One of the silver linings of the COVID-19 pandemic was the rise of telemedicine. The constraints imposed by the pandemic sparked a lasting trend of patients willing and able to seek healthcare solutions virtually. Since the pandemic, the rise of AI and its integration with telemedicine and digital health has seen significant investment growth, leading to enhanced patient care and reduced operation costs for healthcare providers and systems.
The global AI market in healthcare was estimated at USD 19.27 billion in 2023 and is expected to grow at a CAGR of 38.5% from 2024 to 2030. One primary factor driving market growth is the increasing demand in the healthcare sector for enhanced efficiency, accuracy, and better patient outcomes. Specific to telemedicine, estimates suggest that AI-powered telehealth solutions could represent a multi-billion-dollar market segment within the next few years. In 2021, investments in telehealth companies surpassed $29.1 billion, with a substantial portion of this going towards AI-driven solutions.
While the exact dollar figure solely for AI telemedicine investment is fluid, billions are channeled into this space as healthcare shifts towards more AI-driven, remote-first models. The result is the rise of new AI-powered telemedicine tools, dramatically improving patient care when healthcare professionals are unavailable or offline or when patients are unable or unwilling to visit a clinic or hospital. Here are some emerging trends that healthcare providers should adopt to provide more efficient and holistic care.
AI Medical Scribe: The Smart Doctor’s Assistant for Elevating Efficiencies
Virtual doctors or video consultations with doctors are increasingly becoming the norm in modern healthcare. One technology that is instrumental in creating greater efficiencies is an AI Medical Scribe.
By leveraging the microphone on a secure device, the AI scribe transcribes—but doesn’t record—patient encounters. Then, it uses machine learning and natural-language processing to summarize the conversation’s clinical content and produce a note documenting the visit. Thus, the physician has an accurate context to understand and treat the patient efficiently and with surety.
With the advancement of Generative AI and Large Language Models (LLM), digital-based medical conversations can be generated automatically, including info checks, triage, medical conclusions, and e-prescriptions. Based on their medical knowledge library, vocabulary dataset, and secure access to the patient’s medical history, these applications can generate a precise output of patient info, symptoms, medical conclusions, prescriptions, subsequent appointments, etc.
The doctors will review the information before saving it in electronic medical records (EMRs) and transferring it to other digital health systems, i.e., e-pharmacy or digital health insurance. This solution saves considerable time and effort from manually inputting data into the EMR while providing insightful data for health management in aggregate – e.g., detecting the possible outbreak of new diseases and giving warnings to the health system.
When considering AI Medical Scribe and other patient engagement technologies, healthcare providers may require remote patient monitoring and symptom tracking via facial recognition and AI analysis. This would enable proactive, timely interventions and better personalized care. Ultimately, you seek a highly compatible solution with existing healthcare systems and infrastructure while ensuring robust patient data privacy and security.
AI-Driven Diagnostics: Facilitating Accurate Remote Assessment
Today, AI is successfully used to diagnose a wide range of medical conditions even when great distances separate the physician and patient. Training LLMs on extensive datasets that include medical images like X-rays, MRI and CT scans, lab results, and medical records allows for accurate and efficient utility for remote use.
For example, one AI-powered wound assessment device was clinically validated in a trial involving 150 patients with different types of wounds. The device uses dedicated sensors and AI algorithms to remotely collect and analyze wound images to evaluate a wound’s area, depth, and volume. It provides classification based on the Wound Bed Preparation protocol. The results were compared against physician-performed wound classification and tissue segmentation analysis, with a 97% accuracy rate achieved. The trial showed that remote wound assessment using AI technology is as effective as bedside examination, reducing the risk of human error while maintaining high-quality clinical data.
This and similar innovations could greatly enhance healthcare access, aiding providers in informed decision-making for timely and precise care. As healthcare providers expand virtual care options, AI-driven patient triage and diagnosis can establish efficient care pathways. This addresses health disparities, improves access, and enhances patient experiences and outcomes.
Smart Patient Monitoring: Real-Time Insights for Improved Care
Today, data can be analyzed in real-time, equivalent to the blink of an eye. Smart wearables and remote patient monitoring (RPM) solutions capitalize on this to further merge telemedicine and AI for greater efficiencies and better outcomes.
A notable example of AI in real-life RPM situations is computer vision technology, which is used to detect the risk of falls for patients, such as slippery floors, tangled wires, poorly placed furniture, sharp objects, etc. Along with detection from wearable devices or home sensors, computer-vision-aided cameras can hugely improve the quality of care for patients by minimizing the chance of falls, one of the most popular reasons causing patients to go into severe conditions. Implementing this technology in senior care homes can bring peace of mind to caregivers and families alike.
Advanced data analytics on lifestyle and behavior are also considered a helpful and feasible application of AI. By continuously collecting data via smart wearable devices, smartphones, and sensors, AI-infused digital assistants can provide patients and their caregivers with more precise and on-time suggestions, improving patients’ health quality in the long term.
These capabilities empower healthcare providers to monitor their patients’ health round the clock and facilitate early detection and elimination of potential health problems based on vital indicators spotlighted in the data reporting. In a survey conducted by MIT, 57% of healthcare professionals showed interest in deploying AI-enabled wearables, with more than half currently doing so.
Criteria for Selecting a Solution Provider.
While the rise of AI-powered telemedicine solutions has been significant over the past few years, there are still challenges to consider when considering adoption.
AI Telemedicine solution providers must understand HIPAA and other healthcare regulations to ensure patient data privacy and security. Ensuring AI systems meet these standards is critical, and IT professionals considering a solution provider need to work with those who understand this on a regional and global scale.
AI is only as good as the data used to inform the LLM. AI models rely on high-quality data to make accurate predictions. Overcoming biases in datasets and ensuring diverse, representative data is essential. Alongside this issue is the thorny problem of integration. Healthcare systems and networks are often connected with various middleware solutions to overcome legacy system limitations. Working with a solution provider that understands and is experienced in integrating AI-powered telemedicine with existing systems ensures a lower entry cost and swifter ROI.
The promise of AI is greater efficiency. The rise of AI telemedicine provides these efficiencies, which not only enhance patient well-being but can also solve the ongoing issues of complexity that cause healthcare systems to be costly.
About Chu Canh Chieu
Chu Canh Chieu is the Managing Director of the Global Healthcare Center at FPT Software. Since joining the company in 2006, he has quickly advanced through multiple managerial roles, leading efforts to expand his unit’s size and market reach. Chieu also plays a pivotal role in establishing the foundation and growth of FPT Software’s strategic healthcare solutions unit.
