Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
Department of Quantitative Health Sciences, Mayo Clinic, MN, 55905, Rochester, USA.
Trials. 2021 Sep 16;22(1):635. doi: 10.1186/s13063-021-05546-5.
Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care.
To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance.
This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor.
ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start.
姑息治疗是一门以提高患有复杂或危及生命疾病的患者生活质量（QOL）为中心的医学专业。姑息治疗的需求正在增加，因此需要对分诊工具进行严格测试，这些工具可以快速、可靠地识别可能受益于姑息治疗的患者。
为此，我们将开展一项两臂、阶梯式楔形、集群随机试验，该试验将在两家住院医院实施，以评估机器学习算法是否能准确识别可能受益于姑息治疗专家全面评估的患者，并减少在医院接受姑息治疗咨询的时间。这是一项单中心研究，将于 2019 年 8 月至 2020 年 11 月在明尼苏达州罗切斯特的圣玛丽医院和卫理公会医院进行，这两家医院都隶属于梅奥诊所。集群将是护理单元，这些单元将从心脏病学、重症监护和肿瘤学中选择混合有复杂患者，并与姑息医学建立了先前的关系。阶梯楔形设计将有 12 个单元分配到 5 个治疗楔形的设计矩阵中。每个楔形持续 75 天，因此研究期为 12 个月的招募期，除非另有说明。数据将使用贝叶斯分层模型进行分析，置信区间表示统计学意义。
这种干预措施为使用机器学习为有需要的住院患者提供专业姑息治疗提供了一种实用方法，从而提供了高价值的护理和改善的结果。仅仅通过发表显示预测性能的研究来利用人工智能是不够的；迫切需要临床试验来证明更好的结果。此外，部署人工智能算法是一个复杂的过程，需要具有不同技能的多个团队。为了评估部署的人工智能，实用临床试验可以适应临床实践的困难，同时保持科学严谨性。
ClinicalTrials.gov NCT03976297. 于 2019 年 6 月 6 日注册，在试验开始之前。
