Trends in health technology: what HIMSS 2024 left behind

From medical burnout to closing healthcare gaps and from ethics to success stories, the main uses of AI and how it is radically changing the healthcare industry

By Manuel Allegue, founder and CEO of Zentricx

From Zentricx we had the opportunity to be present at the Global Health Conference and Exhibition HIMSS, where, personally, I had the honor of being a speaker at the event's Latin American dinner.

Artificial intelligence (AI) proved to be a dominant theme of the meeting – as it has been in other events focused on health technology – and left important trends for the industry where it would like to focus.

In fact, the core of the day's opening speech revolved around the potential of (AI) to help people around the world live healthier lives. Emphatically, Robert C. Garrett, CEO of Hackensack Meridian Health, stressed that healthcare requires a “radical transformation” that, with the help of AI, will cause a “revolutionary approach on a scale and pace never seen before.”

During his presentation, Garrett spoke enthusiastically about how AI can help improving people's access to good healthcare, but also how AI can lead to more optimal outcomes. He pointed out how predictive analysis serves to more quickly identify the onset of diseases, allowing doctors to offer more personalized treatment. Also the ability of this technology to develop drugs and vaccines more quickly.

Likewise, he referred to how AI can reduce inequities -for example in the case of health care for underserved communities-. He commented on the possibility of identifying the social determinants of health (those population groups that are at risk of suffering or being more exposed to certain dangers) and providing them with adequate care and resources.

A point that Garrett especially indicated and that we always seek to reinforce at Zentricx is the relationship between AI success and data quality: there is no generative AI without accurate data and the power of AI in relation to healthcare is indisputably linked to data quality, accessibility and standardization . Likewise, a challenge where emphasis should be placed is that of data interoperability: in the complex health model, lThe ability of applications and systems to exchange data securely and automatically is the main asset.

It was very interesting to hear Garrett refer to how AI is used in specific cases such as Hackensack Meridian, a system with 18 hospitals and 500 care sites. At the heart of the use of this technology There is a “strategic approach” where “responsible use and with humans always at the center” is the order of the day.

For example, they are using AI-based solutions to help radiologists prioritize the review of critical cases and the detection of advanced kidney disease, delaying the need for dialysis treatment and even the need for transplants. 

At the same time, Garrett noted that AI-powered chatbots help improve patient experience. “At Hackensack Meridian School of Medicine, students are learning how to integrate AI into their training and prioritize ethics and patient protection,” he said.

Medical burnout, in the spotlight

Speaking at one of the HIMSS panels, Alexander Ding, MD, a member of the board of directors of the American Medical Association, commented that a great potential for AI revolves around an area that could be outside the clinical: the expansion of solutions to reduce “the pain points” of healthcare administration. What would this entail? Alleviating some bureaucratic problems for doctors could be the key to addressing “widespread burnout” among these professionals.

In this sense, Ding pointed to studies that show that approximately 2 out of 3 doctors experience some exhaustion symptoms (a trend that was accentuated by the pandemic), with a clear link between professional disconnection and dissatisfaction and the administrative burden.

In fact, a few days before, within the framework of a webinar held by the LOVE with a focus on doctors' burnout, AI was also defined as an “augmented intelligence” that can help the healthcare community. In particular, we sought to emphasize that doctors should focus more on patient care and less on administrative issues. The specialists agreed that Health systems need to find ways to reduce operational tasks, documentation, as well as approval of treatments and medications.  In this sense,  AI-based solutions that record and summarize conversations with patients could help with the workload, but also, these types of tools could automate all administrative processes.

On the panel, Brian Anderson, MD, executive director of the AI for Health Coalition, also explained how AI could contribute to the shortage of medical personnel, a growing problem. Basically, increasing access, that is, making it easier for patients to navigate a platform and find the right doctor for their needs in a safe and effective way, whether through telehealth or augmented form, especially in the case of patients from the rural and remote communities.

The debate for ethics and responsibility It was a trend that was not left out of the conversation.

Already at the opening, Garrett insisted on the need to implement AI safely through effective governance and knowing that it can never replace human intervention and oversight. “We must obtain and ensure accurate data, protect patient privacy and commit to eliminating any potential for bias,” he said.

Sunil Dadlani, chief information and digital officer and chief information security officer at Atlantic Health System in New Jersey, warned during the panel that organizations They can make mistakes if they get caught up in the “cool” of technology. and that it is necessary to start with the use case, never with the technology: “Health organizations need to identify the problem they are trying to solve,” he said, adding that for now, many organizations They are not focused on clinical solutions for AI, as they require a higher validation threshold to be used safely in patient care.

Anderson closed with a crucial point that he would like to emphasize: The healthcare industry needs to reach a consensus on defining the responsible use of AI in healthcare because there is a lack of agreement on measuring the performance and accuracy of large linguistic models.. There is still no agreement on how to define these basic concepts, about what responsible, fair, equitable, safe and effective AI looks like, particularly in the generative AI space. “I would say that perhaps the most urgent thing we need to do is define what those terms are,” Anderson stressed, and I agree that It is a central axis where we must focus if we want a health ecosystem that incorporates AI in the most sensible, efficient and coherent way possible.

By Manuel Allegue, founder and CEO of Zentricx

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