Telehealth is the provision of healthcare via digital information and communication technologies. Most often employed via computers, tablets and smartphones, telehealth also includes an emerging range of health products such as remote monitoring devices, digital biomarkers and wearable technology.
While telehealth adoption had been growing steadily over the last decade, its role in facilitating care during the COVID-19 pandemic cemented its place as an essential healthcare delivery channel.
While telehealth is presently most often employed through video consultations between patient and provider, it encompasses a broad array of clinical and nonclinical uses such as:
Aggregate patient data
Prescription management and adherence
This list is only a small selection of the current ways in which telehealth is deployed. Over the next few years, we’ll continue to see the scope of telemedicine expand into new arenas while growing even more capable in current fields like:
From robotic surgery to telehealth, digital advances are driving innovation in all areas of healthcare, a trend that can be expected to accelerate during and after this era of pandemic-caused isolation.
We see dramatic changes in these areas: (1) Sensors and wearables; (2) Virtual Reality and Augmented Reality; (3), 3D printing; (4) AI driving analytics, automation, and robotics and; (5) The rise of chatbot. In fact, we are already experiencing the impact of the coronavirus isolation in some areas, such as telehealth and 3D printing.
On the grand scale, robots have been proven to be more precise than surgeons and AI can diagnose cancers with a success rate of 99%. In 2020 cost pressures –compounded by the coronavirus initiative- and regulatory change will act as the major catalysts for digital health treatments, which have a crucial role to play in delivering effective, fast, and cost-efficient patient care.
For instance, the pandemic isolation combined with digital health advances are helping shift care to be based around people’s homes.
Local care is not just more convenient and less stressful for patients, it also makes financial sense, when you consider the average hospital stay in the US is upwards of $10,000, totaling over $1 trillion annually in hospital services, and that 60 percent of all bankruptcies in the US are related to medical expenses.
The transformation of traditional value systems in healthcare will continue to accelerate as patients increasingly become better-informed health “consumers”. Thanks to digital, the “value pool” is shifting in this industry, resulting in cost savings for patients thanks to better system efficiency. 2020 will also see the introduction of standalone 5G, which will enable the adoption of an almost limitless number of applications involving AI, big data and the IoT. Many healthcare-related high-bandwidth projects will be set free by 5G’s connectivity, bringing therapies from within hospitals into the field.
From a senior care perspective, we are starting to see many senior living communities shift their focus towards putting technology first. In fact, the shift over the last three years is exponentially more than all the progress from the last ten years combined.
As we continue to see an increase in the implementation of technology, we’ll also see residents’ quality of life improve because we are enabling them to age in place longer and remain in their preferred care setting.
In actuality, technological advancements and innovation are more likely to come to the senior living industry over any other care setting. Since these types of facilities are largely privately funded, senior living facilities are more likely to adopt these new innovations over those organizations that are funded by the government.
Overall, technology is starting to be more widely implemented to improve senior care by managing resident data more efficiently, all with a primary focus of helping our seniors to maintain the independence, health, and general wellness.
We have officially entered into a New Normal and technology overall will continue to play a larger role within the senior living space. Mobile technology will be even more critical and engaging family in care through the use of family engagement solutions will become foundational.
Leveraging an EHR as an underlying platform to improve overall care quality allows care providers to truly see resident needs and find creative ways to address them.
By taking a comprehensive approach to an EHR, providers in the senior living space can gain insight into the community’s key operating metrics, then adapt and adjust accordingly by regularly tracking clinical outcomes, staffing, and quality indicators.
From a data perspective, more and more senior living communities are recognizing the importance of interoperability. Data being collected shouldn’t just tell us where we are at, it should tell us where we are going by helping us predict potential issues before they happen.
With every technological advancement, we’re working toward a mostly digitized healthcare system. And, if the current results are anything to go by, the future is bound to be an exciting one. That said, though, we’ve still got a long way to go.
Healthcare is slowly embracing AI and other technologies to improve services to clients. Six out of 10 healthcare companies already use some form of internet of things (IoT).
We could do better when it comes to incorporating AI, but at least we’re making some progress. In this post, we’ll look at how higher levels of digitization will improve the healthcare industry.
More Digitization Means More Personalized Service
It seems paradoxical, but our current drive toward better efficiency has ignored the human aspect. Doctors today receive a lot of information, most of it digital. Their concern is that by needing to analyze these reams of data, they’ve got less time to deal with their patients.
