Article
The AI remedy: Curing healthcare's biggest challenges
Innovember: Unpacking the role of AI in shaping the future of work
Nov. 10, 2025 · Authored by Steve Salata
The healthcare industry stands at the inflection point. For decades, healthcare has been characterized by slow-moving adoption of technology, with administrative burdens that have contributed to physician burnout and early retirement. But today, artificial intelligence (AI), particularly generative AI and AI agents, is set to fundamentally improve how care is delivered, how clinicians work, how back-office tasks are handled and how patients experience the healthcare system. AI is poised to address healthcare’s most persistent challenges in ways that were unimaginable just a few years ago. The benefits mostly span two areas: clinical excellence and operational efficiency.
Why now? Many factors make this moment uniquely positioned for AI transformation in healthcare. Advances in large language models, retrieval-augmented generation and cloud computing have created the technical foundation for AI to handle the complexity of medical data, understand context and seamlessly integrate into key healthcare systems and processes. At the same time, urgent workforce challenges demand action. Despite improvements since the pandemic, nearly half of physicians still report symptoms of burnout, driven by volumes of administrative work that consume hours of their time daily. The competitive landscape adds further urgency, with 65% of U.S. healthcare organizations recognizing AI’s profound impact on their operations.
Where is AI already making an impact?
AI is actively delivering results across multiple use cases:
Clinical documentation
AI-powered voice-enabled solutions are streamlining clinical workflows by generating highly accurate draft notes in multiple languages within minutes. These systems listen to patient-physician interactions and automatically create summaries of what was discussed and decided, updating health records in real time.
Appointment and administrative automation
AI agents are being embedded directly into workflows for patient registration, pre-appointment briefings and follow-up management. They can automate the filling out of lengthy, repetitive forms and provide physicians with patient briefing, including medical history, recent test results and visit reasons.
Diagnostic support
Advanced AI agents can analyze medical imaging, laboratory results and patient histories to support diagnostic decisions. By comparing individual patient data against vast repositories of clinical information, these systems help identify patterns and potential treatment options that might be missed otherwise.
Patient engagement:
AI-powered digital assistants are helping patients navigate the complex healthcare system, answer questions about their care and manage their health more effectively. These tools provide 24/7 support and guidance, improving patient experiences and outcomes.
Predictive analytics
Healthcare organizations are using AI to identify patients at risk for readmission, predict potential complications and optimize resource allocation. This proactive approach helps prevent adverse events and improve overall care quality.
Revenue cycle management
AI is transforming billing and coding by automating claims processing, identifying errors before submission and flagging potential denials. This reduces the administrative burden and accelerates reimbursement cycles.
Supply chain optimization
AI algorithms forecast demand, manage inventory and optimize procurement strategies. This ensures critical supplies are available when needed while minimizing waste and overstock.
Workforce management
AI tools can help human resources and operations teams forecast staffing needs and schedule shifts efficiently. They also assist in identifying burnout risks and improving employee retention.
What are the challenges of adopting AI in healthcare?
While AI’s potential is immense, organizations must navigate challenges to reap the benefits:
Security: Healthcare data is among the most sensitive information that exists, making security paramount. AI systems require access to vast amounts of patient data, including electronic health records, lab results and diagnostic images. Healthcare providers or organizations should ensure:
- Robust data encryption and access controls
- Secure cloud infrastructure that meets industry standards
- Protection against data breaches that could expose patient information
- Compliance with data residency requirements and cross-border data transfer regulations
- Safe integration points between AI systems and existing IT infrastructure
Risks: Beyond security, several operational and clinical risks should be addressed:
- AI systems should have consistently high accuracy in clinical decision support, as errors could have life-threatening consequences
- Integrating AI agents into complex healthcare technology ecosystems without disrupting workflows
- Risk that clinicians might become overly dependent on AI, potentially leading to reduced critical thinking
- Unclear frameworks for liability when AI contributes to clinical decisions
Ethics, oversight and governance: One of the challenges facing healthcare AI adoption relates to ethical considerations and governance:
- AI systems trained on historical data may perpetuate existing biases, disadvantaging certain patient populations
- Patients have the right to understand when AI is used in their care and how it influences treatment decisions
- AI must augment rather than replace human judgment and the physician-patient relationship
- Clear policies needed for how patient data is used to train and improve AI systems while ensuring privacy protection
- Governance structures required for clinical validation, performance monitoring and problem resolution
How should healthcare organizations prepare for the future with AI?
Healthcare organizations that want to succeed with AI can take several preparatory steps:
Assess current state
Conduct a thorough assessment of existing technology infrastructure and data quality. Identify gaps that that are supposed to be addressed before AI implementation can succeed.
Build foundational capabilities
Ensure basic prerequisites are in place, including data governance, cloud infrastructure and interoperable systems. These foundations are essential for AI success.
Identify high-impact use cases
Focus initial implementations on use cases that deliver clear value. Clinical documentation automation, for example, offers immediate benefits with relatively lower risk.
Invest in change management
Technology alone doesn't drive transformation. Invest in training, communication and change management to help staff adapt to new AI-powered workflows. The entire workforce should lean into the transformation.
Develop governance frameworks
Establish the ethical guidelines and oversight mechanisms needed to deploy AI responsibly. These frameworks should balance innovation with safeguards.
Foster a learning culture
AI systems improve over time through learning and feedback. Organizations can create processes for refinement of AI implementations.
Build partnerships
Few healthcare organizations possess all the expertise needed for AI adoption internally. Strategic partnerships with technology vendors and consulting firms (like Oracle and Baker Tilly) can accelerate capabilities development.
How can Baker Tilly help?
AI transformation in healthcare requires deep healthcare industry expertise combined with deep technical expertise . As a premier Oracle PartnerNetwork member, Baker Tilly brings together both elements through its strategic alliance with Oracle. With proven healthcare implementations across integrated health systems, academic medical centers, not-for-profit healthcare organizations and many more organizational structures since 2010, Baker Tilly's Oracle practice understands the complexities and compliance requirements of the industry.
See what’s possible with AI in healthcare!
Innovember: Unpacking the role of AI in shaping the future of work
The Innovember series demystifies artificial intelligence (AI) and provides actionable insights on how you can harness its potential responsibly and strategically. Throughout November, we'll discuss AI's impact on business strategy, dive deep into industry-specific applications and provide guidance for building an AI road map and its implementation. Stay tuned for our next topic: The AI advantage in HCM.
