These findings imply that treatment strategies for individuals at UHR for psychosis should be comprehensive, promoting resilience as well as targeting the reduction of positive and negative symptoms to foster social reintegration and recovery.

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Delirium can be defined as "Acute Brain Dysfunction". Compared to dementia which is a disease that deteriorates the brain function in chronic course, delirium shows very similar symptoms but mostly ameliorates when the causative factors are normalized. Due to its heterogeneity of etiologies and symptoms, people including health care workers often mistake delirium for dementia or other psychiatric disorders. Delirium has attracted the global interest increasingly enough to gather vast amount of research on its management in various ways. Experts on the field have consistently suggested that systematic interventions should be implemented through a team-based multicomponent approach aimed to reduce the incidence and duration of delirium. Surgery involves many health care workers with different expertise who are not familiar with delirium. For the team-based approach on the management, it is vital that all medical personnel concerned have common understanding and consistent communication of delirium. Postoperative delirium is a common complication and exerts enormous burden on patients, their families, hospitals, and public resources. To alleviate this burden, this article is aimed to review general features and latest evidence-based knowledge of delirium with the focus on postoperative delirium.

Background To motivate people to adopt medical chatbots, the establishment of a specialized medical knowledge database that fits their personal interests is of great importance in developing a chatbot for perinatal care, particularly with the help of health professionals. Objective The objectives of this study are to develop and evaluate a user-friendly question-and-answer (Q&A) knowledge database–based chatbot (Dr. Joy) for perinatal women’s and their partners’ obstetric and mental health care by applying a text-mining technique and implementing contextual usability testing (UT), respectively, thus determining whether this medical chatbot built on mobile instant messenger (KakaoTalk) can provide its male and female users with good user experience. Methods Two men aged 38 and 40 years and 13 women aged 27 to 43 years in pregnancy preparation or different pregnancy stages were enrolled. All participants completed the 7-day-long UT, during which they were given the daily tasks of asking Dr. Joy at least 3 questions at any time and place and then giving the chatbot either positive or negative feedback with emoji, using at least one feature of the chatbot, and finally, sending a facilitator all screenshots for the history of the day’s use via KakaoTalk before midnight. One day after the UT completion, all participants were asked to fill out a questionnaire on the evaluation of usability, perceived benefits and risks, intention to seek and share health information on the chatbot, and strengths and weaknesses of its use, as well as demographic characteristics. Results Despite the relatively higher score of ease of learning (EOL), the results of the Spearman correlation indicated that EOL was not significantly associated with usefulness (ρ=0.26; P=.36), ease of use (ρ=0.19; P=.51), satisfaction (ρ=0.21; P=.46), or total usability scores (ρ=0.32; P=.24). Unlike EOL, all 3 subfactors and the total usability had significant positive associations with each other (all ρ>0.80; P<.001). Furthermore, perceived risks exhibited no significant negative associations with perceived benefits (ρ=−0.29; P=.30) or intention to seek (SEE; ρ=−0.28; P=.32) or share (SHA; ρ=−0.24; P=.40) health information on the chatbot via KakaoTalk, whereas perceived benefits exhibited significant positive associations with both SEE and SHA. Perceived benefits were more strongly associated with SEE (ρ=0.94; P<.001) than with SHA (ρ=0.70; P=.004). Conclusions This study provides the potential for the uptake of this newly developed Q&A knowledge database–based KakaoTalk chatbot for obstetric and mental health care. As Dr. Joy had quality contents with both utilitarian and hedonic value, its male and female users could be encouraged to use medical chatbots in a convenient, easy-to-use, and enjoyable manner. To boost their continued usage intention for Dr. Joy, its Q&A sets need to be periodically updated to satisfy user intent by monitoring both male and female user utterances.