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: 10051319
• 负责人: Katherine JoAnn Hoggatt
• 金额: --
• 依托单位: VA GREATER LOS ANGELES HEALTHCARE SYSTEM
• 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/10051319
• 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性能监测广泛使用基于诊断的质量测量， 仅在符合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数据进行计算，从而实现低成本、近实时的性能监控。 然而，如果目标条件是根据或不根据，基于诊断的措施可能具有关键的有效性问题。 不同程度的过度诊断当诊断和编码发生变化时， 衡量绩效的排名可能会产生误导：高绩效的设施可能得分很低， 表演设施可以得分很高，具有相同真实的性能的设施可以落在相反的两端。 设施等级分布。因此，使用基于诊断的过程措施可能会破坏 质量测量的主要目的：设施和系统的比较。此外，诊断- 不能使用基于的措施来检测患有目标疾病的患者在获得护理方面的差距 但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，基于诊断的 不能利用措施来发现保健差距并采取行动。基于诊断的问题 如果有真实的流行率数据，可以采取补救措施： 基于诊断与基于患病率的措施将检测出具有异常诊断率的设施， 区分真实表现的差异和病例发现的差异。然而，对于许多情况， 电子健康记录（EHR）不包含真实流行率的数据。 目标：本项目的目标是开发一种通用方法，用于改善基于诊断的 在没有现成的有效流行率数据时采取措施。我们建议建立一个模型来预测 使用现有数据的多个来源，并通过一次性收集黄金来验证流行率 标准结局数据（基于调查的SUD患病率）。通过有针对性地收集 模型开发和验证数据是改进基于诊断的措施的一种具有成本效益的策略 而不需要持续的、昂贵的疾病监测。关注物质使用障碍（SUD）护理， 例如，本研究的目的是：（a）通过以下方式评估SUD诊断不足或过度的程度 比较VA管理数据中编码SUD诊断的患者比例与SUD 在30 VA Healthcare进行的患者调查中使用经验证的指标获得的患病率估计值 （B）使用多个系统改进和验证用于预测VA患者中SUD患病率的模型 现有数据来源;以及（c）通过比较诊断率与调查结果，评估SUD诊断的差异- 基于患者年龄、性别和种族/民族的SUD患病率估计。 方法：我们将使用经验证的 仪器我们将对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诊断和真正的SUD患病率之间的预期差异。我们 将观察到的诊断率与基于调查的患病率估计值进行比较。我们将改进SUD原型 预测模型使用作为输入人口SUD监测数据的退伍军人从国家调查 药物使用和健康，来自VA公司数据仓库的EHR数据，以及来自 VA药物和酒精计划调查。该模型将使用基于调查的SUD进行开发和验证 流行作为结果。我们将使用传统方法和更现代的机器学习来拟合模型 算法，并将选择基于预测有效性的既定标准的最终模型。我们将计算 使用诊断率与预测患病率进行设施性能排名，以评估 性能的变化可以反映诊断或编码的变化。最后，我们将评估可能的差异 通过比较诊断和估计患病率之间的差距，