Healthcare Chief Information Officers (CIOs) are navigating a moment of unprecedented complexity. The rapid expansion of digital health tools, electronic health records, connected devices, and patient-generated data has created a data ecosystem of enormous scale and fragmentation. At the same time, CIOs are under mounting pressure to improve patient outcomes, enhance the patient experience, and reduce caregiver burnout, often with limited resources and aging infrastructure.
The sheer volume of data is staggering. A single patient generates an estimated eighty megabytes of data annually, while a single hospital can produce roughly one hundred thirty-seven terabytes of data every day. Complicating matters further, approximately eighty percent of healthcare data is unstructured, locked away in formats such as clinical notes, images, and patient messages. This data is rich with insight, yet difficult to access, analyze, and operationalize at scale.
Against this backdrop, healthcare leaders are increasingly looking to advanced analytics, generative AI, and predictive technologies to turn data into meaningful action. Organizations like Onix are positioning augmented intelligence as a way forward, helping CIOs modernize infrastructure, unlock clinical insight, and deliver measurable improvements without adding administrative burden.
Turning Clinical Data Into Actionable Insight
For healthcare organizations, the quality and accessibility of clinical data directly influence patient outcomes. Yet clinicians often struggle to retrieve relevant information quickly within electronic health record systems.
“To deliver the best patient outcomes, providers depend on the quality and accessibility of clinical data,” says Ron Rerko, Practice Director – Healthcare & Life Sciences at Onix. He explains that Onix develops innovative applications in collaboration with Google and clients. These tools are designed to reduce friction in clinical workflows while surfacing critical information at the point of care to support providers
One example is Search and Summarization which Onix built in collaboration with Google that integrates directly into the MEDITECH Expanse EHR system. Onix’s Search & Summarization application, which enables clinicians to quickly locate and synthesize patient information across large, unstructured data sets. Rather than scrolling through lengthy records, providers receive concise, relevant summaries that support faster and more informed decision-making.
What “Enterprise Grade AI” Means in Practice
Enterprise-grade AI is often used as a marketing phrase, but in healthcare, it carries specific and important operational and regulatory implications. According to Jay Grewal, Vice President of Sales, Strategic Accounts, Health Care & Life Sciences at Onix, the foundation matters as much as the intelligence layer itself.
“Onix builds its applications on the Google Cloud Platform, a global infrastructure renowned for its worldwide availability, immense scalability, and robust data security,” Grewal says. Google Cloud’s adherence to HIPAA compliance standards ensures that patient data is handled securely, providing healthcare organizations with a reliable foundation for AI-powered solutions.
By combining cloud modernization with proprietary intellectual property, Onix positions AI as something that can scale safely across the enterprise—not as a one-off pilot, but as a sustainable operational capability.
Measurable Results From Pre-Trained AI Models
Healthcare CIOs are increasingly asked to justify technology investments with clear, quantifiable outcomes. Pre-trained AI models deployed by Onix support a range of use cases, including medical claims processing and health risk stratification.
Grewal points to tangible performance metrics from real-world deployments. “The Search and Summarization application, for instance, has shown a ninety-one percent user adoption rate and an eighty-six percent user satisfaction rating,” he says. “More importantly, it has saved providers approximately forty seconds per patient in administrative work.”
At scale, those seconds translate into significant time savings, allowing clinicians to reallocate effort from documentation to direct patient care. These efficiency gains also contribute to broader organizational goals, such as reducing patient re-admission rates by ten to twenty-five percent and lowering mortality rates by five to fifteen percent.
Reducing Burnout Through Automated Clinical Documentation
Administrative burden remains one of the most persistent contributors to clinician burnout. Studies show that physicians spend an average of four and a half hours per day on EHR documentation, often extending work well beyond scheduled clinical hours.
“Caregivers are facing a documentation crisis,” Rerko says. He explains that AI-powered tools capable of automating clinical documentation, such as generating patient chart summaries and discharge instructions, can dramatically reduce this burden.
