In recent years, a technique called light therapy has been gaining attention with studies reporting demonstrable benefits of this technique. As the name suggests, in light therapy (also referred to as phototherapy), a light source is used to improve a patient’s health, spanning from mental health to physical pain. Here we will focus on two areas where light therapy has been shown to be effective.

Seasonal affective disorder

A type of depression, Seasonal Affective Disorder (SAD) is a mood disorder that occurs at the same time every year. In most cases, SAD symptoms start appearing in the fall, continue during the winter months, and resolve during the spring months. The disorder reduces a person’s energy, making them feel moody and leading to social withdrawal in many cases.

SAD is one of the main conditions for which light therapy has been shown to be useful. The technique is thought to affect brain chemicals linked to sleep and mood, hence easing SAD symptoms.

During light therapy, the patient sits or works near a device called a light therapy box (shown below). The box gives off bright light that mimics natural outdoor light, thus mitigating the effect of changing seasons.

Foot pain

One of the newest proposed applications of light therapy is to treat foot pain. Recently, an Ohio-based company called Bright Health Therapy has introduced a Red Light Therapy Foot Pain Relief Device as a pain relief solution. The product, which is an over-the-counter Class II Medical Device with FDA clearance, is designed primary to help people suffering from foot and heel pain using infrared and red light LEDs.

The device, pictured below, resembles a body weight scale. It contains 124 medical-grade 880nm invisible infrared and 660nm visible Red Light LEDs. Typical recommended light therapy exposures are 20 to 30 minutes, and at most 3 times a day.

According to Bright Health, the device can help relieve pain associated with an array of conditions, including arthritis, neuropathy, tendonitis, and plantar fasciitis. The company also asserts that the device can help repair tissue and relax muscles.

To back its claims, Bright Health states that red light therapy increases adenosine triphosphate (ATP) production in the body cells, thus boosting the patient’s energy. ATP is the molecule that provides energy to drive many processes in living cells.

The company also claims that the device results in faster cell regeneration and repair, increased nitric oxide release, and collagen production. To support its claims, Bright Health cites studies that support the benefits of red light therapy, including healing and tissue repair, improved blood flow, improved joint health, relief from pain and inflammation, exercise and injury recovery, and improved bone repair.

Light therapy is still a new field and under development. But, if further studies continue supporting the potential of the technique, light therapy may soon become an easy, affordable tool in health care.

About That Medic Network

If you like this article, be sure to follow us for more content on our Website, Facebook, Twitter, Instagram, LinkedIn.

Feeling friendly or excited for our next article? Subscribe to our Medium publication to stayed tuned.

Email us at info@thatmedicnetwork.com if you have any questions.

References

• Foot Pain? Soak in Red Light for Relief
• Light therapy
• Seasonal affective disorder (SAD) - Symptoms and causes
• What Is Light Therapy and Is It Right For You?

Light Therapy: What is it? was originally published in That Medic Network on Medium, where people are continuing the conversation by highlighting and responding to this story.

With the dawn of the COVID-19 pandemic, the international community and professionals in essentially all fields started collaborating more than ever to facilitate the quality of life in a time like no other. And these efforts became possible courtesy of technology. Meetings started happening online; restaurants began delivering food to houses as an alternative to dining; online examinations became more common than ever, etc. (the list goes on and on and on!). In the realm of health care, digital technologies have aided the pandemic fight by strengthening core human capacities in various domains. Here, we will take a look at three of these areas: population surveillance, contact tracing, and enhancing communication with the public.

Population Surveillance

A core public-health function of outbreak management is understanding infection transmission in time, place, and person and identifying risk factors for the disease to guide effective interventions. Various digital data sources are being used to enhance and interpret critical epidemiological data gathered by public-health authorities for COVID-19. These digital sources drastically reduce the time it takes to interpret the data. Hence, digital health especially aids epidemiologists by enhancing the development of online data sources for early disease detection and providing data-visualization tools to ease decision-making.

Contact Tracing

Digital contact tracing has been effective in improving the control of the spread of COVID-19. Using digital proximity tracing and exposure notification tools, digital contact tracing can identity more contacts than traditional contact tracing methods and at a faster rate:

• Digital contact tracing uses Bluetooth or GPS technologies to estimate the proximity and duration of a person’s exposure to individuals diagnosed with COVID-19. This can significantly speed up the contact tracing process.
• Digital contact tracing generates and sends automated notifications regarding potential exposure via phone, email, or SMS.
• Digital contact tracing provides elicited contacts with potential next steps and refers them to the relevant health department for follow-up actions.

