。这一原型将为第二阶段的系统扩展提供坚实的基础，以涵盖广泛的疾病和状况。 与公共卫生相关：尽管补充和替代医学(CAM)疗法日益流行，但它们很少成为医疗专业人员和患者之间讨论的话题；患者与提供者的沟通和患者披露率因语言障碍而进一步复杂化(Eisenberg等人)。1993年)。缺乏关于CAM的沟通可能会将患者置于危险之中，因为一些草药产品可能与药物发生不良反应或导致有害的副作用(Kupiec等人。2005)。本应用程序中设想的计算机辅助专家系统将填补我们医疗保健系统在彻底记录患者用药历史中使用CAM的能力方面的一个重要空白，使提供者能够做出更明智的临床决策，并通过避免或更早检测到草药与药物的相互作用，提高患者的安全性和护理质量。

DESCRIPTION (provided by applicant): Due to its complex, multi-herb nature, many patients who use Complementary and Alternative Medicine (CAM) do not have full knowledge of the ingredients included in their remedies. Lack of disclosure about CAM can place patients at risk, as some herbal products can have adverse interactions with pharmaceuticals or cause harmful side effects. This proposal seeks to address that challenge by developing a computer-aided expert system that can algorithmically determine the content of a patient's CAM herbal remedy. In the absence of reliable patient recall, the envisioned system employs proven analytics techniques and a known set of data- including historical patient records and patient-reported data, such as patient demographic and symptomatic details-to statistically generate a prescription list. The envisioned analytics engine will be a powerful clinical decision support tool that enhances patient safety and patient-provider communication surrounding CAM use. Phase I activities will generate a prototype for evaluating one frequently encountered condition: the common cold. This prototype will provide a solid foundation for system expansion, in Phase II, to cover a broad range of diseases and conditions. PUBLIC HEALTH RELEVANCE: Despite their growing prevalence, Complementary and Alternative Medicine (CAM) therapies are seldom a topic of discussion between healthcare professionals and patients; patient-provider communication and patient disclosure rates are further complicated by the language barrier (Eisenberg et al. 1993). Lack of communication about CAM can place patients at risk, as some herbal products can have adverse interactions with pharmaceuticals or cause harmful side effects (Kupiec et al. 2005). The computer-aided expert system, as envisioned in this application, will fill a critical void in our healthcare system's ability to thoroughly document CAM usage in patient medication history, equip providers to make more informed clinical decisions, and-by avoidance or earlier detection of herb-drug interactions-improve patient safety and quality of care.