Transforming healthcare with evidence-based digital health innovations

In the fast-evolving healthcare environment, the integration of advanced diagnostics, innovative patient engagement strategies and cutting-edge artificial intelligence (AI) tools powered by evidence-based digital technologies is vital for enhancing patient outcomes and operational efficiency. Recent research has brought significant advancements in AI, social intelligence and digital interventions. This editorial explores some of these challenges1 2 and highlights key advancements from recent studies, which are driving digital health innovations to improve healthcare.

AI is transforming clinical informatics, particularly in analysing patient-reported experience measures (PREMs). A participatory action research study conducted in Australian public hospitals has developed AI-based workflows to manage the overwhelming volume of PREMs data. These workflows aim to automate analysis, enhancing consumer input focus and accelerating quality improvement cycles. The study’s AI workflows are ready for clinical piloting, promising to streamline data management and improve healthcare services.3

In a recent study on diagnostic prediction models for elevated low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels in young Thai adults, researchers addressed a critical gap in atherosclerotic cardiovascular disease (ASCVD) risk management. With 94.4% of young Thai adults unaware of their cholesterol levels, developing these models is essential. By using retrospective data, the research identified gender and metabolic age as significant predictors for LDL-C, while gender, diastolic blood pressure and metabolic age were keys for non-HDL-C. The models demonstrated excellent discrimination ability and clinical utility, highlighting their potential in routine screenings to mitigate underdiagnosed population.4

In low-resource countries like Honduras, social media has become a vital tool for healthcare engagement. A study analysing Facebook posts from top-followed healthcare organisations revealed disparities in post frequencies and public health topics. Food-related posts garnered the most engagement, indicating a preference for more practical and relevant content for health education. In addition, the study showed that overall patient engagement remained low, suggesting the need for refined social media strategies to engage and empower chronic patients such as those with diabetes with enhanced interaction for healthcare improvement.2

Telecare consultations offer a promising alternative for post-acute stroke care. A study on a nurse-led telecare programme demonstrated high feasibility and acceptability among stroke survivors, with significant improvements in their activities of daily living and quality of life. The programme was perceived as time-friendly and cost-saving, highlighting telecare’s potential to provide continuous, effective care while reducing healthcare burdens.5

To tackle stigma-related challenges and manpower shortages in mental healthcare, a systematic review protocol assesses the effectiveness of chatbot-based interventions in Asia. By performing a meta-analysis of randomised controlled trials, the study seeks to provide in-depth insights into the impact of chatbots on improving mental well-being. This scalable and stigma-free approach could offer essential support for individuals in need, thereby addressing significant gaps in current mental health services.6 In addition, the intersection of loneliness and insomnia is a growing concern in our increasingly isolated and ageing society. A study using sentiment analysis of tweets provided valuable insights by examining the frequency of words associated with loneliness and insomnia. The research found a strong link mediated by depressive symptoms. This novel approach emphasises the importance of leveraging social media data to gain intimate, real-time insights into mental health conditions, paving the way for targeted interventions.7

The search for efficient prognostic models that predict multiple clinical outcomes from 67 predictors is demonstrated by a study identifying a reduced set of key predictors. These predictors, spanning various health categories, performed comparably to models using sets of extensive clinical predictors. This research highlights the feasibility of developing versatile, robust models that can be implemented via a single platform, streamlining prognostic assessments and aiding clinical decision-making.8

This special issue highlights the transformative potential of digital health technologies in revolutionising accurate diagnostics, enhancing patient engagement and integrating AI into healthcare. From improving cardiovascular risk assessment and post-stroke telecare consultations to advancing mental health support and analysing patient experiences, these innovations play a vital role in addressing current healthcare gaps and challenges as well as driving progress toward more efficient, patient-centred and evidence-based care. By embracing these advancements, we are shaping the future healthcare with improved outcomes and greater accessibility for diverse populations.