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What to Prioritize in Health IT in 2025
Next year, health IT leaders must prioritize areas such as precision medicine, cyber resilience, and health equity in AI, experts say.
7 Min Read
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Heading into 2025, healthcare organizations still face workflow shortages, both on the clinical and IT side. Growth in artificial intelligence and automation will enable tech leaders to address these workflow shortages at health systems.
However, as healthcare IT leaders continue experimenting in generative AI (GenAI), it may not be as much of a top priority as you might think, according to IDC’s Worldwide C-Suite Tech Survey, which was conducted in September and October 2024.
“Interestingly, given all the focus on generative AI, only 25% of healthcare respondents reported implementing AI/GenAI as their organization’s top priority for the next 12 months,” Lynne A. Dunbrack, group vice president for the public sector at IDC, says in an email interview.
The IDC report lists the top three health IT priorities in healthcare as investing in security technologies (36.5%), improving customer-focused digital experiences (36.1%), and advancing digital skills across the organization (33%).
Here, InformationWeek offers insights from several industry experts on the top priorities for health IT leaders in 2025.
Addressing Data Storage Needs
Modernizing their infrastructure in the cloud to manage increasing data volumes should be a priority for health IT leaders, according to the IDC FutureScape: Worldwide Healthcare Industry 2025 Predictions report.
“Cloud solutions and platforms offer more than just expanded technology capacity, scalability, and access to managed services,” the report stated. “They also act as a catalyst for data exchange and interoperability, enabling seamless integration of third-party applications and other platforms, creating a more open, dynamic, and innovative ecosystem.”
Scaling Precision Medicine to a Broader Population
In 2025, health IT leaders should expand precision medicine to a wider population, says Brigham Hyde, cofounder and CEO of Atropos Health, which offers a cloud-based analytics platform for converting healthcare data into personalized evidence. Precision medicine uses AI and digital tools to make better target treatments possible. The technology could support drug development and personalized therapies for patients. To scale precision medicine, the healthcare industry must keep data specific and personalized, according to Hyde.
“Precision medicine traditionally focuses on small, highly specific patient cohorts with unique genetic, environmental, or lifestyle factors,” Hyde says via email. “Scaling it involves extending this level of personalized care to larger and more diverse populations by leveraging technologies like AI and real-world data."
AI delivers the ability to drill down on insights for specific conditions at a granular level, Hyde explains.
“Once these models produce tailored recommendations, they can scale to address broader populations by combining multiple focused models or synthesizing data from different specialties,” he says.
Implementing Generative AI
Healthcare organizations will move on from simply experimenting with GenAI to carrying out enterprise-wide AI strategies, according to the IDC FutureScape report.
Although healthcare GenAI investments are expected to triple in healthcare by 2026, 75% of these healthcare Gen AI initiatives will fail to achieve their “expected benefits” by 2027 due to issues around trustworthiness of data, “disconnected workflows, and end-user resistance,” IDC reported.
In the meantime, in 2025, health IT leaders will need to prioritize quality assurance and physician trust with GenAI and large language models (LLMs), according to Hyde.
“We will need to scrutinize applications for their clinical accuracy, transparency, and alignment with ethical standards,” Hyde says.
In the coming year, health IT leaders will prioritize evaluating the accuracy they get from AI algorithms, according to Michael Gao, CEO and cofounder of SmarterDx, which builds clinical AI applications to allow hospitals to achieve revenue integrity, such as checking for billing coding errors and revenue leakage.
“As we see more widespread adoption of AI and especially generative AI in healthcare, health IT leaders are going to be prioritizing not just how to supervise an algorithm to understand what level of accuracy you’re getting, but also determining how to even pick what level of accuracy you want in the first place,” Gao says in an email interview. “For example, you want extremely high accuracy algorithms for clinical care. There are a lot of learnings around that before we can really use algorithms effectively in healthcare.”
Adopting Ambient AI
Hyde advises that health tech leaders prioritize ambient AI, which operates in the background using advanced computing and AI to detect and generate insights without a user’s involvement. The technology can automate tasks, as well as personalize care delivery, he says.
