2024 outlook: AI focus will shift to specialized machine learning that addresses specific business problems
By 2024, artificial intelligence in healthcare will boom, leading to greater scrutiny of the various processes of AI. The rise of AI will lead to a skills shortage and a need for more specialized IT training. And while the use of AI in healthcare is growing, it won't be generative AI like ChatGPT.
These are predictions for next year from Maxime Vermeir, senior director of AI strategy at ABBYY, an intelligent automation company. With ten years of experience in products and technology, Vermeir works to increase customer value with emerging technologies across industries, including healthcare.
His expertise in artificial intelligence helps enable powerful business systems and transformation initiatives through large language models and other advanced applications of AI. Its mission is to help client organizations achieve their digital transformation goals and unlock new opportunities with AI.
Healthcare IT news spoke with Vermeir to have him explain his predictions in depth and provide healthcare provider organization CIOs, fellow C-suite executives, and healthcare IT leaders with advice on AI for the year ahead.
Q. You suggest that AI will grow, but not generative AI like ChatGPT. What kind of AI will grow and where? And why won't generative AI grow?
A. Today, using generative AI to search and summarize data requires ten times the energy of a normal search. It is simply not sustainable and not relevant to most business cases. Regulatory scrutiny is also likely to increase to ensure the safe and ethical use of AI in healthcare.
This could include rigorous validation of AI solutions such as ChatGPT models to ensure accuracy, transparency in AI decision-making, and compliance with patient data privacy laws.
In healthcare, the focus will shift from general AI to more specialized, contextual AI and machine learning systems that effectively address specific business problems.
Specialized AI systems can be developed to address precise medical challenges such as disease diagnosis, treatment planning and patient management. Unlike general AI, these specialized solutions can be customized to comply with medical protocols, understand medical bills and codes, understand healthcare regulations, and ensure patient safety, making them more suitable for healthcare applications.
Healthcare IT leaders will find that they can solve many of their business challenges using purpose-built applications – 90% of which stem from the need for access to and human understanding of their own data and processes.
Purpose-built AI can reduce administrative burdens and speed up patient care, such as quickly referring to specialists or obtaining approval for life-saving medications. For example, only 54% of faxed referrals result in an appointment, leading to patient attrition, delays in care, and worsening overall health outcomes.
By applying AI technologies to the referral process, healthcare providers can automatically identify and extract handwritten and text notes of referral reasons, and prioritize urgent referrals with all the strict data protection and auditability required in healthcare.
A recent Chime-Cerner study found that nearly 40% of healthcare provider participants lose at least 10% of patient revenue due to referral leakage. And unprocessed referrals cost hospitals between $821,000 and $971,000 per physician per year.
Q. You say artificial intelligence will result in a skills shortage and a need for more specialized IT training. Please provide some more information.
A. Recent nationwide strikes by healthcare professionals have increased workers' need for better work-life balance. Therefore, more AI will be used to augment staff with their administrative tasks, ranging from appointment schedulers to emergency room staff to physicians.
AI will help healthcare professionals understand patient records and recommend and process authorization forms and claims 50% faster.
While frontline workers do represent that more than 70% of the U.S. workforce, a recent survey found that only 14% say they have received training on how AI will impact their jobs. One of the main reasons for the failure of automation projects is the lack of staff training, according to a study commissioned by ABBYY.
Healthcare leaders must take the initiative and ensure staff are properly trained. As we anticipate the integration of AI into healthcare, it is important to learn lessons from historical trends in technology adoption. The digital divide, a term used to describe the gap between demographic groups with easy access to digital technology and those without, provides valuable insights into potential differences in AI adoption in healthcare.
Recent statistics underline this gap. According to Pew Research Center, the share of Americans in each income category who have broadband or a smartphone at home did not change significantly from 2019 to 2021. This indicates a persistent gap in technology access between different income groups, which could translate into varying levels of AI adoption within the healthcare workforce.
In addition, rural adults are less likely than their urban and suburban counterparts to have access to broadband at home and are less likely to own digital devices such as smartphones, tablets or computers. This rural-urban divide could potentially be reflected in healthcare, affecting both healthcare providers and patients across geographic areas.
The historical adoption trends of the Internet and e-commerce show that while new technologies eventually become widespread, the pace and extent of adoption can vary widely among different demographic groups.
This suggests that as AI becomes more prevalent in healthcare, there will be varying levels of willingness and ability to use these technologies effectively. This underlines the importance of targeted training and education programs to ensure equitable access to AI tools and their benefits.
To ensure that no demographic is left behind in this technological shift, healthcare leaders must prioritize upskilling and reskilling initiatives. These should be designed to meet diverse learning needs and backgrounds, and ensure that all healthcare professionals, regardless of their starting point, can use AI effectively in their roles.
Furthermore, it is crucial to consider the end-users of healthcare services, who may also face barriers to accessing and benefiting from AI-driven healthcare due to the digital divide.
So while AI offers transformative potential in healthcare, its equitable adoption and beneficial impact across all demographics depends on proactive and inclusive training strategies. These strategies should be based on an understanding of existing disparities in access to and use of technology, as highlighted by the phenomenon of the digital divide.
As a result, training and retraining will be a key priority in healthcare in 2024 and beyond to ensure workers are up to date. There are numerous workshops, webinars and open source tools available, as well as a more intensive offering from the likes of Coursera, Udemy and edX on relevant topics such as machine learning, deep learning and AI applications.
Healthcare IT leaders must also ensure that vendors deploying their automation are offering the appropriate skills training.
Q. You predict there will be a boom in AI in healthcare, leading to increased oversight of processes. What do you mean? What does this mean for healthcare IT leaders at healthcare organizations?
A. The sudden growth of generative AI has reignited calls for stricter regulation, with President Joe Biden recently signing a sweeping deal new executive order to place guardrails on AI use and development, such as subjecting major AI models such as OpenAI's GPT-5 to review before release.
The executive order will also take steps to set new standards for the safety and security of AI, and protect the privacy and civil rights of Americans.
This will lead to advances in explainable AI (XAI). XAI could become critical in healthcare to provide clear explanations of AI-driven diagnoses and treatment recommendations. This transparency can help build trust between healthcare providers and patients, and ensure that AI acts as a supportive tool rather than an opaque decision maker.
As a result, healthcare organizations will need to understand exactly how their information and data is being processed, especially with the massive growth in digital transformation, as many healthcare companies now see AI investments as one of the key objectives for the coming years.
This growth in automation means that current processes are coming under scrutiny, not only for efficiency and success, but also for compliance reasons. However, this analysis of processes will have to be carried out effectively, especially with new research showsUp to 70% of automation projects fail.
Relying on employee feedback to drive change can lead to incorrect or biased information, which can lead to the wrong processes being automated. Healthcare organizations will need to establish a data-driven decision-making process for their automation project by implementing analytics technology to gather deep insights and inform strategic choices.
The rise of such analytics tools, powered by artificial intelligence and machine learning, allows companies to understand their own processes and operations more deeply – before moving forward with changes.
One such technology is process intelligence, which combines task mining and process mining to display an accurate and detailed model of a workflow in real time to better identify opportunities for automation. Furthermore, with the strict data protection and auditability required in healthcare, IT leaders can use technology to ensure compliance by establishing pre-established business rules that highlight any violations, helping organizations maintain regulatory standards.
IDC has confirmed the growing use of this type of process mining technology in digital transformation, stating that IDC was the fastest growing subset of the intelligent process automation market, with a 2022-2026 CAGR of 50.5% and revenue of $3 billion. in 2026.
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