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

Brown, Sherry-Ann; Chung, Brian Y.; Doshi, Krishna; Hamid, Abdulaziz; Pederson, Erin; Maddula, Ragasnehith; Hanna, Allen; Choudhuri, Indrajit; Sparapani, Rodney; Pour, Mehri Bagheri Mohamadi; Zhang, Jun; Kothari, Anai N.; Collier, Patrick; Caraballo, Pedro J.; Noseworthy, Peter A.; Arruda‐Olson, Adelaide M.

doi:10.1186/s40959-022-00151-0

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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(1) Objective: Atopic dermatitis (AD) is a recurring skin disease that affects children’s daily activities and sleep quality. Due to the limitations of children’s understanding and ability to express themselves, shared decision making (SDM) is often made by guardians, which thus affects the acceptance and effectiveness of children’s treatments. Previous studies have demonstrated that involving both children and parents in decision making may help improve treatment outcomes; thus, we designed a multimedia mixed reality (MR) interactive game of SDM for children with moderate to severe AD. (2) Methods: Research participants included 6–18-year-old patients with moderate to severe AD. This research consisted of the following steps: designing SDM; character setting and visual design; performing games; system modification and optimization; screen editing and dubbing; and user testing and questionnaires by the System Usability Scale (SUS). (3) Results: We completed the SDM design for children with moderate to severe AD. Four different treatments were biologics, oral immune-modulating drugs, phototherapy, and wet wrap. An animated PowerPoint slide showed the AD apple rolling around before treatments and the AD apple sleeping soundly after treatments. Instructions with video teaching for the four different treatments were played, and then, the MR was turned on so that the patients could help the AD apple in the metaverse to undergo these four treatments. A total of 12 moderate to severe AD patients and six control patients used the game, all aged between six and eighteen years old, with an average SUS score of 81.0 and a standard error of 2.1 points. Adjective ratings yielded a rating between good and excellent. The game showed acceptable usability. We found no statistically significant differences in SUS scores between patients with and without moderate to severe AD or between boys and girls nor significant associations between SUS and age or severity. The analysis identified that the two items with the lowest SUS scores were “I think that I would need the support of a technical person to be able to use this product” and “I needed to learn a lot of things before I could get going with this product”. Both of these comments show the limitations of this game. (4) Conclusions: Overall, this study provides the first MR SDM game that has passed the SUS and can be used as an aid in clinical SDM.

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Section: Introductionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%