If AI isn’t ready to diagnose patients, where can it be used in health care?

It’s hardly controversial to claim that rapid advancements in AI technologies are destined to completely revolutionize all aspects of our lives. Generative AI tools are already leveraged for content marketing, code development, video generation, and nearly everything else under the sun. It’s no wonder that 40% of organizations aim to increase their investment in AI as a result of its advances as they seek to leverage the technology to boost productivity and scale their operations.

Amid this “AI revolution,” and the subsequent demand for AI-powered business products, is the desire to automate the more tedious, repetitive, and painstaking tasks that comprise parts of certain jobs. While many industries are accelerating AI integration, however, the health care industry noticeably lags in its adoption of more recent AI tech—most notably with generative AI.

AI in health care

The health care industry has historically held a large dose of skepticism when it comes to adopting new technologies—it comes with the territory. Much of this hesitancy stems from regulations, workflow disruptions, a preference toward waiting for more research, and patient concerns. All of these are challenges AI is still grappling with.

Although businesses are keen to innovate health care-specific solutions through the power of AI, their appropriation within the industry remains limited, albeit with growing prevalence.

Nearly four years removed from the COVID-19 pandemic, we all witnessed the strain health care providers came under in the U.S. and elsewhere. And while that crisis has been largely mitigated, many of those burdens remain. Hospitals, clinics, and other health care networks have become prime targets for cybercriminals. Additionally, the industry faces troubling shortages of nurses, physicians, and other administrative personnel, including revenue cycle staff. In many places, especially in the U.S., all this equates to a health care industry in need of operating more efficiently with fewer resources.

Despite the slow adoption, plenty of research has been conducted on how AI can help alleviate these burdens. From easing administrative workflows and virtual nursing assistants to diagnosing patients and fraud prevention, many experts have opined and pondered how AI will disrupt the health care industry.

Health care’s hesitancy to undergo technological upgrades seems justified when reading about generative AI chatbots misdiagnosing patients or hallucinating results. And there is a long way to go to address those prevailing mishaps. But is there a place within health care that AI can help now?

What role can AI play now?

Considering AI’s potential limitations in diagnosing patients or designing treatment plans, it’s understandable that physicians and other medical professionals would prefer to keep its clinical use at arm’s length for the time being.

That being said, AI can still assist health care organizations in numerous other ways. Whether easing some of the administrative burdens within health care organizations or reducing the time health care providers spend on patient medical records, AI can provide immediate value within the industry.

For instance, health care administrators spend a large portion of their workday dealing with paperwork and repetitive tasks. Autonomous platforms powered by machine learning can help administrators—as well as physicians and clinicians—automate many of these non-clinical tasks like drafting patient summaries, thus freeing up health care professionals to focus on treating patients and other big-picture responsibilities.

Reducing some of the burdens on administrators and doctors alike can create real ripple effects within a health organization that ultimately lead to a higher quality of patient care. And that can occur thanks to improvements in unexpected places. When a hospital, clinic, or any other type of health care organization has an efficient billing department, for example, it leads to financial stability and flexibility, additional resources, a wider range of services, and often more affordable care.

Medical coders work behind the scenes to translate details from patient medical documents (progress notes, consultations, operative reports, diagnostic procedures, and more) in a standardized manner to facilitate proper billing and bookkeeping. As the nerve center of a health care organization’s billing department, they assist health care providers in optimizing the revenue-management cycle—which involves reimbursements from insurance companies and strict compliance with regulatory statutes.

With a shortage of medical coders and a reliance on outdated legacy computer-assisted coding (CAC) software—machine learning and deep learning can help these professionals streamline the revenue-management cycle through automation to benefit patients and care providers.

With minimal human oversight, autonomous AI-powered coding solutions can relieve medical coders and health administrations of a major stressor and optimize the revenue-management cycle in the process. However, it's paramount that health care decision-makers diligently research these autonomous coding solutions just as they would with any AI tool to ensure the product will boost productivity and integrate into existing systems. Many solutions claiming autonomy consist of AI tools that provide more efficient workflows, yet lack the type of full automation and accuracy needed to remove the strain on medical coders and organizations.

AI has become a ubiquitous term in technology and business, but it's crucial to understand that not all AI platforms are created equal. The landscape of AI is diverse, encompassing a wide range of capabilities from basic automation tools to advanced, self-learning systems. This variation means that the effectiveness, sophistication, and applicability of different AI solutions can vary significantly.

Many companies leverage the term 'AI' as a buzzword to capitalize on its cutting-edge connotation, even when the technology they offer may be relatively simplistic. As a result, discerning the true capabilities and innovativeness of AI-powered solutions requires looking beyond the marketing hype, emphasizing the need for potential users to critically evaluate the specific features and benefits of each AI platform they consider.

We can expect health care organizations, and doctors specifically, to continue being cautious in integrating powerful AI tools. However, the case for using AI to alleviate administrative burdens remains strong. In the short term, it can also be used by imaging centers, radiology clinics, and health care practices to enhance existing systems and empower radiologists to be more efficient.

Furthermore, health care organizations weary of how AI will impact patient satisfaction and disrupt current systems and processes can gain familiarity with the technology within an administrative sandbox to better understand how and where to expand its use within a clinical setting down the road. AI developers eager to break into the healthcare industry should also keep this in mind as an inroad to expanding their adoption footprint.
About the author:

Yossi Shahak is the founder and CEO of Maverick Medical AI. Shahak brings over 25 years of combined experience in the world of digital health and artificial intelligence sectors—with a specialty in AI medical imaging. Prior to founding and leading Maverick Medical AI, Shahak served as Vice President of Sales and Business Development at Zebra Medical Vision. Additionally, Shahak has a wealth of experience with startups and Fortune 15 companies, including serving in executive leadership roles for McKesson. As founder and CEO of Maverick, he is fully dedicated to enhancing the productivity and efficiency of the revenue cycle management process for health care providers and payers.

This article was initially published by our sister publication, Medical Economics.