FW-HTF-RM: Introducing Patient-Specific Therapy Profiles in Electronic Health Records for Guiding Treatment Selection in the Era of Genomic Medicine

临床医生(工人)对电子健康记录(EHR)系统(人-技术前沿)进行评级，该系统用于审查和记录患者的健康状况，并输入药物处方(工作)订单，可用性为F。具体地说，电子健康记录经常被视为护理的障碍，而不是促进高质量护理的工具。这在一定程度上是由于大量的EHR警报是自动生成的，在开出治疗疾病(如抑郁症)的药物时必须加以处理。这类警报通常针对从危及生命到轻微反应的潜在不良反应，是基于人群研究，并不是针对患者的。目前的电子病历警报也只建议“不做”，而没有提供关于“做什么”的指导(这是一个重大的知识差距)(例如，应该考虑使用哪种替代药物(S))。因此，临床医生花费大量时间处理无用的EHR警报(导致高度的工作压力和职业倦怠)，并采用代价高昂的“反复试验”方法来选择药物。临床医生需要一种便于护理的技术接口--一种无缝地为特定患者提供给定药物的疗效和不良药物反应的估计可能性的接口。“针对患者的药物电子病历警报”将促进患者护理(更快地缓解抑郁症)，促进临床医生和患者之间的共同决策(更多信息可随时用于个性化治疗)，并减少工作人员的压力和职业倦怠的风险(通过提高电子病历的可用性，改善人类技术前沿)。鉴于新药以前所未有的速度被发现，临床证据继续积累，表明为个体化治疗而开发的几种基因测试改善了患者的结果，并证明显著节省了医疗成本，因此该项目具有重大的公共卫生意义。教育活动包括为一门关于机器学习和基因组医学基础的新课程编制课程。研究人员还将让本科生和代表性不足的社区参与拟议的研究活动。该项目的总体目标是促进基于机器学习的预测分析与电子病历系统的集成，该系统使用基因组和临床数据为患者量身定制治疗。以下目标有助于实现首要目标：(1)开发一种多任务机器学习模型，该模型可以使用患者的基因组、临床和社会人口学数据同时预测药物治疗的疗效和相关的不良反应。将探索不同的预测方法，如任务聚类和任务关系，以提供最佳的预测性能。这项技术是通过使用来自Mayo Clinic Biobank和临床试验的患者数据实现的，并将在Mayo Clinic罗切斯特和佛罗里达校区的常规实践中在预期患者队列中得到验证；(2)进行一项“系统可用性研究”，以证明“针对患者的药物反应概况”(即疗效和不良反应)可以改善EHR的可用性，从而转化为减少工作压力，并使临床医生感受到附加值；以及(3)建立临床医生对旨在个体化治疗的基因组技术的附加值的看法，从而表征针对基因组定制的EHR药物警报的促进者和障碍。作为一个案例研究，该项目将利用梅奥诊所生物库中10,000多名患者的数据，将重点放在用于治疗严重抑郁障碍的抗抑郁药物上。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Clinicians (workers) rate electronic health record (EHR) systems (human-technology frontier), used to review and document patient's health status and enter orders for drug prescription (work), an 'F' for usability. Specifically, the EHR is often seen as a barrier to care, rather than a tool to facilitate high quality care. This is due, in part, to high volumes of EHR alerts that are automatically generated and must be addressed when prescribing medications to treat conditions (e.g., depression). Such alerts, which typically address potential adverse reactions ranging from life threatening to minor reactions, are based on population studies and are not patient-specific. Current EHR alerts also only advise what "not to do" and do not offer guidance (representing a significant knowledge gap) as to "what to do" (e.g., which alternative medication(s) should be considered instead). As a result, clinicians spend substantial amounts of time dealing with unhelpful EHR alerts (contributing to high work stress and burnout) and employ a costly "trial-and-error" approach to selecting drugs. Clinicians need a technology interface that facilitates care - one that seamlessly provides an estimated likelihood of efficacy and adverse drug reactions of a given medication for a particular patient. A "patient-specific drug EHR alert" would advance patient care (faster remission from depression), foster shared decision-making between clinicians and patients (more information readily available to individualizing therapy), and reduce worker stress and risk of burnout (improved human-technology frontier by improving EHR usability). This project is of significant public health importance given that new drugs are discovered at unprecedented rates and clinical evidence continues to accumulate showing that several genetic tests developed to individualize therapy have improved patient outcomes and demonstrated significant savings in healthcare costs. Education activities include a curriculum development for a new course on fundamentals of machine learning and genomic medicine. The researchers will also involve undergraduate and underrepresented community in the proposed research activities.The overarching goal of this project is to facilitate the integration of machine learning-based predictive analytics into EHR systems that use genomic and clinical data to tailor therapy for patients. The following objectives help achieve the overarching goal: (1) Develop a multi-task machine learning model that can simultaneously predict efficacy and associated adverse reactions to drug therapy, using patient's genomic, clinical and sociodemographic data. Different predictive approaches such as task clustering and task relation will be explored to provide the best predictive performance. This technology is enabled by the use of patient data from Mayo Clinic Biobank and clinical trials, and will be validated in a prospective patient cohort in routine practice at Mayo Clinic's Rochester and Florida campuses; (2) Conduct a "system usability study" to demonstrate that "patient-specific drug response profile" (i.e., efficacy and adverse reactions) improves EHR usability, which translates into reduced work stress, and perceived added value by clinicians; and (3) Establish clinician perceptions of added value in genomic technologies designed to individualize therapy, thereby characterizing facilitators and barriers of genomic-tailored EHR drug alerts. As a case study, this project will focus on antidepressant drugs used to treat major depressive disorder, leveraging data from over 10,000 patients in the Mayo Clinic Biobank.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.