Developing a Clinical Decision Support Tool that Assesses Risk of Opioid Use Disorder Using Natural Language Processing, Machine Learning, and Social Determinants of Health from Clinical Notes
开发一种临床决策支持工具,利用自然语言处理、机器学习和临床记录中的健康社会决定因素来评估阿片类药物使用障碍的风险
基本信息
- 批准号:10675434
- 负责人:
- 金额:$ 18.64万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-08-15 至 2026-07-31
- 项目状态:未结题
- 来源:
- 关键词:AddressAffectAmericanAssessment toolCaringClinicalCodeComputerized Medical RecordCountyDataData ScientistDiabetes MellitusDiagnosisDocumentationDrug PrescriptionsEducationEvaluationFoundationsGoalsHIVHealthHealth Insurance Portability and Accountability ActHospitalsICD-10-CMInformation SystemsInstitutionInstitutional Review BoardsInternationalInterventionInterviewKnowledgeLogical Observation Identifiers Names and CodesMachine LearningManualsMapsMeasurementMeasuresMedicalMentorsMentorshipMetadataModelingNatural Language ProcessingOutcomeOutputParticipantPatient-Focused OutcomesPatientsPerformancePhysiciansPlayProceduresQuestionnairesRecommendationResearchResearch DesignResearch MethodologyResearch PersonnelResourcesRiskRisk AssessmentRisk FactorsRoleRunningSNOMED Clinical TermsSamplingSpecialistSurveysSystemTechnologyTerminologyTestingTextTimeTrainingVocabularyWorkaddictionbiomedical informaticscare outcomescareercareer developmentclinical decision supportdisorder riskexperiencefood insecurityheuristicshousing instabilityimprovedinnovationknowledgebasemachine learning algorithmmedication-assisted treatmentnovelopioid epidemicopioid mortalityopioid overdoseopioid useopioid use disorderpatient orientedprescription opioidrecruitsafety netskillssocialsocial health determinantsstatisticssuccesssupervised learningsupport toolstherapy designtherapy developmenttooltrial readinessusability
项目摘要
PROJECT SUMMARY/ABSTRACT
In 2017, 1.7 million Americans suffered from opioid use disorders (OUD), which led to 47,000 American deaths
from opioid overdose. Social determinates of health (SDoH) affect patients' OUD risk level and
physicians' opioid prescribing. Physicians lack the tools to quickly and accurately assess SDoH associated
with OUD, and lack knowledge of relevant resource for intervention. Clinical decision support (CDS) could
quickly assess a patients' SDoH factors associated with OUD risk and provide actionable recommendations,
which would reduce OUD risk assessment time and address knowledge gaps. In 2018, UCSF researchers
created the Compendium of Medical Terminology Codes for Social Risk Factors that maps SDoH risks to
medical vocabularies. However, most SDoH are documented in clinical notes. My long-term career goal is
research independence with expertise in: 1) OUD risk assessment, 2) SDoH research, and 3) intervention
development, implementation, and evaluation. Related to these goals, this study will use natural language
processing (NLP) to identify SDoH in clinical notes, examine associations between SDoH and OUD,
and develop a CDS tool to assess OUD risk. We will then assess usability, acceptability, and feasibility
of using the CDS tool in clinical settings. This research will help physicians quickly and accurately assess
OUD risk, intervene earlier, and improve care. Our research aims include: Aim 1. Use NLP to identify SDoH in
clinical notes and examine associations between SDoH and OUD. We will use the Compendium and NLP to
extract new SDoH in clinical notes. Two raters will manually validate the new SDoH, and use descriptive
statistics to characterize associations between SDoH and OUD. (training goals 1 and 2). Aim 2: Develop a
CDS tool to assess OUD risk. We will use SDoH and OUD associations from aim 1 to develop a supervised
machine learning algorithm for our CDS tool. We will validate the CDS tool by measuring its ability to correctly
assess OUD risk in patients' EHR data (training goals 1 and 2). Aim 3: Test the usability, acceptability, and
feasibility of physicians' use of the CDS tool. 40 physicians will be asked to assess sample patient cases, then
given CDS results on those same cases. Physicians will indicate whether they would follow the CDS's
recommendations. Additionally, participants will be asked to complete an interview and questionnaire to
evaluate usability and acceptability. We will assess feasibility by examining recruitment, implementation, and
metadata. (training goal 3). These aims are achievable because I have experience in NLP and machine
learning and my mentors are experts in OUD research, SDoH research, and intervention design; and have an
outstanding record in career development. This K01 will help me achieve researcher independence by
providing a) skills to develop an OUD risk assessment intervention; b) expertise in a novel growing SDoH field;
c) an innovative trial-ready scalable intervention; and d) preliminary data for an R01.
项目总结/文摘
项目成果
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{{ truncateString('William Brown', 18)}}的其他基金
Developing a Clinical Decision Support Tool that Assesses Risk of Opioid Use Disorder Using Natural Language Processing, Machine Learning, and Social Determinants of Health from Clinical Notes
开发一种临床决策支持工具,利用自然语言处理、机器学习和临床记录中的健康社会决定因素来评估阿片类药物使用障碍的风险
- 批准号:
10352097 - 财政年份:2022
- 资助金额:
$ 18.64万 - 项目类别:
Low Cost OCT Angiography with Spectroscopic Contrast
低成本 OCT 血管造影与光谱对比
- 批准号:
10156095 - 财政年份:2021
- 资助金额:
$ 18.64万 - 项目类别:
Low Cost Spectroscopic OCT for GI Applications
适用于 GI 应用的低成本光谱 OCT
- 批准号:
10384636 - 财政年份:2021
- 资助金额:
$ 18.64万 - 项目类别:
UCSF Data Science Training to Advance Behavioral and Social Science Expertise for Health Research (DaTABASE) Program
加州大学旧金山分校数据科学培训,以促进健康研究的行为和社会科学专业知识 (DaTABASE) 计划
- 批准号:
10324595 - 财政年份:2020
- 资助金额:
$ 18.64万 - 项目类别:
UCSF Data Science Training to Advance Behavioral and Social Science Expertise for Health Research (DaTABASE) Program
加州大学旧金山分校数据科学培训,以促进健康研究的行为和社会科学专业知识 (DaTABASE) 计划
- 批准号:
10544029 - 财政年份:2020
- 资助金额:
$ 18.64万 - 项目类别:
Low cost retinal optical coherence tomography for point of care use
用于护理点使用的低成本视网膜光学相干断层扫描
- 批准号:
9515362 - 财政年份:2016
- 资助金额:
$ 18.64万 - 项目类别:
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