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8 Ways AI Will Improve Healthcare Post-Pandemic – and Beyond

While every industry has felt the effects of the global COVID-19 pandemic to some extent, it’s safe to say that none has been hit harder in 2020 than the healthcare industry. Because in addition to dealing with all the business issues – loss of revenue, strained workforce, interruptions in the supply chain, and general uncertainty – healthcare also has to bear the brunt of dealing with it. 

Fortunately, with a vaccine beginning to roll out it looks like relief will be in sight in 2021. While that doesn’t mean we’re now completely safe – predictions are that things will continue to get worse for the first couple of months before they get better – there is a better perspective at least somewhat within view. 

Once this pandemic is behind us, and healthcare organizations have once again caught their collective breaths, it will be time to look back on what happened to determine how to prevent this same situation from happening again and prepare properly.

Artificial intelligence (AI) promises to help create order from the 2020 situation and drive multiple improvements. With that in mind, here are eight pressing questions along with my predictions for what the future will hold. 

How will AI affect the healthcare supply chain in terms of optimizing enterprise processes?

Government and law enforcement agencies estimate that fraud in healthcare costs the U.S. as much as $300 billion annually, which is 10% of the yearly expenditure on healthcare. One way we will see AI, with its ability to discern subtle patterns and correlations between data, being applied is to look at incoming invoices for evidence of potential fraud.

AI will also be used more frequently to examine processes for waste and duplication, helping drive greater efficiency. The past year has been devastating financially for many providers and payors, and they will need to recover all the revenue they can. AI will help them find money that’s already in the system. 

With what we experienced with COVID-19, where some areas had an abundance of supplies while others had great need, how will AI be used to anticipate shortages and move supplies where they’re needed more effectively?

Many of the issues we saw in 2020 were the results of reacting to events as they occurred. Hospitals, for example, could see that their supplies of personal protective equipment (PPE) or ventilators were running low, then they would set off the alarms and scramble to obtain more. Often providers found themselves competing with each other and with their governmental organizations for whatever supplies were available.

AI will enable providers and government entities to recognize the potential for shortages much earlier based on behavior and economic activities so they can plan better. It will also help manufacturing organizations anticipate needs more effectively so they can prepare to fill demand sooner. While no one can predict when and how a pandemic will occur, they can certainly recognize we are on the path to one earlier and take steps to mitigate its worst effects on the supply chain – including ensuring adequate supplies of the raw materials needed to product PPE, medications, and other necessities. 

How will AI affect patient care at the point of service?

In other words, how can it help frontline physicians and nurses with diagnosis and care? Physicians and nurses will be able to input patient data and receive more precise information to help them with their decisions about treatment. It will also help them consider possibilities they might not have thought of otherwise, based on available research, guidelines, and literature.

For example, instead of using imaging to only scan the area where a lump has been found or is expected, AI may evaluate the entire image to recognize patterns and suggest additional examination. Again, it’s about using the evidence to anticipate issues rather than reacting to them while at the same time eliminating the waste of running a block of tests “just in case”. You’ll be doing things because the data suggests there could other issues. 

Read More: Manufacturing AI Startup ExLattice Joins NVIDIA Inception

How can AI be used to improve the overall patient experience?

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Healthcare professionals currently spend far too much of their valuable time looking for and through data, images, laboratory reports, etc. to discover what is relevant. In the future, AI will be able to predict what information physicians are most likely to need for each appointment. That information can then be brought to the top so it is immediately available.

The physician can then spend more time talking to the patient, understanding her/his current situation and delivering truly personalized care. Increasing direct contact and having the time to really listen to the patient will most likely have a huge impact not only on patient satisfaction scores but also on outcomes. 

How can AI help healthcare organizations and government bodies avoid the next potential pandemic?

By looking for patterns in our hyperconnected world and helping them understand what health threats are occurring or developing both within and outside of their borders. But rather than relying solely on medical or claims data, which can lag the trend, they’ll be able to draw on broader data such as what’s trending on search engines to see which trends are emerging right now. A simple example is an increase in the number of searches for flu symptoms and treatments, or how effective flu vaccines are.

An increase of any of those in a particular region is an indicator that this region probably has an issue that should be addressed. By applying AI on a global scale, we will be able to determine where abnormal situations or even hot spots are developing so they can be addressed more quickly. 

Since data is such a key driver of AI, what effect do you think the current pandemic will have on data sharing moving forward?

I believe that as a result of this pandemic, healthcare organizations and government entities are going to re-think their data-sharing policies.

Up until now, there has been token discussion about opening up data sharing, but not a lot of movement. The pandemic has forced healthcare organizations to collaborate and they’re seeing the value of it. That said, too much data can be just as bad as too little, so it’s important that there is a purpose in sharing the data. So you have a situation where access to all, or nearly all, data should be made available but that doesn’t mean organizations should just pull it all in wholesale. Instead, whoever is requesting the data should determine what they aim for, then obtain the right data for that purpose. That will be a challenge, to parse it out in a way that only gives them what they need instead of a massive data dump, decreasing noise and wasted computation effort.

AI can support matching what’s coming in with what’s needed and reject anything that isn’t required for that particular purpose. While this is a more efficient way to work, it will also help drive privacy because less of an individual’s full data set will be in multiple locations. 

What about all the data being generated on personal devices such as smartphones and watches? How should it be managed and made available to healthcare organizations?

This is something society is going to have to determine, i.e., where personal health data should be processed. A lot of it can be processed right there on the device itself. Unless there is a medical reason to monitor your resting and active heart rate, such as you have congestive heart failure, or are recovering from a coronary event, your medical team probably doesn’t need that information.

On the other hand, if there is a large-scale study going on that might ultimately benefit your health, you may want share some of that data in a de-identified way with the research team. We need to decide what is the right amount of data to share and why are we sharing it. We also need to determine which data can only be processed on a large cloud application versus what can and should be processed locally on a mobile device. The more focused we are with the data we share, the more effective AI will be in drawing insights from it while preserving privacy at the same time. 

Will healthcare move toward larger, more centralized patient record storage or will we all carry our own health records with us from provider to provider?

That’s a topic that is still very much up for debate. Both, in a way, are an attempt to address interoperability. If you store health data centrally it becomes accessible to all providers, but it becomes a massive undertaking to maintain and protect it. If you make it portable and under the control of the patients it also becomes accessible to their providers and enables patients to exert more influence over who can see it and for what reasons, but you also increase privacy risks if the device is stolen or isn’t cleaned before being disposed of.

There are arguments to be made both ways. As healthcare continues to become more patient-centric, however, we will be turning over more control to the patient in the future. That said, there are some strategies out there for storing patient health data on a mobile device or smartwatch but it’s still early. 

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