Kogan Alexandra, Tu Samson W, Peleg Mor
University of Haifa, Haifa, Israel.
Stanford University, Stanford, CA, USA.
AMIA Annu Symp Proc. 2018 Dec 5;2018:690-699. eCollection 2018.
Computer-interpretable guidelines (CIGs) are based on clinical practice guidelines, which typically address a single morbidity. However, most of the aging population suffers from multiple morbidities. Currently, there is no demonstrated effective mechanism that integrates recommendations from multiple CIGs. We are developing a goal-based method that utilizes knowledge of drugs' physiological effects and therapeutic usage to combine knowledge from CIGs. It incrementally detects interactions and plans non-contradicting therapies. Our algorithm uses patterns to check consistency and respond to events, including data enquiries, diagnoses, adverse events, recommended medications, tests, and goals. Our method utilizes existing standards and CIG tools, including the Fast Healthcare Interoperability Resources (FHIR) patient data model, SNOMED-CT, and the PROforma CIG formalism with its Alium knowledge-engineering environment and PROforma enactment engine. We demonstrate our approach using a case study involving two clinical guidelines with templates for responding to a new goal and to a medication request that causes an inconsistency which can be automatically detected and resolved based on the knowledge of the two CIGs.
计算机可解释指南（CIGs）基于临床实践指南，后者通常针对单一疾病。然而，大多数老年人群患有多种疾病。目前，尚无已证明有效的机制来整合来自多个CIGs的建议。我们正在开发一种基于目标的方法，该方法利用药物的生理效应和治疗用途的知识来结合来自CIGs的知识。它逐步检测相互作用并规划不矛盾的治疗方案。我们的算法使用模式来检查一致性并对事件做出响应，包括数据查询、诊断、不良事件、推荐药物、检查和目标。我们的方法利用现有标准和CIG工具，包括快速医疗保健互操作性资源（FHIR）患者数据模型、SNOMED-CT以及具有Alium知识工程环境和PROforma制定引擎的PROforma CIG形式主义。我们通过一个案例研究展示了我们的方法，该案例涉及两个临床指南，其中包含用于响应新目标和药物请求的模板，该药物请求会导致不一致情况，而这种不一致情况可基于两个CIGs的知识自动检测并解决。
