Accelerating Digital Transformation in Health Care Through AI Integration

Health care organizations are significantly increasing investments in digital transformation to modernize their operations and enhance service delivery. Data shows that 40 percent of these organizations are investing $50 million to $100 million annually in digital technologies. Additionally, 66 percent of them report actively deploying new digital solutions. These investments aim to improve operational efficiency, enhance patient services, and streamline administrative processes.

Artificial intelligence (AI) has become integral to health care delivery, supporting applications like predictive analytics and clinical decision support. Predictive AI uses machine learning to predict outcomes such as readmission risks, early disease indicators, and treatment suggestions. However, while digital and AI adoption is growing, a gap in readiness persists. A survey by HIMSS Market Insights reveals only 18 percent of health care organizations feel prepared for AI implementation. This gap highlights the challenge of moving from experimentation to dependable enterprise use, focusing on infrastructure, governance, and operational alignment.

actAVA, a health care-native AI lifecycle management platform, addresses this challenge by creating systems that make AI reliable and adaptable in complex health care settings. Introduced by actAVA, Cura is a one‑trillion‑parameter model built specifically for agentic health care. It enables organizations to transform their institutional knowledge into intelligence they own, moving away from renting generalized AI. Cura provides clinician-grade communication and expert clinical reasoning, enhancing execution across complex workflows.

Kevin Riley, CEO of actAVA, emphasizes that the future of enterprise AI lies in specialized systems owned and refined by the organizations using them. This approach is critical in health care, where reliability affects people and processes. Organizations must focus on how AI is deployed and monitored within operational environments. The discussion around enterprise AI often centers on large models, but health care workflows present unique challenges. These operations involve policies, multiple systems, and decisions requiring accuracy and accountability.

Frank Wang, CTO of actAVA, describes this shift as moving from AI capability development to engineering dependable AI systems. While creating AI demonstrations is quick, building systems to perform consistently across scenarios requires careful orchestration and evaluation. Health care AI systems must connect models, workflows, data, and governance processes. Agentic AI systems automate tasks by coordinating tools and actions but need safeguards to operate within boundaries.

actAVA develops systems to manage AI agents, from creation to evaluation and governance. This includes tools for agents to follow policies and operate safely. A key focus is on ownership, where health care organizations maintain control over workflows, models, and knowledge assets. Regulated industries tend to have predictable patterns, potentially shifting 90 percent of AI workloads to commodity models and reserving 10 percent for frontier models for complex use cases.

Weiran Yao, CAIO of actAVA, believes this shift changes how organizations build with AI. AI offers opportunities to integrate organizational knowledge into active capabilities while meeting governance needs. Evaluation of AI systems in health care is increasingly important. actAVA collaborated with health care professionals and academics to create χ-Bench, a benchmark for evaluating AI agents in complex workflows. The benchmark assessed tasks across health care operations.

χ-Bench was designed to reflect enterprise conditions, including multi-step processes and policy requirements. The study showed the strongest agent framework resolved 28 percent of tasks initially, but performance decreased with repeated attempts. These findings underscore the importance of deployment infrastructure, as health care workflows demand systems interpret policies and execute actions within operational boundaries.

Yao notes that benchmarks like χ-Bench help evaluate AI systems against practical requirements. Testing long-horizon workflows aids understanding of where AI agents succeed and where additional engineering is needed. For health care organizations, the future of AI depends on connecting technological capabilities with operational responsibilities. As AI becomes embedded in health care, managing risks and adapting workflows become as crucial as the models themselves. Progress will be measured by how AI supports reliable and practical use across complex environments.