2024 foresees specialized AI dominance in healthcare, replacing generative models. Maxime Vermeir predicts a skills gap and highlights the need for tailored IT training. Specialized AI applications promise to tackle specific medical challenges effectively. Processes will face intense scrutiny, demanding data-driven automation strategies. Vermeir emphasizes XAI’s role in transparency for AI-driven healthcare. Training initiatives must bridge the digital divide. Analytics-driven automation and process intelligence will reshape workflows. Healthcare IT leaders face a pivotal phase requiring focused, inclusive AI adoption.
Maxime Vermeir predicts a transformative shift in AI dynamics within healthcare for 2024. He anticipates a departure from generalized AI to specialized, contextual solutions tailored to medical challenges. Vermeir emphasizes impending challenges like a skills gap and the need for strategic IT training. This change will prompt meticulous scrutiny of processes, emphasizing the importance of data-driven, compliant automation. Transparency via explainable AI emerges as crucial. His insights underscore the significance of inclusive training strategies to bridge technological disparities.
The imminent rise of artificial intelligence in the healthcare sector is poised to trigger increased scrutiny into the various facets of AI processes. However, this surge will expose a notable skills gap, necessitating specialized IT training. While AI’s integration in healthcare is on the ascent, it will deviate from the realm of generative AI, unlike systems akin to ChatGPT.
Maxime Vermeir, leveraging a decade of expertise in product and technology, endeavors to enhance customer value by implementing cutting-edge technologies across diverse industries, prominently within healthcare.
His proficiency in artificial intelligence aids in establishing robust business systems and transformational initiatives, leveraging advanced AI applications, including large language models. His primary objective is to empower client organizations in realizing their digital transformation aspirations and discovering novel opportunities through AI.
In a conversation with Healthcare IT News, Vermeir delves into his predictions, offering comprehensive insights to CIOs, fellow C-suite executives, and health IT leaders about the AI landscape in the imminent year.
Q: You suggest that AI will experience growth, but not in generative AI like ChatGPT. What type of AI will see growth, and where? Additionally, why do you anticipate a lack of growth in generative AI?
A: The current energy consumption of generative AI—such as using it for data search and summarization—is ten times higher than that of conventional search methods. This unsustainable energy demand renders it irrelevant for most business scenarios. Furthermore, there’s an expected increase in regulatory scrutiny, aiming to ensure the safe and ethical use of AI within the healthcare domain.
This scrutiny might involve stringent validation of AI solutions like ChatGPT models to guarantee precision, transparency in AI-driven decisions, and compliance with patient data privacy regulations.
The healthcare sector will shift its focus from general-purpose AI to more specialized, context-driven AI and machine learning systems adept at addressing specific business challenges efficiently.
Specialized AI systems can be tailored to tackle precise medical hurdles such as disease diagnosis, treatment planning, and patient management. Unlike generalized AI, these specialized solutions can be customized to adhere to medical protocols, comprehend medical coding, regulatory standards, and uphold patient safety measures, making them more suitable for healthcare applications.
Healthcare IT leaders will realize that many business challenges can be addressed using purpose-built applications—90% of which stem from the necessity for access to and an understanding of their own data and processes, resembling human cognition.
Purpose-built AI solutions can alleviate administrative burdens and expedite patient care, facilitating quicker specialist referrals or swift approvals for life-saving medications. Presently, only 54% of faxed referrals result in appointments, leading to patient care delays and overall health deterioration.
By integrating AI into the referral process, healthcare providers can automatically extract handwritten and textual referral notes, prioritize urgent referrals, and ensure compliance with stringent healthcare data protection and audit standards.
According to a recent Chime-Cerner survey, approximately 40% of healthcare providers are losing at least 10% of patient revenue due to referral leakage. Unprocessed referrals cost hospitals between $821,000 to $971,000 annually per physician.
Q: You anticipate an AI boom in healthcare leading to a skills gap and the need for specialized IT training. Can you elaborate on this prediction?
