In today’s rapidly evolving healthcare landscape, telemedicine is revolutionizing how medical services are delivered. From remote patient monitoring to AI-driven diagnostics, telehealth technologies are bridging gaps in healthcare accessibility and efficiency. Whether you’re a healthcare provider, policymaker, or digital health enthusiast, understanding key telemedicine terms is essential. This glossary provides a structured reference for the most important telemedicine-related terminology.

1. Broad Terms in Digital Health

The field of digital health encompasses a wide range of technologies and services:

• Telehealth – A broad term referring to the use of digital and telecommunication technologies to deliver healthcare services remotely. It includes both clinical (telemedicine) and non-clinical services such as health education, administrative meetings, and remote monitoring.
• Telemedicine – A subset of telehealth that focuses specifically on the remote delivery of clinical medical services, such as diagnosis, treatment, and follow-up consultations by licensed healthcare professionals
• eHealth – The use of digital tools and electronic communication in healthcare.
• mHealth (Mobile Health) – Healthcare delivery through mobile devices, applications, and wearable sensors.
• Connected Health – Integration of technology to create seamless healthcare experiences across multiple platforms.
• Health Informatics – The application of information technology to healthcare for data management and decision support.

2. Service Delivery Modes

Telemedicine services can be delivered in various ways to meet different patient needs:

• Teleconsultation/Teleconsult/Virtual Visit – Remote consultation between a healthcare provider and a patient.
• Teletriage – Remote assessment of symptoms to determine urgency and level of care needed.
• Telemonitoring / Remote Patient Monitoring (RPM) – Continuous monitoring of patient health data remotely using digital devices.
• Telecare – Remote support services, particularly for elderly or chronically ill patients to support care at home
• Tele-expertise – Specialist-to-specialist consultations and knowledge sharing.
• Telementoring – Remote guidance from experts to healthcare providers in training or in low-resource settings.
• Telecoaching – Digital coaching for lifestyle changes and chronic disease management.
• Tele-advice – Non-clinical medical guidance, often used for second opinions.
• Telefollow-up – Remote post-treatment monitoring and check-ups.
• Tele-intervention – Remote healthcare procedures or immediate intervention for acute conditions.

3. Specialty-Specific Telemedicine

Different medical specialties have embraced telemedicine to provide specialized care remotely:

• Telepsychiatry – Remote mental health consultations and therapy.
• Teledermatology – Remote diagnosis and treatment of skin conditions.
• Teleradiology – Remote analysis of radiology images such as X-rays, CT scans, and MRIs.
• Telecardiology – Remote diagnosis and management of cardiovascular conditions.
• Teleoncology – Remote cancer care, consultation, and treatment planning.
• Telepediatrics – Remote pediatric consultations and care for children.
• Telegeriatrics – Remote care services tailored to elderly patients.
• Teleophthalmology – Remote diagnosis and management of eye diseases and vision care.
• Teledentistry – Remote dental consultations and oral health assessments.
• Telepharmacy – Remote prescription services and medication management.
• Teleorthopedics – Remote musculoskeletal and orthopedic care.
• Teleobstetrics – Remote prenatal and maternal health monitoring.
• Tele-endocrinology – Remote management of diabetes and hormonal disorders.
• Telepathology – Remote pathology services, including digital biopsy analysis.
• Tele-ICU – Remote intensive care unit monitoring and critical care support.

4. Telemedicine Technologies

Innovative technologies enable telemedicine to deliver high-quality care remotely:

• Asynchronous/Store-and-Forward Telemedicine – Sending patient data (e.g., images, reports) for later review by a specialist.
• Synchronous/Real-Time Telemedicine – Live video consultations between patients and doctors.
• AI-Assisted Telemedicine – Use of artificial intelligence to support diagnosis, triage, and decision-making.
• Wearable Health Technology – Devices that monitor patient vitals and transmit data remotely.
• Telepresence – Use of robotics or smart devices to enable a healthcare provider’s remote presence in a clinical setting.

5. Healthcare System & Policy Terms

The implementation of telemedicine requires robust healthcare policies and systems. These terms are often used in the context of implementing telemedicine in the health system.

• Telehealth Infrastructure – Digital platforms, networks, and policies supporting telemedicine services.
• Interoperability in Telehealth – The seamless integration of electronic health records (EHRs) and digital health platforms.
• Digital Therapeutics (DTx) – Evidence-based digital health interventions designed to prevent, manage, or treat diseases.
• ePrescribing (eRx) – Digital prescription services allowing healthcare providers to send prescriptions electronically.
• Digital Public Health – The use of digital technologies in population health and disease prevention efforts.
• Remote Health Management (RHM) – Strategies for delivering remote healthcare services at scale, often for chronic disease management.

