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Leveraging EHR data to evaluate key treatment decisions to prevent suicide-related behaviors

利用 EHR 数据评估关键治疗决策，以预防自杀相关行为

基本信息

批准号：
10311082
负责人：
RONALD C KESSLER
金额：
$ 75.04万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2023-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10311082
关键词：
Acute Address Ambulatory Care Area Behavior Behavior Therapy Case Management Cessation of life Characteristics Child Rearing Clinic Clinical Code Combined Modality Therapy Criminal Justice Data Data Sources Databases Death Records Decision Making Electronic Health Record Emergency Department patient Employment Equilibrium Feeling suicidal Goals Health Personnel Homelessness Hospitalization Individual Inpatients Intake Integrated Health Care Systems Link Marital Status Measurement Mental disorders Mentally Ill Persons Methods Modeling Modernization Natural Language Processing Observational Study Outcome Outpatients Patients Pattern Performance Pharmacotherapy Population Precision therapeutics Prevalence Primary Care Physician Probability Psychiatrist Psychotherapy Public Health Randomized Reaction Records Reporting Research Research Design Resources Risk Sampling Statistical Methods Structure Suicide Suicide attempt Suicide prevention System Trauma Validation Veterans Health Administration Visit Work base care providers case-by-case basis clinical practice collaborative care common treatment comparative treatment completed suicide design follow-up high risk ideation indexing machine learning method optimal treatments personalized medicine primary outcome prospective reducing suicide response secondary outcome social health determinants success suicidal behavior suicidal morbidity suicidal patient suicide rate treatment effect treatment planning treatment trial unethical virtual

项目摘要

PROJECT SUMMARY/ABSTRACT Key focus: Research leveraging EHR data (PAR-18-929) to prevent suicide-related behaviors (SRBs) Objectives: To use augmented Veterans Health Administration (VHA) EHR data to develop Personalized Treatment Rules (PTRs) to help guide clinicians in making key treatment decisions for mentally ill patients aimed at reducing SRBs over the next 12 months. Specific aims: We will focus on two decisions: the decision of primary care physicians on how to treat patients coming to them for help with common mental disorders (CMD; “the PCP study”); and the decision of VHA Suicide Prevention Coordinators on whether to hospitalize patients who just made nonfatal suicide attempts or treat them as outpatients (“the SPC study”). Both are recognized as critical decisions, with no globally optimal treatment path for either and little guidance on how to decide among the treatment options. Research design: We will use a prospective observational design. The PCP study will be based on EHR data for the roughly 583,000 incident PCP visits of VHA patients for help with a CMD in 2010-2016. An incident visit will be defined as where the patient had not received other CMD treatment in the prior 12 months. The five broad PCP treatment options are pharmacotherapy, referral to psychotherapy, pharmacotherapy plus psychotherapy, pharmacotherapy plus measurement based collaborative care, and referral to a psychiatrist. The outcomes will be either an SRB (the primary outcome, either suicide death or administratively-recorded nonfatal suicide attempt) over the next 12 months or psychiatric hospitalization with suicidality over the same follow-up period (the secondary outcome). These outcomes occurred after 12,292 2010-2016 incident visits. The SPC study will be based on the 67,196 2010-2016 VHA Suicide Behavior Reports completed after a nonfatal VHA outpatient suicide attempt. Roughly half of these cases were hospitalized and the others treated as outpatients. A repeat SRB occurred over the next 12 months for 19,829 of these cases. Methods: A best-practice method of balancing baseline covariates will be used to adjust for nonrandom assignment across treatment options. Baseline covariates will include: prior EHR data; EHR data available for the focal treatment decision, including information abstracted from clinical notes with natural language processing; small-area geocode data for patient addresses; individual-level data from the LexisNexis Social Determinants of Health Database on patient finances, employment, marital status, and criminal justice involvement; and information about prior practice patterns of treating clinicians and practices-resources of treatment settings. A cutting-edge ensemble machine learning method will be used to analyze these weighted data to develop PTRs. Cross-validation in the 2010-2016 data and validation in 2017-2018 data (not available until the third year of the study) will be used to estimate out-of-sample performance of the PTRs.

项目总结/文摘