Student Perspectives from the International Partnership for Health Informatics Education (IPHIE) 2024 Master Class.

The International Partnership for Health Informatics Education (IPHIE) fosters global collaboration in medical and health informatics education. The 2024 IPHIE Master Class united 20 graduate students and 9 faculty members to examine the use, perceptions, and impact of large language models (LLMs) in healthcare. Students designed, piloted, and analyzed a survey on LLM use by healthcare providers and patients, addressing its impacts.

Endocrinologists' Perceived Benefits and Risks of Digital Transformation for Growth Disorders Care in Taiwan: An Expert Panel Discussion.

Digital health technologies have gained acceptance in paediatric growth hormone (GH) therapy, supporting patients and caregivers in their treatment journey with healthcare professionals (HCPs) playing a pivotal role. Understanding HCPs' perspectives regarding the adoption and recommendation of digital health devices with a connected ecosystem for remote monitoring of real-time adherence is essential for aiding clinical decisions and timely interventions. The results from a participatory workshop in Taiwan involving nine paediatric endocrinologists, exploring the risks and benefits of integrating digital health solutions such as Aluetta® with Smartdot™ and Growzen™ ecosystem (Merck Healthcare KGaA, Darmstadt, Germany) for GH therapy are presented.

Monitoring Adherence to Growth Hormone Treatments Using Digital Health Solutions in Hong Kong: An Expert Panel Discussion.

Digital health solutions have evolved for managing long-term treatment with recombinant human growth hormone, making it essential to understand their features and functionalities for adoption and acceptance by healthcare professionals (HCPs), patients and caregivers. Findings from a participatory workshop in Hong Kong explored HCPs' perspectives on monitoring adherence to growth hormone (GH) therapies using digital health solutions incorporating Aluetta® with Smartdot™ and Growzen™ ecosystem (Merck Healthcare KGaA, Darmstadt, Germany). These insights can empower HCPs to effectively integrate and recommend digital health solutions during GH treatment, fostering successful treatment strategies for patients with growth hormone deficiency.

Unraveling the Role of Gut Microbiota in Colorectal Cancer: A Global Perspectives and Biomarkers as Early Screening Tool for Colorectal Cancer.

Colorectal cancer (CRC), the second deadliest cancer globally, is closely tied to gut microbiota, opening doors for early detection and treatment. This review of 45 studies (2018-2024) highlights microbial biomarkers like Fusobacterium nucleatum, Bacteroides fragilis, and Parvimonas micra, with strong diagnostic accuracy (AUC >80%) across populations. Techniques like 16S rRNA sequencing, metagenomics, and shotgun sequencing revealed these links.

Benchmarking IMIA recommendations for the health informatics undergraduate program in Saudi Arabia: a mixed methodology.

Background: Health informatics is a multidisciplinary field that supports the use and management of health data to enhance the quality and efficiency of healthcare delivery. As digital transformation accelerates in healthcare systems, particularly in Saudi Arabia, there is a growing need to ensure that undergraduate education programs in this field meet both national priorities and international standards.

Aim: This study aimed to evaluate and compare the content of undergraduate health informatics programs in Saudi universities with international curriculum recommendations, and to explore professional stakeholder perceptions on essential educational components.

Engaging with patients with diabetes: the role of social media in low-income healthcare organisations.

Background: Type 2 diabetes is the fastest-growing global health concern, and its global prevalence is projected to affect 643 million individuals by 2030. Social media platforms, like Facebook, have become crucial channels for healthcare organisations to engage with the public to promote prevention and disease management, especially in low-resource settings like Honduras. This study aims to perform a retrospective analysis of Honduran healthcare organisations' Facebook posts to understand how effectively they engage diabetes-related content with their followers.

The Digital Health Competencies in Medical Education Framework: An International Consensus Statement Based on a Delphi Study.

Importance: Rapid digitalization of health care and a dearth of digital health education for medical students and junior physicians worldwide means there is an imperative for more training in this dynamic and evolving field.

Objective: To develop an evidence-informed, consensus-guided, adaptable digital health competencies framework for the design and development of digital health curricula in medical institutions globally.

Evidence Review: A core group was assembled to oversee the development of the Digital Health Competencies in Medical Education (DECODE) framework.

Digital Technology Enablers of Tele-Neurorehabilitation in Pre- and Post-COVID-19 Pandemic Era - A Scoping Review.

Neurorehabilitation (NR), a major component of neurosciences, is the process of restoring a patient's damaged/disorganized neurological function, through training, therapy, and education, while focusing on patient's independence and well-being. Since the advent of the COVID-19 pandemic, various applications of telecare and telehealth services surged drastically and became an integral part of current clinical practices. Tele-Neurorehabilitation (TNR) is one of such applications.