Public Communication: informing populations

Public education and cooperation are among important factors in pandemic management. Digital health provides a means to achieve this by supporting appropriate communication strategies that encourage active community participation to ensure public trust. With 4.1 billion people accessing the internet and 5.2 billion unique mobile subscribers, communication through digital platforms has the potential to rapidly reach billions of people in a couple of femtoseconds.

During the COVID-19 pandemic, online data platforms and especially social media, have had an ongoing, important role in public communication. Amid the information spread through social media, however, there is a high chance of the rise of potentially harmful misinformation. To address this, digital health companies are stepping up efforts to mitigate the spread of misinformation, hence prioritizing trusted news sites. For example, Google’s SOS alert intervention prioritizes the World Health Organization (WHO) and other trusted sources at the top of search results.

Through billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources, and advances in machine learning, digital health has been able to aid the pandemic fight, mainly by lowering the time it takes to take action. As a result, the way in which digital health has been helping pandemic management holds promises for the future of public health to become increasingly digital.

About That Medic Network

If you like this article, be sure to follow us for more content on our Website, Facebook, Twitter, Instagram, LinkedIn.

Feeling friendly or excited for our next article? Subscribe to our Medium publication to stayed tuned.

Email us at info@thatmedicnetwork.com if you have any questions.

References

• Contact tracing: digital health on the frontline
• Digital technology and disease surveillance in the COVID-19 pandemic: a scoping review protocol - PubMed
• Digital technologies in the public-health response to COVID-19 - Nature Medicine

Two Years Later: COVID-19 and Digital Health was originally published in That Medic Network on Medium, where people are continuing the conversation by highlighting and responding to this story.

Into the deep world of medical data — ft. a brief history

Over the past decade, key terms such as artificial intelligence (AI), machine learning, neural networks, and deep learning have found their place in our daily vocabulary. However, while closely related, there are subtle yet significant differences between these terms. Hence, to precisely understand how data is ingested, analyzed, and returned, we must understand the difference between these terms. In this article, we will focus on modern technology’s oceanic term: deep learning. Then, considering modern medicine’s inevitable reliance on computing, we will focus on the applications of deep learning in health care.

What is Deep Learning?

Also known as deep structured learning or hierarchical learning, deep learning is a subfield of machine learning that uses a layered algorithmic architecture to analyze data. Since the algorithm is usually comprised of a large of number of layers (or neurons), it is referred to as deep.

• In deep learning models, data is filtered through a cascade of multiple layers, with each successive layer using the output from the previous one to inform its results. Deep learning models can become more and more accurate as they process more data, essentially learning from previous results to refine their ability to make connections and correlations.

Deep learning is loosely based on the way biological neurons connect with one another to process information in our brains. Similar to the way electrical signals travel across the cells of living creates, each subsequent layer of nodes is activated when it receives stimuli from its neighboring neurons.

Into the Deep Past — a brief history of deep learning

1943Logician Walter Pitts and neurophysiologist Warren McCulloch used the neural networks of the human brain to create a computer model based on it.

• Pitts and Warren employed a combination of mathematical equations and algorithms they called “threshold logic” to mimic the human thought process.

1960Mathematician Henry J. Kelley developed the basics of a continuous Back Propagation Model, introducing the concept of back propagation: the backward propagation of errors for training purposes.

1965The earliest developments of deep learning algorithms appeared.

• Ukrainian mathematician Alexey Grigoryevich Ivakhnenko developed the Group Method of Data Handling.
• Valentin Grigorʹevich Lapa wrote Cybernetics and Forecasting Techniques.
• They both used models with polynomial activation functions that were then analyzed statistically. From each layer, the best statistically chosen features were forwarded on to the next layer in a slow, manual process.

From the 1970s to the early 2000s, the world of machine learning experienced a widespread rejection from the scientific community as a result of its great promises (such as creating machines that operate as good as — or even better — than the human brain) that couldn’t be kept. As a consequence of the subsequent lack of funding, the development of both deep learning and artificial intelligence became limited. Despite this, there were still individuals who carried on the research without funding.

2001A research report by META Group (now called Gartner) described the increasing volume of data and the increasing speed of data generation. This was a call to prepare for handling these large volumes through innovations like deep learning.

2012 Google Brain released the results of The Cat Experiment. The use of deep learning in this project proved this tool’s significant advantages in terms of efficiency and speed. As a result, many other projects began implementing deep learning more commonly.

