Background: Despite the increasing availability of clinical decision support systems (CDSSs) and rising expectation for CDSSs based on artificial intelligence (AI), little is known about the acceptance of AI-based CDSS by physicians and its barriers and facilitators in emergency care settings. Objective: We aimed to evaluate the acceptance, barriers, and facilitators to implementing AI-based CDSSs in the emergency care setting through the opinions of physicians on our newly developed, real-time AI-based CDSS, which alerts ED physicians by predicting aortic dissection based on numeric and text information from medical charts, by using the Unified Theory of Acceptance and Use of Technology (UTAUT; for quantitative evaluation) and the Consolidated Framework for Implementation Research (CFIR; for qualitative evaluation) frameworks. Methods: This mixed methods study was performed from March to April 2021. Transitional year residents (n=6), emergency medicine residents (n=5), and emergency physicians (n=3) from two community, tertiary care hospitals in Japan were included. We first developed a real-time CDSS for predicting aortic dissection based on numeric and text information from medical charts (eg, chief complaints, medical history, vital signs) with natural language processing. This system was deployed on the internet, and the participants used the system with clinical vignettes of model cases. Participants were then involved in a mixed methods evaluation consisting of a UTAUT-based questionnaire with a 5-point Likert scale (quantitative) and a CFIR-based semistructured interview (qualitative). Cronbach α was calculated as a reliability estimate for UTAUT subconstructs. Interviews were sampled, transcribed, and analyzed using the MaxQDA software. The framework analysis approach was used during the study to determine the relevance of the CFIR constructs. Results: All 14 participants completed the questionnaires and interviews. Quantitative analysis revealed generally positive responses for user acceptance with all scores above the neutral score of 3.0. In addition, the mixed methods analysis identified two significant barriers (System Performance, Compatibility) and two major facilitators (Evidence Strength, Design Quality) for implementation of AI-based CDSSs in emergency care settings. Conclusions: Our mixed methods evaluation based on theoretically grounded frameworks revealed the acceptance, barriers, and facilitators of implementation of AI-based CDSS. Although the concern of system failure and overtrusting of the system could be barriers to implementation, the locality of the system and designing an intuitive user interface could likely facilitate the use of optimal AI-based CDSS. Alleviating and resolving these factors should be key to achieving good user acceptance of AI-based CDSS.

Background: Post-intensive care syndrome (PICS) is the long-lasting impairment of physical functions, cognitive functions, and mental health after intensive care. Although a long-term follow-up is essential for the successful management of PICS, few reviews have summarized evidence for the efficacy and management of the PICS follow-up system. Main text: The PICS follow-up system includes a PICS follow-up clinic, home visitations, telephone or mail follow-ups, and telemedicine. The first PICS follow-up clinic was established in the U.K. in 1993 and its use spread thereafter. There are currently no consistent findings on the efficacy of PICS follow-up clinics. Under recent evidence and recommendations, attendance at a PICS follow-up clinic needs to start within three months after hospital discharge. A multidisciplinary team approach is important for the treatment of PICS from various aspects of impairments, including the nutritional status. We classified face-to-face and telephone-based assessments for a PICS follow-up from recent recommendations. Recent findings on medications, rehabilitation, and nutrition for the treatment of PICS were summarized. Conclusions: This narrative review aimed to summarize the PICS follow-up system after hospital discharge and provide a comprehensive approach for the prevention and treatment of PICS.

Intensive care unit survivors experience prolonged physical impairments, cognitive impairments, and mental health problems, commonly referred to as post-intensive care syndrome (PICS). Previous studies reported the prevalence, assessment, and prevention of PICS, including the ABCDEF bundle approach. Although the management of PICS has been advanced, the outbreak of coronavirus disease 2019 (COVID-19) posed an additional challenge to PICS. The prevalence of PICS after COVID-19 extensively varied with 28–87% of cases pertaining to physical impairments, 20–57% pertaining to cognitive impairments, and 6–60% pertaining to mental health problems after 1–6 months after discharge. Each component of the ABCDEF bundle is not sufficiently provided from 16% to 52% owing to the highly transmissible nature of the virus. However, new data are emerging about analgesia, sedation, delirium care, nursing care, early mobilization, nutrition, and family support. In this review, we summarize the recent data on PICS and its new challenge in PICS after COVID-19 infection.

Background: The assessment of post-intensive care syndrome (PICS) is challenging due to the numerous types of instruments. We herein attempted to identify and propose recommendations for instruments to assess PICS in intensive care unit (ICU) survivors. Methods: We conducted a scoping review to identify PICS follow-up studies at and after hospital discharge between 2014 and 2022. Assessment instruments used more than two times were included in the modified Delphi consensus process. A modified Delphi meeting was conducted three times by the PICS committee of the Japanese Society of Intensive Care Medicine, and each score was rated as not important (score: 1–3), important, but not critical (4–6), and critical (7–9). We included instruments with ≥ 70% of respondents rating critical and ≤ 15% of respondents rating not important. Results: In total, 6972 records were identified in this scoping review, and 754 studies were included in the analysis. After data extraction, 107 PICS assessment instruments were identified. The modified Delphi meeting reached 20 PICS assessment instrument recommendations: (1) in the physical domain: the 6-min walk test, MRC score, and grip strength, (2) in cognition: MoCA, MMSE, and SMQ, (3) in mental health: HADS, IES-R, and PHQ-9, (4) in the activities of daily living: the Barthel Index, IADL, and FIM, (5) in quality of life: SF-36, SF-12, EQ-5D-5L, 3L, and VAS (6), in sleep and pain: PSQI and Brief Pain Inventory, respectively, and (7) in the PICS-family domain: SF-36, HADS, and IES-R. Conclusion: Based on a scoping review and the modified Delphi method, 20 PICS assessment instruments are recommended to assess physical, cognitive, mental health, activities of daily living, quality of life, sleep, and pain in ICU survivors and their families.