A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.

Background: Sepsis, an acute and potentially fatal systemic response to infection, significantly impacts global health by affecting millions annually. Prompt identification of sepsis is vital, as treatment delays lead to increased fatalities through progressive organ dysfunction. While recent studies have delved into leveraging Machine Learning (ML) for predicting sepsis, focusing on aspects such as prognosis, diagnosis, and clinical application, there remains a notable deficiency in the discourse regarding feature engineering.

Data-Driven Identification of Factors That Influence the Quality of Adverse Event Reports: 15-Year Interpretable Machine Learning and Time-Series Analyses of VigiBase and QUEST.

Background: The completeness of adverse event (AE) reports, crucial for assessing putative causal relationships, is measured using the vigiGrade completeness score in VigiBase, the World Health Organization global database of reported potential AEs. Malaysian reports have surpassed the global average score (approximately 0.44), achieving a 5-year average of 0.

Call for the responsible artificial intelligence in the healthcare.

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare.

Incidence and Mortality of Malignant Brain Tumors after 20 Years of Mobile Use.

(1) Objective: This population-based study was performed to examine the trends of incidence and deaths due to malignant neoplasm of the brain (MNB) in association with mobile phone usage for a period of 20 years (January 2000-December 2019) in Taiwan. (2) Methods: Pearson correlation, regression analysis, and joinpoint regression analysis were used to examine the trends of incidence of MNB and deaths due to MNB in association with mobile phone usage. (3) Results: The findings indicate a trend of increase in the number of mobile phone users over the study period, accompanied by a slight rise in the incidence and death rates of MNB.

Assessment of effects of moon phases on hospital outpatient visits: An observational national study.

Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases.

Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.

Empowering Patients and Transforming Healthcare in the Post-COVID-19 Era: The Role of Digital and Wearable Technologies.

The COVID-19 pandemic has dramatically impacted the global healthcare system, revealing critical gaps in our capacity to provide efficient and effective care to patients, particularly those with chronic diseases [...

An Enhanced Standardization and Qualification Mechanism for Heterogeneous Healthcare Data.

Given the challenge that healthcare related data are being obtained from various sources and in divergent formats there is an emerging need for providing improved and automated techniques and technologies that perform qualification and standardization of these data. The approach presented in this paper introduces a novel mechanism for the cleaning, qualification, and standardization of the collected primary and secondary data types. The latter is realized through the design and implementation of three (3) integrated subcomponents, the Data Cleaner, the Data Qualifier, and the Data Harmonizer that are further evaluated by performing data cleaning, qualification, and harmonization on top of data related to Pancreatic Cancer to further develop enhanced personalized risk assessment and recommendations to individuals.

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information.

(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients.

Investigating Students' Perceptions towards Artificial Intelligence in Medical Education.

Implementing a reform in medical education requires students' awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students' perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait.

Accelerating digital health literacy for the treatment of growth disorders: The impact of a massive open online course.

Background: Growth hormone deficiency (GHD) is a rare disorder characterized by inadequate secretion of growth hormone (GH) from the anterior pituitary gland. One of the challenges in optimizing GH therapy is improving adherence. Using digital interventions may overcome barriers to optimum treatment delivery.

Measuring the Burden of Infodemics: Summary of the Methods and Results of the Fifth WHO Infodemic Management Conference.

Background: An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose.

A Scoping Review and a Taxonomy to Assess the Impact of Mobile Apps on Cancer Care Management.

Mobile Health (mHealth) has a great potential to enhance the self-management of cancer patients and survivors. Our study aimed to perform a scoping review to evaluate the impact and trends of mobile application-based interventions on adherence and their effects on health outcomes among the cancer population. In addition, we aimed to develop a taxonomy of mobile-app-based interventions to assist app developers and healthcare researchers in creating future mHealth cancer care solutions.

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers.

Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology.

A scoping review to assess the effects of virtual reality in medical education and clinical care.

Due to the challenges and restrictions posed by COVID-19 pandemic, technology and digital solutions played an important role in the rendering of necessary healthcare services, notably in medical education and clinical care. The aim of this scoping review was to analyze and sum up the most recent developments in Virtual Reality (VR) use for therapeutic care and medical education, with a focus on training medical students and patients. We identified 3743 studies, of which 28 were ultimately selected for the review.

Artificial intelligence based personalized predictive survival among colorectal cancer patients.

Background And Objective: Colorectal cancer is a major health concern. It is now the third most common cancer and the fourth leading cause of cancer mortality worldwide. The aim of this study was to evaluate the performance of machine learning algorithms for predicting survival of colorectal cancer patients 1 to 5 years after diagnosis, and identify the most important variables.