A clinician loses minutes to a frozen workstation. A researcher waits for an app that won’t load. Across a large health system, small delays like these add up to real time taken away from patients and discovery.
That is why technology sits so close to the mission in healthcare and life sciences, and why three priorities stand out: free clinicians and researchers from technology friction, protect patient and research data from attacks, and build a data foundation ready for AI and discovery.
The pressure is real: tight budgets, regional regulations, and teams stretched thin. More tools rarely help. The organizations making progress scale the right technology with strong governance, and they thread AI through the work instead of treating it as a side project.
Here’s how the three priorities play out, and what practical progress looks like on each.
Priority 1: Free clinicians and researchers from technology friction
Burnout remains one of the sector’s most urgent problems. Recent research found that 43% of U.S. primary care providers report burnout, with administrative burden as a leading cause. Every extra login, ticket, and workaround adds to that load. When technology gets in the way, people who are trained to care for patients spend their energy fighting their tools instead.
Progress starts with simple, consistent IT support. One large U.S. hospital system, with approximately 150,000 employees and 35,000 clinicians across more than 1,000 care sites, had accumulated hundreds of resolver groups and vendors as it grew. Support quality depended on where you worked. Our fix was consolidation: one national service desk, available around the clock, reachable through any channel.
AI is taking on a growing share of that work. An agentic service desk resolves routine requests without human intervention. A digital assistant gives staff instant answers and self-service across channels. Self-healing catches some issues before they ever reach the desk. Password resets and access requests clear faster, so support staff and clinical teams get their time back.
Similar solutions are working in the research space. Unisys supports 18,000 users and more than 39,000 devices across a global biotech organization, with walk-up tech cafés at key research sites. Scientists stay focused on developing new treatments rather than chasing IT fixes.
Priority 2: Protect patient and research data from attacks
Healthcare data is a frequent target, and the financial consequences sting. IBM’s Cost of a Data Breach Report 2025 ranks healthcare the most expensive industry, averaging about $7.42 million per incident. The risk also sits well beyond hospital walls. The American Hospital Association reports that more than 80% of stolen protected health information comes from third-party vendors and business associates.
That pattern shows where defense is needed: at every site, system, and endpoint, with identity and Zero Trust at the center. Strong device management is a practical starting point. In the hospital system mentioned above, unified endpoint management brought clinical and administrative devices under one framework, and endpoint compliance monitoring gave IT a consistent, auditable view of device health across every facility. Detecting unmanaged devices reduced shadow IT risk across the network.
AI sharpens the response. AI-assisted threat detection and managed detection and response (MDR) help teams spot and contain incidents faster, with less disruption to care.
Other risks are still forming. Quantum computing will eventually break the encryption that guards data today, and attackers are already harvesting encrypted data to decrypt once the technology matures. Health records that must stay private for decades are a prime target. Post-quantum cryptography readiness, built on the standards NIST finalized in 2024, protects that data before the threat fully arrives.
Priority 3: Build a data foundation ready for AI and discovery
AI has moved out of pilot projects and into daily operations. The leaders seeing returns scale the right technology with governance, clean data, and clear accountability. They are modernizing applications, improving data quality, and connecting systems so information flows where it’s needed and stays protected.
Consider a global health and security services firm operating in more than 80 countries. When it needed to modernize its data infrastructure, Unisys deployed a data ingestion platform on AWS that improved data quality by 98%, ingested 45 million employee data points, and reached full HIPAA compliance in under four months. A clean, governed base is what makes AI dependable rather than risky.
With that foundation in place, AI can support the people doing the work without taking decisions away from them. AI serves as decision support, keeping clinicians in control. AI workflows run on the enterprise platforms organizations already operate, instead of forcing a rebuild.
The same foundation carries into life sciences research, where AI and quantum computing tackle the same hard problems. AI already speeds discovery, predicting how proteins fold and screening drug candidates faster than lab work alone. Some problems still exceed what today's computers can model. A polypeptide with 101 amino acids has an astronomical number of possible configurations, far beyond what classical computing can handle. Quantum simulation can work through that complexity directly. Used together, AI and quantum point toward faster drug discovery and more precise medicine, and both depend on the clean, connected data underneath them.
What is an outcomes-focused IT strategy?
An outcomes-focused IT strategy judges every technology decision by its effect on care and discovery, not cost or convenience alone. It keeps three questions in view:
- Does this help clinicians and researchers do their work?
- Does it protect sensitive data?
- Does it prepare the organization for AI and what comes next?
When leaders measure investments against those questions, technology supports the mission instead of competing with it.
How healthcare IT leaders can act on today’s priorities
These are the steps where health systems gain traction first. Each is small enough to start this quarter, and each sets up the next.
- Map where support is fragmented. Count your resolver groups and vendors, and find where the employee experience varies by site or role.
- Put AI on the routine first. Start with high-volume requests like password resets and access, where automation returns time quickly.
- Treat identity and endpoints as security priorities. Most exposure starts outside the electronic health record (EHR) and beyond hospital walls, so visibility across devices and third parties matters.
- Fix the data before scaling AI. AI only earns trust when the data beneath it is clean, compliant, and well-managed.
- Get ahead of what’s coming. Begin post-quantum readiness and next-generation computing now, while there’s room to move deliberately.
Building your IT strategy
Freeing staff from friction, protecting data, and readying it for AI and beyond all draw on the same foundation of reliable, well-governed technology, so progress on one clears the way for the next. To see how Unisys helps healthcare and life sciences organizations turn these priorities into results, explore our healthcare and life sciences solutions or contact us to start a conversation.
