Objective Outcomes Evaluation of Innovative Digital Health Curricula. Comment on "Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study"

Implementation of a digital health module for undergraduate medical students: A comparative study on knowledge and attitudes

BACKGROUND

Digital competencies are more and more required in everyday work, and training future healthcare professionals in digital health is highly important.

OBJECTIVE

Aim of this study was to assess medical students' gain of knowledge by participation in a teaching module "Digital Health", and to evaluate their attitudes towards digital health and its role in medical education.

METHODS

Students of the module were asked to complete a questionnaire and a multiple-choice-test before and after completing the classes. Students of the same educational level in different modules served as reference group.

RESULTS

34 students took part (n= 17 "Digital Health group"; n= 17 "reference group"). There was no significant difference in pre-existing knowledge between the groups. After having completed the module, participants reached significantly higher scores, compared to their preexisting knowledge (p< 0.05) and the reference group (p< 0.05). Most students found that digital medicine is not sufficiently represented in undergraduate medical education, but will influence everyday work of physicians in the next five years.

CONCLUSIONS

Students showed a high awareness for the impact of digital health on physicians' work. The results suggest that the format can sufficiently transfer knowledge about digital health. Teaching of digital knowledge and competencies should be firmly implemented into medical education to form digitally competent future doctors.

Prediction of Foot Ulcers Using Artificial Intelligence for Diabetic Patients at Cairo University Hospital, Egypt

Introduction

In Egypt, diabetic foot ulcers markedly contribute to the morbidity and mortality of diabetic patients. Accurately predicting the risk of diabetic foot ulcers could dramatically reduce the enormous burden of amputation.

Objective

The aim of this study is to design an artificial intelligence-based artificial neural network and decision tree algorithms for the prediction of diabetic foot ulcers.

Methods

A case-control study design was utilized to fulfill the aim of this study. The study was conducted at the National Institute of Diabetes and Endocrine Glands, Cairo University Hospital, Egypt. A purposive sample of 200 patients was included. The tool developed and used by the researchers was a structured interview questionnaire including three parts: Part I: demographic characteristics; Part II: medical data; and Part III: in vivo measurements. Artificial intelligence methods were used to achieve the aim of this study.

Results

The researchers used 19 significant attributes based on medical history and foot images that affect diabetic foot ulcers and then proposed two classifiers to predict the foot ulcer: a feedforward neural network and a decision tree. Finally, the researchers compared the results between the two classifiers, and the experimental results showed that the proposed artificial neural network outperformed a decision tree, achieving an accuracy of 97% in the automated prediction of diabetic foot ulcers.

Conclusion

Artificial intelligence methods can be used to predict diabetic foot ulcers with high accuracy. The proposed technique utilizes two methods to predict the foot ulcer; after evaluating the two methods, the artificial neural network showed a higher improvement in performance than the decision tree algorithm. It is recommended that diabetic outpatient clinics develop health education and follow-up programs to prevent complications from diabetes.

Artificial intelligence and robot nurses: From nurse managers' perspective: A descriptive cross-sectional study

AIM

This research was planned to identify nurse managers' opinions on artificial intelligence and robot nurses.

BACKGROUND

As the concepts of artificial intelligence and robot nurses are becoming widespread in Turkey, nurse managers are expected to guide and cooperate with nurses in the future in regard to these technologies.

METHODS

The sample of the study consisted of 326 manager nurses, who were reached via the online questionnaire during the period of September to November 2021. A Nurse Managers Information Form and a Question Form on Artificial Intelligence and Robot Nurses were used to collect data. Data in this cross-sectional descriptive study were collected between September 2021 and November 2021 by the online survey method. The descriptive statistics of the data were analysed with numbers and percentages. The difference between the knowledge of artificial intelligence and robot nurses and demographic characteristics was analysed with the chi-square test.

RESULTS

According to the findings, 66.9% of the nurse managers reported having heard the concepts of artificial intelligence and robot nurses previously. 67.2% stated that they thought that robot nurses would benefit the nursing profession, but 86.2% voiced disbelief that robots would replace nurses.

CONCLUSIONS

The majority of the participating nurse managers reported that artificial intelligence and robot nurses would not replace nurses but would be beneficial for nurses and would reduce their workload.

IMPLICATIONS FOR NURSING MANAGEMENT

It should be ensured that the nurse managers plan the areas in the hospital where artificial intelligence and robot nurses will be used and determine the possible risks. Awareness should be increased with in-service trainings, and patient safety and ethical problems regarding the use of artificial intelligence and robot nurses should be identified.