News & Comment

Navigating contemporary healthcare, wearable technology and smartphones are marking the dawn of a transformative era in patient observation and personalised care. Wearables, equipped with various sensing technologies (e.g., accelerometer for movement, optics for heart rate), are increasingly being recognised for their expansive potential in (remote) patient monitoring, diagnostics, and therapeutic applications which suggests a plausible move towards a more decentralised healthcare system. This shift is evident as healthcare providers and patients alike are becoming increasingly accepting of wearable-driven tools, as they enable continuous health monitoring outside of traditional clinical settings. Equally, the ubiquitous nature of smartphones, now more than mere communication tools, is being harnessed to serve as pivotal health monitoring instruments. Their added sensing capabilities with Internet of Things (IoT) driven connectivity enable a (relatively) seamless transition from conventional health practices to a more interconnected, digital age. However, this evolving landscape is not without its challenges, with concerns surrounding data privacy, security, and ensuring equitable access to digital advances. As we delve deeper into digital healthcare, we must harness the full potential of those technologies and ensure their ethical and equitable implementation, envisioning a future where healthcare is not just hospital-centric but is part of our daily lives.

Contemporary wearables like smartwatches are often equipped with advanced sensors and have associated algorithms to aid researchers monitor physiological outcomes like physical activity levels, sleep patterns or heart rate in free-living environments. But here’s the catch: all that valuable data is often collected separately because the sensors don’t always play nice with each other, and it’s a real challenge to put all the data together. To get the full picture, we may often need to combine different data streams. It’s like putting together a puzzle of our health, instead of just looking at individual pieces. This way, we can gather more useful info and better understand health (it’s called digital twinning). Yet, to do so requires robust sensor/data fusion methods at the signal, feature, and decision levels. Selecting the appropriate techniques based on the desired outcome is crucial for successful implementation. An effective data fusion framework along with the right sensor selection could contribute to a more holistic approach to health monitoring that extends beyond clinical settings.

Gregoor et al. evaluated the healthcare implications and costs of an AI-enabled mobile health app for skin cancer detection, involving 18,960 beneficiaries of a Netherlands insurer. They report a 32% increase in claims for premalignant and malignant skin lesions among app users, largely attributed to benign skin lesions and leading to higher annual costs for app users (€64.97) compared to controls (€43.09). Cost-effectiveness analysis showed a comparable cost to dermatologist-based diagnosis alone. This editorial emphasizes the balance in AI-based dermatology between increased access and increased false positives resulting in overutilization. We suggest refining the diagnostic schemas with new referral pathways to capitalize on potential savings. We also discuss the importance of econometric analysis to evaluate the adoption of new technologies, as well as adapting payment models to mitigate the risk of overutilization inherent in AI-based diagnostics such as skin cancer detection.

AI-based prediction models demonstrate equal or surpassing performance compared to experienced physicians in various research settings. However, only a few have made it into clinical practice. Further, there is no standardized protocol for integrating AI-based physician support systems into the daily clinical routine to improve healthcare delivery. Generally, AI/physician collaboration strategies have not been extensively investigated. A recent study compared four potential strategies for AI model deployment and physician collaboration to investigate the performance of an AI model trained to identify signs of acute respiratory distress syndrome (ARDS) on chest X-ray images. Here we discuss strategies and challenges with AI/physician collaboration when AI-based decision support systems are implemented in the clinical routine.

Artificial intelligence systems are increasingly being applied to healthcare. In surgery, AI applications hold promise as tools to predict surgical outcomes, assess technical skills, or guide surgeons intraoperatively via computer vision. On the other hand, AI systems can also suffer from bias, compounding existing inequities in socioeconomic status, race, ethnicity, religion, gender, disability, or sexual orientation. Bias particularly impacts disadvantaged populations, which can be subject to algorithmic predictions that are less accurate or underestimate the need for care. Thus, strategies for detecting and mitigating bias are pivotal for creating AI technology that is generalizable and fair. Here, we discuss a recent study that developed a new strategy to mitigate bias in surgical AI systems.

Health and wellness/well-being are multifaceted topics further complicated when trying to understand the environmental impact. Typically, there has been a one size fits all approach when trying to understand the 3-way interaction, but that is a limited approach. Equally, measurement (of each) has often used a limited set of outcomes during short periods to provide insight. A more robust understanding of health and well-being within environments may require longitudinal/continuous assessment that holistically targets individuals. Therefore, there is a growing requirement for careful data management, individual-first methodologies, scalable research designs and new analytical approaches, e.g., artificial intelligence. That presents many challenges but interesting research opportunities for the field of digital medicine.

Digital health technologies (DHTs) have brought several significant improvements to clinical trials, enabling real-world data collection outside of the traditional clinical context and more patient-centered approaches. DHTs, such as wearables, allow the collection of unique personal data at home over a long period. But DHTs also bring challenges, such as digital endpoint harmonization and disadvantaging populations already experiencing the digital divide. A recent study explored the growth trends and implications of established and novel DHTs in neurology trials over the past decade. Here, we discuss the benefits and future challenges of DHT usage in clinical trials.

The usage of digital devices in clinical and research settings has rapidly increased. Despite their promise, optimal use of these devices is often hampered by low adherence. The relevant factors predictive of long-term adherence have yet to be fully explored. A recent study investigated device usage over 12 months in a cohort of the electronic Framingham Heart Study. It identified sociodemographic and health-related factors associated with the long-term use of three digital health components: a smartphone app, a digital blood pressure cuff, and a smartwatch. The authors found that depressive symptoms and lower self-rated health were associated with lower smartwatch usage. Female sex and higher education levels were associated with higher app-based survey completion. Here, we discuss factors predictive for adherence and personalized strategies to promote adherence to digital tools.

Artificial intelligence (AI) and natural language processing (NLP) have found a highly promising application in automated clinical coding (ACC), an innovation that will have profound impacts on the clinical coding industry, billing and revenue management, and potentially clinical care itself. Dong et al. recently analyzed the technical challenges of ACC and proposed future directions. Primary challenges for ACC exist at the technological and implementation levels; clinical documents are redundant and complex, code sets like the ICD-10 are rapidly evolving, training sets are not comprehensive of codes, and ACC models have yet to fully capture the logic and rules of coding decisions. Next steps include interdisciplinary collaboration with clinical coders, accessibility and transparency of datasets, and tailoring models to specific use cases.

Even as innovation occurs within digital medicine, challenges around equity and racial health disparities remain. Golden et al. evaluate structural racism in their recent paper focused on reproductive health. They recommend a framework to Remove, Repair, Restructure, and Remediate. We propose applying the framework to three areas within digital medicine: artificial intelligence (AI) applications, wearable devices, and telehealth. With this approach, we can continue to work towards an equitable future for digital medicine.