Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design

Abstract

Background The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. Objectives To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. Design This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. Summary This trial will determine whether a clinical decision aid tool improves cancer survivors’ medication use and imaging surveillance recommendations aligned with current medical guidelines. Trial registration ClinicalTrials.Gov Identifier: NCT05377320

Multimedia Mixed Reality Interactive Shared Decision-Making Game in Children with Moderate to Severe Atopic Dermatitis, a Pilot Study

“…The World Health Organization (WHO) proposed the topic of World Patient Safety Day 2023 as Engaging Patients for Patient Safety. The application of SDM is quite extensive, including the treatment and prevention of diseases [ 30 , 31 ]. Clinical decision support systems have shown the potential for diagnosis and monitoring of allergic diseases [ 1 ].…”

“…Personalized precision medicine is expected to apply to SDM because of differences in treatment guidelines for children and adults with AD [ 3 , 24 ]. Application of artificial intelligence machine learning algorithms in clinical decision can support children with AD in the future [ 31 ].…”