Development and validation of an electronic health record prediction tool for first-episode psychosis

首发精神病电子健康记录预测工具的开发和验证

• 批准号: 10305682
• 负责人: Ben Y Reis
• 金额: $ 76.86万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-05 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10305682
• 关键词: Accident and Emergency department; Adolescent Medicine; Adolescent and Young Adult; Advocate; Affect; Boston; Calibration; Caring; Clinic; Clinical; Code; Communities; Data; Detection; Development; Diagnosis; Early Diagnosis; Early Intervention; Electronic Health Record; General Population; Goals; Health system; Healthcare Systems; Inpatients; Intervention; Laboratories; Lead; Manuals; Measures; Medical; Medical center; Mental Health; Modeling; National Institute of Mental Health; Outcome; Patients; Pediatric Hospitals; Performance; Pharmaceutical Preparations; Phenotype; Population; Primary Health Care; Procedures; Process; Psychoses; Public Health; Research; Research Personnel; Research Support; Risk; Risk Factors; Sampling; Screening procedure; Sensitivity and Specificity; Site; Specialist; Suicide; Testing; Time; Training; Validation; Work; World Health Organization; base; clinical care; cost; data resource; design; duration of untreated psychosis; experience; first episode psychosis; health care settings; improved; improved outcome; individual patient; interest; medical schools; medical specialties; novel; open source; outpatient programs; predictive modeling; prototype; psychosis risk; random forest; socioeconomics; substance abuse treatment; support vector machine; tool; treatment program

Psychosis is a major public health challenge, with approximately 100,000 adolescents and young adults in the US experiencing a first episode of psychosis (FEP) every year. Early intervention following FEP is critical for achieving improved outcomes, yet treatment of FEP is often delayed between 1 and 3 years in the US due to delays in detection and referral. The World Health Organization has advocated shortening the duration of untreated psychosis (DUP) to three months or less. The goal of this study is to develop and validate a universal EHR-based screening tool for early detection of FEP across large clinical populations in diverse healthcare settings. In order to maximize the impact and generalizability of the tool across a wide range of healthcare settings, we will rely only on coded medical information collected in the course of care and thus widely available in EHRs. The tool will be developed and validated with data from three diverse health systems that cover over 8 million patients spanning a wide range of demographic, socioeconomic and ethnic backgrounds: Partners Healthcare System, Boston Children's Hospital, and Boston Medical Center. The study will be conducted by a closely collaborating interdisciplinary team of clinical specialists, psychosis researchers, and risk modeling experts based at these health systems and Harvard Medical School, with extensive experience in treating psychosis patients, and developing strategies for detecting FEP and EHR-based risk screening tools for early detection of various clinical conditions. Our preliminary studies show that EHR-based risk models can be used to sensitively and specifically detect FEP cases, on average 2 years before the first psychosis diagnosis appears in their EHR. Our specific aims include: 1. Define a robust cross-site case definition for FEP that relies only on information commonly available in EHRs and validate it through expert chart review; 2. Train and validate a predictive model for early detection of FEP based on large samples of patient data from the three sites; 3. Develop and validate FEP early detection models for key subpopulations, including patients receiving care at mental health clinics, adolescent medicine outpatient programs, and substance abuse treatment programs; and 4. Engage clinical stakeholders in the process of developing a prototype clinician-facing EHR-based risk screening tool for FEP, and release it as an open source SMART App, enabling further validation and clinical integration across a wide range of healthcare settings. Completion of these aims would provide a novel, clinically deployable, and potentially transformative tool for improving the trajectory of those affected with psychosis and reducing the burden and costs of untreated illness.

精神病是一项重大的公共卫生挑战，在美国约有10万青少年和年轻人 我们每年都会经历第一次精神病发作(FEP)。FEP后的早期干预对 取得了更好的结果，但在美国，FEP的治疗通常会推迟1至3年，原因是 检测和转介方面的延误。世界卫生组织主张缩短 未经治疗的精神病(DUP)至三个月或以下。本研究的目标是开发和验证一种 基于EHR的通用筛查工具在不同地区的大量临床人群中早期检测FEP 医疗保健设置。为了最大限度地提高该工具在各种领域的影响力和通用性 在医疗环境中，我们将仅依赖在护理过程中收集的编码医疗信息，因此 在电子病历中广泛使用。该工具将用来自三个不同卫生系统的数据进行开发和验证 它覆盖了800多万名患者，涵盖了广泛的人口、社会经济和种族 背景：合作伙伴医疗系统、波士顿儿童医院和波士顿医疗中心。这项研究 将由一个由临床专家、精神病研究人员、 以及基于这些卫生系统和哈佛医学院的风险建模专家，具有广泛的 治疗精神病患者的经验，以及开发检测FEP和EHR风险的策略 筛查工具，用于早期检测各种临床情况。我们的初步研究表明，基于电子病历的 风险模型可用于敏感和特异地检测FEP病例，平均在首次发病前2年 精神病诊断出现在他们的EHR中。我们的具体目标包括：1.定义一个强大的跨站点案例 仅依赖EHR中常见信息的FEP定义，并通过专家进行验证 图表回顾；2.基于大样本的FEP早期检测训练和验证预测模型 来自三个地点的患者数据；3.开发和验证关键亚群的FEP早期检测模型， 包括在精神卫生诊所、青少年医学门诊项目接受护理的患者，以及 药物滥用治疗计划；以及4.让临床利益攸关方参与制定 面向临床医生的基于EHR的FEP风险筛查工具原型，并作为开源SMART发布 APP，在广泛的医疗保健环境中实现进一步的验证和临床集成。完成 这些目标将提供一种新颖的、临床上可部署的、具有潜在变革性的工具，用于改善 改善精神病患者的生活轨迹，减少未经治疗的疾病的负担和费用。