2020 Health IT Trends: Revisited
By Susan deCathelineau, senior vice president sales and services, Hyland Healthcare.
Much like the formation of New Year’s Resolutions, the prediction of technology trends for the coming year has become a tradition among pundits, analysts and vendors alike. As the calendar turned to 2020, Hyland, like many, took the opportunity to look into a crystal ball to predict what the future might hold for the software industry at large, as well as many of the key vertical markets in which it operates.
For example, Hyland leadership revealed six overarching trends for enterprise technology as well as key trends to watch for health IT. At the time, none of us could have foreseen that a global pandemic was coming that would turn all of these predictions on their collective ears.
Of course, the healthcare industry has been particularly impacted by COVID-19. Provider organizations have justifiably focused their attention on responding to the new patient care and staffing needs brought about by the virus. That said, all of the health IT trends Hyland outlined at the beginning of 2020 (interoperability, artificial intelligence and cloud adoption) still have relevance in today’s unprecedented landscape. Although, admittedly, the reasons these topics are trending are for vastly different reasons than we originally anticipated.
I want to revisit these trends under the lens of COVID-19 as well as add a few more to the list in light of current circumstances.
Original insight: Secure access to patient information at any facility throughout a care continuum is an imperative for delivering a longitudinal digital record that travels with the patient. The key is to ensure tight integration between disparate IT systems, and to include unstructured data in the interoperability equation. As much as 80% of essential patient information is in an unstructured format – such as digital photos and videos, or physician notes – and not natively included in an electronic medical record (EMR) system. When removed from a clinician’s view, the patient record is incomplete.
New relevance: Health IT interoperability was important prior to COVID-19, and it’s even more critical now. Providers, patients and public health officials need all-encompassing data in a standardized format to better understand this evolving illness and develop guidelines. The effort to identify risk, control spread and manage the treatment of afflicted patients is a coordinated effort among multiple healthcare providers and external care partners. The easier information can be shared among these varied stakeholders, the better equipped we’ll be to combat the virus.
Artificial Intelligence (AI)
Original insight: Realistic applications of AI are coming into focus in healthcare, showing where the technology will help providers optimize workflows and better analyze the vast amounts of information needed to support improved decision making. Experts view AI technology as complementary and a true asset when it comes to helping physicians analyze the overwhelming amount of patient data they receive daily. Physicians can implement AI to streamline or eliminate tedious tasks, such as manual documentation and data search, or cull information to help them focus on a key area of interest.
The medical imaging space in particular provides a tremendous area for the growth of AI and machine-learning technologies. Clinicians can use them to analyze thousands of anonymized diagnostic patient images to identify and detect indicators of everything from lung cancer to liver disease. These technologies are also being used to accelerate research.
New relevance: AI is being used in a number of ways to address the challenges of COVID-19. For example, AI algorithms have been used to identify the spread of new clusters of unexplained pneumonia cases. Other AI applications are being used to spot signs of COVID-19 infections in chest X-rays and identify patients at high-risk of coronavirus complications based on their pre-existing medical conditions. Still others are scanning the molecular breakdown of the virus itself as well as those of existing drug compounds to identify medications that can potentially target the virus and shorten the span of the illness or lessen the severity of the symptoms. In all of these scenarios, AI is quickly analyzing large segments of data to accelerate research and treatment. This automation is indispensable in an environment where medical staff are stretched to their limits, and the act of saving time could save lives.