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

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

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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.

项目摘要/摘要我（理查德·K·勒克特（Richard K. Leuchter） 2022年7月在加州大学洛杉矶分校的科学家。我将练习以医学医学和购买健康服务研究为重点关于识别和减少医疗废物 - 患者护理在某些临床方面没有净收益场景，也可能造成伤害。我将基于我收到的出色的医疗服务研究培训通过R38 Starr计划，并继续使用机器学习（ML）来识别和最小化医疗废物。不必要的住院是最大的医疗储量之一浪费和不成比例的伯恩伯恩种族和少数民族，但是解决这个问题的努力是由于缺乏可以前瞻性地确定住院的措施而受到阻碍准确性。衡量和减少不必要住院的关键障碍是索赔数据（例如，提交给付款人的计费信息）缺乏足够的临床细节来准确将住院分类为 “不必要。”用更丰富的电子健康记录（EHR）数据补充索赔数据的数据提供了潜力提高预测精度，但EHR数据通常不包括离散的患者报告结果（PRO）量化从主观症状中恢复（例如，呼吸急促），使其难以调整需要接受诸如心力衰竭或肺炎等疾病的入院。促进我的职业目标和为了减少浪费医疗习惯引起的危害的总体目标（尤其是在处境不利的患者），我提出了一种确定不必要住院的新方法：火车预测ML 来自EHR数据的模型可以识别出很可能不必要的入院，然后使用临床优点和EHR结果的组合评估模型性能。我的总体目标是通过成为主要的首席研究员，减少浪费和不平等的医疗保健实践开发创新的艺术方法，以最大程度地减少医疗废物。为了实现这一目标，我寻求NHLBI K38职业发展奖的支持。我将获得技能编码和使用ML来预测健康结果，衡量和分析专业人士以及健康/医疗保健差异研究。我提出了两个具体的研究目的，这些研究与我的职业发展目标保持一致：1）开发可以识别心肺疾病的急诊科（ED）入院的ML模型不必要的很有可能是不必要的，2）衡量这些模型的预期性能利用将从出现到ED的患者收集的PROS和EHR数据的组合。我会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年提交的单个中心实用试验试验的NHLBI K23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分配的新兴方法。