Using Machine Learning and Patient-Reported Outcomes to Identify Unnecessary Hospitalizations
使用机器学习和患者报告的结果来识别不必要的住院治疗
基本信息
- 批准号:10696203
- 负责人:
- 金额:$ 11.19万
- 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:2022-09-05 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:Accident and Emergency departmentAddressAdmission activityAffectAmbulatory CareAwardCOVID-19COVID-19 pandemicCardiopulmonaryCaringCause of DeathCharacteristicsChronic Obstructive Pulmonary DiseaseClassificationClinicalCodeCrowdingDangerousnessDataDefensive MedicineDisadvantagedDiseaseElectronic Health RecordEmergency department visitEnsureEthnic OriginExposure toFacultyFoundationsFrightFutureGoalsHarm ReductionHealthHealth ExpendituresHealth Services ResearchHealthcareHeart failureHospitalizationHospitalsHumanInpatientsInternal MedicineJudgmentK-Series Research Career ProgramsKnowledgeLearningMachine LearningMeasuresMediatingMedicalMedical ErrorsMedical WasteMedicineMethodsModelingMoralsMorbidity - disease rateMyocardial InfarctionNational Heart, Lung, and Blood InstituteNatural experimentOutcomeOutpatientsPatient CarePatient Outcomes AssessmentsPatientsPerformancePhysiciansPneumoniaPrincipal InvestigatorProviderPublishingRaceRandomizedRecoveryReportingResearchResearch PersonnelResearch TrainingRiskRunningSARS coronavirusScientistSensitivity and SpecificityShortness of BreathSourceStrokeSymptomsTestingTimeTrainingUncertaintyVulnerable PopulationsWorkadjudicationarmcareercareer developmentcaregivingcostelectronic patient reported outcomesethnic minorityexperiencehazardhealth care disparityhealth equityhospital careimprovedinnovationinpatient servicemachine learning modelmachine learning predictionmarginalized populationmodel buildingmortalityovertreatmentpandemic diseaseperformance testspilot trialpoint of carepredictive modelingprogramsprospectiveracial minorityresponseskill acquisitionskillssuccesstoolunethical
项目摘要
PROJECT SUMMARY/ABSTRACT
I (Richard K. Leuchter, MD) am a UCLA Internal Medicine resident who will be joining the faculty as a clinician-
scientist at UCLA in July 2022. I will practice hospital medicine and pursue health services research focused
on identifying and reducing medical waste - patient care that provides no net benefit in certain clinical
scenarios, and can also cause harm. I will build upon the excellent health services research training I received
through the R38 StARR program, and continue my research using machine learning (ML) to identify and
minimize medical waste. Unnecessary hospitalizations represent one of the single largest reservoirs of medical
waste and disproportionately burden racial and ethnic minorities, but efforts to address this problem have been
hindered by a lack of measures that can prospectively identify hospitalizations as unnecessary with acceptable
accuracy. A critical barrier to measuring and reducing unnecessary hospitalizations is that claims data (e.g.,
billing information submitted to payers) lack enough clinical detail to accurately classify a hospitalization as
“unnecessary.” Supplementing claims data with richer electronic health record (EHR) data offers potential to
improve predictive accuracy, but EHR data do not routinely include discrete patient-reported outcomes (PROs)
to quantify recovery from subjective symptoms (e.g., shortness of breath), making it difficult to adjudicate the
necessity of admissions for diseases such as heart failure or pneumonia. To advance my career goals and
work toward my overall aim of reducing the harms arising from wasteful medical practices (especially among
disadvantaged patients), I propose a new method to identify unnecessary hospitalizations: train predictive ML
models from EHR data that can identify admissions with a high likelihood of being unnecessary, and then
assess model performance using a combination of clinical PROs and EHR outcomes. My overarching goal is
to reduce wasteful and inequitable healthcare practices by becoming a leading principal investigator
developing innovative and state of the art methods to minimize medical waste.
To achieve this goal, I seek support from the NHLBI K38 Career Development Award. I will acquire skills in
coding and using ML to predict health outcomes, measuring and analyzing PROs, and health/healthcare
disparities research. I propose two specific research aims that align with my career development goals: 1)
develop ML models that can identify Emergency Department (ED) admissions for cardiopulmonary illnesses
with a high likelihood of being unnecessary, and 2) measure the prospective performance of these models
using a combination of PROs and EHR data that will be collected from patients presenting to the ED. I will
apply knowledge learned from my training to accomplish these aims, and plan to use the products of this
research to inform an NHLBI K23 proposal for a single center pragmatic pilot trial that I plan to submit in 2023.
The K38 Award would provide me with the training and skills needed to become a national leader in using
emerging methods to reduce medical waste and its associated healthcare disparities.
项目总结/文摘
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
When peer comparison information harms physician well-being.
- DOI:10.1073/pnas.2121730119
- 发表时间:2022-07-19
- 期刊:
- 影响因子:11.1
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Richard K Leuchter其他文献
Richard K Leuchter的其他文献
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{{ truncateString('Richard K Leuchter', 18)}}的其他基金
Using Machine Learning and Patient-Reported Outcomes to Identify Unnecessary Hospitalizations
使用机器学习和患者报告的结果来识别不必要的住院治疗
- 批准号:
10509614 - 财政年份:2022
- 资助金额:
$ 11.19万 - 项目类别:
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