Digital measurement standards being applied often leave doctors frustrated. They feel that they have to work toward standards that have little relation to the overall quality of work.
Artificial intelligence could change that. Not only can AI speed the diagnosis of conditions, but it can also provide a more rounded analysis of a doctor’s performance. AI can assess a range of factors quickly and easily.
Using AI can make it possible to assess how rules affect doctors at the ground level properly. That could lead to more rules that make sense once implemented, which, in turn, could lead to the scrapping of onerous regulations that get in the way of successful patient outcomes.
Digitization Can Fill Healthcare Data Gaps
If we look at the way that healthcare systems collect data, we see huge gaps. Most of the time, data is only collected when patients interact with the system. That is when they’re ill and need to see a doctor. This leads to a system of reactive treatments.
A genuinely useful healthcare system, though, should be able to predict potential health risks, give patients advice on how to manage those risks, and to collect as much data as possible when the person is feeling well.
We’ve had a range of monitoring tools for some years now. Fitbits, home blood pressure checkers, daily blood glucose monitoring kits are all examples of monitoring tools most of us have access to. Many of these tools can now be connected online. That leaves us with a wide range of options that can give our healthcare system a far more complete picture of our health.
Your Fitbit, for example, logs how many steps you walk on any given day. Your blood pressure kit can point out times when your blood pressure is particularly high.
Information that the machines can’t provide, such as how much food you ate, or how you’re feeling, could be entered into an app built for the purpose.
Hospitals and healthcare systems are benefitting from unprecedented innovation in information technology, helping improve everything from facility operations to patient care. But with these advancements come massive amounts of data—clinical research, digital imaging, and other patient data—that are taxing IT’s ability to cost-effectively manage and store in way that is secure, compliant, and always accessible.
Between the introduction of smart connected medical devices, plummeting costs of genome sequencing, and increasingly higher-resolution medical imaging, we are generating a wealth of information that is too expensive to store, yet too valuable—and, in many cases, unlawful—to throw away. Analysts from IDC predict that healthcare data will reach 2.3 zettabytes (ZB) by 2020. Imagine the discoveries that await, if only there was an affordable way to store it all.
Connected Medical Devices Mean Better Care, nd More Data To Store
According to the U.S. National Library of Medicine, within the next three years, 40% of the projected $117 billion IoT industry will be related to healthcare. The IoMT will generate exabytes of additional data, a portion of which compliance regulations will mandate you save. But what if we could store it all? What breakthroughs await when the power of analytics and machine learning are unleashed on vast archives of medical data?
The Internet of Medical Things (IoMT)
Real-time diagnostic data from connected medical equipment and home-health wearables promises to revolutionize medicine. Patients with long-term or chronic conditions can be monitored from the comfort of their homes. Instant access to information will speed diagnoses and response times. But perhaps the greatest potential of the Internet of Medical Things (IoMT) lies in the ability to save and analyze all the data these interconnected devices will generate over time.
Medical Imaging and Records
Hospitals and healthcare facilities are drowning in data as highly sensitive cameras, light wave and electron microscopy, and new modalities like 3D mammography and ultrasonic holography produce higher resolutions and larger file sizes. Many organizations adopt a “save everything” approach to ensure compliance with complicated regulations. To mitigate the high cost of storing all this data, complicated storage tiers and data lifecycle management solutions are implemented. But trying to figure out what doctors and researchers need access to on a regular basis and what can safely go into cold storage makes these complicated tiering strategies even more complex … and expensive.
We tend to have a negative view of risk, regarding it as a danger to the business. But, it also presents opportunities to push boundaries. If we reframe risk as a change-maker, then what degree of risk is acceptable? The healthcare industry faces this conundrum at every turn. Whether testing a toxic chemotherapy drug that could be lifesaving, or adopting IoT devices that provide detailed analytics, these advances can all expand the threat landscape.
Unlike testing pharmaceuticals in a controlled lab setting, the world of cyber and its risks are in constant flux. Healthcare data is at the top of cybercriminals’ lists, contributing to a record amount of breached health records in the past year. Full patient medical records are a valuable commodity on the dark web and?sell for up to $1,000?each.
Now, healthcare organizations can’t stay stagnant in implementing protections.
