Pain Care Quality and Integrated and Complementary Health Approaches

疼痛护理质量以及综合和补充的健康方法

基本信息

批准号：
8757682
负责人：
CYNTHIA A. BRANDT
金额：
$ 45.64万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8757682
关键词：
Accounting Acupuncture procedure Address Adopted Award Benchmarking Caring Code Cognitive Therapy Comorbidity Complex Data Databases Development Dimensions Disadvantaged Disease Education Electronic Health Record Foundations Health Healthcare Healthcare Systems Hypnosis Institute of Medicine (U.S.)Intervention Link Machine Learning Manuals Massage Measures Medical Mental Health Mental disorders Methodology Methods Metric Military Personnel Modeling Musculoskeletal Pain Natural Language Processing Outcome Pain Pain Measurement Pain Research Pain management Pain quality Patient Education Patient Preferences Patients Performance Persons Policies Prevalence Procedures Provider Public Health Publishing Quality Indicator Quality of Care Quality of life Recommendation Research Research Personnel Resources Role Sampling Self Management Solutions Spinal Manipulation Structure Substance abuse problem Testing Time Treatment Effectiveness Veterans Work base evidence base health administration improved improved functioning innovation mindfulness-based stress reduction population based primary care setting public health relevance shared decision making tool treatment planning

项目摘要

DESCRIPTION (provided by applicant): Improving the quality of pain management is a high priority for the Veterans Health Administration (VHA). VHA has published policy guidance that establishes an innovative stepped care model of pain management (SCM- PM) as the single standard of pain care. The SCM-PM provides the ability to assess and treat pain in primary care settings, while maintaining the capacity to escalate treatment options to include specialized care, if necessary. The model further emphasizes the importance of an individually tailored, integrated, multi-modal approach to pain management that takes into account mental health comorbidities and that incorporates complementary health approaches (CHA) to promote optimal pain control and improved function and quality of life. Despite great strides in promoting this model, pain management performance improvement efforts have been hampered by the limited availability of reliable Pain Care Quality indicators and metrics for key dimensions of pai management in order to promote their utilization in systematic quality improvement efforts. Other than pharmacological and procedure based interventions in which specific, easily retrievable codes are used to document care in the VHA's electronic health record (EHR), it is difficult to capture the broader array of CHA or key aspects of integrated care. These gaps in the EHR and VHA database pose serious barriers to promoting performance improvement efforts including implementation of the SCM-PM. The proposed project extends prior research by our investigator team by using Natural Language Processing (NLP) and Machine Learning (ML) to automate a previously validated approach to identify and quantify key dimensions of Pain Care Quality, namely assessment, especially functional assessment, integrated treatment plans, reassessment (outcomes), and patient education from the EHR. Once this automated solution is validated, we intend to apply it to a national sample to test important questions about Pain Care Quality among veterans with comorbid mental health conditions, access to CHA, and the SCM-PM. This innovative solution to identifying key dimensions of healthcare has potential applicability to improving the management of other complex health problems for which existing quality of care indicators and metrics are limited.

描述（由申请人提供）：改善疼痛管理的质量是退伍军人卫生管理局（VHA）的重点。 VHA发布了政策指导，建立了创新的疼痛管理阶梯护理模型（SCM-PM）作为疼痛护理的单一标准。 SCM-PM提供了评估和治疗初级护理环境中疼痛的能力，同时保持治疗方案的升级能力，以便在必要时包括专业护理。该模型进一步强调了一种单独定制的，综合的，多模式的方法对疼痛管理的重要性，该方法考虑了心理健康合并症，并结合了互补的健康方法（CHA），以促进最佳的疼痛控制和改善的功能和生活质量。尽管在促进该模型方面取得了长足的进步，但可靠的疼痛护理质量指标和指标的可靠性有限，可以促进其在系统的质量改进工作中的利用，从而阻碍了疼痛管理绩效的改善工作。除了基于药理和程序的干预措施之外，该干预措施在其中使用特定的，易于检索的代码来记录VHA的电子健康记录（EHR）中的护理，很难捕获更广泛的CHA或综合护理的关键方面。 EHR和VHA数据库中的这些差距在促进绩效改进工作中构成了严重的障碍，包括实施SCM-PM。拟议的项目通过使用自然语言处理（NLP）和机器学习（ML）来扩展我们的研究人员团队的先前研究，以自动使用先前验证的方法来识别和量化疼痛护理质量的关键维度，即评估，尤其是功能评估，综合治疗计划，Reassessment（Reassessment）（ETCOMESMENT（OUTCOMESS）以及EHR的患者教育。一旦该自动解决方案得到验证，我们打算将其应用于国家样本，以测试有关的重要问题具有合并性心理健康状况的退伍军人的疼痛护理质量，获得CHA和SCM-PM。确定医疗保健关键方面的这种创新解决方案具有改善其他复杂健康问题的管理的可能性，该问题现有的护理指标和指标受到限制。