How Can We Optimize Healthcare for a Digital World?

The COVID-19 pandemic forced everyone to evaluate the healthcare system at a microscopic level. While developments were occurring at a faster pace in certain areas, such as the COVID-19 vaccines, there were still significant inefficiencies requiring changes.

For one, the need for healthcare to leverage technology for dispensing services and closing gaps in access became a need of the hour. While the basic outline was available, more fine-tuning to suit the consumer’s healthcare needs was required.

This is why in 2020 global digital health ventures saw a massive rise in investment of almost 19.8 billion USD, 10 times its value in 2016. Nevertheless, while funding is now more widely available, researching the needs of the consumers and patients plays a crucial role in optimizing digital health for the future.

What Is Digital Healthcare?

Digital healthcare covers a wide spectrum of healthcare categories, including health research, information, telehealth services, wearable devices, and diagnostics which use technology to support clinical practice.

Digital health innovation has received a significant boost in the past decade, especially during the COVID-19 pandemic. The primary focus of digital health is to detect and prevent disease and to improve the long-term quality of life.

Why Is Digital Healthcare Important?

The key facets of digital healthcare are providing more insightful data for innovation and encouraging individuals to take control of their health. Making these the cornerstones of technological innovation also enables a more personalized approach to dispensing healthcare, which has so far been lacking.

Patients often feel left out when it comes to their treatment plans. Studies have indicated that healthcare providers in certain circumstances overlook the true intensity of patients’ symptoms as they experience them. Patients feel as though they are not taken seriously enough for their ailments, especially the symptoms that do not have sufficient explanation through diagnostic procedures.

This is where digital healthcare can assist. Through the use of wearables and tracking devices, patients nowadays can document most of their symptoms as they experience them. Some symptoms, such as breathlessness or a nagging cough, may not appear during a clinical visit due to luck. However, if patients can track them when they do appear and present these data during clinical examination, it provides a more holistic and accurate picture for the healthcare provider.

Additionally, stakeholders within digital healthcare spaces are looking toward reducing errors and inefficiencies currently noted in the system. Data that is collected can often be transferred seamlessly between different systems to further both research and personalized healthcare.

Big data within the digital healthcare space is improving AI and machine-learning models so they can predict disease outcomes better. This helps to create models that speed up innovation times, such as we have seen with the COVID-19 vaccine delivery with the technological infrastructure we currently have.

How Is Digital Healthcare Transforming the Health Industry?

One of the foremost concerns within the healthcare system currently is its accessibility and the limited resources available to provide healthcare for all. This poses a problem, especially given that having access to life-saving healthcare is a basic human right.

One of the ways that technology is transforming digital healthcare is through more efficient and continuous tracking of symptoms, as mentioned above. It is a notable deficiency that during case presentations not all symptoms and signs of the underlying disease can be observed. Changes in signs such as a cough, temperature, skin growths, or pain over time are more accurately documented when tracked continuously.

Advances in digital health can also enable the combination of different technologies, such as those observed with robotics and telehealth. This was implemented on a small scale during this pandemic and has great scope to improve the outreach of healthcare services if expanded, especially in remote locations.

Technological automation can also be used in health manufacturing, such as of drugs or equipment, but using advanced machine learning to help reduce errors in product development. This also improves outcomes in diagnostics and treatment plans as the system will be able to document certain inefficiencies earlier than possible with the manual processes

AI models can also help in understanding how chronic diseases progress over time. While most chronic diseases evolve within a certain predictable framework, individual trajectories can differ based on lifestyle, genetics, and other factors. Termed precision medicine, individuals with chronic diseases should be able to receive more individualized management based on AI interpretation of their health factors, which can predict disease outcomes and suggest more appropriate treatments.

Finally, nanomedicine is also a heavily researched field of digital health. Its benefits are being explored in the fields of tracking, diagnosis, and treatment. Combining this technology with existing tools such as MRI scanning can help with more precise diagnosis. Nanomedicine will also enable early detection of metabolic changes which eventually precipitate disease, such as cancer, enabling early intervention and progression.

Case Studies of Recent Breakthroughs in Digital Health

Due to the struggle of accessibility, affordability, availability of resources, and errors in outcomes, a shift to integrate technology into the delivery of healthcare is taking place. This has increased exponentially since the pandemic due to the need for efficient healthcare access for all even at a distance.

The next section documents a few projects around the world that have explored how the integration of digital health affects communities.

AI in Medical Imaging (Israel)

Radiologists’ workloads are increasing, especially with the increase in accessibility for imaging procedures at most medical centers. Today, radiology forms a key diagnostic tool. However, this has also shortened the time available for radiologists to accurately diagnose details within the scans.

Aidoc, a technological startup in Israel, analyzed the pain points of radiologists regarding their availability and time-sensitive scans limiting their workflow. They initially used AI to detect brain scans showing signs of severe diagnoses requiring immediate attention, such as a brain hemorrhage. These scans were then moved to the top of the list. After further development, this AI technology was tested in other areas of diagnosis, such as pulmonary embolism, tumors, and abdominal air.

Aidoc currently has a 96% accuracy rate. Therefore, it is not a replacement or competitor for human radiologists but its automated priority adjustments have reduced waiting times for critical cases by around a third.

CANImmunize (Canada)

Noting the complexities of the immunization system, CANImmunize a web platform and app was launched in 2014 to enable a seamless experience to record and manage immunizations. The need came from the nationwide differences in immunization schedules, especially for children. Additionally, public health reporting of immunization data also faced struggles due to varying data reporting standards.

