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
基本信息
- 批准号:10057390
- 负责人:
- 金额:$ 74.88万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-02-05 至 2022-11-30
- 项目状态:已结题
- 来源:
- 关键词:Accident and Emergency departmentAdolescent MedicineAdolescent and Young AdultAdvocateAffectBostonCalibrationCaringClinicClinicalCodeCommunitiesDataDetectionDevelopmentDiagnosisEarly DiagnosisEarly InterventionElectronic Health RecordGeneral PopulationGoalsHealth systemHealthcare SystemsInpatientsInterventionLaboratoriesLeadManualsMeasuresMedicalMedical centerMental HealthModelingNational Institute of Mental HealthOutcomePatientsPediatric HospitalsPerformancePharmaceutical PreparationsPhenotypePopulationPrimary Health CareProceduresProcessPsychosesPublic HealthResearchResearch PersonnelResearch SupportRiskRisk FactorsSamplingScreening procedureSensitivity and SpecificitySiteSpecialistSuicideTestingTimeTrainingValidationWorkWorld Health Organizationbaseclinical carecostdata resourcedesignexperiencefirst episode psychosishealth care settingsimprovedimproved outcomeindividual patientinterestmedical schoolsmedical specialtiesnovelopen sourceoutpatient programspredictive modelingprototyperandom forestsocioeconomicssubstance abuse treatmentsupport vector machinetooltreatment program
项目摘要
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,在美国大约有10万名青少年和青壮年
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ben Y Reis其他文献
Harnessing the Power of Generative AI for Clinical Summaries: Perspectives From Emergency Physicians.
利用生成式人工智能的力量进行临床总结:急诊医生的观点。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:6.2
- 作者:
Y. Barak;Rebecca Wolf;R. Rozenblum;Jessica K. Creedon;Susan C. Lipsett;Todd W. Lyons;Kenneth A. Michelson;Kelsey A. Miller;Daniel Shapiro;Ben Y Reis;Andrew M Fine - 通讯作者:
Andrew M Fine
Ben Y Reis的其他文献
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{{ truncateString('Ben Y Reis', 18)}}的其他基金
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 - 财政年份:2019
- 资助金额:
$ 74.88万 - 项目类别:
Improved multifactorial prediction of suicidal behavior through integration of multiple datasets
通过整合多个数据集改进自杀行为的多因素预测
- 批准号:
9762979 - 财政年份:2018
- 资助金额:
$ 74.88万 - 项目类别:
Integrative Methods for Improved Pharmacovigilance
改善药物警戒的综合方法
- 批准号:
8232024 - 财政年份:2010
- 资助金额:
$ 74.88万 - 项目类别:
Integrative Methods for Improved Pharmacovigilance
改善药物警戒的综合方法
- 批准号:
8055383 - 财政年份:2010
- 资助金额:
$ 74.88万 - 项目类别:
Integrative Methods for Improved Pharmacovigilance
改善药物警戒的综合方法
- 批准号:
7764278 - 财政年份:2010
- 资助金额:
$ 74.88万 - 项目类别:
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:
8065527 - 财政年份:2009
- 资助金额:
$ 74.88万 - 项目类别:
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 - 财政年份:2009
- 资助金额:
$ 74.88万 - 项目类别:
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:
8053207 - 财政年份:2009
- 资助金额:
$ 74.88万 - 项目类别:
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:
7652734 - 财政年份:2009
- 资助金额:
$ 74.88万 - 项目类别:
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:
7784567 - 财政年份:2009
- 资助金额:
$ 74.88万 - 项目类别:
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