Currently, deep Learning is still evolving…

Deep Learning in Medicine

Over the past couple of years, healthcare has shown to immensely benefit from deep learning because of the large volume of data being generated (about 150 exabytes, or 1018 bytes, in the United States alone). However, deep learning can especially assist health care with medical diagnosis, and more specifically with medical imaging.

• Diagnostic mistakes are common. In fact, between 12 to 18 million Americans face some type of misdiagnosis each year. There is hope that deep learning can change this unsettling situation for the better.
• Medical imaging: In recent years, deep learning has been used to analyze medical images in various fields, showing excellent performance. One of the most widely used deep learning algorithms in this field is convolution, from which the convolutional neural network (CNN) is derived. This system is inspired by the primary visual cortex in animals. It has the capacity to decipher and learn the most complex patterns existing in a set of images, with applications in analyzing X-rays, MRIs, and CT scans. For example, in 2017, Stanford University computer scientists created a CNN model trained on 130,000 clinical images of skin pathologies to detect cancer.

With current advancements in the field of machine learning — and specifically deep learning — the field of health care seems to be able to take advantage of powerful tools. With this in mind, it is safe to say that the ever-evolving intersection of medicine and technology is undergoing a big change: a deep revolution!

About That Medic Network

If you like this article, be sure to follow us for more content on our Website, Facebook, Twitter, Instagram, LinkedIn.

Feeling friendly or excited for our next article? Subscribe to our Medium publication to stayed tuned.

Email us at info@thatmedicnetwork.com if you have any questions.

References

1. Bresnick, Jennifer. “What Is Deep Learning and How Will It Change Healthcare?” HealthITAnalytics, HealthITAnalytics, 18 Dec. 2019, https://healthitanalytics.com/features/what-is-deep-learning-and-how-will-it-change-healthcare.
2. “Deep Learning in Medical Diagnosis: How Ai Saves Lives and Cuts Treatment Costs.” AltexSoft, AltexSoft, 23 Apr. 2020, https://www.altexsoft.com/blog/deep-learning-medical-diagnosis/.
3. Esteva, Andre, et al. “A Guide to Deep Learning in Healthcare.” Nature News, Nature Publishing Group, 7 Jan. 2019, https://www.nature.com/articles/s41591-018-0316-z.
4. Foote, Keith D. “A Brief History of Deep Learning.” DATAVERSITY, 31 Jan. 2017, https://www.dataversity.net/brief-history-deep-learning/.
5. Markoff, John. “How Many Computers to Identify a Cat? 16,000.” The New York Times, The New York Times, 25 June 2012, https://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html.

Deep learning in health care was originally published in That Medic Network on Medium, where people are continuing the conversation by highlighting and responding to this story.

Artificial Intelligence! Many were unfamiliar with this word at the beginning of the 21st century, but it has now become established in vocabularies. As the name suggests, this branch of computer science focuses on creating an “artificial” form of thinking similar to human thinking yet even faster and more accurate. In the realm of healthcare, AI is currently being used to mimic doctors and medical researchers in disease diagnosis as well as analysis and presentation of medical data.

So how is this different from traditional forms of technology?

Well, what distinguishes AI is its ability to gather, process, and derive a conclusion from the data. In other words, the need for human intervention in AI-powered services is minimized. This is achieved through AI’s two powerful tools: machine learning algorithms and deep learning.

What is Machine Learning?

The International Business Machines Corporation, otherwise famously known as IBM, defines machine learning as a technology that “focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.” In short, what machine learning algorithms do is: learn from data, identify patterns, make decisions. Sounds familiar? That is actually precisely how we — humans — operate. We learn from our experiences (walking during childhood, exams during school, interactions during adulthood, etc.). Then, we use those experiences to make decisions in our daily lives. For example, when you experience that a particular food tastes “good,” it is very likely that you will choose it over a less-favorable food whenever given a choice. Or, when you experience that a specific behavior puts you in danger, you will most likely try to avoid that action.

Machine learning encompasses several strategies, usually broken down into three categories: supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, a machine is given example inputs and their corresponding outputs. Subsequently, these act as a “teacher” for the algorithm, hence enabling it to “learn” a general rule that can be used to map the inputs to outputs. Now, whenever given an input, the machine will be able to predict the outcome based on the general rule that it learned earlier.

Unsupervised learning is different. In this strategy, only inputs are given to the algorithm, without any labels — i.e., a set of numbers are given without knowing whether they represent a car’s value or a house’s value. Hence, the algorithm is left to find structure in the inputs.

