Enhanced Risk Adjustment Using Laboratory Test and Pharmacy Data

使用实验室测试和药房数据增强风险调整

基本信息

批准号：
8195231
负责人：
Amresh Hanchate
金额：
--
依托单位：
EDITH NOURSE ROGERS MEMORIAL VETERANS HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-01 至 2013-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8195231
关键词：
Abdominal Aortic Aneurysm Accounting Acute Kidney Failure Acute myocardial infarction Administrator Admission activity Affect Alcoholic Hepatitis Ambulatory Care Back Benchmarking Cardiac Surgery procedures Caring Cessation of life Chronic Obstructive Airway Disease Cirrhosis Clinical Code Community Hospitals Complement Congestive Heart Failure Coronary Artery Bypass Creatinine Data Data Element Data Sources Databases Decision Support Systems Diagnosis Disease Eligibility Determination Enrollment Ensure Equilibrium Exclusion Fee-for-Service Plans Future Gastrointestinal Hemorrhage Geographic Locations Goals Gold Health Services Research Healthcare Healthcare Systems Hip Fractures Hospitalization Hospitals Hypotension Incidence Inpatients Intensive Care Units Laboratories Lead Length Length of Stay Light Linear Regressions Logistic Regressions Measures Medical Medical Records Medicare Methods Mission Modeling Monitor Operative Surgical Procedures Outcome Outpatients Patient Care Patient Outcomes Assessments Patients Peer Review Performance Pharmacy facility Pneumonia Provider Quality Indicator Quality of Care Relative (related person)Reliance Reporting Research Personnel Resources Risk Risk Adjustment Risk Factors Sampling Severities Socioeconomic Status Source Spironolactone System Testing Time Time Study United States Centers for Medicare and Medicaid Services Validation abstracting acute stroke base burden of illness care seeking clinically relevant cohort comparative cost cost effective demographics dual eligible health care quality improved indexing interest model development mortality primary outcome programs repaired secondary outcome sound therapy design tool

项目摘要

Abstract Project Background: Risk adjustment is recognized as critical for accurate assessment of quality, fair comparison of providers, and benchmarking across healthcare systems. As chart-based data -- the "gold standard" for risk adjustment -- are costly and time-consuming to collect, administrative data have remained the basis of risk adjustment, despite inadequacies in capturing patient severity. Recent initiatives by the Agency of Healthcare Quality and Research (AHRQ) to enhance administrative data with automated data on laboratory tests and vital signs are an important step towards improving the accuracy of risk-adjustment models. Project Objectives: Taking advantage of readily available VA automated data, the primary goal of this project is to develop cost-effective and clinically sound enhanced risk-adjustment models to profile VA facilities on 30-day mortality following hospital admission. Enhanced risk-adjusted mortality rates -- and length of stay and readmission (secondary outcomes of interest) -- will be obtained for selected patients cohorts by combining administrative data with automated data on pharmacy claims, laboratory test measures, and vital signs. Our specific objectives are to: 1) examine performance of models for predicting 30-day mortality and length of stay using administrative data; 2) estimate the improvement in model performance from sequentially adding risk factors identified in pharmacy claims, laboratory tests and vital signs data; and 3) assess the impact of enhanced risk adjustment on facility rankings of risk-adjusted mortality. Project Methods: Separate enhanced risk-adjustment models will be estimated for nine medical conditions that account for a sizable proportion of all VA admissions (16 percent) and inpatient deaths (30 percent) - acute myocardial infarction, congestive heart failure, cirrhosis and alcoholic hepatitis, chronic obstructive pulmonary disease, gastrointestinal hemorrhage, hip fracture, pneumonia, acute renal failure and acute stroke. Based on all admissions to VA facilities during FY2003- 2008, disease-specific cohorts will be extracted. We will merge VA inpatient and outpatient administrative data with laboratory test and pharmacy claims data from Decision Support System (DSS) files and vital signs data from Corporate Data Warehouse (CDW). Starting with a standard risk-adjustment model based on administrative data, we will evaluate the improvement in predicting 30-day mortality and length of stay with each increment of additional data from other sources. Model development and hypothesis testing will be based on estimation of hierarchical, multivariate logistic and linear regression models and bootstrap sampling. Project Implications: Findings from this study can become the basis for developing formal quality monitoring and improvement mechanisms for medical inpatient admissions, similar to VA's National Surgical Quality Improvement Program (NSQIP) for surgical admissions.

抽象的项目背景：风险调整被认为对于准确评估质量、公平比较至关重要提供商，以及跨医疗保健系统的基准测试。作为基于图表的数据——“黄金风险调整的“标准”——收集管理数据既昂贵又耗时尽管在捕捉患者严重程度方面存在不足，但仍然是风险调整的基础。医疗保健质量与研究机构 (AHRQ) 最近采取的举措旨在增强管理数据以及实验室测试和生命体征的自动化数据是重要的一步提高风险调整模型的准确性。项目目标：利用现成的 VA 自动化数据，该项目的主要目标是开发具有成本效益且临床合理的增强风险调整模型来分析 VA 入院后 30 天死亡率的设施。风险调整死亡率增加将获得利率——以及住院时间和重新入院时间（感兴趣的次要结果）通过将管理数据与自动化数据相结合，针对选定的患者群体药房声明、实验室测试措施和生命体征。我们的具体目标是：1) 使用以下方法检查预测 30 天死亡率和住院时间的模型的性能行政数据； 2）依次估计模型性能的改进添加药房声明、实验室测试和生命体征数据中确定的风险因素；和 3) 评估加强风险调整对设施风险调整死亡率排名的影响。项目方法：将为九种医疗状况估计单独的增强风险调整模型占退伍军人管理局所有入院人数 (16%) 和住院患者死亡人数 (30%) 的相当大比例 %) - 急性心肌梗塞、充血性心力衰竭、肝硬化和酒精性肝炎，慢性阻塞性肺病、胃肠道出血、髋部骨折、肺炎、急性肾功能衰竭和急性中风。根据 2003 财年 VA 设施的所有入院人数计算 - 2008 年，将提取特定疾病的队列。我们将合并 VA 住院和门诊来自决策支持的实验室测试管理数据和药房索赔数据来自公司数据仓库 (CDW) 的系统 (DSS) 文件和生命体征数据。从一个开始基于管理数据的标准风险调整模型，我们将评估改进情况随着额外数据的增加，预测 30 天死亡率和住院时间其他来源。模型开发和假设检验将基于估计分层、多元逻辑和线性回归模型以及引导抽样。项目影响：这项研究的结果可以成为制定正式质量监控和内科住院病人入院的改进机制，类似于 VA 的国家外科手术手术入院质量改进计划 (NSQIP)。