Human-Machine Collaborations to Improve Prognosis and Clinical Decision-Making in Advanced Cancer

人机协作改善晚期癌症的预后和临床决策

基本信息

  • 批准号:
    10284721
  • 负责人:
  • 金额:
    $ 23.12万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-07-05 至 2026-06-30
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY/ABSTRACT Advance care planning and palliative care represent evidence-based, high-quality care for patients with advanced cancer. Early identification of patients at risk of short-term mortality is a promising strategy to increase advance care planning and palliative care. However, this is limited by prognostic inaccuracy among oncology clinicians, who overestimate prognosis for 70% of their patients with advanced cancer. While recent advances in electronic health record (EHR) infrastructure and machine learning (ML) have allowed accurate identification of patient' mortality risk, there is a fundamental gap in understanding how to integrate ML prognostic algorithms alongside clinician intuition (“human-machine collaborations”) in the routine care of patients with cancer. Dr. Parikh's research objective is to develop and test human-machine collaborative systems that leverage ML algorithms to improve clinicians' prognostic accuracy in order to prompt earlier advance care planning and palliative care among patients with advanced cancer. In prior work, Dr. Parikh has prospectively validated and embedded into the EHR an automated ML algorithm to predict short-term mortality risk among patients with cancer. In this application, Dr. Parikh proposes to take a fundamental next step in this work by exploring strategies to improve prognostic accuracy and decision-making among oncologists treating patients with advanced cancer. In Aim 1, Dr. Parikh will retrain and validate the existing ML mortality risk prediction algorithm by integrating recently-available patient-generated health data. In Aim 2, Dr. develop prognostic that Parikh will a vignette-based survey to assess optimal strategies of presenting ML predictions to improve accuracy. He will administer this survey to a large national sample of medical oncologists to ensure clinician perspectives are incorporated into interventions.In Aim 3, Dr. Parikh will develop two models of human-machine collaborative systems to generate real-time mortality estimates that integrate clinician and algorithm predictions. In a pragmatic multi-institutional clinical trial among patients with advanced cancer, Dr. Parikh will test the impact of human-machine collaborations on prognostic accuracy and rates of advanced care planning and palliative care referral. These findings will have important implications for patients with cancer, their caregivers, oncology clinicians, and health systems. More broadly, the methods proposed may serve as a blueprint to develop and evaluate human-machine collaborations in oncology. This facilitate judgment highly-qualified Dr. development improving research will t raining in areas vital to Dr. Parikh's career goals: dvanced predictive modeling, survey methods and and decision-making, human-machine interfaces, and pragmatic clinical trials. Dr. Parikh has two and committed mentors: Dr. Justin Bekelman, an expert i n cancer care delivery r esearch, and Jinbo Chen, an expert in EHR-based predictive model development. The proposed research and career plan will enable Dr. Parikh to transition to an independent physician-scientist devoted to the quality and applicability of predictive analytics in the care of patients with cancer. a
项目摘要/摘要 提前护理计划和姑息治疗代表了对患有以下疾病的患者的循证、高质量护理: 晚期癌症早期识别有短期死亡风险的患者是一种有希望的策略, 增加提前护理规划和姑息治疗。然而,这是有限的预后不准确, 肿瘤临床医生高估了70%晚期癌症患者的预后。虽然最近 电子健康记录(EHR)基础设施和机器学习(ML)的进步使得准确的 识别患者的死亡风险,在理解如何整合ML方面存在根本性的差距 预后算法与临床医生的直觉("人机协作")在日常护理 癌症患者。Parikh博士的研究目标是开发和测试人机协作 系统利用ML算法来提高临床医生的预后准确性, 先进的护理计划和姑息治疗的晚期癌症患者。在之前的工作中,Parikh博士 前瞻性地验证并嵌入到EHR中的自动ML算法,以预测短期死亡率 癌症患者的风险。在这个应用程序中,Parikh博士建议采取一个基本的下一步, 通过探索策略来提高肿瘤学家的预后准确性和决策, 晚期癌症患者。在目标1中,Parikh博士将重新培训和验证现有的ML死亡风险 通过整合最近可用的患者生成的健康数据来预测算法。在目标2中,博士 发展 预后 的 帕里克将 一个基于插图的调查,以评估呈现ML预测的最佳策略, 精度他将对全国范围内的肿瘤学家进行调查,以确保 在目标3中,Parikh博士将开发两种模型, 人机协作系统,以生成实时死亡率估计, 算法预测在一项针对晚期癌症患者的多机构临床试验中, Parikh将测试人机合作对预后准确性和晚期癌症发生率的影响。 护理规划和姑息治疗转诊。这些发现将对患者具有重要意义。 癌症患者、他们的护理人员、肿瘤学临床医生和卫生系统。更广泛地说,所提出的方法可以 作为开发和评估肿瘤学人机协作的蓝图。这 促进 判断 高素质 博士 发展 改善 研究将 在对Parikh博士的职业目标至关重要的领域进行培训:先进的预测建模,调查方法和 和决策,人机界面,以及实用的临床试验。帕里克医生有两个 和坚定的导师:贾斯汀·贝克曼博士,癌症护理服务专家, Jinbo Chen,基于EHR的预测模型开发专家。建议的研究和职业 该计划将使帕里克博士过渡到一个独立的医生,科学家致力于 预测分析在癌症患者护理中的质量和适用性。 一

项目成果

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Ravi Bharat Parikh其他文献

Ravi Bharat Parikh的其他文献

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{{ truncateString('Ravi Bharat Parikh', 18)}}的其他基金

Human-Machine Collaborations to Improve Prognosis and Clinical Decision-Making in Advanced Cancer
人机协作改善晚期癌症的预后和临床决策
  • 批准号:
    10445051
  • 财政年份:
    2021
  • 资助金额:
    $ 23.12万
  • 项目类别:
Human-Machine Collaborations to Improve Prognosis and Clinical Decision-Making in Advanced Cancer
人机协作改善晚期癌症的预后和临床决策
  • 批准号:
    10656477
  • 财政年份:
    2021
  • 资助金额:
    $ 23.12万
  • 项目类别:
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