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)拥有巨大的希望，以改善护理，降低患者的生命限制疾病的成本。因此，世界各地的医院都在大力投资此类项目，事实上，这些项目在美国医院的渗透率在过去十年中增加了157%，预计今年将覆盖84%的至少有50张床位的医院。然而，现有的实验证据有限，无法量化IPCS在可概括的现实世界环境中的影响，关于哪些类型的服务对哪些类型的患者最有效，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、务实的试验，以评估重症患者默认姑息治疗咨询对临床、以患者为中心和经济结果的影响。这项试验将在9家医院中进行，使用美国最大的非营利性医疗系统阿森松健康内的相同电子健康记录，初步数据显示，该系统将招募超过15,000名符合条件的患者。Tria将使用阶梯式楔形集群随机设计，在不同的时间随机分配医院采取干预措施，使姑息治疗咨询成为选定的患有限制生命的疾病的患者的默认做法。我们的全自动化方法不是依赖购买医生的操作来确定IPCS的需求并下相应的订单，而是使用医院的电子健康记录默认为常规筛查中被确定为可能受益的患者订购IPCS。因此，干预措施将IPCS的实践从以医生为中心的选择加入模式转变为选择退出模式，在这种模式下，姑息治疗团队定期与患者和家属接触，除非患者、家人或医生积极选择其他方式。在最初的UH2(规划)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条件的患者，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，开发方法以确保从参与医院传输高保真数据，建立机制和材料，向患者和提供者通报研究，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领域进行有史以来最大规模的随机试验，回答一些关键问题，例如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CAE医生与护士从业者的人员配备)，以及哪些类型的患者受益最大(例如，那些在他们的疾病病程中出现得更早的患者，或那些在ICU与医院病房的患者)。与此同时，这项试验将提供默认姑息治疗的试验性测试--这是一种有希望的方法，可以增加IPCS在选定患者中的渗透率，如果成功，可以快速轻松地扩展到世界各地的医院实施。

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.