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
• 负责人: Richard K Leuchter
• 金额: $ 11.19万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-05 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10509614
• 关键词: Accident and Emergency department; Address; Admission activity; Affect; Ambulatory Care; Award; COVID-19; COVID-19 pandemic; Cardiopulmonary; Caring; Cause of Death; Characteristics; Chronic Obstructive Pulmonary Disease; Clinical; Code; Dangerousness; Data; Defensive Medicine; Disadvantaged; Disease; Electronic Health Record; Emergency department visit; Ensure; Exposure to; Faculty; Foundations; Fright; Future; Goals; Health; Health Expenditures; Health Services Research; Healthcare; Heart failure; Hospitalization; Hospitals; Human; Inpatients; Internal Medicine; Judgment; K-Series Research Career Programs; Knowledge; Learning; Machine Learning; Measures; Mediating; Medical; Medical Errors; Medical Waste; Medicine; Methods; Modeling; Morals; Morbidity - disease rate; Myocardial Infarction; National Heart, Lung, and Blood Institute; Natural experiment; Outcome; Outcome Measure; Outpatients; Patient Care; Patient Outcomes Assessments; Patients; Performance; Physicians; Pneumonia; Principal Investigator; Provider; Publishing; Randomized; Recovery; Reporting; Research; Research Personnel; Research Training; Risk; Running; SARS coronavirus; Scientist; Sensitivity and Specificity; Shortness of Breath; Source; Stroke; Symptoms; Testing; Time; Training; Uncertainty; Vulnerable Populations; Work; adjudicate; arm; career; career development; cost; ethnic minority; experience; hazard; health care disparity; health equity; improved; innovation; inpatient service; learning outcome; machine learning model; machine learning prediction; marginalized population; mortality; overtreatment; pandemic disease; performance tests; pilot trial; point of care; predictive modeling; programs; prospective; racial and ethnic; racial minority; response; skills; success; tool; unethical

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.

项目摘要/摘要 我(Richard K.Leuchter，医学博士)是加州大学洛杉矶分校内科住院医生，将作为临床医生加入教职员工- 2022年7月，加州大学洛杉矶分校的科学家。我将在医院行医，从事专注于健康服务的研究 关于识别和减少医疗废物--在某些临床上没有提供净效益的病人护理 场景，也可能造成伤害。我将在我接受的出色的医疗服务研究培训的基础上再接再厉 通过R38 Starr计划，并使用机器学习(ML)继续我的研究，以识别和 尽量减少医疗废物。不必要的住院是最大的医疗资源之一 这是对少数族裔的浪费和不成比例的负担，但解决这一问题的努力 受阻于缺乏措施，能够前瞻性地确定住院是不必要的，但可以接受 精确度。衡量和减少不必要住院的一个关键障碍是索赔数据(例如， 提交给付款人的账单信息)缺乏足够的临床细节来准确地将住院归类为 “没有必要。”使用更丰富的电子健康记录(EHR)数据补充索赔数据有可能 提高预测准确性，但EHR数据通常不包括离散的患者报告结果(PRO) 量化从主观症状(例如，呼吸急促)中恢复的情况，这使得很难判断 因心力衰竭或肺炎等疾病住院的必要性。为了提升我的职业目标和 努力实现我的总体目标，即减少浪费的医疗实践造成的危害(特别是在 弱势患者)，我提出了一种识别不必要住院的新方法：训练预测性ML 来自电子病历数据的模型，可以识别极有可能是不必要的录取，然后 结合临床经验和EHR结果评估模型的性能。我的首要目标是 通过成为领先的首席调查员来减少浪费和不公平的医疗实践 开发创新和最先进的方法，最大限度地减少医疗废物。 为了实现这一目标，我寻求NHLBI K38职业发展奖的支持。我将在以下方面获得技能 编码和使用ML来预测健康结果、测量和分析PRO以及健康/医疗保健 差异研究。我提出了两个与我的职业发展目标相一致的具体研究目标：1) 开发可识别心肺疾病急诊科(ED)入院情况的ML模型 很可能是不必要的，以及2)测量这些模型的预期性能 使用PRO和EHR数据的组合，这些数据将从提交给急诊室的患者那里收集。这就做 应用我从培训中学到的知识来实现这些目标，并计划使用 研究为NHLBI K23关于单中心务实试点试验的提案提供参考，我计划在2023年提交该提案。 K38奖将为我提供所需的培训和技能，使我成为全国使用 减少医疗废物及其相关的医疗保健差距的新方法。