Medication management of patients with polypharmacy is highly complex. We aimed to validate a novel Artificial Pharmacology Intelligence (API) algorithm to optimize the medication review process in a comprehensive, personalized, and scalable way. The study was conducted on anonymized retrospective electronic health records (EHR) of 49 patients. Each patient's file was reviewed by the API system, a clinical pharmacist, and a judging committee. Validation was assessed by comparing the overall agreement of the judging committee (as the gold standard, blinded to the identity of the analyzer) to both the API system and clinical pharmacists' conclusions. Five medication-related problem (MRP) categories were assessed: duplication of therapy, age-related issues, incorrect dose, current side effects and future side effects' risk. For each category the overall validity parameters, agreement, positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity were analyzed. The agreement between the API system and the judging committee was 93.5 % (95 % CI 92.7–94.4), while the agreement between the clinical pharmacists and the judging committee was 73.9 % (95 % CI 72.5–75.3). The PPV was 92.2 % (90.9–93.5) and NPV was 94.2 % (93.1–95.2) for the API system and 76.3 % (69.8–82.8) and 73.5 % (72.3–74.8) respectively for the clinical pharmacists. AI systems can equip clinicians with sophisticated tools and scale manual processes such as comprehensive medication reviews, thus reducing MRPs and drug-related hospitalizations related to multidrug treatments. The API system validated in this study provided comprehensive, multidrug, multilayered analysis intended to bridge the innate complexity of personalized polypharmacy treatment. The API system was validated as a tool for providing actionable clinical insights non-inferior to a manual clinical review of a clinical pharmacist. The API system showed promising results in reducing MRPs.

中文翻译：

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验证用于管理多重用药患者的新型人工药理学智能 (API) 系统

多重用药患者的药物管理非常复杂。我们的目标是验证一种新颖的人工药理学智能 (API) 算法，以全面、个性化和可扩展的方式优化药物审评流程。该研究针对 49 名患者的匿名回顾性电子健康记录 (EHR) 进行。每个患者的档案均由 API 系统、临床药剂师和评审委员会审查。通过比较评审委员会（作为黄金标准，不了解分析仪的身份）与 API 系统和临床药剂师结论的总体一致性来评估验证。评估了五个与药物相关的问题（MRP）类别：重复治疗、年龄相关问题、不正确的剂量、当前的副作用和未来副作用的风险。对于每个类别，分析总体有效性参数、一致性、阳性预测值（PPV）、阴性预测值（NPV）、敏感性和特异性。 API系统和评审委员会之间的一致性为93.5%（95% CI 92.7–94.4），而临床药师和评审委员会之间的一致性为73.9%（95% CI 72.5–75.3）。 API系统的PPV为92.2%（90.9-93.5），NPV为94.2%（93.1-95.2），临床药师分别为76.3%（69.8-82.8）和73.5%（72.3-74.8）。人工智能系统可以为临床医生配备先进的工具，并扩展综合药物审查等手动流程，从而减少与多药治疗相关的 MRP 和药物相关住院治疗。本研究中验证的 API 系统提供了全面的、多药物的、多层的分析，旨在弥合个性化多药治疗固有的复杂性。 API 系统被验证为一种提供可行临床见解的工具，其效果不逊于临床药剂师的手动临床审查。 API 系统在降低 MRP 方面显示出可喜的成果。