Summary
AI may dominate the headlines, but healthcare organizations getting the most out of new technologies in 2025 are those that first built a strong data foundation. This article explores the current state of healthcare technology, the rising role of AI, and the critical infrastructure (cloud, remote monitoring, data governance, and integration) required to ensure these tools deliver lasting impact. Learn how healthcare leaders can position their organizations for sustainable success by setting the right foundation for AI.
Across the industry, everyone’s talking about what healthcare technology advances can mean for the future of health system operations and patient care. While AI gets the buzz, our conversations with numerous healthcare leaders consistently point to a more urgent priority: establishing the reliable, effective data infrastructure and capabilities that make AI viable.
Healthcare organizations feel pressure to implement whatever AI they can, but it’s worth stepping back first to assess. For our clients and the patients they serve, AI’s impact hinges on having those data foundations firmly in place.
Building Blocks of the Modern Healthcare System
The future of healthcare is more connected, more convenient, and more personalized. Successful healthcare organizations aren’t waiting for change; they’re building it. Leaders across the industry are focused on healthcare technology that delivers meaningful results for both providers and patients: improving outcomes, expanding access, and easing the strain on overburdened systems. Here are three top focus areas for transformation:
- Cloud Infrastructure: Moving data environments to the cloud is now recognized as essential for scaling analytics and enabling AI and greater remote capabilities. Organizations that have made this transition report greater agility, resilience, and improved support for data-driven care decisions.
- Remote Patient Monitoring: The shift toward home-based care is accelerating, with connected monitoring technologies helping manage chronic conditions outside of facilities. These tools improve outcomes through increased engagement, early intervention, and reduced readmissions, all while relieving pressure on brick-and-mortar facilities.
- Mobile Diagnostics: Advances in portable testing technology are improving access to diagnostic services in previously underserved settings. From rural communities to in-home hospital programs, mobile diagnostics expand reach and enable earlier, more convenient care delivery.
Key drivers behind healthcare technology change investment
Three critical challenges are driving healthcare organizations to invest in smarter, more adaptive technology. As pressure for efficiency, cost reduction, and better patient outcomes mounts across the system, technology solutions must support care delivery, ease workforce strain, and sustain long-term viability.
- Addressing Workforce Challenges: Persistent staffing shortages force healthcare organizations to do more with less. Technologies that automate routine tasks, extend clinical capacity, or reduce administrative burden are highly valuable, improving both care quality and staff retention.
- Improving Patient Experience: As healthcare becomes more consumer-centric, patients increasingly base their care decisions on the quality of their experience. Healthcare organizations can enhance patient satisfaction with technology, from streamlining care navigation to personalizing interactions.
- Operational Efficiency: With tighter margins and rising demand, efficiency is vital for long-term sustainability. Successful healthcare organizations are prioritizing technology that helps reduce costs while maintaining or improving outcomes, creating more sustainable models for care delivery.
The AI trend: What everyone’s talking about
To address these challenges and get to their ideal future state, there’s a strong consensus among healthcare executives that AI is the most promising area for healthcare technology investment. Leaders are identifying specific, high-value applications where AI delivers measurable returns like:
- Ambient AI for Clinical Documentation: By far the most frequently mentioned application, AI-powered documentation tools reduce administrative burden on clinicians. Using natural language processing (NLP), these tools allow providers to focus on patient care rather than typing notes, bringing back the human side of healthcare work and improving empathy in patient interactions. The technology is advancing beyond basic transcription to offer clinical decision support and coding assistance.
- AI-Driven Automation: Healthcare organizations that move beyond the excitement of generative AI are deploying practical AI applications to streamline workflows across revenue cycle management, patient access, and clinical operations. Many leaders we’ve spoken with note that while they can secure funding for projects labeled as “generative AI,” the real value often comes from more straightforward automation applications that deliver immediate ROI.
- Predictive Analytics: AI models that can predict patient deterioration, readmission risks, and resource needs help organizations improve outcomes and operational efficiency simultaneously. These tools are particularly valuable for hospitals seeking to avoid Medicare readmission penalties.
- AI Agents: More advanced AI systems that can perform complex tasks independently are emerging as the next frontier, particularly for patient navigation, call centers, and administrative processes.
The reality check: Are you ready for AI?
Before pursuing these trends and making big investments in AI, healthcare organizations need to ask themselves a critical question: Do we have the proper foundations and data governance set up to support AI initiatives?
If you haven’t tackled data governance, you’re probably not ready for AI yet.
Successful AI implementation depends on several foundational elements:
- Clean, High-Quality Data: AI systems are only as good as the data they’re trained on. Without clean, accurate data, even the most sophisticated AI will produce unreliable results.
- Record Linkage: The ability to accurately match patient records across disparate systems is crucial for creating a comprehensive view of each patient’s health journey.
- Data Integration: Bringing together data from different sources—including patient information, state and government data, and other external datasets—enables the kinds of insights that make AI truly valuable. (Think about receiving text reminders for your next vaccine; this requires seamless integration of multiple data sources.)
We’ve observed that many healthcare organizations already recognize the significance of data governance, but the effectiveness of implementation varies considerably. The organizations achieving the greatest success with AI are those that have prioritized data governance as a strategic imperative, not just a compliance obligation.
The Resultant approach: Building the right data foundation for AI
Our experience with healthcare organizations consistently demonstrates that effective AI begins with strong data fundamentals. Organizations that rush into AI without addressing data governance often find their sophisticated tools producing unreliable results.
Our Strategic Data Roadmap addresses this by meeting healthcare organizations where they are. We view data governance not just as compliance, but as the essential foundation for enabling and enhancing AI initiatives. The roadmap helps organizations understand their current data and create a practical, phased plan toward AI readiness.
We help clients succeed by blending our deep healthcare knowledge with our technical data expertise. We’ve learned that purely technical or solely healthcare-focused solutions often fall short. This integrated approach allows us to address both the technical aspects of data governance and the crucial people and process dimensions. We help organizations establish data stewardship, define quality standards, and implement governance that balances innovation with necessary controls.
For AI readiness, our approach includes evaluating data for completeness and biases, establishing data quality accountability, and creating adaptable governance structures. We’ve found that organizations building this foundation first implement AI solutions that truly deliver on their promise, avoiding costly, unscalable experiments.
Conclusion: Looking ahead in healthcare technology
As healthcare technology evolves through 2025, the organizations achieving meaningful AI outcomes will be those who have prioritized strong data foundations. This deliberate approach ensures sustainable, scalable results that transform operations and patient care.
Effective AI adoption looks beyond the newest shiny tools to first create the right technology environment. By putting data governance and quality first, healthcare organizations ensure their AI delivers real value, not just costly experiments.
This foundation also provides future flexibility. With robust data governance, organizations can adapt to rapidly advancing AI capabilities and implement new technologies more quickly than those still facing basic data challenges.
For healthcare leaders making these complex decisions, understanding data readiness establishes the roadmap for strategic investments that build toward long-term success.
Find out where your organization stands on the data maturity scale.
