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：
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
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：
https://reporter.nih.gov/project-details/10696203
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 Classification Clinical Code Crowding Dangerousness Data Defensive Medicine Disadvantaged Disease Electronic Health Record Emergency department visit Ensure Ethnic Origin Exposure to Faculty Foundations Fright Future Goals Harm Reduction 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 Outpatients Patient Care Patient Outcomes Assessments Patients Performance Physicians Pneumonia Principal Investigator Provider Publishing Race 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 adjudication arm career career development caregiving cost electronic patient reported outcomes ethnic minority experience hazard health care disparity health equity hospital care improved innovation inpatient service machine learning model machine learning prediction marginalized population model building mortality overtreatment pandemic disease performance tests pilot trial point of care predictive modeling programs prospective racial minority response skill acquisition skills success tool unethical

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.

项目概要/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师，将以临床医生的身份加入该学院- 2022 年 7 月成为加州大学洛杉矶分校的科学家。我将从事医院医学工作并从事以健康服务为重点的研究识别和减少医疗废物 - 在某些临床中不提供净效益的患者护理我将在我接受的优秀卫生服务研究培训的基础上进行研究。通过 R38 StARR 计划，并使用机器学习 (ML) 继续我的研究来识别和最大限度地减少不必要的住院治疗是最大的医疗资源之一。浪费并给少数种族和族裔带来不成比例的负担，但解决这一问题的努力一直在由于缺乏可以前瞻性地确定住院治疗为不必要且可接受的措施而受到阻碍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分类为 “没有必要。”用更丰富的电子健康记录（EHR）数据补充索赔数据可以提供以下潜力：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) 量化主观症状（例如呼吸短促）的恢复情况，这使得很难判断因心力衰竭或肺炎等疾病入院的必要性，以推进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（尤其是在弱势患者），我提出了一种新方法来识别不必要的住院治疗：训练预测机器学习来自 EHR 数据的模型可以识别很有可能是不必要的入院，然后我的首要目标是结合临床 PRO 和 EHR 结果来评估模型性能。成为领先的首席研究员，减少浪费和不公平的医疗保健实践开发创新和最先进的方法来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，我将获得以下方面的技能。编码并使用 ML 来预测健康结果、测量和分析 PRO 以及健康/医疗保健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）开发可以识别急诊室 (ED) 因心肺疾病入院的 ML 模型很可能是不必要的，并且 2）衡量这些模型的预期性能我将结合从向 ED 就诊的患者收集 PRO 和 EHR 数据。应用从我的培训中学到的知识来实现这些目标，并计划使用此产品我计划在 2023 年提交一项 NHLBI K23 单中心实用试点试验提案。 K38 奖将为我提供成为国家领导者所需的培训和技能减少医疗废物及其相关医疗保健差异的新兴方法。

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.

当同行比较信息损害医生的健康时。

DOI：
发表时间：
2022-07-19
期刊：
影响因子：
11.1
作者：
Reiff, Joseph S;Zhang, Justin C;Gallus, Jana;Dai, Hengchen;Pedley, Nathaniel M;Vangala, Sitaram;Leuchter, Richard K;Goshgarian, Gregory;Fox, Craig R;Han, Maria;Croymans, Daniel M
通讯作者：
Croymans, Daniel M