Default palliative care consultation for seriously ill hospitalized patients

描述（由申请人提供）：住院姑息治疗咨询服务（IPCS）对改善生命限制疾病患者的护理和降低成本有着巨大的希望。因此，世界各地的医院都在大力投资此类项目，事实上，在过去十年中，美国医院的此类项目普及率提高了157%，预计今年将达到84%的拥有至少50张床位的医院。然而，可用于量化IPCS在可推广的现实环境中的效果的实验证据有限，而关于哪种类型的服务最适合哪种类型的患者，或如何确保及时提供IPCS的证据就更少了。因此，研究人员提出了一项大型、实用的试验，以评估默认姑息治疗咨询对重症患者的临床、以患者为中心和经济结果的影响。该试验将在美国最大的非营利性医疗系统Ascension health的9家医院中使用相同的电子健康记录进行，初步数据显示，将招募超过1.5万名符合条件的患者。该试验将采用楔形分步聚类随机设计，随机分配医院在不同时间采用一种干预措施，使选定的患有限制生命疾病的患者默认接受姑息治疗咨询。我们完全自动化的方法不是依靠医生的行动来确定IPCS的需求并下相应的订单，而是使用医院的电子健康记录来默认为通过常规筛查确定可能受益的患者订购IPCS。因此，干预措施将实践从以医生为中心的IPCS选择加入模式转变为选择退出模式，在这种模式下，姑息治疗团队通常会让患者和家属参与，除非患者、家属或医生主动选择其他方式。在最初的UH2（计划）年，多学科研究团队将开发电子病历流程，以识别符合姑息治疗触发器的患者，验证所有建议结果的数据捕获和跟踪电子生成的姑息治疗订单遵守情况的方法，开发确保参与医院高保真数据传输的方法，建立通知患者和提供者有关研究的机制和材料。并完成所有监管要求。在随后的UH3（实施）阶段，该团队将在姑息治疗方面进行有史以来规模最大的随机试验，回答关键问题，如IPCS的效果如何，这些服务的哪些结构特征使它们特别有效（例如，姑息治疗cae医生与护士从业人员的人员配置），以及哪种类型的患者获益最大（例如，在病程较早与较晚的患者，或在ICU与医院病房的患者）。与此同时，该试验将提供默认姑息治疗的实验性测试，这是一种在选定患者中增加IPCS普及的有希望的方法，如果成功，可以迅速和容易地在世界各地的医院推广实施。

DESCRIPTION (provided by applicant): Inpatient palliative care consultative services (IPCS) hold great promise to improve care and reduce costs for patients with life-limiting illnesses. As such, hospitals worldwide are heavily investing in such programs, and indeed, their penetrance in U.S. hospitals has increased 157% in the past decade and is projected to reach 84% of hospitals with at least 50 beds this year. However, there is limited experimental evidence available to quantify the effects of IPCS in generalizable, real-world settings, and even less evidence about which types of services work best for which types of patients, or how to ensure timely IPCS delivery. Thus, the investigators propose a large, pragmatic trial to assess the effects of default palliative care consultation for seriously ill patients on clinical, patient-cenered, and economic outcomes. The trial will be conducted among 9 hospitals using the same electronic health record within Ascension Health, the largest non-profit health system in the U.S., and preliminary data suggest that it will enroll more than 15,000 eligible patients. The tria will use a stepped-wedge, cluster randomized design in which hospitals will be randomly assigned to adopt, at different times, an intervention that makes palliative care consultation the default for selected patients with life-limiting illnesses. Instead of relying on the actions of buy physicians to identify needs for IPCS and place corresponding orders, our fully automated approach uses hospitals' electronic health records to order IPCS by default for patients identified on routine screening as likely to benefit. The intervention therefore shifts practice frm a physician-centered, opt-in model for IPCS to an opt-out model whereby palliative care teams routinely engage patients and families unless patients, families, or physicians actively choose otherwise. During the initial UH2 (planning) year, the multidisciplinary research team will develop electronic medical record processes to identify patients meeting palliative care triggers, validate data capture of all proposed outcomes and methods for tracking adherence to the electronically generated palliative care orders, develop methods to ensure high-fidelity data transfer from participating hospitals, establish mechanisms and materials for notifying patients and providers about the research, and complete all regulatory requirements. During the subsequent UH3 (implementation) phase, the team will conduct the largest randomized trial ever in palliative care, answering key questions such as how well IPCS works, which structural characteristics of these services make them particularly effective (e.g., staffing by palliative cae physicians vs. nurse practitioners), and which types of patients derive the greatest benefits (e.g., those seen earlier vs. later in their disease courses, or those in the ICU vs. hospital ward. At the same time, the trial will provide an experimental test of default palliative care - a promising approach for increasing penetration of IPCS among selected patients that, if successful, could be quickly and easily scaled for implementation at hospitals around the world.