The AI hype cycle is currently in an unusual place, especially in the court of how public opinion and disciplines are portraying the technology’s potential.
Andy Sajous is a field CTO and healthcare practice leader at Ahead, a digital transformation company. In meetings with healthcare chief information officers and other IT decision makers, he says, no one was willing to sign on to a specific AI product or service for more than 12 months.
In this interview, Saju explains why he thinks so. He discusses the rapid changes in the AI market, the challenges of building and buying, and the key actions healthcare CIOs should take as we look ahead to 2025, when AI is expected to transform.
Q. In working with CIOs and other technology decision makers in the healthcare industry this year on digital transformation, you said that no one signs on to any AI product or service for more than 12 months. What did you learn from these experiences and what do you think it means for the future of AI in healthcare?
A. The reluctance to enter into AI contracts longer than 12 months reflects the profound uncertainty in the AI landscape in healthcare. CIOs and other decision makers are wary of overcommitting to tools in a rapidly evolving environment.
AI vendors are constantly releasing new products, but the market is full of startups and small businesses with uncertain futures. A system that looks promising today could become obsolete within a year, or worse, the company running it could be acquired or go out of business entirely. There is a big concern.
The rate of change in AI technology, especially after the announcement of generative AI tools like ChatGPT, has created an environment where healthcare organizations are forced to think short-term when adopting new technology.
However, this does not indicate that we do not fully believe in the potential of AI. On the contrary, healthcare organizations are acutely aware of the potential of AI to transform patient care, improve operational efficiency, and streamline administrative processes.
But they also recognize that the technology remains in a state of flux, with new players constantly entering and exiting the market. CIOs want flexibility. This means you can quickly pivot if better technology comes along, or if the AI tools you invested in don’t achieve the results you expected. They want to avoid being locked into long-term contracts with vendors whose products may not keep up with rapidly advancing cutting-edge technology.
for While this cautious approach to the future of AI in healthcare may slow adoption in the short term, it may ultimately drive more thoughtful and strategic integration of AI into healthcare workflows. As the market matures and more stable, proven systems emerge, healthcare organizations may become more comfortable with long-term commitments.
Until then, flexibility and adaptability remain key. Healthcare departments must remain agile and continually evaluate new technologies to ensure patient care is not compromised by unproven or rapidly outdated systems.
Q. You cite the rapid turnover of market leaders in AI. Who are the past and current market leaders? Why has the change occurred?
A. The dynamic nature of AI means that today’s market leader may not be tomorrow’s market leader. The AI landscape is seeing significant changes in terms of market leadership, both through innovation and consolidation. A few years ago, big technology companies like IBM Watson and Google’s DeepMind were pioneers in healthcare AI, especially in areas like diagnostic imaging and predictive analytics.
The market continues to expand due to rapid developments and new AI players. Startups and niche companies are building highly specialized systems to serve very specific medical needs, such as AI-powered clinical decision support and AI-based diagnostic tools for radiology and oncology. Companies are emerging.
Companies like NVIDIA that provide the hardware backbone for AI development have become essential, especially in areas like machine learning and computer vision. Epic, which is integrating AI into electronic health record systems, is also making significant progress by providing a comprehensive AI-enhanced system that is more tightly integrated with existing hospital workflows.
These companies leverage their extensive platforms to deploy AI capabilities, which can make it difficult for smaller, more specialized vendors to compete unless they offer a truly unique value proposition. There is a gender.
Changes in market leadership are driven by several factors. First, the rapid pace of AI innovation means that vendors must continually update and improve their products to remain competitive. Number 2, When AI technology is integrated into large platforms like Epic, the need for standalone AI vendors is reduced.
Finally, many healthcare organizations continue to grapple with regulatory and ethical concerns regarding AI. This means that companies that can deliver not only innovative systems, but reliable, secure, and compliant systems will ultimately lead the market. These changes indicate that the AI landscape will remain unstable until a few clear leaders emerge.
Q. What are the challenges in building versus buying AI tools in the healthcare sector?
A. Deciding whether to build or buy AI tools in healthcare is not an easy decision, and each path presents unique challenges. Building AI tools in-house allows healthcare organizations to customize systems to fit their needs. They can develop models tailored to their unique datasets and workflows, ensuring that their AI systems are finely tuned to their organization’s demands.
However, this approach requires significant resources, both in terms of financial investment and technical talent. Many healthcare organizations are facing 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 in-house developed AI tools up to date with the latest advancements in the field can place an additional strain on resources.
Conversely, purchasing pre-built AI tools requires less upfront development effort and speeds implementation. These tools often come with vendor support to help healthcare organizations get up and running quickly.
However, this approach is not without risks. The healthcare AI market is crowded with vendors, many of which are startups that may not be around for the long term. CIOs are concerned about contracting with vendors whose products may not evolve at the same pace as the organization’s needs or whose business models may not be sustainable.
Additionally, pre-built AI tools may not integrate seamlessly with existing health IT, leading to inefficiencies and potentially hampering the effectiveness of the technology.
Another key challenge when purchasing AI tools is vendor lock-in. Once a healthcare organization becomes dependent on a particular AI tool, it may be difficult to switch to another tool in the future if the vendor stops innovating or a better system becomes available. There is.
This can lead to a situation where organizations continue to use suboptimal tools, or worse, vendors go out of business and health systems are left scrambling for replacements. there is. Healthcare organizations should carefully weigh the risks and benefits of building versus purchasing AI tools, considering not only the immediate costs and benefits but also the long-term impact on IT infrastructure and patient care. There is a need.
Q. What are the key actions healthcare CIOs and other health IT leaders need to take toward 2025?
A. As healthcare organizations look to 2025, CIOs and healthcare IT leaders must focus on three key areas: cloud optimization, talent development, and data governance. Cloud optimization is critical because many healthcare organizations operate in a hybrid cloud environment with both on-premises and cloud-based systems.
Optimization of cloud usage This not only allows for scalability and flexibility, but also helps reduce costs. This is an increasingly important factor given the financial pressures many healthcare organizations face. Ensuring cloud infrastructure is secure and efficient allows health systems to take advantage of AI and other emerging technologies without being burdened by legacy systems or prohibitive infrastructure costs.
Talent development is another key area where CIOs need to focus their efforts. There is a huge talent gap across the technology industry, but especially in healthcare IT, especially when it comes to AI and cloud engineering. CIOs must invest in training programs to improve the skills of existing staff while finding creative ways to attract new talent in a competitive market.
This may include building partnerships with educational institutions, offering specialized certification programs, or working with vendors to offer joint training initiatives. Improving the skills of your internal teams is critical to ensuring your healthcare organization not only implements cutting-edge technology, but also maintains and evolves as the industry advances.
Finally, data governance is a top priority for healthcare leaders as we head into 2025. As AI and data analytics become more integrated into healthcare operations, ensuring the security, privacy, and ethical use of patient data becomes a top priority. This includes implementing a strong governance framework that can manage the vast amounts of data being generated while adhering to regulatory requirements such as HIPAA.
Additionally, CIOs must proactively develop strategies to address potential risks associated with AI, such as algorithmic bias and data privacy concerns. Building a strong data governance infrastructure is critical not only to reduce risk but also to foster trust in AI-driven healthcare tools.
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