Rewriting the Script: How Gen AI Is Influencing Healthcare M&A Trends

The Gen AI Revolution in Clinical Documentation Software

Besides tending to patients, physicians are constantly taking notes of their interactions to keep track of progress and paint the full picture of healthcare treatments. What many don’t know is that this isn’t a small task for them. The American Medical Association reported that doctors might spend more time on electronic health records (EHR) than actually seeing patients (36 minutes on average versus 30 minutes).

So, when gen AI popped up with a potential tool that could spare most of the time spent on clinical documentation to instead care for more patients, do other administrative tasks, or just take a break, it became all the rage, showcasing its potential value.

Today, the technology can take over these tasks in healthcare:

• Automatically generate progress notes, discharge summaries, and other clinical documents.
• Deliver real-time transcriptions and summaries of patient-physician interactions during visits.
• Extract key insights and data points from unstructured clinical text.
• Personalize and adapt to documentation styles.

As a result, gen AI’s support has made physicians more efficient at their job by reducing administrative burdens, can potentially improve accuracy and completeness of clinical documentation, enhance data quality for health analytics and research, reduce operational costs, and increase the chances of better patient outcomes through comprehensive records. Evidently, this has also attracted venture capital investments and increased funding.

Gen AI in Healthcare by the Numbers

Making life that much easier for doctors and the healthcare sector in general has meant this AI branch is growing exponentially — more specifically, 12.5% annually until 2030. In fact, 75% of leading healthcare companies are experimenting with or planning to scale gen AI across the enterprise, and industry experts predict that roughly 30% of the healthcare market will be using an AI scribe by the end of 2025.

How Gen AI is Reshaping Healthcare M&A Trends for Clinical Documentation

As more companies witness the advantages of gen AI in the field, they’re becoming increasingly interested in adopting the services firsthand, leading to more private investment flowing into AI healthtech startups and M&A deals.

For example, healthcare providers are acquiring emerging AI healthtech startups to improve their services, including streamlining workflows with gen AI clinical documentation, with the help of cutting-edge tech (the highly disputed UnitedHealth and Amedisys deal comes to mind, among many others).

Consequently, this rising interest has meant valuations for seed and early-stage healthcare startups have remained steady with some increases despite the rough fundraising landscape.

A New Door of Opportunities in Healthcare

This newfound potential for gen AI in healthcare is opening doors for tech companies and private equity firms to enter the M&A space and come out on the winning end despite the uncertainty surrounding the technology.

For instance, according to Bain & Company, companies striking at least one healthcare acquisition deal experienced 12.2% growth in total shareholder returns (TSRs) as opposed to the 0.2% of those that didn’t — the biggest gap for any industry.

This positive track record, plus gen AI becoming a hot topic in health services and life sciences, could reignite the M&A spark between tech and health organizations.

But, for deals to reap their benefits, traditional due diligence processes must also shift. With gen AI in the picture, the healthcare industry is keeping a close eye on data privacy pertaining to EHRs, potential algorithmic bias, heightened regulatory scrutiny, and other underlying aspects the technology carries along.

Likewise, gen AI can bring new complexities when it comes to integration into healthcare processes, whether those issues are tech or even workforce-related. So, while success prospects are ripe, so are the risks of failed M&As.

Navigating the Risks: Due Diligence in the Age of Gen AI

Novel technologies are as exciting as they are tricky to adopt. This stems mostly from the fast-paced regulatory environment surrounding them, making adoption a challenging endeavor on the legal front and requiring specialized expertise. So, how should organizations go about due diligence in a healthcare M&A involving gen AI?

Technical Challenges

Vetting the capabilities of gen AI models might be the most challenging task of all, but one worth pursuing. This is especially true in healthcare, which thrives on accuracy, safety, and compliance.

To start, M&A teams must evaluate the robustness, accuracy, and reliability of the gen AI algorithms they’re acquiring — are they biased? Is their training data diverse enough? Does it produce hallucinations? Compliance is directly related to this step, ensuring the product follows the required security protocols and data privacy controls depending on its applied jurisdiction.

After all, AI adoption failure rates dramatically surged (from 17% in 2024 to 42% this year), mostly due to data privacy and security risks.

Additionally, the team must assess the scope of the technology and whether its scalability and integration capabilities are on par with what their company requires. Can it perform at the enterprise level and adapt to its pace and growth?

Lastly, checking every aspect surrounding IP is crucial, especially as AI is still under fire for blurring the lines of legal IP usage in training data and what it generates. The issue is multi-fold and heavily varies depending on the jurisdiction, making it even more difficult for providers to properly integrate the technology without crossing legal boundaries.

Operational Due Diligence

Although gen AI is a relatively new technology in healthcare — or anywhere else — scanning the industry for adoption rates and user satisfaction can help benchmark what companies should expect when integrating it.

This includes getting staff and patients on board, exploring common adoption pitfalls and working to mitigate them, and assessing other documented risks against the specific gen AI model being adopted.

Taking the workforce into consideration during the due diligence process is vital, ensuring there’s proper training and support involved to make the adoption process smooth.

Plus, one of the most important facts teams must gather during the due diligence process is the cost-effectiveness and ROI of the solution they’re adopting. Is it truly worth diving into this technology? The answer can massively vary from company to company.

Regulatory and Compliance Implications

Finally, a gen AI solution is only as functional as its security measures and compliance efforts. And, as a heavily regulated sector, healthcare organizations must take a closer look at how tech companies operate before acquiring them.

For example, in the US, being HIPAA compliant is a non-negotiable requirement. This regulation defines how sensitive patient data should be handled, secured, and divulged by covered organizations, and enforces hefty fines and penalties if healthcare providers fail to comply.

And, depending on where the organization operates, regulations around gen AI are a roller-coaster of sorts, evolving by the minute. As such, it’s crucial to stay on top of regulatory changes and ensure the AI company does the same, making shifts in their product as needed.

The Insurance Lens: Mitigating Emerging Risks

Insurance can be a sure-fire way to dive into new territories with more certainty and support if things don’t go as planned. Here are some key trends we’ve noticed in gen AI adoption in the healthcare sector, and our considerations when wanting to adopt it.

The Evolving Risks of Gen AI

Arguably no other technology has created as much debate as gen AI in recent times. From IP implications to bias, infusing gen AI into a sensitive process such as clinical documentation brings its own sets of evolving challenges and risks.

A few salient issues with this technology include algorithmic bias that can lead to errors or inconsistencies in documentation, data breaches that leak sensitive patient information processed by the AI, and reliance on AI-generated documentation which can trigger professional liability claims.

And — we can’t stress this enough — gen AI also brings IP matters to the forefront, making it a top priority to study the AI model’s training data for possible infringement.

Insuring Your Company Accordingly

Knowing the inherent risks attached to gen AI paints a clear path toward better risk management solutions each company should adopt. Insurance can protect valuable assets and be an effective part of these preventive measures. A few policies to consider are:

• Cyber Liability: Vital coverage when there’s risk of data breaches and cyberattacks — innate for any tech-related product.
• Errors & Omissions (E&O): For general claims arising from errors in AI-generated documentation or negligent implementation.
• Technology E&O:  More specifically tailored for technology providers, potentially covering AI-related failures.
• Intellectual Property: For defense against or pursuit of IP claims.

As always, you’re not in this process alone. Gen AI is an ever-evolving technology bringing uncountable advantages to healthcare organizations, yet it’s important to approach its adoption with caution to ensure you get the desired results from an acquisition.

Consulting insurance experts and discussing the best coverage ahead of your M&A deal helps you land the protection your company needs while mitigating headaches down the line, especially in the context of future acquisitions.

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