The AI hype cycle is in an unusual place right now, especially considering public opinion versus how the professional sector portrays the technology’s potential.
Andy Sajous is the field technology director and healthcare practice leader at digital transformation company Ahead. From all his meetings with healthcare CIOs and other IT decision makers, it appears that none of them are willing to commit to any particular AI product or service for more than 12 months.
In this interview, Sajous explains why he thinks so. Describes some of the rapidly evolving changes in the AI market, build vs. buy challenges, and outlines some key actions healthcare CIOs should take as we enter 2025, a year that will see even greater AI transformation.
Q. You say that as you work on digital transformation this year with CIOs and other technology decision makers in healthcare, none of them will sign any AI product or service for more than 12 months. What lessons do you draw from this experience and what do you think it means for the future of AI in healthcare?
AND. The reluctance to enter into AI contracts longer than 12 months reflects the deep uncertainty in the healthcare AI landscape. CIOs and other decision makers are concerned about overcommitting to tools in a rapidly changing environment.
AI vendors are constantly releasing fresh products, but the market is flooded with startups and smaller companies whose future is uncertain. There is a real fear that a system that seems promising today could become obsolete within a year or, worse still, the company behind it could be taken over or go completely bankrupt.
The pace of change in AI technology, especially with the introduction of generative AI tools such as ChatGPT, has created a climate in which healthcare organizations are forced to think short-term when implementing fresh technologies.
However, this does not mean a complete lack of faith in the potential of AI. On the contrary, healthcare organizations are fully aware of the potential of AI to transform patient care, improve operational efficiency and streamline administrative processes.
However, they realize that technology is constantly changing and fresh players are constantly entering and exiting the market. CIOs are looking for flexibility, and that means being able to change things quickly if better technology comes along or if the AI tool they’ve invested in doesn’t deliver the expected results. They want to avoid being tied into long-term contracts with suppliers whose products may not keep up with the rapidly advancing state of knowledge.
For future of AI in healthcare, this cautious approach may sluggish its adoption in the miniature term, but could ultimately lead to more thoughtful and strategic integration of AI into healthcare workflows. As the market matures and more stable, proven systems emerge, we may see healthcare organizations become more comfortable making long-term commitments.
Until then, flexibility and adaptability will remain key. The healthcare sector will need to remain agile, constantly evaluating fresh technologies while ensuring that patient care is not compromised by unproven or rapidly dated systems.
Q. You refer to the rapid change in artificial intelligence market leaders. Who was and is currently the market leader and where do these changes come from?
AND. The active nature of AI means that today’s market leaders may not be tomorrow’s leaders. The AI landscape has seen significant changes in terms of market leadership, both due to innovation and consolidation. A few years ago, gigantic technology companies such as IBM Watson and Google’s DeepMind were pioneers of artificial intelligence in healthcare, particularly in areas such as diagnostic imaging and predictive analytics.
Thanks to the rapid development and fresh AI players, the market continues to grow. Start-ups and niche companies are emerging with highly specialized systems to address very specific healthcare needs, such as AI-based clinical decision support or AI-based diagnostic tools for radiology and oncology.
Companies like NVIDIA, which provides the hardware framework for AI development, have become necessary, especially in areas like machine learning and computer vision. Epic, which integrates AI into its electronic health records system, is also making significant progress by offering end-to-end AI-enhanced systems that are more closely integrated with existing hospital workflows.
These companies are leveraging their broader platforms to introduce AI capabilities, which could make it harder for smaller, more specialized providers to compete unless they offer a truly unique value proposition.
Changes in market leadership result from several factors. First, the rapid pace of AI innovation means providers must constantly update and improve their offerings to stay competitive. Second, the consolidation of AI technologies on larger platforms such as Epic reduces the need for independent AI providers.
Finally, many healthcare organizations continue to grapple with regulatory and ethical issues related to AI, which means that ultimately the market leaders will be companies that can provide not only creative systems, but also systems that are trustworthy, secure and compliant with regulations. These changes indicate that the AI landscape will continue to be volatile until a few clear leaders emerge.
Q. What are the challenges of building and purchasing AI tools in healthcare?
AND. The decision to build or purchase AI tools in healthcare is not an uncomplicated one, and each path comes with its own set of challenges. Building AI tools in-house allows healthcare organizations to tailor systems specifically to their needs. They can develop models tailored to their unique datasets and workflows, ensuring that AI systems are fine-tuned to meet their organization’s requirements.
However, this approach requires significant resources, both in terms of financial investment and technical talent. Many healthcare organizations face a shortage of skilled AI professionals, and the costs of hiring and retaining such talent can be prohibitive. The ongoing maintenance and updates required to keep internally developed AI tools current and up to date with the latest developments in the field can place additional strain on resources.
On the other hand, purchasing ready-made AI tools provides a faster path to implementation, with less development work. These tools often come with vendor support, which can assist healthcare organizations get up and running quickly.
However, this approach is not without risks. The healthcare AI market is full of vendors, many of which are startups that may not be around for quite some time. CIOs have expressed concerns about engaging with vendors whose products may not evolve at the pace of the organization’s needs or whose business models may not be sustainable.
Additionally, off-the-shelf AI tools may not integrate seamlessly with existing healthcare IT systems, leading to inefficiencies and potentially limiting the effectiveness of the technology.
Another key challenge when purchasing AI tools is vendor lock-in. When a healthcare organization becomes dependent on a particular AI tool, it may be tough to switch to another tool in the future if the vendor stops innovating or if a better system becomes available.
This can lead to an organization being stuck with a suboptimal tool or, worse still, a vendor going bankrupt and the healthcare system scrambling for alternatives. Healthcare organizations must carefully weigh the risks and benefits of building AI tools versus purchasing AI tools, considering not only the immediate costs and benefits, but also the long-term implications for IT infrastructure and patient care.
Q. What are the key actions that healthcare CIOs and other healthcare IT leaders need to take starting in 2025?
AND. As healthcare organizations look ahead to 2025, CIOs and healthcare IT leaders must focus on three key areas: cloud optimization, talent development and data management. Cloud optimization is key because many healthcare organizations operate in a hybrid cloud environment, with both on-premises and cloud-based systems.
Optimizing the exploit of the cloud not only does it provide scalability and flexibility, but it also helps reduce costs – an increasingly critical factor given the financial pressures many healthcare organizations face. Ensuring the security and performance of cloud infrastructure will enable healthcare systems to leverage artificial intelligence and other emerging technologies without being bogged down by legacy systems or prohibitive infrastructure costs.
Talent development is another key area where CIOs must focus their efforts. There is a significant talent gap across the technology industry, but particularly in healthcare IT, especially when it comes to artificial intelligence and cloud engineering. CIOs must invest in training programs to upskill their current employees while finding imaginative ways to attract fresh talent in a highly competitive marketplace.
This may include forming partnerships with educational institutions, offering specialized certification programs, or working with vendors to provide joint training initiatives. Upskilling internal teams will be critical to ensuring healthcare organizations can not only implement cutting-edge technologies, but also maintain and evolve them as the industry continues to evolve.
Finally, data management will be a top priority for healthcare leaders as we enter 2025. As artificial intelligence and data analytics become increasingly integrated into healthcare operations, ensuring the security, privacy and ethical exploit of patient data will be of the utmost importance. This involves implementing a stalwart governance framework that can manage the massive amounts of data generated while still complying with regulatory requirements such as HIPAA.
Moreover, CIOs must actively develop strategies to address potential AI risks, such as algorithmic bias or data privacy concerns. Building a stalwart data management infrastructure will be crucial not only to mitigate risk, but also to augment trust in AI-based healthcare tools.