A: Recent widespread strikes by healthcare professionals have underscored the necessity for improved work-life balance. Consequently, AI will increasingly assist staff in managing administrative responsibilities, spanning from scheduling appointments to supporting emergency room operations and aiding physicians.
AI applications will enable healthcare workers to comprehend patient records swiftly and expedite authorization form processing and claims by 50%.
However, despite frontline workers constituting over 70% of the U.S. workforce, a recent study highlighted that only 14% have received training regarding AI’s impact on their roles. Notably, inadequate staff training remains a primary reason for the failure of automation initiatives, as indicated by a survey commissioned by ABBYY.
Healthcare leaders must take proactive measures to ensure their staff receives adequate training. As AI integration in healthcare looms, drawing insights from historical technology adoption trends is crucial. The digital divide, delineating the gap between demographic groups with varying levels of digital access, offers valuable insights into potential disparities in AI adoption within the healthcare workforce.
Recent statistics demonstrate persistent technology access gaps among different income groups, indicating potential variations in AI adoption rates. Additionally, rural populations exhibit lower access to home broadband and digital devices, potentially replicating disparities in healthcare settings across geographic regions.
Historical trends in internet and e-commerce adoption emphasize the varied pace and extent of technology adoption across demographics. Consequently, as AI pervades healthcare, diverse readiness levels and capabilities to harness these technologies will emerge, necessitating targeted training programs to ensure equitable access and utilization.
Effective training initiatives encompass workshops, webinars, open-source tools, and comprehensive offerings from educational platforms like Coursera, Udemy, and edX. Healthcare IT leaders must also ensure that automation vendors provide requisite skills training.
Q: You predict increased scrutiny of processes due to the AI boom in healthcare. What does this entail, and how will it impact health IT leaders in provider organizations?
A: The surging use of generative AI has prompted calls for stricter regulations. President Joe Biden recently signed an executive order to impose guardrails on AI development and usage, compelling thorough reviews before the release of large AI models like OpenAI’s GPT-5.
This executive order seeks to establish new standards ensuring AI safety, security, and protection of privacy and civil rights.
This shift will underscore the significance of explainable AI (XAI) in healthcare, offering clear insights into AI-driven diagnoses and treatment recommendations. Transparency in AI processes will foster trust among healthcare providers and patients, positioning AI as a supportive tool rather than an opaque decision-maker.
Consequently, healthcare organizations must comprehend the intricacies of their data processing methods, especially amid exponential digital transformation. This growth in automation necessitates a meticulous evaluation of existing processes—not just for efficiency but also for compliance reasons. Nonetheless, this process analysis demands effective execution, considering that up to 70% of automation projects reportedly fail.
Relying on employee feedback to drive automation can result in biased or incorrect information, leading to the automation of incorrect processes. Establishing data-driven decision-making processes by implementing analytics technologies becomes imperative for healthcare organizations embarking on automation projects.
Advanced analytics tools driven by AI and machine learning empower businesses to comprehensively understand their operations and processes before initiating any transformational changes.
Process intelligence, merging task mining and process mining, offers real-time, detailed workflow models for pinpointing automation opportunities. With healthcare’s stringent data protection and audit standards, IT leaders can ensure compliance by setting predefined business rules that flag potential violations.
IDC has identified the exponential growth of process mining technology in digital transformation, projecting a CAGR of 50.5% from 2022 to 2026, with revenues estimated at $3 billion by 2026.
Maxime Vermeir’s foresight highlights AI’s trajectory within healthcare for 2024 and beyond. Specialized AI’s ascendancy, scrutiny of processes, and the imperative need for inclusive, targeted training initiatives define this phase. The shift demands an understanding of XAI’s role and leveraging analytics-driven automation. Bridging the digital divide becomes pivotal for equitable AI adoption. Vermeir’s insights signify a critical juncture for healthcare IT leaders, necessitating proactive measures to navigate this transformative AI revolution.
