The Case for Incremental AI in Healthcare

Updated on December 9, 2021
Artificial intelligence (AI), data mining, expert system software, genetic programming, machine learning, deep learning, neural networks and another modern computer technologies concepts. Brain representing artificial intelligence with printed circuit board (PCB) design.
doug mcdonald

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By Doug McDonald

The tech industry often falls prone to extremely hyperbolic claims. “Cloud will kill the data center.” “Robots will replace all retail workers.” “5G will become the de facto wireless connectivity option.” 

On the one hand, I applaud the optimism and futurism of such visions. But on the other, that’s not how technology really works. Technology doesn’t just upend and transform industries overnight. Progress is iterative, incremental, and at times slow. 

Those of us with experience in the healthcare industry know this well. And it’s easy to see in the case of the healthcare industry adopting artificial intelligence (AI). 

Why “slow to adopt” doesn’t mean “won’t adopt”

The healthcare industry has a reputation of moving slower than other industries when it comes to technology adoption. And it’s easy to understand why: in addition to issues such as the interoperability of legacy systems with newer digital technologies, data compliance challenges, and skills gaps, any potential risk to patients or patient data is a massive concern holding many organizations back. With the number of mission-critical devices and systems running on hospital networks (e.g., monitors, medical scanners, wearables, sensors, implants) — it can be both challenging and precarious for healthcare organizations to rip out old equipment or introduce entirely new solutions. 

Although healthcare is understandably slower to adopt technology, it doesn’t mean they shouldn’t adopt it at all, nor does it mean they don’t want to. In fact, amidst the challenges created by the pandemic, many healthcare organizations are now recognizing the need to adopt new tools and technologies. According to a report published by Accenture this year, 81 percent of healthcare executives say the pace of digital transformation for their organization is accelerating. 

The trick for the healthcare industry is understanding that change doesn’t need to happen in massive waves, or overnight — it can be concerted, gradual, and still carry a massive impact. Such is the case with AI, one of the most talked about technologies in the healthcare industry.

How healthcare organizations can implement “incremental” AI

For years, experts have been hypothesizing about AI’s potential to completely transform healthcare. Not only would it be able to detect patterns that could assist in diagnostics, one day in the future AI could eventreat patients autonomously. While that’s a compelling vision to rally around, we’re still years away from that ever happening. But that’s not to say AI isn’t alreadyimpacting healthcare in smaller, more subtle ways to significantly improve access, affordability, and effectiveness in scenarios like clinical monitoring, patient self-service, and cybersecurity. 

Clinical Monitoring

Experts have long been warning about a potentially catastrophic nursing shortage. A 2017 study predicted that by 2030, there will be a shortage of more than 510,000 registered nurses. COVID-19 has only exacerbated that threat. A recent Washington Post-Kaiser Family Foundation poll found that roughly 3 in 10 healthcare workers have weighed leaving their profession due to the stresses of the pandemic. 

As a result, hospitals around the world are experiencing staffing constraints and severe burnout. AI-powered nursing assistants can greatly reduce the burden on medical staff by alleviating some of their load and allowing them to focus on the most urgent needs. For example, AI can be leveraged to conduct routine monitoring of levels, dosages, and checkups. AI monitoring can even be used after patients leave the hospital, which will help lower re-admittance rates. 

Patient Self-Service

Not only can AI make clinicians’ jobs easier, it can also save patients and hospital staff from time-consuming tasks. Tools like chatbots or other intelligent self-service tools leverage AI to analyze a patient’s information and past touchpoints and customize their experience with personalized recommendations and routing. This results in faster, more convenient processes. For example, these self-service tools allow patients to conveniently schedule appointments, pay bills, and resolve administrative inquiries. Additionally, the use of registration kiosks that can take the usual blood pressure, temperature, and other readings at the self-service check-in enable the staff usually administering these tasks to work on other duties. This can also reduce the time the patent spends at the visit. In turn, hospitals reduce labor costs, reduce staff workloads, and increase patient satisfaction.


AI also offers huge benefits for healthcare IT operations and security teams. Hospitals continue to be a prime target for attackers — a 2020 HIMSS Cybersecurity Survey found that 70 percent of hospitals experienced a “significant security incident” in the last year. Unfortunately, a significant amount of a security analyst’s day is dedicated to sorting through huge amounts of data or responding to “false alarms.” Machine learning and AI can monitor and analyze data across the hospital network as well as proactively detect and remediate cybersecurity anomalies in real time. This alleviates the burden on overtaxed health IT teams, reduces human error, and keeps the hospital and its sensitive data more secure. 

The promise of new technology is always exciting — but it takes time to be fully realized. One day, AI may well treat patients or help conduct surgeries. For now, it’s making small, meaningful waves in the healthcare industry and both hospitals and patients can greatly benefit from what it has to offer.

Doug McDonald is Director of Technology in the Office of the CTO at Extreme Networks. He previously worked at Henry Ford Health System, holding responsibilities that included strategy, architecture, implementation and support for all enterprise networking infrastructure equipment including wired/wireless, cellular DAS, location services, spectrum management, route/switch and LAN/WAN across hundreds of medical facilities. He holds an Executive MBA in healthcare leadership and has recently achieved fellow status with the HIMSS organization. 

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The Editorial Team at Healthcare Business Today is made up of skilled healthcare writers and experts, led by our managing editor, Daniel Casciato, who has over 25 years of experience in healthcare writing. Since 1998, we have produced compelling and informative content for numerous publications, establishing ourselves as a trusted resource for health and wellness information. We offer readers access to fresh health, medicine, science, and technology developments and the latest in patient news, emphasizing how these developments affect our lives.