By automating routine documentation tasks, augmented intelligence allows caregivers to focus on patient interaction rather than data entry. This shift not only improves operational efficiency but also addresses one of the root causes of burnout: the erosion of meaningful patient connection.
Driving Adoption Through User-Centered Design
Technology adoption in healthcare is notoriously challenging, particularly when tools disrupt established workflows. According to Grewal, the success of Onix’s applications is driven by a strong emphasis on usability and immediate value.
“The success of solutions like the Search and Summarization application is driven by a focus on user-centric design and a clear value proposition,” he says. High adoption and satisfaction rates reflect tools that integrate seamlessly into clinical environments rather than forcing clinicians to adapt to new systems.
By saving time and reducing cognitive load, these tools not only improve efficiency but also enhance job satisfaction, an often-overlooked factor in technology adoption.
Innovating Safely With Synthetic Data
One of the most significant barriers to AI innovation in healthcare is the risk associated with using protected health information. Training models and testing new algorithms typically require large data sets, but compliance requirements can slow innovation.
Onix addresses this challenge with its proprietary Kingfisher Synthetic Data Generator. “It uses a proprietary profiler to construct like-for-like synthetic datasets that are statistically representative of real patient data but contain no Protected Health Information,” Rerko explains.
By removing PHI from the equation, healthcare organizations can accelerate development and testing while maintaining strict privacy standards. This approach enables innovation without compromising compliance, a critical requirement for scaling AI initiatives.
Overcoming Barriers to Scalable AI Adoption
Despite growing interest, many healthcare organizations struggle to move AI projects from pilot to production. Common obstacles include poor data quality, integration challenges with legacy systems, high implementation costs, and limited in-house expertise.
Grewal says Onix helps organizations overcome these barriers by focusing on strategy first. “Onix helps organizations overcome these challenges by developing a customized roadmap with defined projects and clear goals,” he explains.
Rather than deploying technology for its own sake, the approach emphasizes solving specific operational problems. Grewal summarizes this philosophy with a guiding principle: “Fall in love with the problem, not the technology.”
The Future of Augmented Intelligence in Healthcare
Looking ahead, both Rerko and Grewal emphasize that the future of AI in healthcare is human-centered. The American Medical Association has endorsed the term “augmented intelligence” to underscore AI’s role in supporting, not replacing, clinical judgment.
“The future of AI in healthcare is human-centered,” Rerko says. Generative and predictive AI are poised to further improve operational efficiency and patient outcomes, but successful adoption requires thoughtful governance and clear use cases.
Rerko also notes that many healthcare employees are already using AI tools informally, a phenomenon often referred to as “shadow AI.” Organizations must provide guidance to protect proprietary information while channeling employee innovation productively.
A Strategic Imperative for CIOs
For healthcare CIOs, augmented intelligence is no longer a futuristic concept; it is a strategic imperative. As data volumes continue to grow and workforce pressures intensify, the ability to transform information into action will define organizational resilience.
By combining cloud modernization, enterprise-grade AI, and secure innovation practices, organizations can move beyond experimentation toward scalable, measurable impact. The challenge is not simply adopting AI, but deploying it in ways that reduce burden, enhance care, and keep humans firmly at the center of healthcare delivery.
For more information, visit onixnet.com/healthcare.
Daniel Casciato is a seasoned healthcare writer, publisher, and product reviewer with two decades of experience. He founded Healthcare Business Today to deliver timely insights on healthcare trends, technology, and innovation. His bylines have appeared in outlets such as Cleveland Clinic’s Health Essentials, MedEsthetics Magazine, EMS World, Pittsburgh Business Times, Post-Gazette, Providence Journal, Western PA Healthcare News, and he has written for clients like the American Heart Association, Google Earth, and Southwest Airlines. Through Healthcare Business Today, Daniel continues to inform and inspire professionals across the healthcare landscape.
