Once, telehealth and telemedicine began as basic video consultations. However, today, such solutions are much more than that. They have been gradually transforming into integrated platforms that embrace scheduling, diagnostics, follow-ups, and chronic care management tools. AI powered healthcare technologies enhance this evolution by automating and optimizing workflows, personalizing care, and predicting patient needs.
By addressing such challenges as fragmented care journeys or data overload, artificial intelligence helps to increase the efficiency of digital tools.
In our article, we will explore how AI is reshaping telehealth and telemedicine in particular by redefining patient experiences and improving their outcomes.
Telehealth and Telemedicine: The Role of Remote Patient Monitoring and EHR Integration
AI in healthcare is going through a major experimentation phase, specifically being integrated into telehealth and telemedicine. They are one of the most developed and widely adopted solutions in medical organizations in the past few years.
These terms are quite often used interchangeably. Nevertheless, they represent different scopes of virtual care.
Telemedicine refers to remote clinical services. Such solutions provide access to virtual consultations, diagnosis, and treatment.
Telehealth is a broader notion. It covers telemedicine but also other non-clinical services, including but not limited to appointment scheduling, patient education, and doctor training.
In 2024, the global telehealth market size was valued at $161.64 billion. Expanding at a CAGR of 22.9%, by 2032, it can reach $791.04 billion.
These are pretty impressive figures. Especially given the fact that before the COVID-19 pandemic, in 2019, the size of this market was $13.7 billion. The pandemic brightly highlighted a huge demand for remote medical services and boosted the market growth.
Remote patient monitoring (RPM) is poised to become a cornerstone of telehealth. This approach presupposes continuous tracking of patients’ vital parameters and health data, such as blood pressure or glucose levels, using connected devices. This real-time data is sent to a centralized platform or app, where medical specialists can get access to it and take timely measures if needed.
Apart from this, telemedicine and telehealth solutions can revolutionize the work with health records. Such digital platforms are often integrated with electronic health records (EHR) systems. As a result, it becomes easier to create seamless digital workflows for doctors, medical staff, and patients. Thanks to this integration, doctors can get access to comprehensive patient data during virtual visits and add new information and documents directly into the EHR. As a result, it is possible to ensure continuity and accuracy of care across multiple providers.
Here’s an example of a comprehensive eHealth system that is fully HIPAA-compliant and automation-driven.
With the progress in the tech space, telehealth solutions are becoming more feature-rich and advanced. However, the industry still struggles with a row of difficulties and pitfalls. Here’s when AI-powered healthcare solutions come into play.
AI in Healthcare: How It Helps to Overcome Common Challenges
Let’s consider the most significant challenges that medical organizations face today and how AI powered healthcare tools can help to eliminate them or at least mitigate their consequences.
Inaccurate Data and Disconnected Systems
In traditional digital systems, like EHRs and various lab apps, patient data is often siloed. As a result, it is practically impossible for doctors to get a complete picture and find the right treatment for each individual case.
AI-powered healthcare solutions can be applied to integrate and harmonize data from multiple sources. Thanks to creating a unified patient profile, medical organizations can ensure better decision-making and coordinated care.
In addition to this, medical organizations can also integrate smart document understanding solutions into their work to reduce inaccuracy. Such ML-driven tools eliminate the necessity to rely on manual data entry or keyword-based searches. They are able to comprehend documents the way a human does. They automatically read, classify, process data, and extract the required information from clinical notes, insurance forms, lab results, and other documents. To get more details, you can read about the white-label document understanding solution developed by Tensorway.
Poor Management of RPM Data
Despite the value that remote patient monitoring systems bring to the healthcare industry, the approaches to interacting with RPM devices still leave much to be desired. Captured data often goes unused and is not securely stored.
AI healthcare tools can automatically collect, sort, and analyze this data. If such tools detect any alarming changes in patients’ vital parameters, they send alerts to medical teams who then properly react and make decisions in real time.
Administrative Overload and Medical Staff Burnout
Medical personnel often spend too much effort on scheduling, documentation, billing, and other related procedures. As a result, the time that can be dedicated to patient care should be reserved for administrative tasks. Moreover, such overload can be a reason for extra stress and reduced performance.
AI automation tools can streamline routine tasks and ensure the possibility for healthcare providers to focus on patients more.
Long Wait Times and Repeated Visits to Emergency Rooms
Overcrowded emergency rooms and long wait times are among the most pressing issues in the modern healthcare system. This happens due to numerous factors. For instance, ER staff often lack sufficient patient data, which leads to time-consuming diagnostics.
Inefficient triage leads to a single undifferentiated queue, in which all patients are treated in the order they arrive—regardless of the urgency of their condition. Apart from that, due to poor follow-up care and underutilized telehealth services, some patients visit ERs even for non-emergency or chronic issues.
AI-powered healthcare solutions can help to improve patient flows. AI chatbots can handle initial consultations, direct non-emergency patients to appropriate care specialists (offline or online), prioritize patients based on urgency, and reduce ER overload.
Medical Supply Waste
Inefficient inventory management processes lead to significant waste in terms of money and resources. For example, expired inventory is often a consequence of overstocking or poor rotation practices. Overordering happens because of a lack of real-time visibility. Meanwhile, a lack of standardized usage tracking makes demand forecasting unreliable.
AI can precisely predict supply needs, monitor usage trends, and automate inventory tracking. All this allows medical institutions to minimize waste and reduce costs.
How AI Tools Can Enhance the Healthcare System
The development and implementation of solutions powered by AI in healthcare can be a rather time- and resource-consuming project. Nevertheless, when properly implemented, AI can optimize numerous processes and greatly enhance patient outcomes. To better demonstrate this, let us turn to real-life AI use cases in this industry.