Last but not least, in reinforcement learning, an algorithm is given a certain goal in an interactive environment. By trial and error and using provided feedback (rewards and punishments for positive and negative behaviors), the program learns from its own actions and experiences.

What is Deep Learning?

First, to understand the relationship between Deep Learning, Machine Learning, and Artificial Intelligence, we can use a Russian dolls analogy; deep learning sits inside machine learning, which sits under artificial intelligence.

In deep learning, an algorithm is given raw inputs, meaning that we don’t specify which features are significant in the data. Instead, the algorithm decides this for itself. In other words, deep learning is essentially the component of AI that aspires to act like the human brain!

An essential aspect of deep learning is neural networks. These are inspired by the biological neurons found in our brains. Neural networks are used to recognize relationships between large amounts of data. A “neuron” — commonly referred to as a node— in these networks is a mathematical function that collects and classifies the data based on a specific architecture. However, it is not just one level of calculations. Neural networks usually have many levels between the input and the output, called layers. Nodes (or neurons) in each layer may be connected to different nodes in layers below or above. Ultimately, these nodes move the data through the network, from one layer to the next. The more layers an algorithm has, the deeper it is called — hence the name “deep learning”!

How are these AI-driven Technologies aiding Healthcare?

Among its vast potential in the field of healthcare, Artificial Intelligence can be especially beneficial for disease diagnosis and treatment recommendations. Indeed, there has been a significant focus on AI-powered diagnosis and treatment techniques since the early 1970s, when MYCIN was developed at Stanford University.

MYCIN

MYCIN was an early AI-driven expert system that was used to diagnose blood-borne bacterial infections. It also recommended antibiotic treatments for the infection, with the dosage determined based on the patient’s body weight. Since many antibiotics have the suffix “-mycin”, the program’s name is derived from these treatments.

MYCIN is considered a simple algorithm when compared to today’s machines. It operated using a knowledge base of approximately 600 rules. The physician would have to answer a series of yes/no or textual questions and, in the end, MYCIN would create a list of possible bacteria that caused the infection, ordered from high to low based on the probability of each diagnosis. The recommended treatment was also listed in front of each bacteria.

Watson

IBM’s Watson Health unit is currently one of the forerunners of AI-based healthcare. One of the general goals of Watson is to aid medical personnel in the treatment of patients. Unlike MYCIN, Watson is not a single algorithm but rather a set of “cognitive services” connected through Application Programming Interfaces — or APIs for short. These services include — but are not limited to — speech and language, vision, and machine learning-based data-analysis programs.

When using Watson, first the physician must pose a query to the system, describing the patient’s symptoms and other related information. Watson then analyzes the input to determine the most critical pieces of information. Besides, the algorithm also delves into patient data to discover other facts relevant to the patient’s medical and hereditary history. Examining the available data and forming and testing hypotheses, the algorithm provides a list of “individualized, confidence-scored” recommendations in the end.

Others

Over the past few years, more and more tech firms and startups have set a goal to aid AI applications in healthcare. Google, for instance, is collaborating with health delivery networks, like the UK National Health Services, to develop and implement prediction models from big data to warn clinicians of high-risk conditions, such as heart failure and sepsis.

There are also several companies and organizations that focus specifically on the diagnosis and treatment for certain diseases — including cancer — based on patient genetic profiles. Such firms can be especially significant considering the complexity of understanding all genetic variants of a disease for physicians without the aid of AI. Foundation Medicine and Flatiron Health, two firms owned by Roche, specialize in this approach.

“Population Health” machine learning models are also becoming essential for predicting populations at risk of particular diseases or outbreaks. Since many factors contribute to outbreaks, including history, environment, and even socio-economic status, the powerful data analysis techniques that these models utilize make them effective at making predictions.

From data analysis to making diagnoses and giving prescriptions, AI-powered services have been tremendously aiding healthcare — especially in the past couple of decades. But, there is still a long way for them to become completely independent of human interference. It is still — in most cases — physicians that input the data in the machine learning algorithms. More importantly, AI algorithms still operate based on the scientific and medical knowledge that humans feed them. Still, AI’s beneficial applications in healthcare by making predictions and analyzing medical data — and ultimately saving lives — are undeniable. And let us not forget how AI has gotten to where it is today: by mimicking our very own beloved, all-powerful brains!

About That Medic Network

If you like this article, be sure to follow us for more content on our Website, Facebook, Twitter, Instagram, LinkedIn.

Feeling friendly or excited for our next article? Email us at info@thatmedicnetwork.com or subscribe to our newsletter.

