Machine Learning to Predict Mortality and Improve End-of-Life Outcomes among Minorities with Advanced Cancer
机器学习预测死亡率并改善少数晚期癌症患者的临终结局
基本信息
- 批准号:10521902
- 负责人:
- 金额:$ 30万
- 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:2022-09-20 至 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdvanced Malignant NeoplasmAlgorithmsAttitudeBeliefCancer PatientCaringCessation of lifeCharacteristicsClinical Decision Support SystemsDataDevelopmentDiscriminationDiseaseDocumentationEducational workshopElectronic Health RecordEmergency department visitEnrollmentEthnic OriginFeedbackFocus GroupsGoalsHealthHealth systemHealthcareHospitalizationIncidenceInferiorInterventionIntervention StudiesInterviewLifeLife ExpectancyLinkMachine LearningMalignant NeoplasmsMinorityModalityModelingMulticenter TrialsNursesOncologistOncologyOutcomePatient-Focused OutcomesPatientsPerceptionPersonsPhysiciansPredictive ValueProceduresProcessPrognosisProviderRaceRandomized Controlled TrialsRecommendationReportingResearchResourcesSafetySocial WorkersSolidSolid NeoplasmStrategic PlanningSystemTimeUncertaintybasecancer careclinical decision-makingclinical implementationdata registrydata toolsdesigndisparity reductionend of lifeexperiencehealth care service utilizationhigh riskhospice environmentimprovedimproved outcomeindividual patientmachine learning algorithmmachine learning modelmachine learning predictionminority patientmortalitymortality riskneoplasm registrypredictive modelingpredictive toolsprognosticprognostic modelprognosticationracial biasstandardized caretool
项目摘要
PROJECT SUMMARY/ABSTRACT
Multiple studies show that minority patients with advanced cancer have inadequate discussions about
treatment, prognosis and goals of care which contributes to higher utilization of health care among
minorities at the end-of-life. A primary contributor to the low rates of prognosis and goals of care
discussions relates to oncologists inability to accurately predict mortality. Clinical decision support systems
(CDSS) are designed to directly aid clinical decision making by utilizing individual patient characteristics to
generate patient-specific assessments. Limited studies indicate CDSS can reduce disparities in process of
care and care standardization. However, existing tools do not identify patients at highest risk of mortality,
have not been linked to patient outcomes and have not been routinely evaluated in minority patients.
Machine learning (ML) predictive models allow more accurate prognoses by modeling patient and disease-
specific interactions and has the potential to obviate the racial bias that exists in the use of oncologist-
specific prognostication. ML models utilizing electronic health record (EHR) data can accurately predict
short-term mortality among oncology patients. However, little evidence exists that these models assist with
clinical decision making or improve outcomes for minority patients with cancer. We will address systemic
race/ethnicity-related barriers that contribute to disparities in end-of-life outcomes among minority cancer
patients by: 1) Developing and validating a predictive model to identify patients with advanced solid cancers
at high risk of death within 90-days; 2) Creating a CDSS system intervention that incorporates mortality
predictive tool data to prompt goals of care conversations for solid cancer patients at high risk of mortality
within 90 days; and 3) Conducting a stepped-wedge cluster randomized controlled trial to evaluate
whether implementing a clinical decision support system for patients with advanced solid cancer at risk of
death within 90 days increases goals of care discussions and decreases utilization of aggressive care at the
end-of-life among minorities versus non-minorities. The predictive model will be created from cancer
registry data linked to the EHR. We will then create a CDSS by conducting focus groups among an
interdisciplinary team of oncology clinicians (physicians, advance practice providers, nurses and social
workers). Additionally, we will conduct co-design workshops with the oncology clinicians to inform the
implementation of the CDSS. Next, we will conduct a stepped-wedge cluster randomized controlled trial to
evaluate whether utilization of the CDSS increases goals of care discussions, and decreases healthcare
utilization at the end-of-life among minority versus non-minority patients with advanced solid cancer at risk
of death within 90 days. Finally, we will perform exit interviews to refine the intervention and study
procedures. Findings will inform a larger multi-center trial aimed at implementation of the CDSS among
those predicted to have high mortality to improve the end-of-life outcomes of minority patients with cancer.
项目总结/文摘
项目成果
期刊论文数量(0)
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Cardinale B Smith其他文献
Cardinale B Smith的其他文献
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{{ truncateString('Cardinale B Smith', 18)}}的其他基金
The Role of Implicit Bias on Outcomes of Patients with Advanced Solid Cancers
隐性偏见对晚期实体癌患者预后的作用
- 批准号:
10383721 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
The Role of Implicit Bias on Outcomes of Patients with Advanced Solid Cancers
隐性偏见对晚期实体癌患者预后的作用
- 批准号:
10653820 - 财政年份:2022
- 资助金额:
$ 30万 - 项目类别:
The Role of Implicit Bias on Outcomes of Patients with Advanced Solid Cancers
隐性偏见对晚期实体癌患者预后的作用
- 批准号:
10211612 - 财政年份:2021
- 资助金额:
$ 30万 - 项目类别:














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