The Canadian Vaccine Catalogue (CVC), a comprehensive database of Canadian vaccines and standard vaccine terminology, was used to develop CANImmunize. Using CVC data assisted with providing a more scalable method to build networks for managing vaccination registries.

With CANImmunize, individuals can record their vaccines for themselves and their children. They also receive recommendations on the different centers available for receiving vaccines across the country. CANImmunize also notifies patients of local outbreaks and possible vaccinations available for disease prevention, such as with the recent COVID-19 pandemic.

Luscii (The Netherlands)

A remote telemonitoring app, Luscii was developed in 2018 with the aim of reducing unnecessary hospital visits and giving patients more control over their health. Luscii measures a patient’s vital stats and symptoms from the comforts of their homes. These can then be relayed to healthcare professionals who can keep a close watch if symptoms deteriorate.

During the COVID — 19 pandemic, Luscii enabled the setup of virtual wards which helped ease the immense pressure healthcare facilities experienced. The algorithm used to develop the app helped to detect cases that might require immediate attention and signal healthcare professionals to look into their case files further to facilitate more effective management of high-risk cases.

Currently, 50% of hospitals in the Netherlands use Luscii to monitor various healthcare conditions. Several countries across the globe such as the UK, Sweden, Ireland, Ghana, and Kenya are also employing Luscii software.

What Are the Disadvantages of Digital Healthcare?

With the rapid evolution of digital healthcare technology, there are bound to be drawbacks.

A primary concern for patients is maintaining privacy and confidentiality between them and their healthcare providers. A diagnosis such as cancer can be significantly personal, which can make patients apprehensive about sharing information over electronic health portals when they do not know who else could have access to it or how securely it is transmitted. Additionally, patients may be reluctant to allow their information to be stored on an e-health database if they know it will be used for research, even if they would remain anonymous.

Another concern is bias experienced when building healthtech systems. Humans all have biases, whether we admit them or not, and these affect the decisions we make. This remains true for AI — since at least the late 80s, machine learning, and AI systems have been found to eerily reproduce biases against a certain race, ethnicity, and gender groups, among other social categories.

These were not hard-coded into these systems. Rather, the systems accurately learned from the material they were given by humans, which was replete with unaddressed implicit biases. Therefore, bias within the e-healthcare system remains a concern for patients and health practitioners, and further that it may be performed by a machine believed to be objective and infallible, which can be harder to fight against. The main aim within digital healthcare currently is to use technology to improve efficiency and reduce human error, but understanding how human bias might be affecting how the systems are built is important and necessary

For healthcare professionals, a primary concern is that AI systems might render them obsolete and lead to job loss. However, experts believe that there are key human traits, such as compassion, that AI and machine-learning systems cannot replicate. The key while building these systems is to effectively streamline health practitioners’ workflow and decrease their workload by automating repetitive tasks, leaving them more time and energy to focus on personal interaction and treatment administration. Additionally, doctors and other healthcare professionals are the main source of feedback for these AI systems currently and will likely continue to be so.

Finally, there are challenges in the implementation of digital healthcare itself. Effective digital healthcare requires the digital infrastructure to be sufficient advance and robust enough to be able to handle it. In rural areas and developing nations, the available internet connections are simply not fast or consistent enough for digital healthcare to be consistently useful. Additionally, implementing digital healthcare across a whole country can be patchy and discontinuous, leading to duplicate and redundant information that is not seamlessly shared among healthcare professionals and mimics or worsens the problems of non-digital healthcare systems.

How Can We Address the Disadvantages of Digital Healthcare?

There is no single way to address all these inefficiencies when building a system for digital health. Currently, detailing these setbacks is the first step to understanding how they can be solved.

When more healthcare settings employ digital health technology, a more holistic understanding of ways to address disadvantages can take place.

To solve the primary concern of privacy and confidentiality, the digital healthcare systems that are built must have privacy at the forefront. Stringent monitoring and misuse of data should be penalized. Understanding what patients consider acceptable to share for research purposes needs to be outlined. Estonia’s digital healthcare system has embraced the blockchain as one method of ensuring patients retain complete control over their data’s visibility.

Additionally, while these systems are being built, legal frameworks need to be drawn out to ascertain the sanctity of basic healthcare is maintained at all levels.

Regarding bias, conducting blind trials of systems globally can help ensure the elimination of bias over time. Big data is one way to improve this. Especially as showcased during this pandemic, where information from around the globe was seamlessly transferred for advancements to tackle COVID-19. Another tool against AI supporting biases is conscious awareness of the biases from the beginning. Part of this is involving patients for whom the algorithm will eventually help make decisions, particularly those from minority groups, and investigating their experience and perception of the AI as it stands. This information will enable the developers to adjust the system to minimize the biases before it is put into full operation.

Conclusion

Digital health technology is laying a foundation for a future where healthcare will become increasingly accessible to all. The key is to optimize healthcare efficiency and delivery. For this to be successful, clear end goals need to be defined for all technology developed. This is because healthcare systems will have to adapt to accommodate new technology in the long run.

Additionally, the people who will be a part of this system require their needs to be addressed, especially doctors and their patients. Those using these technologies will eventually require training to implement them within their communities.

If you are currently working in the field of healthtech, our team would love to have your input on your experience in optimizing the delivery of healthcare. Hyfe aims to revolutionize cough tracking to understand both causes and workable solutions for chronic coughs. Simple changes often make a difference in how a tech product is consumed and optimized for healthcare delivery.