References

1. “Artificial Intelligence (AI).” IBM, 3 Jun. 2020. https://www.ibm.com/cloud/learn/what-is-artificial-intelligence. Accessed 26 Mar. 2021.
2. Brownlee, Jason. “What is Deep Learning?” Machine Learning Mastery, 16 Aug. 2019. https://machinelearningmastery.com/what-is-deep-learning/. Accessed 28 Mar. 2021.
3. Chen, James. “Neural Network.” Investopedia, 23 Dec. 2020. https://www.investopedia.com/terms/n/neuralnetwork.asp. Accessed 28 Mar. 2021.
4. Davenport, Thomas, and Ravi Kalakota. “The potential for artificial intelligence in healthcare.” Future healthcare journal vol. 6,2 (2019): 94–98. Accessed 28 Mar. 2021.
5. “Machine Learning: What is it and why does it matter?” Suit of Analytics Software (SAS), https://www.sas.com/en_ca/insights/analytics/machine-learning.html#:~:text=Machine%20learning%20is%20a%20method,decisions%20with%20minimal%20human%20intervention. Accessed 26 Mar. 2021.
6. Osiński, Błażej, and Budek, Konrad. “What is reinforcement learning? The complete guide.” DeepSense.ai, 5 Jul. 2018. https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/. Accessed 28 Mar. 2021.

AI-Powered Health Services was originally published in That Medic Network on Medium, where people are continuing the conversation by highlighting and responding to this story.

Arianna Huffington, the co-founder of The Huffington Post, once said,

“If you take care of your mind, you take care of the world.”

And yet, although we all know the importance of a relieved and cognitively and emotionally healthy state of mind, throughout history, many little resources within the field of healthcare have been dedicated to mental health. According to the World Health Organization, “one in four people in the world will be affected by mental or neurological disorders at some point in their lives.” And with the current COVID-19 pandemic in hand, many reports (referenced below) suggest the devastating effects of the decrease in social interactions on people’s mental health, especially teenagers. Hence, it is more important than ever to provide mental health services to the public. Through what medium? Well, Digital Health!

First things first:

What is Mental Health?

Mental health is an umbrella term for our emotional, social, and psychological well-being. It is directly correlated with virtually all aspects of one’s behavior, including mood, thinking, and actions. Mental health problems affect not only the individual but also the people that the person interacts with. Thus, maintaining a healthy state of mind is of the interest of a group — i.e., family, school, workplace, etc. — as well as its members.

How can Technology Help?

Digital health can make mental health services more accessible, especially in rural and remote areas and in underdeveloped countries. It also decreases the waiting times, hence resulting in more rapid access. Additionally, technology can provide access to mental health services in many languages, making it more available for the use of people from different races and backgrounds. And, during the current pandemic (and any possible infectious diseases that may emerge in the future), by substituting the in-person component of services with remote ones, digital health decreases the chances of infection.

Technology can also play a significant role in increasing people’s understanding of mental health and its associated disorders. We live in the 21st century, yet many still use terms like “crazy” or “psycho” when talking about people suffering from mental health issues. Therefore, considering the widespread use of technology in the modern world, we can use this as a platform for educating the general public about mental health.

What is Being Done Right Now?

Technology has actually already made its way into the world of mental health. The term “e-Mental Health” refers to the delivery of mental health services through the internet or other forms of technology. Various mental health clinics and companies worldwide have adapted technology-based techniques for the delivery of their services. Here are some of the digital health solutions that are currently being used:

• Online self-help: Includes self-help modules and cognitive behavioral therapy apps
• Crisis support: Includes phone help-lines, text support, and online chat support
• Medical intervention: Includes video-based counseling; telemedicine videoconferences; and text-based counseling, followed by clinical follow-up and referrals
• Peer-led support: Includes online monitoring, peer support apps, social media, chat rooms, instant messaging, gaming
• Coaching: Includes online therapy; video, text, and voice chat

Utilizing digital health platforms to improve the delivery of mental health services can assist many people — specifically tech-savvy teenagers — in overcoming their mental issues. These online platforms can also change the stigma around mental disorders, which many still refer to using the wrong terminology, by increasing the understanding of the general public about mental health. It is now more critical than ever for us not to neglect mental health issues but to conquer them with the help of mental health services and through the means of our good-old-friend — technology.

About That Medic Network

If you like this article, be sure to follow us for more content on our Website, Facebook, Twitter, Instagram, LinkedIn.

Feeling friendly or excited for our next article? Email us at info@thatmedicnetwork.com or subscribe to our newsletter.

