2025 Trends In Health IT

Technology often plays a critical role in strategies to combat rising costs while maintaining quality care. Here are four trends expected to accelerate in 2025.
The post 2025 Trends In Health IT appeared first on Above the Law.

Healthcare costs are expected to rise 8% in 2025, the most significant projected increase in over a decade. These staggering costs affect all of us – from providers to payers, life sciences companies to government agencies, and most certainly affect us as patients. This cost trajectory is not sustainable.

Having spent more than 30 years in the healthcare IT industry, I’ve seen how technology often plays a critical role in strategies to combat rising costs while maintaining quality care. I expect to see these four trends accelerate in 2025.

1. Data sophistication is not optional.

Insurance companies and self-insured employers continue to see significant increases in benefits costs. In one estimate, costs of employer-sponsored healthcare coverage are expected to rise by 9%.

Healthcare data curation and aggregation, plus linking closed claims with clinical data, will become one of the most effective ways for organizations to improve treatment options and tackle affordability. However, this is complex work.

Let’s put this challenge in context using a current issue: managing GLP-1s. A recent survey found 1 in 8 adults have reported taking this increasingly popular class of drugs.  In talking with our clients, we’ve found that about two out of three payers cover GLP-1s for weight loss. They report they use different approaches to manage these drugs for patients with diabetes and those who are using them to address obesity. For example, pharmacy benefit manager step therapy and quantity limits often oversee usage for patients with diabetes, while other patients participate in weight loss management programs. 

The increasing savvy of healthcare consumers further challenges employers and insurance companies. As financial burdens increase, people expect more from their benefits in terms of transparency and access to services, especially mental health and wellness offerings.

Payers need sophisticated analytics to understand how their strategies play out among their members, how they impact utilization rates, and which approach is leading to better outcomes. And we hear from our clients that the last piece – understanding the path to better outcomes – is a complex, difficult task. A deep understanding of these trends enables payers to achieve their goals.

The same is true in the life sciences world. For decades, researchers have used large health data sets, always making the best of what they had access to. Today, however, it’s a different story. Researchers have more data choices than ever before, and that requires a new level of sophistication and data expertise. To gain insights from today’s real-world data, researchers need a foundation of comprehensive, diverse, trusted information built on linkages between data sets. 

2. Use cases will drive the adoption of Artificial Intelligence (AI).

Reality checks have tempered the euphoria about AI’s ability to solve every challenge over the last few decades. While the promise of AI is clear and strong, healthcare is taking a more pragmatic approach these days.

AI isn’t the first technology that has fallen short of being the immediate panacea for all healthcare problems. Blockchain is another example that comes to mind. Full economic value from AI will take five to ten years to be realized. In comparison, mobile phones and internet technology took 20 years to reach their full potential. We can learn from these past experiences and instead put emerging technologies to work in targeted ways. 

We will see many use cases centered around operational efficiency and workplace AI integration. AI will automate administrative tasks such as scheduling, resource allocation, and supply chain management. This automation reduces overhead and enables healthcare professionals to dedicate more time to patient care.

Using AI to alleviate administrative tasks can also help reduce clinician burnout. For example, specific use cases in neurology and stroke care are gaining traction in imaging when AI can help clinicians prioritize patients in the most urgent need of care. AI algorithms assist radiologists by detecting abnormalities in X-rays, CT scans, and MRIs with heightened accuracy. AI can also sift through extensive data sets to serve up trusted information at the point of care, such as a clinician who needs quick and accurate dosing information for a neonatal infant. 

Beyond its search capabilities, AI can also reduce other manual tasks. For example, in government benefit and service delivery, agencies can use AI to analyze and verify documents uploaded by citizen users. This can make processes much more efficient and enable caseworkers to spend more time on complex tasks.

In the life sciences industry, there are numerous use cases where AI-assistants will streamline working methods and drive improved outcomes with ROI for clinical trials. For example, research teams must translate participant data into medical codes. AI-assisted data entry has made this process less manual. It uses predictive suggestions and up-to-date coding dictionaries for consistent, accurate entries. 

These solutions must become ingrained in the technologies to ensure scalability and drive down the overall time and cost of bringing new treatments and solutions to patients.

In the future, predictive analytics powered by Generative AI will enable early interventions and preventative care. By modeling patient outcomes based on genetic predispositions and medical history, AI supports proactive health management and improved long-term care. AI also enables personalized medicine as clinicians analyze genetic, clinical, and lifestyle data to develop customized treatment plans. This shift towards precision and patient-centric care improves health outcomes and minimizes side effects.

AI hype will soon be replaced by use cases for specific AI investments. It will require a clear return on investment before organizations add investment and operating expenses to their budgets.

3. Expect and adapt to new rules in the United States.

The Trump administration has signaled that it plans to change existing federal agencies and programs to lower costs. The president-elect has named several people to lead key federal health agencies, such as the FDA, NIH, and CDC, which some industry observers have called “out-of-the-box choices” and a “colorful cast” that could make significant changes. While many incoming officials have been vocal about reducing costs, the details are still being developed.  

Changes in the United States may also be a catalyst for additional changes from other global regulatory bodies. For example, the European Union (EU) is much more stringent on data privacy than the U.S. If the U.S. further reduces data privacy requirements for companies, EU regulators could require global companies to relocate servers.

As with any change in administration, government agencies responsible for benefits delivery must be ready to adjust. Federal, state, and local agencies must also be ready to communicate with citizens about how these changes impact eligibility and benefits. Technology must be capable of adapting to changes in legislation, policy, and population needs.

4. Cybersecurity is more critical than ever in healthcare.

According to the World Economic Forum, healthcare is the most targeted industry for cyberattacks. In 2023, healthcare data breaches cost an average of about $11 million each, almost double the cost of a breach in the financial industry, which came in second. 

Recent high-profile cyberattacks will impact healthcare organizations for years to come. To mitigate future risk, there is proposed legislation in the U.S.: The Health Infrastructure Security and Accountability Act of 2024. The bill imposes additional security controls and measures, new fines, and penalties for companies who fail to comply and protect health information.

Security concerns are both a driver and barrier to healthcare providers moving systems and data to the cloud. Given healthcare data’s increasing complexity and volume, cloud computing can offer scalability, better security, and more efficient data management. However, securing these systems is paramount, especially with sensitive patient information.

Many healthcare organizations increasingly adopt hybrid cloud models for better data management, interoperability, and cost-efficiency. Cloud technology can facilitate seamless integration between healthcare providers, payers, and life sciences companies while maintaining security. Cloud infrastructure can also accelerate the adoption of AI and big data analytics by providing more accessible and secure environments.

Data sophistication, targeted AI, adapting to new rules, and cybersecurity; healthcare organizations that effectively pursue these aims will be better positioned to reduce costs while maintaining high-quality care.  

Source: metamorworks, Getty Images

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