Using Data Integration and Predictive Analytics to Improve Diagnosis-Based Performance Measures

使用数据集成和预测分析来改进基于诊断的绩效衡量

• 批准号: 10457091
• 负责人: Katherine JoAnn Hoggatt
• 金额: --
• 依托单位: VETERANS AFFAIRS MED CTR SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10457091
• 关键词: Age; Alcohol consumption; Alcohols; Benchmarking; Businesses; Caring; Clinical; Code; Collection; Complex; DSM-IV; DSM-V; Data; Data Sources; Diagnosis; Diagnostic; Disease; Disease Surveillance; Documentation; Drug Use Disorder; Drug usage; Effectiveness; Electronic Health Record; Ensure; Ethnic group; Geographic Locations; Goals; Gold; Guidelines; Health; Health Information System; Health Services Accessibility; Healthcare; Healthcare Systems; ICD-9; Intelligence; Lead; Logistics; Measurement; Measures; Mental Health; Methods; Modeling; Modernization; Monitor; Outcome; Patient Care; Patients; Performance; Pharmaceutical Preparations; Policies; Population; Predictive Analytics; Prevalence; Process Measure; Provider; Qualifying; Research; Resources; Retinal blind spot; Site; Source; Stigmatization; Structure; Substance Use Disorder; Surveys; System; Telephone Interviews; Time; Validation; Variant; Veterans; Work; base; care delivery; case finding; cost; cost effective; data integration; data warehouse; falls; gaps in access; health care delivery; health care disparity; improved; innovation; instrument; machine learning algorithm; model development; operation; patient subsets; performance based measurement; predictive modeling; programs; prototype; racial and ethnic; remediation; sex; social stigma; surveillance data

Background: VA performance monitoring makes extensive use of diagnosis-based quality measures that track delivery of care only among patients who have qualifying ICD-9 diagnosis codes. Diagnosis-based measures can be calculated using existing VA data, allowing for low-cost, near real-time performance monitoring. However, diagnosis-based measures can have critical validity problems if the targeted condition is under- or over-diagnosed to differing degrees across facilities. When variation is diagnosing and coding occurs, facility rankings on measured performance can be misleading: High performing facilities can score poorly, low performing facilities can score well, and facilities with the same real performance can fall at opposite ends of the facility rank distribution. Use of diagnosis-based process measures can therefore undermine one of the primary purposes of quality measurement: The comparison of facilities and systems. In addition, diagnosis- based measures cannot be used to detect gaps in access to care for patients who have a targeted condition but no qualifying diagnosis code. Finally, when diagnosis rates vary across patient subgroups, diagnosis-based measures cannot be used to detect and act on healthcare disparities. Problems with diagnosis-based measures could be remedied if true prevalence data were available: Comparisons of performance based on diagnosis- versus prevalence-based measures would detect facilities with anomalous diagnosis rates and distinguish variation in true performance from variation in case-finding. However, for many conditions, the electronic health record (EHR) does not contain data on true prevalence. Objectives: The goal of the proposed project is to develop a general method for improving diagnosis-based measures when valid prevalence data are not readily available. We propose to build a model for predicting prevalence using multiple sources of existing data and to validate it through a one-time collection of gold standard outcome data (survey-based SUD prevalence). Leveraging existing data with targeted collection of model development and validation data is a cost-effective strategy to improve diagnosis-based measures without requiring ongoing, expensive disease surveillance. Focusing on substance use disorder (SUD) care as an example, the objectives of this study are to: (a) assess the degree of SUD under- or over-diagnosis by comparing the proportion of patients with coded SUD diagnoses in the VA administrative data to SUD prevalence estimates obtained using a validated measure in a patient survey conducted at 30 VA healthcare systems; (b) refine and validate a model for predicting SUD prevalence among VA patients using multiple existing data sources; and (c) assess disparities in SUD diagnosis by comparing diagnosis rates to survey- based SUD prevalence estimates across patient age, sex, and racial/ethnic groups. Methods: We will collect data on DSM-IV and DSM-5-concordant SUD among VA patients using a validated instrument. We will conduct telephone interviews with patients at 30 VA healthcare systems selected based on geographic region and expected differences between observed SUD diagnosis and true SUD prevalence. We will compare observed diagnosis rates to survey-based prevalence estimates. We will refine a prototype SUD prediction model using as inputs population SUD surveillance data for Veterans from the National Surveys on Drug Use and Health, EHR data from VA Corporate Data Warehouse, and organizational survey data from the VA Drug and Alcohol Program Survey. The model will be developed and validated using survey-based SUD prevalence as the outcome. We will fit the model using traditional methods and more modern machine learning algorithms and will select a final model based on established criteria for predictive validity. We will compute facility performance rankings using diagnosis rates versus predicted prevalence to assess the extent to which variation in performance may reflect variation in diagnosis or coding. Finally, we will assess possible disparities in diagnosing by comparing the gap between diagnosis and estimated prevalence across patient groups.