Conclusion

Telemedicine is transforming healthcare by making it more accessible, efficient, and patient-centered. As these technologies continue to evolve, staying informed about key terminologies will help stakeholders navigate this rapidly changing field. Whether you’re a healthcare provider, a digital health entrepreneur, or a policymaker, understanding telemedicine terms is the first step toward leveraging these innovations for better healthcare delivery.

Let us know if there are any other telemedicine-related terms you’d like to see in this glossary!

What if one could save 2 million lives globally by investing as little as US$ 0.24 per patient per year?

Wouldn’t that be a great investment? The WHO report- “Going digital for noncommunicable diseases” shows exactly such a pathway. Noncommunicable diseases (NCDs), particularly cardiovascular diseases, cancers, diabetes and chronic respiratory diseases, are responsible for 74% of deaths globally and negatively impact the lives of millions more. Here are some of the key takeaways from the report:

1. Digital Health’s Role in Addressing NCDs: Telemedicine is particularly well-suited for managing chronic diseases, which require continuous monitoring and long-term care.Digital health technologies, including telemedicine, mobile apps, and chatbots, offer innovative ways to improve prevention, management, and treatment of NCDs.

2. Telemedicine: A Game-Changer for Remote Care: Currently one of the biggest challenges facing the public healthcare system is the sheer cost of building and supporting infrastructure to provide care for rural and underserved communities. Telemedicine allows for remote teleconsultations and continuous monitoring of chronic conditions, reducing the need for hospital visits. One is able to offer real-time care and improved decision-making without any additional time and money spent on travel and other logistical arrangements at the patient’s end.

3. Cost-Effective Solutions with High Returns on Healthcare Outcomes: A minimal investment of US $ 0.24 per patient annually in digital tools could save over 2 million lives and bring US $199 billion in economic benefits by 2033. There is a return on investment of 19 US $ on every US $ invested. Telemedicine reduces hospitalisation rates and long-term healthcare costs, making it a highly cost-effective solution.

4.Barriers and Challenges in implementing Telemedicine: Some key barriers include inadequate digital infrastructure in low-resource settings, limited digital literacy (in both patients and healthcare providers), and concerns over data privacy and regulation. To overcome these, one needs to invest in a variety of digital health technologies, train healthcare providers as well as individuals located within communities, and shape the right kind of digital governance policies and structure to ensure equitable access to digital healthcare.

5. The Future of Healthcare is Digital: Digital health has transformative potential for achieving universal health coverage. Telemedicine, as part of a broader digital health ecosystem, holds the promise of transforming healthcare delivery, making it more inclusive, efficient, and patient-centered. Its potential to improve NCD outcomes, particularly in underserved populations, makes it an indispensable tool for the future of global health.

You can access some notable case studies in the report. The document can be accessed here.

Intelehealth will be working with the task force at WHO to help turn this vision into a reality. We are committed to helping build an ecosystem that provides healthcare to the widest possible cross-section of underserved populations across geographies.

Authors: Ramesh C, CTO

A Force to be reckoned with!

Artificial intelligence (AI) is a high-powered and influential area of computer science, with the ability to radically rebuild the practice of medicine and an enormously impactful delivery of healthcare.

What is AI? Simply put, AI refers to the science and engineering of making intelligent machines, through algorithms or a set of rules, which the machine follows to mimic human cognitive functions, such as learning and problem solving. AI systems have the potential to anticipate problems or deal with issues as they come up and, as such, operate in an intentional, intelligent and adaptive manner. AI’s strength is in its ability to learn and recognise patterns and relationships from large multidimensional and multimodal datasets; for example, AI systems could translate a patient’s entire medical record into a single number that represents a likely diagnosis. Moreover, AI systems are dynamic and autonomous, learning and adapting as more data become available.

In recent years, we have witnessed the integration of Artificial intelligence (AI) and Machine learning (ML) in various industries and healthcare is no exception. Digital healthcare offers a huge range of possibilities and may improve the quality of patient care. The traditional paradigm of clinical history, examination, differential diagnosis, and treatment may be improved by tools such as machine learning, mobile applications and sensors, wearables, and telehealth. The recent pandemic has accelerated the move towards this future, COVID-19 has only enhanced this impact and one of the areas that has seen significant transformation

is healthcare, especially with telemedicine emerging as a critical tool for providing remote healthcare services to patients in the last mile populations.