References

1. “Delivering Mental Health Services in a Digital World.” Digital Health Canada, https://digitalhealthcanada.com/all-blog-posts/delivering-mental-health-services-in-a-digital-world/. Accessed 4 Mar. 2021.Panchai, N., Kamal, R., Cox, C., and Garfield, R. “The Implications of COVID-19 for Mental Health and Substance Use.” Kaiser Family Foundation, https://www.kff.org/coronavirus-covid-19/issue-brief/the-implications-of-covid-19-for-mental-health-and-substance-use/. Accessed 3 Mar. 2021.
2. “e-Mental Health.” Canada Health Infoway, https://www.infoway-inforoute.ca/en/solutions/e-mental-health. Accessed 3 Mar. 2021.
3. “E-Mental Health.” Mental Health Commission of Canada, https://www.mentalhealthcommission.ca/English/what-we-do/e-mental-health. Accessed 4 Mar. 2021.
4. “The World Health Report 2001: Mental Disorders affect one in four people.” WHO, https://www.who.int/news/item/28-09-2001-the-world-health-report-2001-mental-disorders-affect-one-in-four-people#:~:text=One%20in%20four%20people%20in,ill%2Dhealth%20and%20disability%20worldwide. Accessed 3 Mar. 2021.
5. “What is Mental Health?” MentalHealth.gov, https://www.mentalhealth.gov/basics/what-is-mental-health. Accessed 4 Mar. 2021.

Mental Health Services in a Digital World was originally published in That Medic Network on Medium, where people are continuing the conversation by highlighting and responding to this story.

Since the start of the COVID-19 pandemic, the extent to which technology can be used in various areas has become clear to humanity. We go online shopping more than ever, hang out with friends virtually, and go to school on our computers. In other words, it is safe to say that technology has dominated our lives more than ever. One of the fields that can take advantage of technology in these unprecedented times (and of course, beyond) is healthcare. This is done through nothing but the signature feature of digital health: telemedicine.

So, what is it?

Telemedicine refers to the delivery of healthcare services remotely through the means of technology, i.e., phone calls and video calls. In most cases, telemedicine is used to overcome the physical distance between the patient and the care provider or assist patients who have difficulty leaving their house, like the elderly or patients with disabilities. Additionally, in the recent case of COVID-19, telemedicine is being used to substitute the in-person delivery of healthcare, thus reducing the risks of contracting the virus. In fact, the spread of contagious diseases can be reduced through the use of telemedicine since individuals will no longer have to be exposed to infected people in crowded waiting rooms.

There are, in general, three types of telemedicine:

• Interactive Medicine
• Store and Forward
• Remote Patient Monitoring

Interactive Medicine allows for real-time communication between the patient and the physician. It is basically like going to the doctor’s office — but through your computer.

Store and Forward permits a third-party organization to collect the patient’s information and send it to a physician in another location for evaluation. This one is more like communicating with your doctor through email.

Last but not least, Remote Patient Monitoring enables care providers to monitor a patient’s conditions, like blood sugar and pressure, remotely using mobile devices.

Telemedicine vs. Telehealth

Now that we have an idea of what telemedicine is, it is a good time to distinguish between telemedicine and telehealth, two digital health tools that are often interpreted as synonymous. The World Health Organization (WHO) defines telemedicine as “healing from distance”. On the other hand, HealthIT.gov (the Official Website of The Office of the National Coordinator for Health Information Technology in the USA) refers to telehealth as “the utilization of electronic information and telecommunications technologies to support and promote long-distance clinical health care, patient and professional health education, public health and health administration.” These two definitions of telemedicine and telehealth sound almost identical, with one distinguishing difference: unlike telemedicine, in addition to clinical services, telehealth focuses on remote non-clinical services (i.e., medical education, physician training, administrative meetings) as well. In other words, telehealth refers to a broader range of remote healthcare services. Hence, it is all-encompassing, with telemedicine falling under telehealth’s umbrella.

Benefits

As a revolutionizing healthcare infrastructure, telemedicine provides some awesome benefits.

Telemedicine is remote, hence enabling the patients to overcome transportation limitations. During extreme weather, there is no need to travel to the doctor’s office for an appointment; instead, this can easily be done through video consultations. In this way, transportation costs are avoided as well (and we have to mention the escape that telemedicine provides from long hours in traffic, too!).

Thanks to telemedicine, patients with difficulties leaving homes — like seniors and patients with disabilities — can now access healthcare from the comfort of their houses. Scheduling is also made easier through telemedicine since the time spent travelling to and from the doctor’s office is taken out of the process.

