Today, phenomena such as an increase in chronic illnesses, ageing, medical care costs and risks of medical errors are imposing unparalleled challenges upon healthcare practitioners. Traditional healthcare approaches, which focus on treating medical conditions post hoc rather than addressing lifestyle and environmental factors, struggle to manage the strong increase in chronic and severe diseases associated with poor lifestyle choices.
Wearable patient monitoring systems could be one solution to address this challenge. In recent years, commercial AI-enabled wearables – such as smartwatches and smart rings – have gained widespread popularity among consumers across all age groups and geographical regions. These devices, initially designed for fitness and lifestyle purposes, are increasingly equipped with advanced health-tracking features, such as heart rate monitoring, sleep tracking, and blood oxygen level measurement. Their growing capabilities, powered by machine learning and AI algorithms, position them well as potential tools for clinical use. More specifically, AI-enabled wearable devices can transform healthcare by shifting the focus toward predictive diagnosis and more accurate early detection and prevention.
Even if commercial wearables were designed with patient needs in mind, adoption in healthcare will stagnate without support from healthcare professionals.
Why healthcare providers’ views matter
Most adoption studies on smart wearables focus on patients, as they are often considered the primary end-users in healthcare. However, the needs of healthcare providers remain largely overlooked. Neglecting healthcare providers’ perspectives is a significant gap in wearable technology research, especially for vendors aiming to drive adoption. Even if commercial wearables were designed with patient needs in mind, adoption in healthcare will stagnate without support from healthcare professionals.
As said by professors and internists Bodenheimer and Sinsky: “Healthcare is a relationship between those who provide care and those who seek care, a relationship that can only thrive if it is symbiotic, benefiting both partiesˮ. If healthcare professionals believe these devices can enhance diagnostic accuracy, patient monitoring, and workflow efficiency, their satisfaction and willingness to adopt the technology will likely increase.
Perceived benefits of commercial AI wearables
As noted in my research, Belgian healthcare professionals consistently believe that commercial AI-enabled wearables will improve the quality of patient care and enhance their ability to monitor patients more effectively, carry out better diagnoses, and enhance treatment. Specialist practitioners (such as cardiologists, anaesthetists, gynaecologists) and nurses show the highest acceptance rate, while general practitioners remain more skeptical.
Still, healthcare professionals in all kinds of roles agree on the usefulness of AI-enabled wearables and expect them to significantly enhance their job performance. Care providers express that they would consider using AI wearables for three purposes: monitoring chronic conditions, checking health indicators and getting support in diagnosis, and, to a lesser extent, monitoring mental health. The way wearable devices are expected to perform plays a big role in whether people actually want to use them. While user-friendliness and expected effort to set up have a less direct impact on the acceptance of AI-wearables, seamless integration with clinical systems did emerge as a critical factor in facilitating adoption.
Barriers hindering adoption
Many healthcare professionals report difficulty with setting up and integrating AI-enabled wearables into clinical workflows and systems, revealing concerns about a lack of standardized data and integration. It is important to note that these barriers to adoption are influenced by factors such as age and professional role. Healthcare providers in home care settings, for example, expressed the least confidence in having the necessary technical support, which possibly relates to poorer infrastructural facilities and a lack of targeted training. Professionals working in hospitals or outpatient clinics, on the other hand, reported higher confidence in the ease of use and integration with clinical systems, likely due to more structured support systems in those environments.
Opinions around data reliability are surprisingly positive, indicating that healthcare professionals may have more trust in the accuracy and reliability of the data collected by these devices than might be expected. This is a positive sign, as having confidence in the reliability of the data reinforces the intention to adopt wearable devices.
On the other hand, there are large data privacy and security concerns among healthcare professionals. This indicates a need for stringent protective measures to prevent data breaches, especially given the sensitive nature of health information.
Securing healthcare providersʼ buy-in not only increases patient engagement, but also strengthens the overall value proposition of the product.
Policy and governmental implications
Overcoming these barriers and building trust among healthcare professionals calls for targeted actions from policymakers, healthcare institutions, and manufacturers of commercial AI-enabled wearables.
- Improve technical support: policymakers must work with manufacturers and healthcare organisations to offer hands-on training programs and improve technical infrastructure. Special attention needs to go to clinical settings like home care, ensuring that all workplaces can benefit from the use of AI wearables. AI wearables need to work effortlessly with hospitals and other clinical systems to increase adoption.
- Implement clear AI regulations: existing AI guidelines on data security and privacy need to be adapted specifically for healthcare, to promote trust in AI-wearable technologies by healthcare professionals.
- Impose clear data standards: to further enhance trust among healthcare professionals, policymakers should require manufacturers to provide clear validation of the data reliability of their AI-enabled wearable devices.
My research highlights both the promise and challenges of AI-enabled wearables in healthcare, emphasising that addressing data integration and set-up, privacy and security threats, and demographic-specific concerns is key to successful adoption. By addressing these barriers, AI wearables developers and policymakers can create solutions that not only meet patient expectations, but also align with the needs of healthcare professionals.
Securing healthcare providersʼ buy-in not only increases patient engagement, but also strengthens the overall value proposition of the product. This dual focus will help unlock the full potential of AI-enabled wearables in the healthcare industry, leading to a healthcare system that is more efficient, equitable, and better equipped to meet today’s challenges.
This article is based on the author’s thesis as part of the Master of Science in Big Data Business Analytics at ESCP Business School.
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