Pain Care Quality and Integrated and Complementary Health Approaches

描述（由申请人提供）：提高疼痛管理的质量是退伍军人健康管理局（VHA）的重中之重。VHA发布了政策指导，建立了疼痛管理（SCM- PM）的创新阶梯护理模式作为疼痛护理的单一标准。SCM-PM提供了在初级保健机构评估和治疗疼痛的能力，同时保持了升级治疗方案的能力，如有必要，包括专门护理。该模型进一步强调了个性化、综合、多模式的疼痛管理方法的重要性，该方法考虑到精神健康合并症，并结合补充健康方法（CHA），以促进最佳疼痛控制和改善功能和生活质量。尽管在推广这一模式方面取得了很大的进步，但疼痛管理绩效改进的努力受到了可靠的疼痛护理质量指标和疼痛管理关键维度指标的有限可用性的阻碍，以促进它们在系统质量改进工作中的利用。除了使用特定的、易于检索的代码在VHA的电子健康记录（EHR）中记录护理的药理学和基于程序的干预措施之外，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对促进绩效改进工作（包括实施SCM-PM）构成了严重障碍。拟议的项目扩展了我们的研究团队之前的研究，通过使用自然语言处理（NLP）和机器学习（ML）来自动化先前验证的方法，以识别和量化疼痛护理质量的关键维度，即评估，特别是功能评估，综合治疗计划，重新评估（结果）和患者教育。一旦这个自动化解决方案得到验证，我们打算将其应用于一个国家样本，以测试有关的重要问题

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.