背景：VA性能监控可广泛使用基于诊断的质量措施 仅在有资格的ICD-9诊断代码的患者中提供护理。基于诊断的措施 可以使用现有的VA数据来计算，从而允许低成本，接近实时性能监视。 但是，如果目标状况不足或 过度诊断到设施之间的不同程度。当诊断和编码发生变化时，设施 在测量性能上排名可能会引起误导：高性能设施得分很差，很低 表演设施可以得分良好，具有相同真实性能的设施可能会落在 设施等级分布。因此，基于诊断的过程措施的使用可能会破坏其中之一 质量测量的主要目的：设施和系统的比较。另外，诊断 - 基于目标状况的患者无法使用基于措施来检测获得护理差距 但是没有合格的诊断代码。最后，当诊断率在患者亚组之间变化时，基于诊断 措施不能用于检测和对医疗保健差异行动。基于诊断的问题 如果有真实的患病率数据，可以修复措施：基于绩效的比较 诊断与基于患病率的措施将检测具有异常诊断率的设施和 区分真实性能的变化与案例调查中的变化。但是，在许多情况下， 电子健康记录（EHR）不包含有关真实患病率的数据。 目标：拟议项目的目标是开发一种改善基于诊断的通用方法 当不容易获得有效的患病率数据时进行措施。我们建议建立一个预测的模型 使用现有数据的多个来源的流行率，并通过一次性收集黄金来验证它 标准结果数据（基于调查的SUD患病率）。利用有针对性收集的现有数据 模型开发和验证数据是一种具有成本效益的策略，旨在改善基于诊断的措施 不需要进行持续的昂贵疾病监测。专注于药物使用障碍（SUD）护理 一个例子，这项研究的目标是：（a）评估SUD的诊断程度或过度诊断的程度 将VA管理数据中的SUD诊断患者的比例与SUD进行比较 使用经过验证的措施在30 VA医疗保健进行的患者调查中获得的患病率估计值 系统； （b）完善并验证使用多个VA患者预测SUD患病率的模型 现有数据源； （c）通过将诊断率与调查进行比较来评估SUD诊断的差异 - 基于患者年龄，性别和种族/种族群体的基于SUD患病率估计。 方法：我们将使用经过验证 乐器。我们将对根据30个VA医疗保健系统的患者进行电话采访 地理区域和观察到的SUD诊断与真正的SUD患病率之间的预期差异。我们 将将观察到的诊断率与基于调查的患病率估计值进行比较。我们将完善原型SUD 使用AS输入的预测模型从国家调查 毒品使用和健康，来自VA公司数据仓库的EHR数据以及来自的组织调查数据 VA毒品和酒精计划调查。该模型将使用基于调查的SUD开发和验证 流行率是结果。我们将使用传统方法和更现代的机器学习适合模型 算法并将根据既定标准选择预测有效性的标准。我们将计算 使用诊断率与预测患病率的设施性能排名，以评估 性能的变化可能反映诊断或编码的差异。最后，我们将评估可能的差距 通过比较诊断和估计患者群体的估计患病率之间的差异。