The Paradigm Shift

The application of technology and artificial intelligence (AI) in healthcare has the potential to address
supply-and-demand challenges. The increasing availability of multi-modal data (genomics, economic,
demographic, clinical and phenotypic) coupled with technology innovations in mobile, internet of things
(IoT), computing power and data security herald a moment of convergence between healthcare and technology to fundamentally transform models of healthcare delivery through AI-augmented
healthcare systems.
AI-ML is changing the landscape of healthcare by enhancing digital health and telemedicine services. In
this blog post, we will explore how AI-ML is transforming healthcare, specifically through NLP &
chatbots, AI-assisted disease screening, differential diagnosis, clinical decision support systems and
analytics of Electronic Medical Records (EMRs).

NLP and Chatbots

Natural language processing (NLP) is a subfield of AI that enables computers to understand, interpret, and generate human language. NLP based chatbots have significant potential in healthcare, as they can be used to improve communication between patients and healthcare providers. Chatbots can provide patients with accurate and relevant

information, helping them make informed decisions about their health. This especially plays a key role in the management of lifestyle diseases such as Obesity, diabetes, hypertension & heart disease. Imagine a patient seeking advice on diet & lifestyle

mgmt. being greeted by a friendly chatbot that guides based on clinically validated protocols without a health provider’s intervention. Additionally, chatbots can triage patients, helping to reduce wait times and reduce the burden on healthcare professionals. With significant advancements in NLP especially with advanced language models such as ChatGPT, GPT-3 from OpenAI and LaMDA from Google; the world of NLP is here to stay and Telemedicine only stands to gain immensely from this.

AI-assisted disease screening

AI-ML can assist in screening for various diseases such as cataract, anaemia, malnutrition, maternal health care and infant mortality. For example, AI prediction models can analyse eye images to identify early signs of cataract as well as help screen Anaemia by looking closer at the palpebral conjunctiva. In underserved communities, where access to healthcare is very limited, this automated disease screening

can be a boon and help prevent severe disease progression and even deaths. Similarly, ML can identify patterns in malnutrition and maternal healthcare, making timely predictions of childbirth outcomes and related complications. This helps to improve the quality of care and reduce maternal & infant mortality rates. An AI-ML based screening model used in Telemedicine amplifies the impact, in terms of early detection of disease & preventive healthcare especially in hard-to-reach remote communities.

Differential diagnosis

Differential diagnosis is the process of identifying a patient’s medical condition by comparing their symptoms with those of other possible conditions. This process can be challenging, as many diseases have similar symptoms. AI-ML can help with this process by analysing patient data and identifying patterns that are associated with specific diseases. This can help healthcare professionals arrive at an accurate diagnosis more quickly, leading to faster treatment and better outcomes for patients. This can also serve as a “effective eye for blind spots” in medical diagnosis and help physicians arrive at a more accurate diagnosis.

Clinical decision support systems

Clinical decision support systems (CDSS) are computer based tools that provide healthcare professionals with information and knowledge to assist with decision-making in patient care. AI-ML can enhance the capabilities of CDSS by providing real-time analytics and personalized recommendations based on patient data. For example, AI algorithms can analyse a patient’s EMR and recommend specific treatments based on the patient’s medical history, symptoms, and other factors.

Analytics of EMRs

EMRs are digital records of patients’ medical histories, treatments, and outcomes. AI-ML can analyse these records to identify patterns and trends that can help healthcare providers make more informed decisions. For example, analytics of EMRs can help identify disease seasonality patterns and predict outbreaks, enabling healthcare providers to prepare for potential public health crises. Additionally, analytics of EMRs can help

identify high-risk patients/societies who may benefit from preventative interventions. This helps deliver focused & timely interventions that lead to better health outcomes & lower healthcare costs.

Building effective and trusted digital healthcare systems

In conclusion, AI-ML is transforming healthcare by enhancing digital health

and telemedicine services. However, there are also challenges associated with AI-ML when dealing with huge volumes of patient data. Patient data privacy & confidentiality become key aspects to be considered when building software systems. There are several laws & regulations such as HIPAA that

help moderate this and protect patient interest, while still enabling delivery of quality healthcare. As AI- ML continues to evolve, we can expect significant improvements in healthcare delivery and outcomes.

AI today, and in the near future…

At Intelehealth, we constantly leverage advances in technology to build better digital health solutions that are focused on serving the underserved communities. With this we help to bridge the inequities in healthcare and strive to produce better patient outcomes!

Digital health: how it started, how it is going and how it could be. Three panels presenting traditional care (‘How it started’), current care (‘How it is going’) and a possible future care (‘How it could be’) paradigm.

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