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

基本信息

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
项目类别：
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

项目摘要

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 绩效监控广泛使用基于诊断的质量测量来跟踪仅向具有合格 ICD-9 诊断代码的患者提供护理。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，如果目标条件不充分或不充分，基于诊断的措施可能会出现严重的有效性问题。各机构不同程度地过度诊断。当诊断和编码发生变化时，设施衡量性能的排名可能会产生误导：高性能设施的得分可能较差、较低表演设施可以得分很高，而具有相同实际表现的设施可能会落在两端设施等级分布。因此，使用基于诊断的过程措施可能会破坏其中一项质量测量的主要目的：设施和系统的比较。此外，诊断—— 基于基础的措施不能用于发现有目标病症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有所不同时，基于诊断的措施不能用于发现医疗保健差异并采取行动。基于诊断的问题如果有真实的患病率数据，则可以对措施进行补救：基于以下因素的绩效比较基于诊断与基于患病率的措施将检测诊断率异常的设施，并区分真实表现的变化和案例发现的变化。然而，对于许多条件，电子健康记录 (EHR) 不包含真实患病率的数据。目标：拟议项目的目标是开发一种改进基于诊断的通用方法当有效的流行率数据不易获得时采取的措施。我们建议建立一个模型来预测使用现有数据的多个来源来确定流行程度，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经济有效的策略无需持续、昂贵的疾病监测。重点关注物质使用障碍 (SUD) 护理例如，本研究的目标是：(a) 通过以下方式评估 SUD 诊断不足或过度诊断的程度：将 VA 管理数据中编码为 SUD 诊断的患者比例与 SUD 进行比较使用在 30 VA 医疗机构进行的患者调查中经过验证的测量方法获得患病率估计值系统； (b) 使用多种方法完善并验证预测 VA 患者中 SUD 患病率的模型现有数据源； (c) 通过比较诊断率与调查来评估 SUD 诊断的差异基于不同患者年龄、性别和种族/族裔群体的 SUD 患病率估计。方法：我们将使用经过验证的方法收集 VA 患者中符合 DSM-IV 和 DSM-5 的 SUD 数据。乐器。我们将根据情况选择 30 个 VA 医疗保健系统，对患者进行电话采访地理区域以及观察到的 SUD 诊断与真实 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进行比较。我们将完善 SUD 原型使用来自全国调查的退伍军人人口 SUD 监测数据作为输入的预测模型药物使用和健康、来自 VA Corporate Data Warehouse 的 EHR 数据以及来自 VA Corporate Data Warehouse 的组织调查数据退伍军人管理局毒品和酒精计划调查。该模型将使用基于调查的 SUD 来开发和验证患病率作为结果。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，并将根据既定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来评估设施绩效排名性能的变化可能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不同患者组的诊断结果和估计患病率之间的差距来进行诊断。