Intelligent Histories: Detecting Personalized Risk with Longitudinal Surveillance

智能历史：通过纵向监控检测个性化风险

• 批准号: 8249941
• 负责人: Ben Y Reis
• 金额: $ 28.85万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8249941
• 关键词: Accident and Emergency department; Acute; Address; Affect; Boston; Calibration; California; Caring; Case Study; Clinical; Code; Computer software; Data; Databases; Decision Making; Development; Diagnosis; Discrimination; Early Diagnosis; Early treatment; Electronic Health Record; Electronics; Environment; Evaluation; Event; Expert Systems; Frequencies; Generations; Goals; Gold; Grouping; Health; Health Information System; Healthcare; Hospitalization; Hospitals; Human; Imagery; Individual; Interview; Laboratories; Lead; Link; Maps; Masks; Massachusetts; Medical; Medical History; Medical Records; Medicine; Mental Depression; Methods; Modeling; Nature; Outcome; Patient Monitoring; Patients; Pattern; Pediatric Hospitals; Performance; Physicians; Procedures; Process; Public Health; Publishing; Reader; Recording of previous events; Records; Research; Research Design; Risk; Risk Estimate; Risk Factors; Screening procedure; Site; Specialist; Spottings; Staging; System; Time; Vision; Work; base; clinical phenotype; computer based statistical methods; design; end of life; experience; high risk; improved; markov model; mortality; network models; next generation; open source; population health; prescription procedure; programs; prototype; trend

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.

描述（由申请人提供）： 电子健康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尚未开发的机会。然而，医生通常没有时间在短暂的临床相遇中彻底审查历史记录，即使他们这样做，他们也可能会发现很难迅速识别多种数据的长期模式。结果，没有利用电子健康记录的全部潜力，并且通常会错过单个临床相遇不容易诊断的条件。例如，虐待和抑郁症可能多年来无法被识别，因为它们被构成临床相遇基础的其他急性疾病掩盖，回想起来，对纵向记录的审查可能会显示出可分辨的模式。 NLM的战略愿景要求采用系统的医疗保健方法，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，患者监测的纵向记录以及发起警报和提醒。该项目的目的是通过意识到纵向医学信息的全部潜力来改善医疗决策，以接听此通话。这将通过开发智能历史 - 动态的贝叶斯网络模型的纵向医学信息来实现。在为人口健康监视系统开发的方法的基础上，智能历史模型将被纳入个性化的风险监视系统中，该模型将主动监测患者的纵向历史，以实现长期风险相关模式。该系统将以有针对性的，上下文化的方式向临床医生展示信息，从而快速识别长期风险模式。这项工作将分为四个阶段：（1）开发智能历史，贝叶斯网络风险模型，其中包含个人的多年纵向编码医学信息，包括诊断，程序，处方和实验室结果。这些模型的性能将被评估并与其他现有方法进行比较； （2）将这些模型扩展到包括时间趋势和关系的明确表示，包括基于马尔可夫模型的动态贝叶斯网络模型的发展； （3）将这些模型集成到原型的个性化风险监视系统中，该系统生成警报，并为临床医生提供对患者纵向病史的量身定制的视图。 （4）进行形成性评估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，允许早期发现临床状况，并促进更个性化和系统的医学方法。

项目成果

Measuring the impact of health policies using Internet search patterns: the case of abortion.

• DOI: 10.1186/1471-2458-10-514
• 发表时间: 2010-08-25
• 期刊: BMC public health
• 影响因子: 4.5
• 作者: Reis BY;Brownstein JS
• 通讯作者: Brownstein JS