LEVERAGING AI AND IOT TECHNOLOGIES FOR IMPROVED DECISION-MAKING IN FAMILY MEDICINE

Abstract

Background: AI and IoT technologies have now become seen as robust approaches for the improvement of decision-making in the healthcare field. These aim to manage care, monitor, and enhance flow in a family medicine practice. The use, though, remains suboptimal, and the positive and negative effects of applying AI and IoT in family medicine are worth further examination.

Objective: As such, it is the purpose of this study to assess its utilization, comprehension, and consequence in family medicine AI and IoT cases, while also unveiling the limitations to their operationalization.

Methods: A quantitative, cross-sectional study was undertaken whereby data was collected through a structured questionnaire from 250 family medicine practitioners comprising general practitioners, nurses, and specialty practitioners.

Methodology: The survey included questions related to the frequency of the usage of artificial intelligence and IoT, benefits, and potential challenges, and contribution to the decision-making process in the clinical environment. The first analysis was descriptive statistics, and Cronbach Alpha was used in determining reliability. Established normality tests for major variables involved the Shapiro-Wilk test and inferential statistics examined the relationship between technology adoption and perceived value.

Results: It was indicated in the study that family medicine uses AI moderately; most of those who participated in the study used it occasionally or sometimes. Correspondingly, the main IoT devices that residents received recommendations for chronic diseases include heart rate and blood pressure monitors. Not surprisingly, the Shapiro-Wilk test pointed at the non-normal distribution of the key variables, meaning that the confidence and perceived helpfulness of AI are different across practitioners. The work presented here is not perfect since the internal reliability was moderate according to Cronbach’s Alpha (.463). Some of the challenges to scaling the use of AI were a lack of training and data security issues.

Conclusions: The noted technologies AI, and IoT are seen as helpful for tackling challenges in family practices; chronic disease monitoring is a significant area of interest But, their implementation is not equal. Particularly, there are four primary concerns for further development: increased emphasis on training and attention to privacy. The use of these technologies has to be propelled by the best efforts to tackle these challenges and offer enough support to healthcare providers. More studies and policy measures should be carried out to increase the incorporation of AI and IoT into the field of family medicine.