Abstract
BackgroundPalliative 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.MethodsTo 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.DiscussionThis 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.Trial registrationClinicalTrials.gov NCT03976297. Registered on 6 June 2019, prior to trial start.
Highlights
Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses
Evaluation of artificial intelligence (AI) Despite the large number of published risk scores in the literature for palliative care needs, to the best of our knowledge, there has been a lack of research assessing wide scale evaluation and implementation of palliative risk scores into clinical practice
Aims of the study The objective of this study is to evaluate whether the Control Tower solution 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
Summary
Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses through the prevention and management of pain and other problems, be it physical, psychosocial, and/or spiritual [1]. Palliative care expertise is often an underutilized resource, and the current model of care and incentives may relegate palliative care as a “last resort” after all attempts at cure have been exhausted [4] This delay can lead to suboptimal symptom management for pain, lower quality of life, and even reduced survival [5,6,7,8]. Triage tools can be used quickly and reliably to identify patients that may benefit from palliative care, thereby leading to high value care and improved outcomes
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