Improving Palliative Measurement Application with Computer-Assisted-Abstraction Study

背景：退伍军人事务部需要在衡量绩效时纳入退伍军人和家庭的声音， 改善护理，使退伍军人和家庭更深入地参与改善。更是如此 姑息治疗和临终关怀，考虑到生命最后一年的终身医疗保险费用的30%， 美国人对临终关怀表达了不同的观点。我们已经确认少校 退伍军人管理绩效存在差距，但需要优先考虑退伍军人和家庭的指标 展望未来，提高措施的可行性。后者将培养更好的VA质量和经验， 护理和促进监测支付的潜在影响，非VA照顾重病退伍军人。 目的：计算机辅助抽象研究在姑息测量中的应用 （ImPACS），将优先考虑措施，并将高、中、低可行性的子集付诸实施 姑息治疗和临终癌症护理的过程和利用措施。我们的目标是： 目标1：从两个德尔菲小组中征求优先事项-一个是退伍军人和家属小组，另一个是专家小组 关于先进癌症护理的过程和医疗保健使用质量措施，包括 42癌症质量援助措施，以提取，并就如何整合进行访谈 退伍军人和家庭在VA测量和改善姑息治疗和临终癌症护理，以及 目标2：使用VA的自然语言处理提取高优先级过程和利用措施 图表和管理数据，并在一个样本中从斯坦福大学和达纳法伯的医疗保健的 史诗系统，专注于疼痛和阿片类药物，心理健康和护理目标的领域 通信和 目标3：检查提取的度量与退伍军人特征的关联，重点是 农村和非白人退伍军人的护理和姑息治疗使用的差异。 方法：对于目标1，我们将招募两个德尔菲小组-一个是退伍军人，家庭成员，另一个是 专家利益相关者。退伍军人家庭成员将有癌症的经验。专家将拥有专业知识 质量测量的方法和应用。有目的抽样将侧重于关键属性 (e.g., #21453;，这可能会影响优先级。小组成员将在高， 中等和低可行性，通过对干预措施和影响的证据进行审查， 患者和护理人员的绩效差距。我们还将采访退伍军人，家庭成员和VA 根据临终前丧亲家庭调查，高绩效和低绩效VA设施的领导人 经验，看看如何更深入的退伍军人家庭参与可能会加强质量和经验， 临终关怀对于目标2，我们将操作目标1中的优先过程度量的子集 包括使用文本注释和机器的自然语言处理的癌症质量ASSIST措施 学习在目标3中，我们将描述具有退伍军人特征的度量的变化，重点是 农村和非白人退伍军人和姑息治疗服务使用之间的已知差距。 影响：我们将制定退伍军人和家庭知情的优先事项，为姑息治疗制定平衡的措施。 和癌症临终关怀。我们将告知退伍军人和家庭如何更深入地参与 促进质量和经验的提高，并促进学习型医疗保健系统。最后我们将 使用最先进的方法提取优先措施，以提高其可行性， 退伍军人水平的差异集中在已知的差异和缓解这些差异的姑息治疗潜力上。

Background: VA needs to incorporate the Veteran and family voice in measuring performance, and it may improve care to involve Veterans and families more deeply in improvement. This is especially true for palliative and end of life care, given the 30% of lifetime Medicare costs in the last year of life, and the divergent perspectives that Americans have expressed regarding end of life care. We have identified major gaps in VA performance, but work is needed to prioritize indicators from the Veteran and family perspective and to improve measure feasibility. The latter will foster better VA quality and experience of care and facilitate monitoring the potential impact of paid, non-VA care on seriously ill Veterans. Aims: The Improving Palliative Measurement Application with Computer-Assisted-Abstraction Study (ImPACS), will prioritize measures and operationalize a subset of higher, intermediate, and lower feasibility process and utilization measures for palliative and end of life cancer care. We aim to: Aim 1: Solicit priorities from two Delphi panels - one of Veterans and families and a second of experts regarding process and healthcare use quality measures for advanced cancer care, including which of the 42 Cancer Quality ASSIST measures to extract, and conduct interviews regarding how to integrate Veterans and families in VA measurement and improvement for palliative and end of life cancer care, and Aim 2: Extract high priority process and utilization measures using natural language processing of VA charts and administrative data and in a sample drawn from Stanford and Dana Farber’s Healthcare's Epic systems, focusing on the domains of pain and opioids, mental health, and goals of care communication, and Aim 3: Examine associations of the extracted measures with Veteran characteristics, focusing on disparities in the care of rural and nonwhite Veterans and palliative care use. Methods: For Aim 1, we will recruit two Delphi panels - one of Veterans, family members, and a second of expert stakeholders. Veteran-family members will have experience with cancer. Experts will have expertise in the methods and application of quality measures. Purposive sampling will focus on critical attributes (e.g., race) that may affect priorities. Panelists will rate and rank measures within tiers of high, intermediate, and low feasibility, informed by reviews of the evidence for intervention and impact of performance gaps on patients and caregivers. We will also interview Veterans, family members and VA leaders at high and low performing VA facilities based on the Bereaved Family Survey of end of life experience, to see how deeper Veterans-family involvement might strengthen quality and experience of end of life care. For Aim 2, we will operationalize a subset of prioritized process measures from Aim 1 including Cancer Quality ASSIST measures using natural language processing of text notes and machine learning. In Aim 3, we will characterize variation in measures with Veteran characteristics focusing on known disparities among rural and nonwhite Veterans and palliative care services use. Impact: We will produce Veteran and family-informed priorities for a balanced measure set for palliative and end of life cancer care. We will inform how Veterans and families might be more deeply engaged in fostering improved quality and experience and fostering a learning healthcare system. Finally, we will extract prioritized measures using state of the art methods to improve their feasibility, characterize variation at the Veteran level focused on known disparities and palliative care potential to mitigate them.