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What’s next for cybersecurity and AI in healthcare


Although AI shows promise in streamlining administrative tasks like documentation and billing, its integration into clinical decision-making processes sparks some challenges.

cybersecurity | © RoBird -

© RoBird -

Recent ransomware attacks have brought cybersecurity and AI to the forefront of discussions in healthcare. In fact, these incidents highlighted the vulnerability of healthcare systems to cyber threats, prompting organizations to strengthen their defenses. The surge in interest in cybersecurity sessions and discussions at industry events indicate a growing recognition among healthcare providers—that robust cybersecurity measures to protect patient data and ensure uninterrupted services is critical in today’s landscape.

At the same time, the adoption of AI technologies in healthcare is gaining momentum. Organizations are grappling with whether to develop AI tools in-house or purchase them, and how to govern and scale these innovations effectively.

Although AI shows promise in streamlining administrative tasks like documentation and billing, its integration into clinical decision-making processes sparks some challenges, like regulatory compliance and clinician acceptance. As healthcare providers navigate these complexities, 2024 is likely to be a year of exploration and collaboration aimed at leveraging AI to improve patient care while addressing regulatory and operational concerns.

Elevating cybersecurity concerns: Reflections on recent ransomware attacks

At ViVE 2024, the Change Healthcare ransomware attack was the topic that garnered the most overall attention and even overshadowed the numerous conversations related to artificial intelligence. There was heightened awareness around the cybersecurity issue, but it became poignant for numerous provider organizations who suddenly saw an immediate financial impact on their bottom line due to an inability to submit claims for a significant portion of their business. Expect to see an increase in healthcare organizations doubling down on this area for the remainder of 2024 and ensure they have sufficient systems and processes in place.

Staffing hurdles and updates to onboarding

Health systems continue to struggle with staffing, especially around nursing and ancillary services (e.g., pharmacy and imaging technicians). Now that telehealth adoption has peaked and largely stabilized to a consistent level (outside of mental and behavioral health), several telehealth/virtual care vendors have shifted to emphasize how their solutions helped address some of these staffing challenges, especially virtual nursing, and specialty care.

Additionally, there’s been a shift in increase of digital health vendors who offer modules that ensure new clinical staff could be onboarded as quickly and efficiently as possible (e.g., credentialing), as well as helping health systems to continue to recruit clinical staff. Expect staffing concerns to remain a major challenge for health systems through the rest of 2024.

Generative AI shifts in health systems

Health systems are struggling with several topics related to this issue, including whether to build/buy (most seem to be buying and/or expanding their licenses for at least one generative AI solution this year), AI governance structure and standards, what tools to provide to their employees to empower them to come up with own solutions, and how to rollout and scale these innovations across their organizations.

This year very much seems like it will be “trial and error” with pilot-level projects. The range of recent clinical, administrative and research use cases that health systems are attempting to use this technology have provided some insight into the direction this category may be headed this year. Most of the efforts though still seemed focused on administrative/operational use cases with the primary activity focused on AI scribes, autonomous coding, automation around various revenue cycle processes (e.g., prior authorization) and call center operations. It is going to be interesting to see how vendors continue to differentiate themselves from their competitors in this category through additional functionality, integration, and pricing in 2024.

Oversimplifying AI in technologies

Recent industry events, such as ViVE and HIMSS, digital health vendors across the spectrum make an effort to emphasize how their solution was utilizing “artificial intelligence.” One could argue that it’s been a vendor mantra, within the last year. The problem is, there tends to be a lot of ambiguity among vendors with a lot of the same canned language and talking points. An opportunity here would be for vendors to speak more candidly about their product or solution if it does not actually involve AI capabilities.

Digital health vendors who successfully differentiate themselves have a succinct summary on how their solution is currently using AI. However, this tends to be mainly either vendor’s whose core solutions are based on this technology and/or they had a marketing department/agency who crafted standardized language to effectively communicate this feature.

The area of artificial intelligence that tends to generate the most opinions from leaders across the industry is AI focused on clinical decision support. Besides the additional concerns of FDA approval, skeptics tend to focus on how challenging it is to get key clinical leadership buy-in, having a model/algorithm that is well-proven and externally validated, where and how to integrate this solution in a clinician’s workflow, the challenge of working with various gatekeepers (e.g., EHR vendors), and the annual capacity of even a large and mature provider organization to successfully implement and scale these solutions.

Matt Guldin is the Senior Manager of Market Intelligence for Panda Health.

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