Using Computational Approaches to Optimize Asthma Care Management

使用计算方法优化哮喘护理管理

• 批准号: 9982399
• 负责人: Gang Luo
• 金额: $ 79.25万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9982399
• 关键词: Address; Adoption; Affect; Air; American; Asthma; California; Caring; Case Manager; Characteristics; Chronic Disease; Chronic Obstructive Airway Disease; Clinical; Clinical Medicine; Computerized Medical Record; Computers; Consumption; Costs and Benefits; Data; Data Sources; Development Plans; Diabetes Mellitus; Disease; Disease Outcome; Drops; Emergency department visit; Enrollment; Focus Groups; Health Services Research; Healthcare; Healthcare Systems; Heart Diseases; Hospitalization; Hour; Individual; Intervention; Link; Machine Learning; Medicine; Methods; Modeling; Modernization; Outcome; Patient Care Planning; Patient risk; Patients; Pattern; Performance; Physicians; Physicians' Offices; Psychological Transfer; Public Health Informatics; Publishing; Records; Resources; Risk Factors; Scientist; Services; System; Techniques; Time; Training; Universities; Washington; Weather; acute care; adverse outcome; asthmatic patient; barrier to care; base; care costs; computer science; cost; high risk; improved; improved outcome; individual patient; novel; patient population; predictive modeling; prospective; simulation

Abstract The study will develop more accurate, computational predictive models and a novel automatic explanation function to better identify patients likely to benefit most from care management. For many chronic diseases, a small portion of patients with high vulnerabilities, severe disease, or great barriers to care consume most healthcare resources and costs. To improve outcomes and resource use, many healthcare systems use predictive models to prospectively identify high-risk patients and enroll them in care management to implement tailored care plans. For maximal benefit from costly care management with limited service capacity, only patients at the highest risk should be enrolled. But, current patient identification approaches have two limitations: 1) Low prediction accuracy causes misclassification, wasted costs, and suboptimal care. If an existing model were used for care management allocation, enrollment would miss >50% of those who would benefit most but include others unlikely to benefit. A healthcare system often has insufficient data for model training and incomplete data on many patients. A typical model uses only a few risk factors for adverse outcomes, despite many being known. Also, many predictive variables on patient and system characteristics are not found yet. 2) No explanation of the reasons for a prediction causes poor adoption of the prediction and busy care managers to spend extra time and miss suitable interventions. Care managers need to understand why a patient is predicted to be at high risk before allocating to care management and forming a tailored care plan. Existing models rarely give such explanation, forcing care managers to do detailed patient chart reviews. To address the limitations and optimize care management for more high-risk patients to receive appropriate care, the study will: a) improve accuracy of computationally identifying high-risk patients and assess potential impact on outcomes; b) automate explanation of computational prediction results and assess impact on model accuracy and outcomes; c) assess automatic explanations' impact on care managers' acceptance of the predictions and perceived care plan quality. The use case will be asthma that affects 9% of Americans and incurs 439,000 hospitalizations, 1.8 million emergency room visits, and $56 billion in cost annually. Asthma experts and computer scientists will use data from three leading healthcare systems; a novel, model-based transfer learning technique needing no other system's raw data; a novel, pattern-based automatic explanation technique that also improves model generalizability and accuracy; a new data source PreManage to make patient data more complete; and novel features on patient and system characteristics. These techniques can advance clinical machine learning for various applications, improve patient identification, and help form tailored care plans. Focus groups will be conducted with clinicians to explore generalizing the techniques to patients with chronic obstructive pulmonary disease, diabetes, and heart diseases, on whom care management is also needed. The results will potentially transform care management for better outcomes and more efficient resource use.

摘要 这项研究将开发出更准确的计算预测模型和一种新颖的自动解释 更好地识别可能从护理管理中受益最多的患者。对于许多慢性疾病， 一小部分脆弱性高、疾病严重或护理障碍大的患者消耗最多 医疗资源和成本。为了改善结果和资源利用，许多医疗保健系统使用 预测模型，以前瞻性地识别高风险患者，并将其纳入护理管理， 定制的护理计划。为了从服务能力有限且成本高昂的护理管理中获得最大利益，只有患者 风险最高的人都应该参加。但是，目前的患者识别方法有两个局限性： 1)低预测准确性导致错误分类、浪费成本和次优护理。如果现有的模型 用于护理管理分配，则登记将错过>50%的受益最多的人 但包括其他不太可能受益的人。医疗保健系统通常没有足够的数据用于模型训练， 许多患者的数据不完整。一个典型的模型只使用少数风险因素的不利结果，尽管 很多人都知道。此外，尚未找到许多关于患者和系统特征的预测变量。 2)没有解释预测的原因会导致预测的不佳采用和忙碌的护理 管理人员花费额外的时间和错过适当的干预措施。护理经理需要了解为什么 在分配给护理管理并形成定制护理计划之前，预测患者处于高风险中。 现有的模型很少给出这样的解释，迫使护理经理做详细的病人图表审查。 解决局限性，优化护理管理，让更多高危患者接受适当的 该研究将：a）提高计算识别高风险患者的准确性，并评估潜在的 对结果影响; B）自动解释计算预测结果并评估对模型的影响 准确性和结果; c）评估自动解释对护理管理人员接受 预测和感知护理计划质量。用例将是影响9%美国人的哮喘， 439，000次住院治疗，180万次急诊室就诊，每年花费560亿美元。哮喘专家和 计算机科学家将使用来自三个领先的医疗保健系统的数据;一种新颖的基于模型的迁移学习 技术不需要其他系统的原始数据;一种新颖的，基于模式的自动解释技术， 提高了模型的通用性和准确性;新的数据源PreManage使患者数据更加 完整性;以及患者和系统特性的新功能。这些技术可以促进临床 用于各种应用的机器学习，改善患者识别，并帮助制定量身定制的护理计划。重点 将与临床医生一起进行小组讨论，以探索将这些技术推广到慢性 阻塞性肺病、糖尿病和心脏病，也需要对他们进行护理管理。的 这些成果将有可能改变护理管理，以取得更好的成果和更有效地利用资源。