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

• 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
• 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

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 提供者，并在医疗保健系统中进行基准测试。作为基于图表的数据 - “黄金 标准“风险调整 - 昂贵且耗时，收集行政数据 尽管捕获患者的严重程度不足，但仍是调整风险调整的基础。 医疗保健质量与研究机构（AHRQ）的最新举措以增强 具有实验室测试和生命体征的自动数据的行政数据是重要的一步 致力于提高风险调整模型的准确性。 项目目标： 利用随时可用的VA自动数据，该项目的主要目标是 开发具有成本效益和临床声音增强的风险调整模型，以配置VA 住院后30天死亡率的设施。增强了风险调整后的死亡率 将获得费率 - 住宿和再入院时间（感兴趣的次要结果） - 将获得 对于选定的患者队列，通过将行政数据与自动数据相结合 药房索赔，实验室测试措施和生命体征。我们的具体目标是：1） 检查模型的性能，以预测使用30天死亡率和使用时间长度 行政数据； 2）估计从顺序的模型性能的改善 添加药房索赔，实验室测试和生命体征数据中确定的危险因素； 3） 评估增强风险调整对风险调整死亡率设施排名的影响。 项目方法： 对于九种医疗状况，将估计单独的增强风险调整模型 在所有VA入院中（16％）和住院死亡（30个）占相当比例的 百分比） - 急性心肌梗塞，充血性心力衰竭，肝硬化和酒精性肝炎， 慢性阻塞性肺疾病，胃肠道出血，髋部骨折，肺炎， 急性肾衰竭和急性中风。根据2003财年对VA设施的所有入院 2008年，将提取特异性疾病的队列。我们将合并VA住院和门诊 带有实验室测试和药房索赔数据的行政数据来自决策支持 系统（DSS）文件和生命体征来自公司数据仓库（CDW）的数据。从A开始 标准风险调整模型基于行政数据，我们将评估改进 在预测30天死亡率和住院时间时，每次增加了来自 其他来源。模型开发和假设检验将基于对 分层，多元逻辑和线性回归模型以及引导带抽样。 项目影响： 这项研究的发现可以成为开发正式质量监测和的基础 医疗住院治疗的改进机制，类似于VA的国家手术 用于手术入院的质量改进计划（NSQIP）。

项目成果

Induced membrane technique versus one-stage autografting in management of atrophic nonunion of long bone in the lower limb: clinical and health burden outcomes.

• DOI: 10.1186/s13018-023-04296-1
• 发表时间: 2023-11-09
• 期刊: JOURNAL OF ORTHOPAEDIC SURGERY AND RESEARCH
• 影响因子: 2.6
• 作者: Zhang, Hu;Fu, Jingshu;Jie, Shen;Wang, Xiaohua;Wang, Shulin;Wu, Hongri;Hu, Yongjun;Huang, Chunji
• 通讯作者: Huang, Chunji