The World Health Organization (WHO) has adopted several definitions and resolutions to support the adoption of technologies in health care services:
eHealth: the cost-effective and secure use of information communication technologies (ICT) in support of health and health-related fields, including health-care services, health surveillance, health literature, and health education, knowledge and research.
mHealth: mobile health, defined by the use of mobile devices – such as mobile phones, patient monitoring devices, personal digital assistants (PDAs) and wireless devices – for medical and public health practice. Examples cover a broad spectrum: telephone helplines and text message appointment reminders, to mobile telehealth and mobile access to electronic patient information.
Telehealth: the delivery of health care services, where patients and providers are separated by distance. Telehealth uses [information and communications technologies (ICT)] for the exchange of information for the diagnosis and treatment of diseases and injuries, research and evaluation, and for the continuing education of health professionals. Telehealth can contribute to achieving universal health coverage by improving access for patients to quality, cost-effective, health services wherever they may be. It is particularly valuable for those in remote areas, vulnerable groups and ageing populations.
eLearning: the use of ICT for education.
Electronic health records (EHRs): real-time, patient-centred records that provide immediate and secure information to authorized users. EHRs typically contain a patient’s medical history, diagnoses and treatment, medications, allergies, immunizations, as well as radiology images and laboratory results. A National Electronic Health Records system is mostoften implemented under the responsibility of the national health authority and will typically make a patient’s medical history available to health professionals in health care institutions and provide linkages to related services such as pharmacies, laboratories, specialists, and emergency and medical imaging facilities.
Source: Global diffusion of eHealth: making universal health coverage achievable. Report of the third global survey on eHealth. Geneva: World Health Organization; 2016. Licence: CC BY-NC-SA 3.0 IGO.
Have you ever received a text message from a healthcare provider to remind you of an upcoming appointment? Did you have a phone consultation with your GP during the COVID-19 pandemic? Perhaps you were told to use a contact-tracing app? These are only a few examples of mHealth (use of a mobile device for medical and public health practice). According to the WHO and several studies, mobile wireless technologies have the potential to revolutionize how populations interact with national health services. mHealth has been shown to improve the quality and coverage of care, increase access to health information, services and skills, as well as promote positive changes in health behaviours to prevent the onset of acute and chronic diseases, as stated in the WHO Executive Board report from 2017.
The same report emphasizes on the significant potential of mHealth to accelerate the progress towards universal health coverage, particularly:
A key objective to implementing digital health, and in particular mHealth, is to increase access to health services through the effective and timely sharing of health data, particularly for hard-to-reach populations. For example, the ability to attach specialized devices and sensors, combined with the inherent capability of mobile technologies, increase their reach and power in disease diagnosis, monitoring, management and research. Moreover, information and communication technologies support a variety of critical health system functions by improving the ability to gather, analyse, manage, deliver and exchange information in all areas of health.
The goal of integrating mHealth across the reproductive, maternal, newborn and child health continuum focuses on strengthening the quality, coverage and affordability of validated health interventions. This includes: electronically registering clients, assessing and monitoring those in need of services, having the necessary human resources and commodities in adequate supply, and ensuring that beneficiary populations are empowered; and that the health workforce is responsive to their needs, tracking and responding to health events in a timely manner, in order to improve outcomes and reduce mortality.
Other opportunities for using mobile technologies include improving awareness to bring about change on the key noncommunicable diseases risk factors (including tobacco use, alcohol use, unhealthy diet and lack of physical activity), improving disease diagnosis and tracking, as well as self-care and home care and overall management of chronic conditions (including diabetes, cardiovascular disease, cancers and respiratory diseases).
The limitations of current approaches to surveillance of both communicable and noncommunicable diseases and the increase in the public’s use of the internet and mobile telephones have prompted new approaches for obtaining information directly from the public to support disease surveillance. These approaches include, for example, gathering information and data on epidemics and health indicators directly from affected populations or other stakeholders, through approaches such as “crowdsourcing” or community reporting.
The concept of making international patient summary data available through mobile technologies will increase the safety and quality of care by providing secure access to the information needed by the attending physicians at the time of care. This is particularly important in the event of disasters, emergencies and other unplanned care. Mobile technologies allow individuals to have access to their own summary health records and give physicians timely access to these records, which is particularly important when patients seek care outside of their normal care settings.
The framework on integrated, people-centred health services intends to make health care systems more responsive to people’s need by putting patients and their families at the centre of health care systems. Creating delivery systems that support self- and family-driven care through digital, and in particular mHealth, solutions will be a key development in the near future.
- All Member States of the WHO made a commitment to achieve universal health coverage back in 2005.
- In some more remote or underserved areas and communities, mobile communications infrastructure may have been prioritized over infrastructure supporting Web access
- There is a similar rate of mobile phone subscriptions in many low and middle income countries when compared to high income countries
- The cost of using mobile phone technologies is lower than the cost of providing in person services in many low and middle income countries
This suggests that there exists a potential for mHealth to contribute to achieving universal health coverage (UHC).
The main applications of mHealth are:
Between patients and health services
Call centres, telephone helplines, emergency toll-free
Between health services and patients
Reminders for treatment adherence and appointments, health campaigns
Between healthcare professionals
Emergency management systems, decision support systems, mLearning, patient records, etc.
