While we are fighting the battle against COVID-19 and adjusting to the new normal, technology is helping to rewrite the playbook of the health care industry. Modeling, artificial intelligence, remote monitoring and chat bots are paving the way for a better tomorrow.
If you haven’t yet connected to a doctor remotely via an app, chances are during COVID-19 you will. Using a combination of video conferencing, remote monitoring through health/fitness trackers, electronic consults and e-records, doctors can assess the symptoms and make recommendations about the course of action.
These telehealth visits serve dual purposes: first they allow patients to get access to treatment while mitigating the risk of viral transmission. Secondly, it helps prevent the use of short-in-supply Personal Protective Equipment (PPE).
Though these systems have been in place for a few years, the inability of patients to visit hospitals due to exposure risk has resulted in an enormous surge in demand for telemedicine.
This has led to a paradigm shift supported by relaxing licensing regulations that prevent doctors in one state from remotely treating patients in another state. Additionally, Congress has taken initiatives to allow Medicare insurers to provide telehealth services so that more elderly patients, who are at greater risk from the virus, can receive care at home.
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A cultural shift ensued when the patients started experiencing first-hand that virtual care is actual care and guarantees immediate attention.
Dr. Richard Zane, chief innovation officer at UCHealth and chair of Emergency Medicine, said to me in a recent interview: “UChealth has set a robust secure platform within the existing electronic records system that is easily scalable. The risk of regression mostly comes from payer and regulation ends.”
A bottleneck that needs to be overcome is the reluctance of medical companies to accept that virtual care is equivalent to personal care and needs to be paid and respected equally. In addition, there’s a danger that the regulations might tighten again post COVID-19 era.
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Second, the use of various AI-based sorting systems could potentially reduce the clinical load of physicians. An online medical chatbots (robots that can chat through a chat window) could help patients recognize early symptoms, educate people on the importance of social distancing and refer people for medical treatment should symptoms worsen.
Also, chatbots have been extensively used to answer public inquiries regarding the pandemic. Henceforth, creating awareness and reducing the spread of fake news.
An obvious extension beyond individual care is the use of the high-quality patient data collected by telemedicine services with AI to model the spread of pandemic trajectory in both local and global populations.
These and prior information from previous epidemics have built models that have predicted future hotspots with success. Also, these have helped epidemiologists to provide evidence-based statistics that aid in decision-making.
These models have brought to light how COVID-19 disproportionately affects vulnerable populations such as elderly, poor-income neighborhoods and black communities. Further, they have found that commonalities exist between the communities most vulnerable to the effects of other natural crises and those dying from COVID-19.
These insights pave the road to how to develop strategies and treatment keeping social determinants in mind.
This has fostered large-scale public-private sector collaborations to provide regulatory support and accelerate the innovation process. An added out-of-the-gate outcome has been extension of communication channels between the federal and state governments ensuring efficient local implementation.
However, the results from these models should be taken with a grain of salt. The results are only as good as the data and algorithm used at the backend. What we need is to be honest about uncertainty, assumptions and the limitations of the approach.
A good modeling system relies on access to quality data, providing a full picture of clinical, therapeutic and social factors. The lack of data transparency and uniform standard means of data collection has hindered the progress. We are dealing with a double-edged sword aiming to strike a balance between maintaining privacy laws while ensuring safety from the virus.
How can we ensure accuracy and success of these models moving forward? One way is to learn and adapt from other industries such as finance and security that have been using machine learning and AI for decades.
In many ways, all of these more advanced technologies augment and enhance the public-health strategies to combat the coronavirus outbreak.
I believe that though the current global pandemic brings challenges with it, it also serves as a catalyst for innovation and greater integration of the technology in the health care industry.
The successful application of digital technology to help with current global public-health challenge will probably increase the public and governmental acceptance of such technologies for other areas of health care. As Rahm Emanuel said, “Never allow a crisis to go to waste.”
Ankita Arora is a postdoctoral researcher at the University of Colorado-Anschutz Medical Campus in Aurora.
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