Intelligent Histories: Detecting Personalized Risk with Longitudinal Surveillance

描述（由申请人提供）： 电子卫生信息系统中积累的大量纵向数据为改善医疗筛查和诊断提供了一个尚未开发的机会。然而，医生通常没有时间在短暂的临床接触中彻底审查历史记录，即使他们这样做，他们也可能发现很难快速识别多种类型数据的长期模式。因此，电子健康记录的全部潜力没有得到利用，并且经常错过从单一临床遭遇中不易诊断的状况。例如，虐待和抑郁症可能多年未被识别，因为它们被形成临床遭遇基础的其他急性疾病所掩盖，当回顾时，对纵向记录的审查可能会显示出可辨别的模式。NLM的战略愿景要求采用系统方法进行医疗保健，该方法使用下一代电子健康记录来促进以患者为中心的护理、自动化决策支持、用于患者监测的纵向记录以及警报和提醒的生成。该项目的目标是通过实现纵向医疗信息的全部潜力来改善医疗决策来响应这一呼吁。这将通过开发智能历史-个人纵向医疗信息的动态贝叶斯网络模型来实现。在为人口健康监测系统开发的方法的基础上，智能历史模型将被纳入个性化风险监测系统，该系统将主动监测患者的长期风险相关模式的纵向历史。该系统将以有针对性的、情境化的方式向临床医生提供信息，从而能够快速识别长期风险模式。这项工作将分四个阶段进行：（1）开发智能病史，贝叶斯网络风险模型，其中包括个人多年的纵向编码医疗信息，包括诊断，程序，处方和实验室结果。（2）扩展这些模型，使其包括时间趋势和关系的显式表示，包括发展基于马尔可夫模型的动态贝叶斯网络模型;（三）将这些模型集成到原型个性化风险监测系统中，该系统生成警报并向临床医生呈现患者纵向的定制视图。历史(4)进行形成性评价，以确定原型系统是否可以提高临床医生检测和估计临床风险的能力。我们寻求改善医疗决策，允许更早地检测临床状况，并促进更加个性化和系统化的医学方法。

DESCRIPTION (provided by applicant): Vast amounts of longitudinal data accumulating in electronic health information systems present an untapped opportunity to improve medical screening and diagnosis. Yet doctors typically do not have the time to thoroughly review historical records during a brief clinical encounter, and even when they do, they may find it difficult to rapidly identify long-term patterns across multiple types of data. As a result, the full potential of the electronic health record is not utilized, and conditions that are not easy to diagnose from a single clinical encounter are often missed. For example, abuse and depression may go unrecognized for years as they are masked by other acute conditions that form the basis of clinical encounters, when in retrospect, a review of the longitudinal record may show a discernable pattern. The NLM's Strategic Vision calls for a systems approach to health care that uses next generation electronic health records to facilitate patient-centric care, automated decision support, longitudinal records for patient monitoring, and generation of alerts and reminders. The goal of this project is to answer this call by realizing the full potential of longitudinal medical information to improve medical decision-making. This will be accomplished by developing Intelligent Histories - Dynamic Bayesian Network models of an individual's longitudinal medical information. Building on methods developed for population health surveillance systems, Intelligent History models will be incorporated into a personalized risk surveillance system that will proactively monitor patients' longitudinal histories for long-term risk-associated patterns. The system will present the information in a targeted, contextualized fashion to clinicians, enabling rapid identification of long-term patterns of risk. The work will be carried out in four stages: (1) Developing Intelligent Histories, Bayesian Network risk models that incorporate an individual's multi-year longitudinal coded medical information, including diagnoses, procedures, prescriptions, and laboratory results. The performance of these models will be evaluated and compared with other existing approaches; (2) Extending these models to include explicit representation of temporal trends and relationships including the development of Markov-model based Dynamic Bayesian Network models; (3) Integrating these models into a prototype personalized risk surveillance system that generates alerts and presents the clinician with a tailored view of a patient's longitudinal history. (4) Conducting a formative evaluation to determine whether the prototype system can improve clinicians' abilities to detect and estimate clinical risk. We seek to improve medical decision-making, allowing for earlier detection of clinical conditions, and facilitating a more personalized and systematic approach to medicine.