SMART HEALTH: UTILIZING AI AND IOT FOR REAL-TIME PATIENT ENGAGEMENT IN FAMILY MEDICINE

Abstract

Background: Both AI and IoT technologies are implemented in the healthcare domain with a special emphasis on the FM, with the capabilities of improving timely patient interactions. However, the level and nature of their influence on the patient’s perceived satisfaction and interaction level is still a grey area.

Objective: The general objective of this research was to establish an objective measure of how information technologies with a focus on AI and IoT revolutionize family medicine by enhancing engagement and overall satisfaction in real-time.

Methods: An online self-completed cross-sectional survey took place with 250 participants, featuring patients, family physicians, nurses, and HCLIT members. The survey conducted in this study measured the use, perception, and impact of AI and IoT through Likert scale items. In testing the hypothesis descriptive statistics, correlation analysis, and regression modeling were used to analyze the data. Cronbach alpha was used to determine reliability while the normality of data was tested using the Sharon-pillai test.

Results: The outcome analysis revealed rather an insignificant link between AI/IoT benefits and overall customer satisfaction with the correlation between the impact of AI on engagement and satisfaction being 0.102 and the correlation of IoT impact and satisfaction being -0.05. As could be observed, the regression analysis yielded an R-squared equal to 0.012, which means that AI and IoT tools account for just 1.2% of satisfaction variability. Internal consistency reliability based on Cronbach’s alpha coefficient was calculated to be 0.059 for the variables measured on the Likert scale. Furthermore, substantial variation in AI/IoT usage frequency was established; most participants reported the occasional or irregular use of such technologies.

Conclusion: None of the uses of AI and IoT technologies has significant value in reality and patient engagement at present even though they have future possibilities in family medicine. Several limitations arise from this study; first, the measurement tools’ reliability is low, and they are difficult to use; second, issues to do with data privacy emerge. The future research direction should aim to tailor measurement instruments, enhance their applicability, and remove these barriers to allow AI and IoT to bring the optimal contribution to the improvement of patient engagement.