The reality of highly-regulated industries is that compliance mandates tend to govern security operations. But where regulations are cut and dry, risks do not fit neatly into boxes of “high risk” and “low risk.” Instead, risk is on a spectrum that requires a holistic cybersecurity strategy to appropriately prioritize and mitigate risk according to what is deemed as acceptable.
To help healthcare organizations mature security policies and become more comfortable with risk, here are three recommendations for 2020 cybersecurity planning:
Imagine for a second: you’re walking through the busy halls of your local hospital, only to notice that the doctors and nurses around you are constantly checking their phones and tablets. It strikes you as odd, and you can’t help but think: Isn’t anyone getting any work done around here?
Actually, they are.
With over 70 percent of examined patients using at least one health app to manage their diagnosed condition, and more than 318,000 mobile healthcare apps available in top app stores worldwide, the picture of doctors and nurses relying on their devices as literal “mobile assistants” is becoming a highly sought-after reality.
While this perspective is often bolstered by positive reviews of hand-held computer use by healthcare professionals – where digital assistant devices improved physician effectiveness during patient documentation, patient care, information seeking and professional work patterns — the mHealth industry still has a lot of room to grow in terms of digital health infrastructure.
Not to be put off, mHealth developers have nevertheless continued to advance their compliance, security, accessibility, and efficiency practices in the face of wide-scale transformative change. And when asked, most mHealth developers (myself included) will tell you that what motivates us to keep going has to do with the massive potential these technologies have to literally transform the field of medicine as we know it.
And what exactly is thatpotential? Every day our news feeds are inundated with articles promoting the latest in mHealth technology – from mobile apps that can perform an ultrasound, to apps that help patients track their own symptoms – so it can be hard to navigate the ever-widening world of mobile healthcare.
In light of such a big subject then, I’ve often taken to cementing my own understanding of mobile health by thinking about the ways in which these applications are already affecting physicians, clinicians, and other practitioners at every stage of their medical career.
Put differently, from the time that an aspiring healthcare professional begins their educational journey, to their first-accepted payment for needed treatment, mobile health apps are helping doctors transform the field of medicine before our very eyes. Here’s how:
In a lot of our popular media, physician education is represented as an arduous journey from beginning to end. With long nights studying, cadavers to examine, and an infinite amount of medical information to digest, med students are flocking to (mobile) medical education applications that can help them test their own knowledge in a way that suits their learning style.
It began in the 1980s with those wonderful word processors. Electric typewriters bit the dust, and health records could be entered and saved on floppy discs. This was only the beginning.
We’ve come along way, baby. As technology came to disrupt every sector of the economy, healthcare was no exception. Consider all that has happened in this sector and where we are today.
Consolidated health records in the cloud
Anyone who has been to a doctor recently understands this. That doctor may have your entire health history, from multiple providers, all in one place. This technology allows any provider to provide better care protocols according to each individual’s unique history and make recommendations for testing, etc. that will not be duplicating those already done.
Patients can also access their full health histories and provide access to family members as well. This allows more control of patients over their own healthcare and allows them to make better decisions for future care.
Use of big data for treatment protocol decisions
Now that providers have access to health data from all over the globe, they can review research studies, identify effectiveness based on specific symptoms, DNA makeups, and more. The net effect is this: research from all over the world is now available through tools that gather data, churn it, categorize it, and provide reports based on specific queries. Ultimately, better care for all can occur because of this shared data. Amy Castello, a healthcare writer for Trust My Paper, says this: “I conduct a lot of research on a number of healthcare topics. One of the most interesting is the strides that have been made in the use of big data. I see a future of customized care solutions that
Use of AI and machine learning to identify and predict disease outbreaks
When artificial intelligence is applied to bag data gathering, environmental conditions can be analyzed for their contributions to disease outbreaks. Likewise, when there are higher than average disease conditions among certain demographics or in certain geographical areas, AI can analyze data and report common characteristics that may be contributing to those outbreaks.
Development of vaccines
Every year, a number of medical reporting organizations isolate the specific viruses that have resulted in flu outbreaks. All of this information is then physically reported during a consolidated meeting, and decisions are made for the next vaccine composition. Now, all of the data can be digitally reported, and the recommended vaccine compositions determined by the use of artificial intelligence. Ultimately, this can serve to reduce some of the human “guesswork” that now occurs.
