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

精神病是一个重大的公共卫生挑战，在美国约有10万青少年和年轻人。 美国每年都有精神病首次发作（FEP）。FEP后的早期干预对于 尽管在美国，FEP的治疗通常会延迟1 - 3年， 发现和转诊的延误。世界卫生组织主张缩短 未经治疗的精神病（DUP）的三个月或更短。这项研究的目标是开发和验证一个 一种通用的基于EHR的筛查工具，用于在不同的临床人群中早期检测FEP 医疗保健设置。为了最大限度地扩大这一工具在广泛领域的影响和普遍性， 在医疗保健环境中，我们将仅依赖于在护理过程中收集的编码医疗信息， 在EHR中广泛使用。该工具将使用来自三个不同卫生系统的数据进行开发和验证 覆盖了800多万患者，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。研究 将由临床专家，精神病研究人员， 和风险建模专家在这些卫生系统和哈佛医学院，与广泛的 治疗精神病患者的经验，并制定检测FEP和EHR风险的策略 筛查工具，用于早期发现各种临床状况。我们的初步研究表明， 风险模型可用于敏感和特异性地检测FEP病例，平均在第一次发病前2年， 精神病诊断出现在他们的电子病历中。我们的具体目标包括：1.定义可靠的跨站点案例 FEP的定义仅依赖于EHR中常用的信息，并通过专家验证 图表审查; 2.训练和验证预测模型，用于基于大样本的FEP早期检测， 来自三个站点的患者数据; 3.开发和验证关键亚群的FEP早期检测模型， 包括在心理健康诊所接受治疗的患者，青少年医学门诊计划， 药物滥用治疗方案;和4.让临床利益相关者参与制定 面向临床医生的FEP基于EHR的风险筛查工具原型，并作为开源SMART发布 应用程序，可在广泛的医疗保健环境中进行进一步验证和临床集成。完成 这些目标将提供一种新的，临床上可部署的，并有可能变革的工具，以改善 这将有助于改善精神病患者的生活轨迹，并减少未经治疗的疾病的负担和费用。

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.