Telemedicine is a sub-domain of telehealth that involves a healthcare provider providing a medical service (the focus is on the curative aspect of care) to a patient while not being in the physical presence of each other. It can be in real time (audio or video call) or asynchronous (store-and-forward: data is sent to the healthcare provider, who reviews it in their own time, e.g. via email). Several medical specialties are already known to offer these services in some regions: primary care, paediatrics, thoracic, heart health, renal, endocrinology, eating disorders, genetics, neurology, oncology, multiple sclerosis, pharmacy, psychiatry, etc.
Telehealth on the other hand includes disease prevention and digital health promotion on top of curative care.
Telemedicine for remote monitoring
Remote patient monitoring (RPM) is a category of ambulatory healthcare where a patient uses a mobile medical device to self-monitor while a medical staff has real-time access to the data. Such device can be a glucometer, a blood pressure monitor, a vital sign monitor, etc.
Health care systems around the world have been rolling out digital patient monitoring system to support the remote management of patients living at home or in care homes. In England, Liverpool was a trailblazer in 2013 when they launched a large-scale telehealth program providing education and remote monitoring tools to support people living at home with lung conditions, heart failure or diabetes. For the East London NHS Foundation Trust for example, the aspiration of such a program was to empower patients to self-manage at home, improve patient care and reduce demand on the local care economy. For NHS England, the COVID-19 pandemic caused its ‘At Home’ rollout scheme to be ramped up, benefiting to thousands of respiratory patients who were given home monitoring devices and apps so that their condition could be monitored remotely by healthcare professionals.
While different reports claim different growth rates for the global telehealth market, they all anticipate a substantial growth accelerated by the COVID-19 pandemic for the period spanning 2020-2024.
The Japanese Shibuya-ku’s outlook is more conservative with a compound annual growth rate (CAGR) of 7.95% to reach US$20.18 billion in 2024 whereas Frost and Sullivan anticipate 38.2% CAGR.
It is important to note that regulatory frameworks are very much country-, even state- or region-specific.
Mordor Intelligence’s expected market growth trends for remote patient monitoring systems (2017-2025):
- Asia-Pacific: high (fastest growing)
- Middle-East and Africa: low
- Europe: medium
- North America: medium (largest market)
- South America: low
Several technologies such as artificial intelligence (AI) and the Internet of Things (IoT) can augment the telemedicine offering, and standards known as Health Level 7 make collaboration possible across technologies and devices.
Even though AI was already being developed in the 1950s, its applications only really took off in the 2000s for the field of medicine when significantly more computing capacity became widely available and more medical data had been compiled and shared between institutions worldwide. Prior to that, a lack of computing power and the scarcity of large datasets meant that machine learning algorithms were too focused on a specific dataset to accurately process new datasets.
Click here to learn more on the top 4 applications of AI in medicine, namely AI for diagnostics, drug development, treatment personalisation and gene editing.
AI in medical imaging has a strong potential to improve accuracy, consistency, and efficiency in reporting. AI can also assist with lesion detection, diagnosis, predictions (for prognosis and response to treatment) and medical report composition.
- Arterys was the first U.S. FDA–approved clinical cloud-based deep learning application in health care back in 2017. They offer cardiac, respiratory, chest & musculoskeletal as well as neurologic AI solutions. For example, their Chest and Musculoskeletal AI can be used to detect seven common abnormalities seen on x-ray images in emergency departments: fracture, dislocation, elbow joint effusion, pleural effusion, pulmonary nodule, pulmonary opacity, and pneumothorax while Neuro AI has unique applications for stroke detection on CT images and tumour detection on magnetic resonance images.
Several other recent advances in the field of AI in medical imaging have made headlines in 2020 as reported by Analytics Insight. A deep learning algorithm developed by EndoAngel Medical Technology Company achieved a performance comparable to expert radiologists when detecting COVID-19 in chest CT scans. Nanox developed the Nanox System, a mobile digital x-ray system that uses AI cloud-based software for early diagnosis and is in the process of taking their technology further to develop a new x-ray machine for tomographic imaging of the lungs.
AI feeds heavily on data in order to improve its accuracy and specificity; it makes for a very organic match with the Internet of Things, and its passive accumulation of data.
Internet of Things (IoT)
The Internet of Things (IoT) is a network of things that use the Internet to communicate and exchange data between each other.
Such things are physical objects or devices that are embedded with sensors, software and other technologies. They are already widely available commercially with some well-known examples such as smart LED bulbs, Alexa, smart robot vacuum cleaners, etc. In the medical world, they take the form of patches that can detect and communicate physiological parameters, smart pills that contain a miniature sensor that starts transmitting data once swallowed, bed that can detect movement and notify the medical staff, smart ambulance systems to efficiently route ambulances, etc. According to Goldman Sachs, the Internet of Medical Things (IoMT) has the potential to unlock savings of $300 billion annually for the healthcare industry. The global IoMT market was valued at $44.5 billion in 2018 and is expected to grow to $254.2 billion in 2026, according to AllTheResearch.
Health level 7 (HL7)
Hugo Peixoto, Tiago Guimarães, Manuel Filipe Santos, A New Architecture for Intelligent Clinical Decision Support for Intensive Medicine, Procedia Computer Science, Volume 170,2020, Pages 1035-1040, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.03.077.
Vivek Kaul, Sarah Enslin, Seth A. Gross, History of artificial intelligence in medicine, Gastrointestinal Endoscopy, Volume 92, Issue 4, 2020, Pages 807-812, ISSN 0016-5107, https://doi.org/10.1016/j.gie.2020.06.040.