Automating Performance Metrics for Quality Improvement in Complex Chronic Disease

自动化绩效指标以提高复杂慢性疾病的质量

• 批准号: 8596733
• 负责人: MARY K. Goldstein
• 金额: --
• 依托单位: VETERANS ADMIN PALO ALTO HEALTH CARE SYS
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8596733
• 关键词: Accounting; Address; Area; Beds; Caring; Characteristics; Chronic; Chronic Disease; Clinical; Clinical Management; Clinical Practice Guideline; Comorbidity; Complex; Conflict (Psychology); Data; Detection; Development; Disease; Electronics; Exclusion Criteria; Funding; Guideline Adherence; Guidelines; Health; Health Professional; Healthcare; Healthcare Systems; Informatics; Information Systems; Information Technology; Interview; Knowledge; Lead; Light; Measurement; Measures; Medical; Methods; Metric; Patients; Performance; Procedures; Process; Quality of Care; Recommendation; Research Infrastructure; Research Project Grants; Structure; System; Testing; Time; To specify; United States National Library of Medicine; Veterans; Work; Writing; base; clinical care; clinical data warehouse; design; electronic data; evidence base; evidence based guidelines; health care quality; health information technology; improved; insight; iterative design; knowledge base; meetings; method development; multiple chronic conditions; pharmacy benefit; programs; tool

DESCRIPTION (provided by applicant): Background: Many Veterans receiving medical care in VA have multiple coexisting chronic conditions ("comorbidities"); yet, performance measures typically examine only one disease at a time. Clinical practice guidelines (CPGs) synthesizing evidence into recommendations form the basis for most performance measures. Because of the limitations of previously available systems for automated processing of performance measures, the measures address only small portions of the CPG recommendations, and rarely take account of clinical complexity even within single diseases. In order to make performance measures applicable to the patients included in the denominator for the measure, many exclusion criteria are often applied, resulting in absence of any performance measure for the substantial proportion of patients who have an exclusion criterion. Furthermore, even for patients remaining in the denominator, evidence-based recommendations may call for alternate treatments due to specific clinical characteristics of the patient, resulting in non-applicability of the simple performance measures. Newer information technology can be applied to enhance performance measures. Encoded knowledge bases of detailed clinical knowledge allow for automated processing of patient data to draw conclusions about the state of the patient (including attainment of performance measure clinical targets) and highly-individualized recommendations for next steps in therapy. Application of such knowledge bases can provide a nuanced view of guideline-adherence and detection of presence or absence of valid reasons, such as comorbid illnesses and their treatments, for altering treatment or targets. Objectives: The overall objective of this project is to develop new informatics methods to automate quality improvement measures for patients with complex clinical scenarios. The Specific Aims are (1) To create an automated knowledge base for current performance measures with clinical detail about alternate evidence- based recommendations based on detailed clinical characteristics; (2) To conduct stakeholder interviews with key VA program offices and quality managers regarding prioritization and coordination of recommendations for patients who have multiple comorbidities; (3) To elaborate the knowledge base with additional clinical knowledge about prioritization and coordination of recommendations based on findings from Specific Aim 2; (4) To develop systems to provide clinical decision support (CDS) across multiple chronic conditions to health professionals in the Patient-Aligned Care Teams (PACTs) to improve performance. Design/Methods: The project will use informatics development methods to encode the clinical knowledge base, building on prior (CDS) work and drawing on insights about integrating multiple guidelines from an informatics project recently-funded by National Library of Medicine (NLM). Stakeholder interviews with key staff in quality management and in several domains of clinical care including experts in multiple co-existing chronic conditions will be synthesized to guide prioritization and coordination of recommendations. The VISN21 Pharmacy Benefits Management clinical dashboard will serve as the test-bed for providing CDS to PACT health professionals providing patient-centric care for Veterans with complex conditions. Agile development procedures will be employed based on frequent interaction with potential end-users in iterative design and development cycles. Informatics tools developed in this project will be evaluated by constraint verification, testing of accuracy of recommendations, assessment of suitability to clinical workflow, and stakeholder input. Potential Impact: This informatics research project will develop new methods to address quality measurement and improvement for the many Veterans with multiple chronic conditions. These methods can potentially automate quality measurement for quality managers and clinical program offices, and automate CDS to assist health professionals in PACTs for quality improvement in patient-centric care.

描述（由申请人提供）： 背景资料：许多退伍军人在VA接受医疗护理有多种共存的慢性疾病（“合并症”）;然而，性能指标通常只检查一种疾病的时间。临床实践指南（CPG）将证据合成为建议，构成了大多数性能指标的基础。由于以前可用的系统的性能指标的自动化处理的限制，措施解决只有一小部分的CPG的建议，很少考虑到临床的复杂性，即使在单一的疾病。为了使性能指标适用于指标分母中包含的患者，通常应用许多排除标准，导致具有排除标准的大部分患者缺乏任何性能指标。此外，即使对于仍在分母中的患者，基于证据的建议也可能由于患者的特定临床特征而要求替代治疗，从而导致简单性能指标不适用。可采用较新的信息技术来加强业绩计量。详细临床知识的编码知识库允许自动处理患者数据，以得出有关患者状态的结论（包括性能测量临床目标的实现）和针对治疗中的后续步骤的高度个性化的建议。这些知识库的应用可以提供对指南遵守的细致入微的观察，并检测是否存在有效的原因，例如共病及其治疗，以改变治疗或目标。目的：该项目的总体目标是开发新的信息学方法，以自动化复杂临床情况下的患者质量改进措施。具体目的是：（1）为当前性能指标创建自动化知识库，其中包含基于详细临床特征的替代循证建议的临床详细信息;（2）与主要VA项目办公室和质量经理进行利益相关者访谈，讨论对患有多种合并症的患者的建议的优先级和协调;（3）根据具体目标2的发现，利用关于优先排序和协调建议的额外临床知识来阐述知识库;（4）发展系统，为“病人为本护理队”的医护专业人员提供多种慢性病的临床决策支援以提高性能设计/方法：该项目将使用信息学开发方法对临床知识库进行编码，建立在先前（CDS）工作的基础上，并借鉴国家医学图书馆（NLM）最近资助的信息学项目中关于整合多个指南的见解。利益相关者采访质量管理和临床护理的几个领域的关键工作人员，包括在多种共存的慢性疾病的专家将被合成，以指导优先级和建议的协调。VISN 21药房福利管理临床仪表板将作为向PACT卫生专业人员提供CDS的测试平台，为患有复杂疾病的退伍军人提供以患者为中心的护理。在迭代设计和开发周期中，将根据与潜在最终用户的频繁互动采用敏捷开发程序。本项目中开发的信息学工具将通过约束验证、建议准确性测试、临床工作流程适用性评估和利益相关者输入进行评价。潜在的影响：这个信息学研究项目将开发新的方法来解决质量测量和改善许多退伍军人与多种慢性疾病。这些方法可能会自动化质量管理人员和临床项目办公室的质量测量，并自动化CDS，以协助PACTs的卫生专业人员提高以患者为中心的护理质量。