Clinical Decision Support Systems (CDSS)
AI algorithms can process huge volumes of EHR data practically in no time and suggest diagnoses or treatment options based on the conducted analysis.
One of the most well-known systems of this kind is ClinicalKey by Elsevier. It is designed to provide nurses, doctors, and pharmacists with reliable content in different formats, including quick answers to their questions and full-text materials.
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Benefits of CDSS:
- Increased diagnostic accuracy;
- Faster treatment decision-making processes;
- Reduced risk of errors;
- Improved patient safety.
Predictive Analytics for Readmission Prevention
Machine learning models can be used to analyze patient histories, lab results, and socio-economic data to flag individuals at high risk for returning to the hospital.
This allows doctors to take proactive measures to reduce readmission rates. Such tools have already been implemented by some healthcare organizations.
For example, it is known that the largest hospital network in New York City, Mount Sinai Health System, relies on predictive analytics in its processes.
Benefits of such AI powered healthcare tools:
- Proactive care planning and follow-ups;
- The reduced number of avoidable ER visits and rehospitalizations;
- Improved long-term outcomes.
AI-Driven Inventory Management
ML models can forecast supply needs based on usage trends. This helps to automate inventory ordering and expiration tracking while minimizing overordering and stockouts.
IDENTI Medical is a good example of how healthcare inventory management can benefit from using AI. The systems relied on sensors, RFID tags, and ML to monitor inventory levels and automate order processes. Apart from ML for predictive analytics, it also utilizes image recognition technology to capture product details.
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Benefits of AI-powered inventory management systems:
- Reduced expired or unused medical stock;
- Decreased costs;
- Improved supply availability;
- Sustainable hospital operations.
Smart Scheduling and Staffing
AI-driven platforms can analyze real-time patient volumes, seasonal trends (like flu spikes), and staff availability to optimize schedules. By predicting demand, they can optimize scheduling automatically.
Solutions like iQueue Scheduling and Qventus can efficiently transform manual workflows into electronic processes.
Very often, such tools can be integrated into bigger AI-powered resource planning systems.
Accurate forecasts of patient demand far enough in advance help to properly align not only staffing but also rooms and equipment. All this is absolutely essential for delivering timely and efficient care.
Benefits of smart scheduling tools:
- Reduced appointment delays;
- Lower staff burnout;
- Increased operational efficiency;
- Balanced resources across departments;
- Improved patient satisfaction.
GenAI and AI Agents in Telemedicine
Both Generative AI and Agentic AI development are believed to be the future of telemedicine. They can make it more human-like, responsive, and scalable.
GenAI in healthcare is able to produce specialized content, including text, images, voice, and video. In the healthcare industry, this technology is the backbone of smart documentation systems, health summaries, chatbots, and other tools.
Agentic AI goes beyond that. AI agents are built to act autonomously to achieve goals and dynamically interact with users and environments.
Here’s how these technologies are used to power telemedicine systems today.
- Automated medical documentation. These tools help to transcribe and summarize doctor-patient conversations in real time. This reduces physician overload and improves record accuracy.
- AI chatbots and virtual assistants. They can share medication guidance, answer FAQs, and book appointments based on the provided requests.
- Personalized care pathways. AI features can create dynamic treatment plans and recommendations based on real-time inputs, including changing vitals and new symptoms.
- AI-generated education for patients. This technology can be applied to prepare personalized explainer videos, medication guides, or wellness tips in multiple languages and formats. This content can significantly boost health literacy and engagement.
Future Trends to Watch Out For
Generative AI and other advanced technologies like NLP, speech recognition, and computer vision are quickly developing and transforming virtual care into a smarter and more conversational experience. Their efficient use and sophisticated combination of their capacities promise to make patient care much more proactive and personalized than it is now.
Here are the key trends that are shaping the telemedicine landscape of the near future.
Agentic Care Coordinators
AI chatbots are used by some healthcare providers already today. They can gather symptoms, provide first recommendations, help patients make telemedicine and in-person appointments, and fulfill other isolated tasks. The entire patent experience still remains rather fragmented. AI agents will manage a patient’s digital journey end-to-end, from appointment scheduling to follow-up reminders.
Real-Time Co-Pilots for Clinicians
AI co-pilots can act as consultants for doctors during virtual and traditional visits. Trained on large data sets of medical information, they will be able to offer real-time recommendations, suggest diagnostic tests, identify alarming symptoms, and help clinicians make informed treatment decisions faster.
Explainable AI Models
One of the key concerns about AI in healthcare is related to the fact that, quite often, neither doctors nor patients can understand why it has made this or that decision. This problem is highly serious when it comes to diagnoses and treatments. It is expected that explainable AI models will ensure better transparency and auditability, especially in agentic systems.
Wrapping Up
Artificial intelligence is one of the most transformative and powerful technologies of our time. It can completely change the way medical services are delivered and continuously elevate the quality of patient care.
However, even with all the benefits of AI for telemedicine and in-person visits, it is essential to bear in mind that this technology is not replacing human expertise or compassion. Instead, it is augmenting human capabilities, enhancing decision-making, and driving better health outcomes.
At Tensorway, our AI development team specializes in building tailor-made AI solutions for different domains, including healthcare. Are you planning to implement AI-powered virtual assistants, predictive analytics, automated documentation, or any other advanced tools powered by emerging technologies? We can help you build secure and scalable systems that will meet your goals. Contact us to start your AI journey!
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