What was the impact of COVID on HealthTech?
With technology evolving at a staggering rate, COVID has had a profound impact on the healthtech industry.
Since the start of the COVID-19 pandemic about two years ago, many facets of life have changed. Healthcare systems have seen many changes throughout the pandemic. We’ve seen hospitals overloaded with coronavirus patients struggle to keep up with the demand. The pandemic highlighted how inept our healthcare systems were in dealing with this type of crisis, while still providing care to existing patients. It also highlighted the inability to easily collect and analyze data on patients in an efficient matter, thus providing care to people more quickly. How has technology impacted healthcare systems during the pandemic, and where do we go from here?
During the pandemic, people have started to take a closer look at their health, not only to prevent contracting coronavirus, but also to manage other conditions they may have. Despite the drop in new cases, many people have not been able to even see a primary care physician in two years due to the risk associated with meeting a physician in-person. Technology has played a major role in increasing access to healthcare, at a time where people are worried about exposure.
Telehealth was quickly adopted by many people that were in need of medical care. While patient sentiment wasn’t high before the pandemic, the idea that anyone can get immediate care by just downloading an app was revolutionary. I had a close friend that needed to see a physician around the time COVID started, but he was immunosuppressed. He was able to use Teledoc to quickly get the help he needed, without leave his house. At a time when coronavirus cases only seemed to be going up, being able to take care of your health from the comfort of your own home was not only convenient, but it was also safer.
With the increased adoption and even reliance on digital health solutions during the pandemic, it appears telehealth is here to stay. Patients find it more convenient and have become more comfortable with virtual appointments. Providers are also finding it is more efficient and sometimes effective to reach as many patients as possible. McKinsey estimates that investments in digital health solutions in the first half of 2021 had already surpassed all investments in 2020. I believe we will see more digital health solutions in the market as healthcare continues to evolve, and as people continue to adopt solutions like telehealth.
The use cases for artificial intelligence in healthcare has increased over the years. However, researchers and companies still find the lack of organized data a huge problem when developing AI models. In a field as complicated as healthcare, where the stakes are quite high, access to high quality and high-volume data is essential. In addition to the lack of data, the data itself isn’t always comprehensive, and can sometimes be disorganized. Data sets from different sources, such as providers and insurers, aren’t always compatible and are often times stored in different ways, so using them together is quite difficult. This data can paint a completely different picture of patients, and often times, this data either isn’t collected, or it isn’t useful to be used with a machine learning algorithm. In 2020, Google Health researchers developed a deep learning model that could diagnose diabetic retinopathy using images of eyes. During internal testing, they found that the system worked with 90% accuracy. However, during real world testing a large amount of variation in performance across the 11 clinics they used the system. They found that there were inconsistencies in where images were collected and the quality of the data, as well as differences in the workflows nurses used during screening and the resources the clinics had access to. These factors, as well as others, contributed to an AI model that wouldn’t work when the data it was using lacked consistency and met certain quality requirements.
Because of COVID, there has been a push to digitize healthcare, and more reasons to collect healthcare data. There have been more use cases for AI models, such as predicting patient exposure to coronavirus to diagnose and treat patients more quickly. Researchers had to collect and share large amounts of data in order to study and even find treatments for coronavirus, and this data has helped train AI models to COVID screenings, diagnostics, and predictions for many patients. Mt. Sinai Health System in New York used an algorithm to highlight patients that needed to be isolated based off the results of CT scans. The algorithm had a high sensitivity to images and clinical data compared to radiologists, 84 percent versus 75 percent. The algorithm also recognized 68 percent of patients with negative CT scans as COVID-positive, reducing the number of patients released with a false negative. This is one of many examples of AI helping healthcare organizations deliver better service to patients and having an impact on society.
The trend of collecting large amounts of data for analysis will likely increase moving forward, given how effective this data was in combatting COVID and finding new treatments, such as vaccines. The idea of using large amounts of data to train AI models to solve healthcare problems is exciting. It will become easier to use these AI solutions as we collect more useful data that is less fragmented and more comprehensive. As the government and other institutions introduce and improve data standards, such as HL7, there will likely be an increase in AI applications as the data used to train models will be more effective in producing efficient and accurate AI solutions.
While this future is exciting, as more and more data on patients is collected, it will need to be stored safely. In 2021, there were over 680 healthcare data breaches, resulting in nearly 45 million patient records being leaked or stolen. This data can include healthcare records of patients, but it can also include financial data. However, there is potential medical device data and access to be compromised as well. IoT devices are starting to become more widely adopted across healthcare systems, in place of traditional monitoring systems. These devices can be hacked, and they need to be protected to prevent any impacts to patient care. Patients care about data privacy now more than ever, and with more data being shared between healthcare organizations during the pandemic, securing this data will allow companies to continue providing great service to their patients in a responsible manner.
Technology has evolved at a record pace, and with healthcare organizations facing increased demand and challenges, they need to adapt to technological changes in the market to stay competitive. COVID-19 has already forced many healthcare providers, insurers, and other entities to adopt newer technologies, such as cloud, AI, and IoT, and we’re just getting started.
References and Works Cited
Beede, Emma, Google Health, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, and Et Al. “A Human-centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.” ACM Conferences. 01 Apr. 2020. Web. 26 Feb. 2022.
Bestsennyy, Oleg, Greg Gilbert, Alex Harris, and Jennifer Rost. “Telehealth: A Quarter-trillion-dollar Post-covid-19 Reality?” McKinsey & Company. McKinsey & Company, 22 July 2021. Web. 26 Feb. 2022.
“Health Level Seven International.” Health Level Seven International — Homepage. Web. 26 Feb. 2022.
“How COVID Increased the Cybersecurity Threat to Healthcare Companies.” The Wall Street Journal. Dow Jones & Company, 07 Dec. 2021. Web. 26 Feb. 2022.
“How COVID-19 Is Changing Healthcare in America.” How COVID-19 Is Changing Healthcare in America | Maryland Smith. Web. 26 Feb. 2022.
“Largest Healthcare Data Breaches of 2021.” HIPAA Journal. 30 Dec. 2021. Web. 26 Feb. 2022.
Mount Sinai Health System. “Mount Sinai First in U.S. to Use Artificial Intelligence to Analyze Coronavirus (COVID-19) Patients.” Mount Sinai Health System. Mount Sinai Health System, 19 May 2020. Web. 26 Feb. 2022.