Telemedicine enables patients to overcome childcare and eldercare challenges as well. Those who struggle to find care options for a vulnerable group no longer need to leave the house to visit their doctor, making healthcare more accessible to them.

Shortcomings

Despite the numerous benefits of telemedicine, there are some areas that can be considered as “downsides”.

Technology is expensive

The essential component of telemedicine is some form of access to technological devices and the internet. Poor to no access to the internet in some rural areas and in some underdeveloped countries makes telemedicine less accessible to all, a point that might result in telemedicine being reserved for the richer people only.

Unclear policies

Despite improvements in the recent years to regulate telemedicine, because technology is growing at a fast pace, there are still some uncertainties regarding policies. The areas under these uncertainties are reimbursement policies (should physicians be paid the same for telemedicine as in-person care), privacy protection, and healthcare laws.

Fewer face-face interactions

As humans, we are social beings. Hence, telemedicine might not always be our preferable form of contact with our physician. Furthermore, power outages and lost internet connections sometimes make it harder to have a smooth visit, something that does not happen very often in a face-to-face interaction. And, we cannot deny the fact that technology cannot always capture what humans can do, and for the diagnoses that require a physician to physically touch some part of the body, telemedicine fails to provide all the essential needed components (unless being able to touch someone from afar becomes possible some day!).

Considering telemedicine’s benefits and still-to-be-solved shortcomings, it has played a significant role in making healthcare more accessible to everyone. At the current pace at which technology is becoming an inseparable part of our daily lives, telemedicine holds great promises to facilitate the fair distribution of health — especially during unprecedented times, i.e., a worldwide pandemic (watch the video bellow). We should all keep an eye on telemedicine; after all, it is referred to by many as “The Future of Healthcare.”

About That Medic Network

If you like this article, be sure to follow us for more content on our Website, Facebook, Twitter, Instagram, LinkedIn.

Feeling friendly or excited for our next article? Email us at info@thatmedicnetwork.com or subscribe to our newsletter.

References

1. Cranford, Lauren. “Telemedicine vs. Telehealth: What’s the Difference?” Chiron, https://chironhealth.com/blog/telemedicine-vs-telehealth-whats-the-difference/. Accessed 17 Feb 2021.
2. Monaghesh, E., Hajizadeh, A. “The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence.” NCBI, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395209/. Accessed 16 Feb 2021.
3. “Telemedicine vs Telehealth: Here’s How Telehealth and Telemedicine Differ From Each Other.” Dapasoft.com, https://www.dapasoft.com/telehealth-vs-telemedicine/. Accessed 17 Feb 2021.
4. “What is Telemedicine?” VSee.com, http://tlp.law.pitt.edu/ojs/index.php/tlp/article/view/160/172. Accessed 16 Feb 2021.

Telemedicine: What is it? was originally published in That Medic Network on Medium, where people are continuing the conversation by highlighting and responding to this story.

During the past couple of decades, it has become clear that when referring to the “future” of almost all disciplines, there is one thing that comes to mind: technology. The breathtaking pace at which artificial intelligence and information technology are changing the world is undeniable. One field where we see this pace is medicine; the intersection of new technologies and healthcare is referred to as “Digital Health.” This is virtually a simple concept: using technological methods and processes to improve people’s well-being while also empowering patients to make better-informed decisions about their health. Yet what makes digital health so fascinating is the complexities that result from this relatively simple definition. From wearable devices to telemedicine, digital health is revolutionizing the field of healthcare. In this blog, we will decipher this revolution by a journey through the history of digital health and what it can bring — and is right now bringing — to the world.

Let us begin by discussing the origins of digital health.

As the American poet Maya Angelou beautifully says,

“If you don’t know where you’ve come from, you don’t know where you’re going.”

In other words, studying the history of digital health will enable us to further understand its roots and conclude where it is taking us. In order to review this history, we must go back to the end of the 19th century, and the beginning is with a subfield of digital health that is now becoming more important than ever: telemedicine.

The Beginning (1897)

As one of the most comprehensive areas of digital health, telemedicine encompasses using electronic forms of communication (i.e., phone and video calls) to perform medical diagnosis. According to Adam Darkins and Margaret Cary in their book Telemedicine and Telehealth: Principles, Policies, Performances and Pitfalls, it was in 1897 that the first documented telemedicine happened. A child was diagnosed with croup — a viral infection in the airways characterized by a distinctive cough — during a telephone consultation with a physician. Despite its early appearance, until nearly 9 decades after this incident, long distance diagnosis was reserved for Antarctica expeditions and, later, for space missions.

