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Background The smart hospital's concept of using the Internet of Things (IoT) to reduce human resources demand has become more popular in the aging society. Objective To implement the voice smart care (VSC) system in hospital wards and explore patient acceptance via the Technology Acceptance Model (TAM). Methods A structured questionnaire based on TAM was developed and validated as a research tool. Only the patients hospitalized in the VSC wards and who used it for more than two days were invited to fill the questionnaire. Statistical variables were analyzed using SPSS version 24.0. A total of 30 valid questionnaires were finally obtained after excluding two incomplete questionnaires. Cronbach’s α values for all study constructs were above 0.84. Result We observed that perceived ease of use on perceived usefulness, perceived usefulness on user satisfaction and attitude toward using, and attitude toward using on behavioral intention to use had statistical significance (p < .01), respectively. Conclusion We have successfully developed the VSC system in a Taiwanese academic medical center. Our study indicated that perceived usefulness was a crucial factor, which means the system function should precisely meet the patients' demands. Additionally, a clever system design is important since perceived ease of use positively affects perceived usefulness. The insight generated from this study could be beneficial to hospitals when implementing similar systems to their wards.

Alert dwell time, defined as the time elapsed from the generation of an interruptive alert to its closure, has rarely been used to describe the time required by clinicians to respond to interruptive alerts. Our study aimed to develop a tool to retrieve alert dwell times from a homegrown CPOE (computerized physician order entry) system, and to conduct exploratory analysis on the impact of various alert characteristics on alert dwell time. Additionally, we compared this impact between various professional groups. With these aims, a dominant window detector was developed using the Golang programming language and was implemented to collect all alert dwell times from the homegrown CPOE system of a 726-bed, Taiwanese academic medical center from December 2019 to February 2021. Overall, 3,737,697 interruptive alerts were collected. Correlation analysis was performed for alerts corresponding to the 100 most frequent alert categories. Our results showed that there was a negative correlation (ρ = −0.244, p = 0.015) between the number of alerts and alert dwell times. Alert dwell times were strongly correlated between different professional groups (physician vs. nurse, ρ = 0.739, p < 0.001). A tool that retrieves alert dwell times can provide important insights to hospitals attempting to improve clinical workflows.

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