The healthcare landscape is rapidly evolving as advancements in technology continue to drive efficiencies in medical practices, clinical trials, and drug development. While the healthcare industry has turned towards artificial intelligence (AI) to streamline healthcare processes, these rapid innovations have also driven ethical and cultural concerns with adoption, regulations, and patient data security.
AlphaSense Expert Insights boasts an extensive range and depth of healthcare insights from industry experts that can be easily discovered. Leveraging Expert Insights on healthcare trends allows researchers to understand new developments and innovations within the life sciences landscape at a deeper level.
Below we explore key expert insights on AI trends within the healthcare sector from AI-driven efficiencies, implementation challenges, to data regulations.
Key AI Themes Found in the Expert Transcript Library
Through the lens of expert perspectives, the AlphaSense expert transcript library provides a concise exploration of how AI technology, like natural language process (NLP) and generative AI (genAI), drives both efficiency and concerns within the ever-evolving healthcare landscape.
We explore these various topics in greater detail below:
NLP-Driven Efficiencies
A senior healthcare consultant at TQM Solutions, and former practicing cardiologist, expresses that one area for problem solving in clinical practice is the demand to access critical information within large swathes of medical documents and articles. Fortunately, genAI can quickly aggregate medical data to generate concise insights for practitioners in the same capacity as AlphaSense’s Smart Summaries.
“If we talk about something that actually affects even the problems that exist in healthcare, natural language processing, me and you know, we are talking freestyle. To undermine this data instead of even writing it in two or three different ways, you just want to get the most useful, valuable information. Using natural language processing, it can summarize all this in one or two sentences. This is what we needed, this here data.
Even if you look at the physician, for example, of me as a cardiologist, I have more than 15,000 articles published in cardiology, I can’t even read it to be up to date. Imagine a hospital with different multispecialty, subspecialty. A lot of daily updated information, a lot of data, and a lot of variables. Who is going to process this and even get rid of the waste in the freestyling while recording or talking and just give me what I need, when I need it, and how I need it? Machine learning you are talking about a lot of stuff like speech recognition, data analysis, translation, and so on.”
– Senior Healthcare Consultant, TQM Solutions | Expert Transcript
The TQM Solutions expert suggests that the implementation of NLP tools is the most exciting area of competition right now with Cerner, Epic, and Health Catalyst as major competitors.
“Cerner, Epic, and Health Catalyst, they are competing for analytic solution. If you look for Epic, for their basic capability, adopting at least one of the effective adopters, as we call it, natural language processing and the system in this ROI calculation, they did it 100%. It’s awesome. If you look for Health Catalyst, there’s three adopter[s] like the clinical, financial, and operational data. They are doing [an] amazing job.
If you look [at] the competition now, it is built in artificial intelligence tool[s]. When I say artificial intelligence tool, I’m not talking only about basic operational and clinical data analysis or getting any significant value. I believe now the competition between Cerner, Epic, and Catalyst [is] who can really get the practical artificial intelligence tool implementation?
…I believe that the game now, is in artificial intelligence tool[s] which [will] actually affect the population health management and the clinical practice itself. This [is] what I believe. This is the best area of competition right now.”
– Senior Healthcare Consultant, TQM Solutions | Expert Transcript
Implementation Challenges
An associate officer at IERA Medical Physics expert states that C-suite level healthcare executives are in agreement that AI can drive efficiencies in analyzing large amounts of patient data. However, implementation of AI solutions is a challenge because hospitals are suffering from physician shortages.
“There’s physician shortages, which actually the physician shortage plays in well to AI and ML, but to get there, hospitals, they’re still running pretty thin even in 2023. I think that’s a part of it too.
There have been a lot of polls done of healthcare executives, and they all say they believe that AI can be very effective and often effective in improving clinical outcomes, which is true. They understand the idea of AI analyzing large amounts of patient data to spot patterns and make predictions and flag health risks. That’s all positive stuff. That’s all what the C-suite wants to hear, but in a very basic level, they have to be able to buy into the whole thing, to buy into the data analysis and the data collection and be willing to have AI really augment human intervention.
To answer your question, the vendors can train, but I don’t know on the hospital end if they’re able to put as much time and effort into it as they want to because they’re running thin and they’ve got huge data decisions to make. Again, I think they want AI to augment human intervention, they do, but they’ve got to get there. They really got to get there.”
– Associate Officer, IERA Medical Physics | Expert Transcript
The TQM expert also suggests that AI solution vendors need to either have healthcare domain expertise or at least understand the culture of healthcare. The expert emphasizes that a culture shift needs to occur first in order for AI to be widely adopted within healthcare, and it is the responsibility of the AI solution vendors to create that change.
“I think the first mission and the first duty [of] people who are taking the name of AI, when it comes to healthcare, it is better to have people in AI that come from healthcare backgrounds. Why? This is a big gap. People, when they come, they claim they are aware of the complexity of [healthcare]… People who are actually offering the solution, they are responsible about the culture and how people will accept this and to trust it…
These boundaries to AI, actually create a limitation. The successful people that offer AI solutions [are] coming from healthcare and filling the gap, health is a culture. I suppose it’s a culture. Any information technology in healthcare will not improve anything unless you change the culture of people. The limitation of the algorithm that was purely built by information technology or pure data scientists without any domain experience, [is] a big limitation.”
– Senior Healthcare Consultant, TQM Solutions | Expert Transcript
Data Regulations
The IERA Medical Physics expert also states that data sharing and the psychology surrounding AI are the biggest challenges of AI adoption in healthcare. Society as a whole needs to become comfortable with our healthcare data being in the intangible hands of AI rather than humans.
“The bigger problem is, again, the sharing of the data. That’s always going to be a problem for EHR and EMR and in AI and ML as well, sharing the data is a problem, and of course also the psychology around all this, but this is the same issue that has been in our lives for many decades, the first credit card, the first ATM card. We have to, as a society, take our healthcare the same way as we took our finances and say that we’re willing to turn it over to a non-human entity. That’s a big leap. That’s a very, very big leap.”
– Associate Officer, IERA Medical Physics | Expert Transcript
The TQM Solutions expert also provides a similar sentiment that regulations for AI in healthcare has major challenges with data access being the biggest limitation for lawmaking.
“This is a big bounty for this to even be able, regulation, lawmaking to understand all [of] the aspects and to be aware what creates a competition and what is behind this. You have national security of cybersecurity who’s using any technology now, especially in secure data. You have a lot of risk with cybersecurity, with our server, who [is] going to get access to the data, how can we secure our infrastructure, and so on…
The data access, it’s a big limitation to understand the laws, regulation, to reach the lawmakers and to give them a good understanding [of] what is the data and how we can regulate this because this is a big regulator and the misaligned incentives. This is the most crazy thing.”
– Associate Officer, IERA Medical Physics | Expert Transcript
Track New AI Developments in Healthcare with Expert Insights
NLP in healthcare carries incredible potential and will revolutionize the way healthcare professionals access medical and patient data. However, these AI-driven efficiencies also face major challenges in implementation by hospitals and cybersecurity concerns by lawmakers. As these issues are addressed, genAI will usher in a new era of medical practices.
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Ees is a Content Marketing Specialist at AlphaSense and responsible for elevating the brand of Expert Insights, a unique content set of alternative data, through the power of storytelling. Previously, Ees has been a seasoned copywriter across various industries throughout his career and also authored “masala chai,” an illustrated poetry book capturing his journey as a South Asian minority in America.
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