Digital Transformation and AI in Health Care
Health care organizations are rapidly investing in digital transformation to modernize operations and improve service delivery. Industry data indicates that 40% of organizations are allocating $50 million to $100 million annually to digital technologies, while 66% report actively deploying digital solutions. These investments aim to enhance operational efficiency, patient services, and streamline administrative processes.
AI has become increasingly integrated into health care delivery. It supports applications such as predictive analytics and clinical decision support. Predictive AI uses machine learning to estimate outcomes like readmission risks, early disease indicators, and treatment recommendations. Despite the interest in advanced technologies, a HIMSS Market Insights survey reveals only 18% of health care organizations feel prepared for AI implementation. This readiness gap highlights the challenge of transitioning from experimentation to dependable enterprise use, requiring infrastructure, governance, and operational alignment.
Kevin Riley, CEO and co-founder of actAVA
“We believe the future of enterprise AI won’t be defined by a single model rented by everyone, but by thousands of specialized systems owned, shaped and continuously refined by the enterprises that rely on them.”
Facing these issues, actAVA, an AI lifecycle management platform, aims to make AI reliable and adaptable in health care settings. actAVA introduced Cura, a model built for agentic health care. Cura helps enterprises transform institutional knowledge into intelligence they fully control, moving away from generalized AI to domain-native systems. The goal is to support clinician-grade communication, expert reasoning, and reliable execution.
AI’s role in enterprise health care is evolving, focusing not only on larger models but also on enhancing how intelligence is deployed, monitored, and improved. Health care workflows demand accuracy and accountability, raising challenges beyond model performance. Administrative and clinical operations involve policy concerns, multiple systems, and specialized roles.
The shift from developing AI capabilities to engineering dependable AI systems requires orchestrating, evaluating, and continuously improving AI. Health care organizations need infrastructure that connects models, workflows, data, and governance processes. Agentic AI systems can perform multi-step tasks and introduce automation opportunities but require safeguards to operate effectively within boundaries.
actAVA focuses on managing AI agents throughout their lifecycle, developing specialized language models for common healthcare workflows. Ownership is an essential theme, with organizations controlling their workflows, models, and knowledge assets. This may lead to a “90/10” shift, where 90% of AI workloads run on commodity models for repeatable workflows, while 10% rely on frontier models for complex use cases.
Frank Wang, CTO and co-founder
“Building a compelling demonstration of AI can happen quickly, but creating systems that perform consistently across thousands of real-world scenarios requires a different discipline.”
The importance of evaluating AI systems gains focus as researchers test AI agents in realistic health care settings. actAVA’s collaboration with healthcare professionals and academic partners developed χ-Bench, a benchmark evaluating AI agents in complex workflows. The study focused on enterprise operations like multi-step processes and interactions with simulated health care applications. It found varying degrees of success, providing insights into where AI performs effectively and where additional engineering is required.
For health care organizations exploring AI adoption, building a foundation linking technological capability with operational responsibility is crucial. As AI systems integrate further into health care environments, evaluating performance and managing risks becomes vital alongside technological advancements. The progress in health care AI will depend on combining models with governance, evaluation frameworks, and operational readiness, measuring systems by their sophistication and ability to support reliable and practical use.
Overall, health care organizations must address AI readiness, focusing on creating reliable systems that align with their complex environments and supporting responsible deployment.