Automating assessment of obesity care quality

描述（由申请人提供）：目前的趋势表明，肥胖患病率将继续上升，随着人口老龄化，治疗肥胖相关疾病的费用将急剧增加。尽管NHLBI为预防、诊断和治疗成人肥胖提供了指导方针，但大多数医疗保健系统对这一迫在眉睫的公共卫生问题反应迟缓。这种缓慢的反应部分是由于无法评估遵守肥胖诊断和治疗指南。特别是，即使在最先进的电子病历系统（EMR）中，也缺乏适当的健康IT来整合有关肥胖的各种临床数据，因此很难评估护理质量、衡量新干预项目的有效性并在系统、组织和个体患者层面做出合理决策。EMR提供了有效评估大量人群的潜力，然而，肥胖护理评估所需的许多数据无法用于自动化方法，因为它们存在于EMR的文本临床记录中。以前的研究表明，尽管一些感兴趣的数据被记录在容易检索的字段中（例如，体重记录为生命体征;标准诊断代码），大部分治疗信息只能在自由文本临床笔记中找到。本研究旨在开发、验证、应用和评估一种可扩展的方法，用于常规和全面测量门诊肥胖护理质量。为了实现这一点，我们将扩展MediClass（一种“医学分类器”），这是一种经过验证的技术，用于从EMR中的编码数据和自由文本临床笔记中提取护理质量数据。本研究将对两个不同卫生系统的EMR数据进行成人初级保健的回顾性分析： 中型HMO（Kaiser Permanente Northwest，KPNW）和公共卫生诊所联盟（OCHIN），包括西海岸各州（主要是俄勒冈州，但也包括华盛顿和加州）的患者、提供者和医疗保健实践的各种样本。我们建议使用健康信息技术来整合各种数据和知识，以提高该地区有保险和贫困，无保险和保险不足人口的质量。我们将首先使用最新的NHLBI肥胖诊断和治疗指南制定肥胖护理质量（OCQ）措施。接下来，我们将开发和验证一种自动化方法，将这些措施应用于综合EMR数据。在每个研究中心，Medi Class系统将提取编码数据，并对自由文本临床记录使用自然语言处理（NLP），以识别EMR中的OQ相关临床事件。然后，我们将应用OCQ的措施，以评估目前的水平，肥胖保健质量在两个卫生系统。最后，我们将评估推荐的肥胖护理的OCQ指标与提供者特征以及患者的临床结局（包括体重变化）之间的相关性。

DESCRIPTION (provided by applicant): Current trends suggest that obesity prevalence will continue to rise and that costs of treating obesity-related disease will dramatically increase as the population ages. Despite NHLBI guidelines for preventing, diagnosing, and treating obesity among adults, most health care systems have been slow to respond to this looming public health problem. This slow response is partly due to the inability to assess adherence to obesity diagnosis and treatment guidelines. In particular, the lack of appropriate Health IT for integrating diverse clinical data on obesity, even within state-of-the-art electronic medical record systems (EMRs), makes it difficult to evaluate the quality of care, measure the effectiveness of new intervention programs, and make rational decisions at system, organization, and individual patient levels. EMRs offer the potential to efficiently assess large populations, however much of the data necessary for obesity care assessment are unavailable to automated methods because they reside in the text clinical notes of the EMR. Previous studies have shown that although some data of interest are recorded in easily retrievable fields (e.g., body weights recorded as a vital sign; standard diagnosis codes), much of the treatment information is found only in free-text clinical notes. This research aims to develop, validate, apply, and evaluate a scalable method for routine and comprehensive measurement of outpatient obesity care quality. To accomplish this, we will extend MediClass (a "Medical Classifier"), which is a proven technology for extracting care quality data from both coded data and free-text clinical notes in the EMR. This research will perform retrospective analysis of adult primary care from the EMR data of two distinct health systems: a mid-sized HMO (Kaiser Permanente Northwest, KPNW) and a consortium of public health clinics (OCHIN) including a diverse sample of patients, providers, and health care practices of the West Coast states (primarily Oregon, but also Washington and California). We propose to use Health IT to integrate diverse data and knowledge that advance quality improvement for both insured and the indigent, uninsured, and underinsured populations of this region. We will first develop obesity care quality (OCQ) measures using up to-date NHLBI guidelines for diagnosis and treatment of obesity. Next, we will develop and validate an automated method for applying these measures to comprehensive EMR data. At each study site, the Medi Class system will extract coded data and use natural language processing (NLP) on free-text clinical notes to identify OCQ-relevant clinical events in the EMR. Then we will apply the OCQ measures to assess current levels of obesity care quality in the two health systems. Finally, we will evaluate the associations between OCQ measures of recommended obesity care and provider characteristics as well as clinical outcomes for patients, including change in weight.