Broadly defined, telehealth leverages technologies to facilitate the delivery of a wide range of remote healthcare services. These include services provided by various healthcare professionals, such as doctors, nurses, pharmacists, or social workers. In the eyes of many industry experts, any remote medical interaction that incorporates or is facilitated by technology falls under the umbrella of telehealth.
Telehealth is a subset of an even broader concept known as digital health or digital healthcare. This concept represents the intersection of technology and healthcare in general.
While not every aspect of telemedicine incorporates AI, the role of AI in healthcare has witnessed a significant expansion over recent years. The rise and advancement of Generative AI and Large Language Models (LLMs) promise to further transform the landscape of telehealth. As per a report by McKinsey, Generative AI can help unlock a significant portion of the unrealized $1 trillion improvement potential in the healthcare industry, showcasing its vital role in the future of healthcare.
Telehealth services are transforming modern healthcare, facilitating patient care regardless of location and extending the reach of healthcare professionals beyond physical limitations.
Several modes of telehealth care exist:
Generative AI can augment telehealth by enhancing various processes like appointment booking, documentation, virtual consultations, and remote monitoring. It equips healthcare providers with data-driven insights, enabling them to deliver more efficient, personalized care to patients.
The healthcare industry is inundated with data, particularly unstructured data. Generative AI can unlock its potential by labeling and extracting crucial medical information from massive, unstructured data sets. This empowers healthcare providers with streamlined operations and a faster, patient-centric diagnosis.
Conversational AI, a type of artificial intelligence, simulates human-like discussions using natural language processing (NLP) and machine learning techniques. It powers both text-based interactions (chatbots) and voice-based interactions (voicebots). While traditional conversational AI adheres to pre-determined dialogue flows and can only understand expressions it’s trained for, Generative AI, specifically Large Language Model (LLM), introduces a new dimension to these interactions. LLMs can understand a broader range of human expressions and context during real-time conversations beyond their initial training.
In the healthcare sector, this implies that Generative AI-powered chatbots and voicebots can be equipped with specific medical knowledge, enabling them to engage in highly professional, human-like conversations with patients.
Generative AI’s ability to generate and synthesize language could improve the documentation process, i.e. how EHRs (Electronic Health Records) work.
EHRs allow healthcare providers to access and update patient information but typically require manual inputs that are time-consuming and prone to human errors. Generative AI can employ natural language processing (NLP) to transcribe entire remote sessions, and automatically populate the corresponding fields in patients’ EHRs. Clinicians can then review and make any necessary edits.
Generative AI can serve as an invaluable tool for telehealth, paving the way for faster and more personalized diagnosis. Prior to a doctor’s session, generative AI-powered chatbots or voicebots can help book, reschedule and remind patients about their appointments, triage patients and ask about their symptoms. All patient engagement would be conducted in natural, human-like conversations.
Meanwhile, Generative AI can efficiently consolidate patients’ information from various sources (including symptoms collected by chatbots and voicebots), arranging them chronologically to provide a comprehensive view of patients’ medical history. It can also empower a more comprehensive analysis based on diverse data including medical literature, clinical trials, and individual patient’s unique medical conditions. Consequently, generative AI can present tailored treatment options, potential drug interactions, and personalized recommendations to clinicians.
In particular, generative AI finds significant applications in the realm of medical imaging. Trained LLMs can analyze medical images, providing automated diagnosis and saving time for routine diagnosis. This can be especially valuable when patients are located in remote or underserved areas lacking radiologists.
Beyond this, generative AI improves the accuracy of radiological diagnoses by offering radiologists additional insights. AI algorithms can analyze large datasets of medical images, identifying patterns and trends that might not be immediately apparent to the human eye.
Nevertheless, it’s crucial at this stage to continue involving healthcare professionals to safeguard the accuracy of diagnosis and treatment. As highlighted in Microsoft’s recent report, GPT-4V has proven to be an effective AI assistant, capable of generating radiology reports following standard formatting. However, given that these reports have demonstrated inaccuracies, it underscores the importance of having medical professionals meticulously evaluate these reports to confirm their precision.
Generative AI-powered chatbot and voicebot enhance patient care in telehealth, providing round-the-clock services. Through the provision of 24/7 services, patient inquiries can be addressed promptly, efficiently and in natural conversations. This not only enhances patient experience but also significantly reduces the manual labor burden for healthcare providers.
Once a doctor’s session concludes, generative AI can summarize the session and treatment plan in seconds, and deliver that to patients. The Generative AI-powered summaries can simplify clinical jargon and enable patients to better understand their medical conditions.
The role of generative AI also extends well beyond the initial treatment phase. In the post-treatment period, it offers effective follow-up and remote monitoring services, a feature particularly beneficial for managing chronic conditions. Constant monitoring can help catch any alarming symptoms or disease progression early on, potentially preventing complications and improving health outcomes.
Utilizing generative AI-powered voicebots such as WIZ Talkbot, healthcare can become more personalized and accessible. WIZ Talkbot is specifically designed to offer diverse services for patients, including booking and rescheduling appointments, sending appointment reminders, offering 24/7 inquiry support and facilitating regular check-ins. Through routine check-ins, Talkbot continually monitors patients’ conditions, symptoms, and responses to medication. It acts as a virtual healthcare companion, ensuring personalized patient care outside the conventional healthcare setting.
While generative AI holds significant potential in revolutionizing telehealth, we must also consider the complexities and challenges that come with its adoption. As highlighted in the MIT and GE Healthcare study, integration issues present a major hurdle, with 57% of respondents experiencing challenges when incorporating AI applications into their existing systems.
Meanwhile, we can not overlook the critical issues concerning patient privacy, AI accuracy and AI bias. It’s essential for the healthcare industry, AI developers, and regulators to establish strong governance programs.
As we navigate this complex landscape, it’s clear that the integration of Generative AI in telehealth is a journey of continuous learning, adaptation, and above all, a commitment to patient-centered care.