Growth (1950 — 1999)

During the second half of the 20th century, the development of ultrasound imaging techniques, artificial organs, and DNA sequencing clarified the degree to which technology can be used in medicine. These inventions paved the way for digital health to go further than telemedicine and beyond the boundaries of diagnosis to the front lines of providing treatments.

The end of the 80s into the 90s were golden times for digital health. During this time, several professional associations appeared in the US and around the world to improve healthcare delivery through digital communication. Some examples include the International Medical Informatics Association, the American Telemedicine Association, and the European Health Telematics Association (EHTEL). These organizations greatly impacted the transition from traditional healthcare methods to more advanced, technology-driven substitutes.

Maturation (2000–2015)

During this period, we observe an increasing desire to digitize healthcare. Since the beginning of the 21st century, as technological innovations, like the world-wide-web, became more user-friendly, patients gained easier access to the world of medicine. At the same time, new terms in the realm of digital health began to appear, such as mHealth (mobile health), eHealth (electronic health), and Personalized Health.

The first decade of this century was also the birthday of digital therapeutics. This treatment option uses online and digital technologies to prevent and treat medical and psychological disorders. For instance, WellDoc, founded in 2005, is among the first companies in the digital therapeutics industry.

The invention of Fitbit was the climax of this period. In 2007, this company was found to produce wireless fitness devices that help users monitor their health conditions, such as heart rate and the calories burnt. Fitbit started the era of commercial wearable devices in the field of digital health. This was an important step: digital health was no longer reserved for adventurous, expensive missions to the moon or Antarctica — everyone could now use it, even at their homes!

Bright Futures (2015 — )

During the decade that we are in now — from 2010 to 2020 — digital health has grown into an entirely accepted path for achieving higher standards of health.

A significant milestone in the development of digital health was the launch of a digital health unit by the US Food and Drug Administration (FDA) in 2017. The unit’s goal is to expand the opportunities for digital health to become a part of the general healthcare. FDA’s Digital Health Unit has tremendously sped up the development and distribution of digital health technologies.

Investments in digital health are skyrocketing in this age: from $4.1B in 2014 to $9.4B in 2020. In other words, the promising future of digital health is becoming more apparent to the general public. With the development of technological innovations in this era, we see digital health developing into a standalone market — one that improves the hopes of achieving more responsive and sustainable healthcare.

About That Medic Network

If you like this article, be sure to follow us for more content on our Website, Facebook, Twitter, Instagram, LinkedIn.

Feeling friendly or excited for our next article? Email us at info@thatmedicnetwork.com or subscribe to our newsletter.

References

1. Lee, Gabrielle. “A Telehealth Technicality: Pennsylvania’s Outdated Insurance Reimbursement Policies Deter Investment in Modern Telehealth Technology.” Journal of Technology Law and Policy, Fall 2014, http://tlp.law.pitt.edu/ojs/index.php/tlp/article/view/160/172. Accessed 26 Nov. 2020.
2. Macnaughtan, Lorena. “The Curious Case of Digital Health.” InnovateMedTec, 25 May 2015. https://innovatemedtec.com/content/the-curious-case-of-digital-health. Accessed 25 Nov. 2020.
3. Meskó, B., Drobni, Z., Bényei, É., Gergely, B., & Győrffy, Z. “Digital health is a cultural transformation of traditional healthcare.” mHealth, 14 Sept. 2017, https://doi.org/10.21037/mhealth.2017.08.07. Accessed 23 Nov. 2020.
4. “Milestones in Medical Technology.” The New York Times, 10 Oct. 2012. https://archive.nytimes.com/www.nytimes.com/interactive/2012/10/05/health/digital-doctor.html?_r=1#/#time15_343. Accessed 25 Nov. 2020.
5. “Milestones Of Digital Health: Infographic About Its Timeline.” MedicalFuturist, 9 Jul. 2020, https://medicalfuturist.com/milestones-of-digital-health-infographic-about-its-timeline/. Accessed 26 Nov. 2020.
6. “Timeline.” Digital Healthcare Timeline. http://digitalhealthcaretimeline.com/digital-healthcare-timeline/. Accessed 25 Nov. 2020.
7. “What is Digital Health?” US Food and Drug Administration, 22 Sept. 2020, https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health. Accessed 25 Nov. 2020.

A Brief History of Digital Health was originally published in That Medic Network on Medium, where people are continuing the conversation by highlighting and responding to this story.