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

• 批准号: 10521902
• 负责人: Cardinale B Smith
• 金额: $ 30万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10521902
• 关键词: Address; Advanced Malignant Neoplasm; Algorithms; Attitude; Belief; Cancer Patient; Caring; Cessation of life; Characteristics; Clinical Decision Support Systems; Data; Development; Discrimination; Disease; Documentation; Educational workshop; Electronic Health Record; Emergency department visit; Enrollment; Ethnic Origin; Feedback; Focus Groups; Goals; Health; Health system; Healthcare; Hospitalization; Incidence; Inferior; Intervention; Intervention Studies; Interview; Life; Life Expectancy; Link; Machine Learning; Malignant Neoplasms; Minority; Modality; Modeling; Multicenter Trials; Nurses; Oncologist; Oncology; Outcome; Patient-Focused Outcomes; Patients; Perception; Persons; Physicians; Predictive Value; Procedures; Process; Prognosis; Provider; Race; Randomized Controlled Trials; Recommendation; Reporting; Research; Resources; Safety; Social Workers; Solid; Solid Neoplasm; Strategic Planning; System; Time; Uncertainty; base; cancer care; clinical decision-making; clinical implementation; data registry; data tools; design; disparity reduction; end of life; experience; health care service utilization; high risk; hospice environment; improved; improved outcome; individual patient; machine learning algorithm; machine learning model; machine learning prediction; minority patient; mortality; mortality risk; neoplasm registry; predictive modeling; predictive tools; prognostic; prognostic model; prognostication; racial bias; standardized care; tool

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