Automating assessment of obesity care quality

描述(申请人提供)：目前的趋势表明，肥胖率将继续上升，治疗肥胖症相关疾病的费用将随着人口老龄化而大幅增加。尽管NHLBI制定了预防、诊断和治疗成年人肥胖的指南，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。反应迟缓的部分原因是无法评估对肥胖诊断和治疗指南的遵守情况。特别是，缺乏适当的卫生信息技术来整合关于肥胖的各种临床数据，即使在最先进的电子医疗记录系统(EMRS)中也是如此，这使得评估护理质量、衡量新干预计划的有效性以及在系统、组织和患者个人层面做出合理决策变得困难。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通过自动化方法获得，因为它们位于EMR的文本临床笔记中。以前的研究表明，尽管一些感兴趣的数据被记录在易于检索的字段中(例如，记录为生命体征的体重；标准诊断代码)，但许多治疗信息仅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，用于门诊肥胖护理质量的常规和综合测量。为此，我们将扩展MediClass(一种“医疗分类器”)，这是一项成熟的技术，可从EMR中的编码数据和自由文本临床记录中提取医疗质量数据。这项研究将从两个不同的卫生系统的EMR数据中对成人初级保健进行回顾分析： 中型保健组织(Kaiser Permanente Northwest，KPNW)和一个公共卫生诊所联盟(OCHIN)，其中包括西海岸各州(主要是俄勒冈州，但也包括华盛顿州和加利福尼亚州)的患者、提供者和医疗实践的不同样本。我们建议使用健康信息技术整合不同的数据和知识，以促进该地区参保人口和贫困、未参保和参保不足人口的质量改进。我们将首先使用最新的NHLBI肥胖诊断和治疗指南来制定肥胖护理质量(OCQ)衡量标准。接下来，我们将开发并验证一种自动方法，用于将这些措施应用于全面的电子病历数据。在每个研究地点，Medi Class系统将提取编码数据，并在自由文本临床记录上使用自然语言处理(NLP)来识别EMR中与OCQ相关的临床事件。然后，我们将应用OCQ衡量标准来评估这两个医疗系统目前的肥胖护理质量水平。最后，我们将评估推荐的肥胖护理的OCQ测量与提供者特征以及患者的临床结果之间的关联，包括体重变化。

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.