The Prospects of Artificial Intelligence(AI) in Healthcare and how COVID-19 accelerated the Digital Transformation
Against the darkness of the COVID-19 storm, a flickering light gives glimpses of the power of data and digital tools to protect and improve the health and well-being, and inspires hope of what is to come.
Artificial intelligence (AI) has transformed industries around the world, and has shown that it has the potential to radically alter the field of healthcare. Be it the ability to analyze data on patient visits to the clinic, the medications prescribed, results from lab tests, or the procedures performed, or perhaps being able to scrutinize health system such as social media, purchases made using credit cards, census records, internet search activity logs that contain valuable health information, one can get the sense of how AI could transform patient care and diagnoses. From wearable trackers to mental health chatbots to AI-based cancer screening, Artificial Intelligence has made such remarkable strides in healthcare that it not only supports clinicians in decision making and accurate diagnosis, however, it also comes in handy in disease treatment and individualized healthcare delivery. One cannot understate how the global pandemic helped propel the field further as demand for virtual and technology-enabled services like telemedicine and remote patient monitoring shifted from short-term needs to a new normal during the epidemic.
Now, I am of the opinion that the COVID-19 pandemic resulted in massive disruptions within health care, both directly and indirectly. Directly in that there was an infectious disease outbreak and indirectly because of the public health measures to mitigate against its transmission. These disruption caused rapid dynamic fluctuations in demand, capacity, and even contextual aspects of health care. Therefore, the traditional face-to-face patient–physician care model had to be re-examined in many countries, with digital technology and new models of care being rapidly deployed to meet the various challenges of the pandemic.
At the onset of the COVID-19 pandemic, three things must have been top-of-mind for CEOs of most companies: employee safety, ensuring the network remained operational, and adapting strategies to the evolution of technology. I say this because if there was one thing we knew for sure during that period of uncertainity, it was the world wouldn’t come out of the pandemic thinking about healthcare the same way. The most eye-catching of these changes has to be the percieved change in public perception and acceptance of telemedicine as a growing prospect. Take an individual with a chronic conditions planning to see a physician whose office is 45 minutes from his/her home, for example. That clinician offers the patient a telehealth visit, which as a result, saved an hour and a half of driving time, along with time in the waiting room. The individual is also getting the same quality of patient care from the comfort of their home, using this digitized platform, as they would have received in person.The 19th century philosopher, William Shedd once said; “A ship in the harbour is safe, but that is not what ships are built for.” In other words, technology of high potential is of little value if the potential is not exploited. As the shape of 2021 is increasingly defined by the coronavirus pandemic, digitalisation is like a ship loaded with technology that has a huge capacity for transforming mankind’s combat against infectious disease but it was still moored safely in harbour, at least until the pandemic. Overall, there is a general sense of transformation, innovation, and connection that’s happening across the healthcare industry. Some of it occurred before the spread of COVID-19, but overall, the coronavirus was simply a catalyst that accelerated the process.
A report from Stanford University looked at publications in the digital health field from the last 35 years and showed that almost 75% of all the digital health papers have been published in the last five years. This can be attributed to the recent explosion and maturation of the technology in terms of the number of therapeutic areas and use cases. This coupled with wider internet availability, the rise of smartphones with intelligent computing, and the proliferation of mobile applications combines for a fast-growing field. These changes has led to a corresponding change in how patients are taken care of with medical AI. The future of medical AI is one where it is used for disease classification and sensor data classification — such as whether you have a certain heart rhythm or not based on the data that comes from your watch. The future of medical AI is also one where AI is used intelligently by clinicians and in a way that augments their abilities to make better, more accurate, and faster diagnoses. The future of medical AI is one where it is used to parse through large reams of medical information and clinical guidelines to get the data they need to come at the right answers and treatment pathways for their patients.
As widespread use of AI in healthcare is relatively new, there are several unprecedented ethical concerns related to its practice such as data privacy, automation of jobs, and representation biases. What we’re starting to see is that AI is moving into areas of messier and less structured data — looking at prediction, looking at taking unstructured electronic health record data, trying to tackle large enterprise problems in healthcare. AI works well for some things like image detection. But, the major challenges include implementation and adoption of payment models, scaling these across healthcare systems, getting cultural institutions and clinicians to buy in, and figuring out where AI will sit in augmenting the intelligence of a clinician versus an independent diagnostic that runs on its own. That is a graded process that will come through time with ongoing validation testing and regulatory approval. Other challenges with AI include recognizing that it has limitations. Right now, AI doesn’t work where labels aren’t well defined, the quality of the training data isn’t good, or where the problem inherently is not solvable deterministically by the data you possess. I think we need to continue to maintain the humility to understand where it’s going to work well, where it doesn’t, and how it complements what we already do well in healthcare.To illustrate how the Coronavirus helped accelarate the emergence of technology in the healthcare industry, a case study of China’s approach to the first stream of the virus in Febraury 2020 will be examined. As the pandemic hit Wuhan, China costing lives and bringing upheaval, the global scientific community started racing towards effective vaccines and therapeutics. However, China was quick to realize that the most essential defense was prevention with the fundamental of public health measures, such as personal hygiene and mass physical distancing. Contact tracing, testing, and surveillance, each being an essential part of the overall public health measures in keeping the outbreak within a manageable scale — were each augmented in China by data-driven technologies.
One widely used application by the general public was one that allowed people to trace if they were ever on the same train or flight or otherwise in close proximity with any confirmed cases in the past two weeks. The app, first developed by an independent software developer using data crawled from social media and websites where information on cases could be found, later became more reliable after having aggregating data from all-level public surveillance systems and the national transportation authorities. Putting such risk assessments in the hands of the public enabled individuals to be better informed of their exposure level and gave specific instructions on the need to continue practicing social distancing and health monitoring. Three weeks after Wuhan lockdown, more than 140 million searches were made on the platform, which helped over 80,000 travelers discover that they had travelled with confirmed cases. During the outbreak, telemedicine even helped provide mental health support. The vision of rolling out systematic, accessible, and comprehensive mental health support for healthcare workers and general public was made possible by many popular online platforms.
All these set the tone for what is set to be the next revolution in the tech industry. Against the darkness of the COVID-19 storm, a flickering light gives glimpses of the power of data and digital tools to protect and improve health and wellbeing, and inspires hope of what is to come. The monumental challenges brought about by the COVID-19 pandemic have forced the healthcare industry to think creatively to develop the best and the most efficient solutions, systems and processes to help clinical researches deliver safe and effective treatments to the public faster. As a result, many medical companies are now looking to build on this momentum and retool their approach. They are eagar to take advantage of all available resources, technologies and digital capabilities, to execute their clinical trials with greater speed and quality. One of the most significant initiatives accelerated by the pandemic is the drive towards decentralised trials, with protocol designs, technology and processes that support greater remote participation in research.While COVID-19 is the greatest global health challenge to emerge in a generation, and the medical research community has come together to fight it, there is an urgent need to apply some of Covid’s lessons to treatments for other diseases that represent urgent, unmet medical needs.
The 2020’s may turn out to be the decade when digital technology reshapes the health system and thankfully, COVID-19 has driven many developments, as the digital health community continues to navigate how best to bolster classic public health measures. Over time, what we will see is increasing autonomy and independence of AI where it can now work alongside a clinician with a higher level of independence. We will continue to see progressive autonomy with rigorous and thoughtful development and testing for safety and efficacy.
Machine Learning and Artificial Intelligence enthusiast