“By collecting and analyzing real-world data in the background, ambient AI enables more precise and actionable insights for disease management, treatment optimization, and personalized medicine initiatives,” Hyde explains.
Ambient AI can reduce clinician burnout and improve physician retention through ambient dictation and transcription of notes from patient visits, according to Hyde.
Addressing Health Inequities With AI
To address health inequities and avoid the biases in AI models, health IT leaders should prioritize vetting AI use cases, says Ann Bilyew, executive vice president for health and president of the Healthcare Solutions Group at WebMD/Internet Brands.
Keeping AI equitable means paying attention to the social determinants of health, which are factors that influence health such as income, job, education level, and ZIP code.
Although addressing health inequities is a “worthwhile and promising goal” for AI, “it’s important to note that AI is only as good as the material it’s trained on, and that material has inherent biases,” Bilyew tells us via email. “AI can exacerbate those biases, so it is critical that healthcare organizations thoroughly vet these use cases to ensure they meet the intended goal.”
AI models should apply to patients across the board to remain trustworthy and equitable, suggests Dan Stevens, healthcare and life sciences solutions architect at Lenovo.
“To gain trust from care providers and patients to accept AI-generated healthcare recommendations, it will be crucial to ensure the data used for training is representative of the general population, maintains patient data confidentiality, and avoids bias,” Stevens says via email.
Investing in Cybersecurity Tools
In IDC’s Worldwide C-Suite Tech Survey, 46.9% of healthcare respondents cited security concerns as the top challenge their organizations faced when implementing Gen AI.
“Security and cybersecurity tools are a business imperative to protect vulnerable healthcare infrastructure against increasing volumes of insidious ransomware attacks that put patient safety at risk,” Dunbrack says.
Meanwhile, by 2027, increasing cybersecurity risks will drive healthcare organizations to use AI-based threat intelligence solutions to enable continuity of care and protect patients, according to the IDC FutureScape report.
“To safeguard patient safety and ensure uninterrupted healthcare services, it is imperative to make investments in cybersecurity a top priority,” the report stated.
Mitesh Rao, founder and CEO of OMNY Health, notes the security steps healthcare organizations should take in 2025 following the massive healthcare data breaches that occurred in healthcare in 2024, particularly with Change Healthcare.
“More companies need to implement checks and balances on their own operations to prevent leaks and cyberattacks,” Rao says in an email interview. “Beyond that, data providers need to vet their data sharing policies to make sure that patients’ information doesn’t end in the wrong hands.”
As AI models are used more extensively and health data gets spread across diagnostic and financial information as well as multiple types of platforms -- including local devices, mobile, servers and cloud services -- IT leaders will need to prevent risk of security breaches, Lenovo’s Stevens suggests.
“If not managed appropriately, AI workflows risk introducing unanticipated security breaches due to a lack of end-to-end protection keeping data secure across all resources, from an individual’s PC to the cloud,” Stevens says.
Tackling Regulatory Compliance
With the focus on GenAI, healthcare organizations must ensure they understand regulations around compliance in 2025, IDC’s FutureScape report noted.
For 2025, Atropos Health’s Hyde advises that health IT leaders build frameworks that establish trust while adhering to regulatory standards at the same time. These frameworks will depend on the size of the healthcare organization, he says.
“Larger health systems and technology companies with robust resources may prioritize building their own frameworks tailored to their specific needs, ensuring alignment with their internal workflows, patient populations, and operational goals,” Hyde says. “However, the majority are expected to rely on or closely align with emerging regulatory frameworks and standards.”
Prioritize Cyber Resilience
In 2025, health IT leaders should keep cyber resilience in mind to stay prepared for cybersecurity incidents before they occur, advises Ty Greenhalgh, industry principal of healthcare at cybersecurity firm Claroty. Greenhalgh is also an ambassador for the US Department of Health and Human Services 405(d) Task Force and a member of the HHS Healthcare Sector Council Cyber Working Group.
“By leveraging the NIST definition of resilience, organizations can anticipate, withstand, adapt, and recover from threats,” Greenhalgh tells us via email. “This approach emphasizes early detection and mitigation to reduce downtime and financial impact, particularly in the face of persistent threats like ransomware.”
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