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性能监控广泛使用基于诊断的质量度量来跟踪 仅在具有合格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、近乎实时的性能监控。 然而，如果目标条件低于-或，基于诊断的措施可能存在严重的有效性问题 不同设施的过度诊断程度不同。当发生变异诊断和编码时，工具 衡量绩效的排名可能具有误导性：高性能设施的得分可能很低 表演设施可以得分很高，而具有相同真实表现的设施可以落在 设施等级分布。因此，使用基于诊断的流程措施可能会破坏 质量测量的主要目的：对设施和系统进行比较。此外，诊断- 不能使用基于措施的方法来检测有针对性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，当不同患者亚组的诊断率不同时，基于诊断 不能使用措施来发现医疗保健差距并对其采取行动。基于诊断的问题 如果有真实的流行率数据，可以补救措施：基于以下方面的绩效比较 基于诊断与基于流行率的措施将检测具有异常诊断率的设施和 区分真实表现中的差异和病例发现中的差异。然而，在许多情况下， 电子健康记录(EHR)不包含有关真实流行率的数据。 目标：拟议项目的目标是开发一种改进基于诊断的一般方法 在没有有效的流行数据时采取措施。我们建议建立一个预测模型 使用现有数据的多个来源并通过一次性收集黄金进行验证 标准结果数据(基于调查的SUD患病率)。利用现有数据，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经济高效的策略 而不需要持续、昂贵的疾病监测。关注物质使用障碍(SUD)护理，如 例如，这项研究的目标是：(A)通过以下方式评估SUD的诊断不足或过度诊断的程度 比较VA管理数据中编码了SUD诊断的患者与SUD的比例 在30VA医疗保健进行的患者调查中使用验证测量获得的患病率估计 系统；(B)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- 基于患者年龄、性别和种族/民族的SUD患病率估计。 方法：我们将收集VA患者的DSM-IV和DSM-5-一致性SUD的数据 乐器。我们将对30个VA医疗系统的患者进行电话采访，这些系统是根据 观察到的SUD诊断与真实的SUD患病率之间的地理区域和预期差异。我们 将把观察到的诊断率与基于调查的流行率估计值进行比较。我们将提炼一个原型SUD 以全国退役军人人口监测数据为输入的预测模型 药物使用和健康，来自退伍军人事务部数据仓库的EHR数据，以及来自 退伍军人事务部毒品和酒精项目调查。该模型将使用基于调查的SUD进行开发和验证 作为结果的流行率。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，并将根据已建立的预测有效性标准选择最终模型。我们将计算 使用诊断率与预测患病率进行设施性能排名，以评估以下情况的程度 性能的不同可能反映诊断或编码的不同。最后，我们将评估可能存在的差距 通过比较不同患者组之间的诊断和估计患病率之间的差距来进行诊断。