A decade ago, patients had to travel to their doctors’ offices for regular checks on chronic conditions. Now, wearable devices provide ongoing data electronically, so that patients are monitored from home, with alerts to their doctors when conditions change that they might warrant an office visit or hospitalization. Getting real-time data of this sort not only increase efficiency of care but results in lower costs for both providers and patients.
By Brooke Faulkner, freelance writer; @faulknercreek.
Up to a fifth of patients with serious conditions are first misdiagnosed, and that leaves tremendous consequences. With the help of healthcare technology, doctors are able to diagnosis patients more effectively and easier. For example, migrating patient data from paper to online, known as electronic health records (EHRs), has greatly aided the medical world. Technology, especially using artificial intelligence and predictive analytics, has enabled doctors to make faster, more accurate diagnoses, and thus provide better care.
The volume of big data
Duquesne University estimated there to be 150 exabytes of healthcare data collected in 2011. Four years later, they reported about 83 percent of doctors had transitioned from using paper to electronic records. By now, with the ubiquity of the cloud, these numbers have assuredly gone up.
Massive amounts of data make predictive analytics possible, as trends can be spotted and analyzed. By spotting patterns, diagnosis of a disease becomes easier even for doctors unfamiliar with a specific disease or symptom. Uploading symptoms allows a computer to compare records and identify symptoms comorbid of other problems. This allows even specialized doctors to recognize issues outside of their field. Medical mistakes lead to the death of some 440,000 people each year; while misdiagnosis is only a part of this number, correct diagnosis and treatment will reduce it.
Big data can even be collected in the form of PDFs as part of telemedicine. A doctor can send PDFs to patients as part of a poll or survey or simply to collect symptom information from the patient. From there, data entered in the PDF can be collected and analyzed, generating patient data or feedback for the doctor.
Google flu trends
Google ran what can best be called an experiment from 2008 to 2014. Using artificial intelligence, the search engine recorded flu-related searches in an attempt to predict the severity of an outbreak, as well as the affected geographical area.
It was a flawed model, and tried to use big data as a replacement, rather than a supplement, for traditional data collection and analysis. It completely missed a flu outbreak in 2013, the data off by a massive 140 percent, and Google Flu Trends ended its public version in 2014. The algorithm monitoring flu-related search terms was simply not sophisticated enough to provide accurate results. While new data is no longer available to the public, historical data remains available to the Centers for Disease Control and other research groups. It’s possible that once the algorithm and predictive analysis is capable, the program will continue.
Zingbox, provider of healthcare Internet of Things (IoT) analytics platform, announced new research demonstrating that hackers are leveraging error messages from connected medical devices — including radiology, X-ray and other imaging systems — to gain valuable insights. These insights are then used to refine the attacks, increasing the chance of successful hack.
“Hackers are finding new and creative ways to target connected medical devices. We have to be in front of these trends and vulnerabilities before they can cause real harm,” said Xu Zou, Zingbox CEO and co-founder. “We make it our mission to assist and collaborate with device manufacturers to ensure the security and uninterrupted service of connected medical devices.”
Information gathering phase of a typical cyberattack is very time intensive phase where hackers learn as much as they can about the target network and devices. By simply monitoring the network traffic for common error messages, hackers can gain valuable insight into the inner workings of a device’s application; the type of web server, framework and versions used; the manufacturer that developed it; the database engine in the back end; the protocols used; and even the line of code that is causing the error. Hackers can also target specific devices to induce error messages. With this information, the information gathering phase is greatly shortened and they can quickly customize their attack to be tailored to the target device.
Zingbox’s research discovered that:
Information shared as part of common error messages can be leveraged by hackers to compromise target connected devices.
Hackers can “trick” or induce medical devices into sharing detailed information about the device’s inner workings.
Leveraging this information quickens a hacker’s access to a hospital’s network.
“Imagine how much more effective hackers can be if they find out that a device is running on IIS Web Server, using Oracle as backend and even gathering usernames,” said Daniel Regalado, principal security researcher at Zingbox and co-author of Gray Hat Hacking. “That will help them to focus their attack vectors towards the database where PHI data might be stored.”
The research also revealed that the healthcare industry has made great strides in collaborating across providers, vendors and manufacturers: there was rapid response and a willingness to generate patches for their medical devices from three out of seven manufacturers whose devices were included in the study. However, there is still work to be done to bring the urgency of these findings as well as increased collaboration between security vendors and device manufacturers.