In 2025, artificial intelligence will continue to reshape healthcare, offering transformative solutions to long-standing challenges like workforce shortages, operational inefficiencies, and the need for better clinical outcomes. Digital transformation represents an ongoing journey for healthcare executives that will only become more complex every year. The most expensive U.S. cyberattack in healthcare occurred last year, costing more than $2.4 billion. Its effects are still being calculated. As hospitals and health systems' technological capabilities evolve, security protocols must evolve in tandem before the next attack occurs.
The key to successfully deploying AI in healthcare lies in balancing risks against long-term strategy while scoring quick wins that provide tangible returns.
Three themes have emerged in each of these areas, which can help organizations stay the course with a focused and balanced approach:
Quick ROI Wins:
1. Streamlining Documentation and Administrative Tasks
AI offers opportunities to reduce the administrative burden on clinicians and staff. Tools like automated documentation systems can record and organize clinician visit notes or summarize patient interactions in real time, allowing healthcare workers to focus more on patient care. A recent Deloitte survey indicates low-touch patient activities like documentation and administrative work hold the greatest opportunities for AI-driven time savings.
Real-time benefits: Reduced staff burnout, faster workflows, and improved documentation accuracy boost operational efficiency and job satisfaction.
2. Accelerating Clinical Decisions
Predictive and generative AI tools can analyze vast amounts of patient data in real time, helping clinicians make faster, evidence-based decisions. For example, these tools can flag potential health risks or suggest optimal treatment paths, empowering providers to deliver higher-quality care.
Real-time benefits: Optimize resource allocation and improve patient outcomes in acute care settings.
3. Improving Patient Access and Scheduling
AI-powered scheduling systems can dynamically adjust appointments based on patient needs and provider availability. These tools optimize clinic schedules, reduce no-shows, and shorten patient wait times.
Real-time benefits: Enhanced patient satisfaction and increased throughput.
Long-Term AI Strategies: Moving Beyond the Basics
1. A Shift Toward Proactive Denial Management
Traditionally, denial management has focused on back-end processes — fixing errors after claims are rejected. However, healthcare leaders should invest in AI tools that address issues earlier in the revenue cycle. For example, AI can flag missing or inaccurate data at the point of documentation or during claim submission, preventing high-value denials before they occur. Focus on revenue integrity earlier in the process can also leverage predictive analytics for internal alignment among health systems’ departments, as well as between payers and providers, to ensure more accurate reimbursement.
Real-time benefits: Faster reimbursement cycles, reduced revenue leakage, and fewer costly appeals.
2. Leveraging Generative AI for Data Interoperability
One of healthcare’s greatest challenges is siloed data. Generative AI can extract and organize data from unstructured sources, creating more complete longitudinal patient records. This not only supports better clinical decision-making but also strengthens compliance with interoperability standards.
Real-time benefits: Faster access to critical patient information, improved care coordination, and enhanced compliance.
3. Empowering Employees with AI Insights
AI doesn’t just streamline processes — it also prompts healthcare leaders to ask better questions to identify root causes of systemic operational challenges. For example, AI can analyze staff workflows to identify inefficiencies or predict staffing needs, allowing leaders to allocate resources more effectively.
Real-time benefits: Better workforce planning to reduce operational bottlenecks.
Balancing Risk and Reward
As AI adoption accelerates, healthcare leaders must navigate its risks — especially in critical areas like patient safety and data privacy. While some processes, such as clinical decision support, should continue to employ human-in-the-loop development practices and human oversight, others, like claims processing or appointment scheduling, can be safely automated.
Emerging technologies like Retrieval-Augmented Generation (RAG) are reducing risks by ensuring AI outputs are accurate and grounded in reliable data. Regardless of how the regulatory environment may evolve at the federal level, forward-thinking healthcare executives can continue to support responsible AI use by:
- Investing in scalable technologies that address immediate pain points while building toward long-term goals.
- Partnering with trusted AI vendors who align with their organization’s values and regulatory requirements.
- Focusing on measurable ROI by implementing tools that deliver real-time benefits, such as faster workflows and reduced costs that ultimately benefit the patient experience.
Armed with these core areas of focus, healthcare organizations of any size can implement AI in innovative ways – delivering short-term wins and long-term transformation – while navigating its risks in healthcare for a balanced approach to drive meaningful change.