Using Machine Learning and Patient-Reported Outcomes to Identify Unnecessary Hospitalizations

项目总结/摘要 I（Richard K. Leuchter，医学博士）是加州大学洛杉矶分校内科住院医师，将加入学院担任临床医生- 2022年7月，加州大学洛杉矶分校的科学家。我将在医院行医，并从事卫生服务研究， 确定和减少医疗废物-在某些临床治疗中没有净效益的病人护理 情节，也可能造成伤害。我将在我所接受的出色的卫生服务研究培训的基础上再接再厉， 通过R38 StARR计划，并继续我的研究使用机器学习（ML）来识别和 减少医疗废物。不必要的住院治疗是最大的医疗费用来源之一， 浪费和不成比例的负担种族和族裔少数，但努力解决这一问题， 由于缺乏措施，无法前瞻性地确定住院治疗是不必要的， 精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分类为 “不必要的。”用更丰富的电子健康记录（EHR）数据补充索赔数据， 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） 为了量化主观症状的恢复（例如，呼吸急促），使其难以判断 因心力衰竭或肺炎等疾病而必须入院。为了实现我的职业目标， 努力实现我的总体目标，即减少浪费性医疗行为造成的危害（特别是在 弱势患者），我提出了一种新的方法来识别不必要的住院治疗：训练预测ML 从EHR数据中建立模型，可以识别不必要的可能性很高的入院，然后 使用临床PRO和EHR结果的组合评估模型性能。我的首要目标是 通过成为领先的首席研究员，减少浪费和不公平的医疗保健做法 开发创新和最先进的方法，以尽量减少医疗废物。 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，测量和分析PRO以及健康/医疗保健 差距研究。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： 开发可识别心肺疾病急诊科（艾德）入院的ML模型 很有可能是不必要的，2）衡量这些模型的预期性能 使用PRO和EHR数据的组合，这些数据将从向ED就诊的患者中收集。我将 运用我在培训中学到的知识来实现这些目标，并计划使用这些产品。 研究，为我计划在2023年提交的单中心务实试点试验的NHLBI K23提案提供信息。 K38奖将为我提供培训和技能，使我成为使用 减少医疗废物及其相关医疗差异的新兴方法。

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.