The role of Artificial Intelligence (AI) in health care has rapidly become a central theme in healthcare innovation.
But what does this growing focus mean for providers, IT leaders, and patients?
The long-term success of AI in healthcare ultimately depends on one critical factor: the ability to validate and trust its reliability.
How AI Is Supporting Healthcare Delivery
EHR Healthcare providers spend at least a quarter of their time on administrative and repetitive tasks. AI can ease some of that burden, allowing clinicians to focus more on direct patient care.
Since the 1950s, AI has steadily advanced, with early healthcare applications emerging in the 1970s. Technologies such as diagnosis ranking and natural language processing (NLP) paved the way for innovations like speech recognition, text analysis and language translation (AI's Ascendance in Medicine: A Timeline, April 2023).
More recently, Generative AI has unlocked new possibilities for capturing information through means such as ambient listening, synthesizing unstructured data, analyzing findings, and summarizing insights to support research and clinical decision-making.
Today, AI algorithms are outperforming radiologists in detecting malignant tumors, guiding clinical trial cohort selection, identifying missed diagnoses, improving medical coding accuracy, and helping providers design personalized treatment plans for their patients.
The measurable benefits are real: streamlined chart reviews, reduced documentation time, optimized reimbursement, higher quality scores, and more opportunities for engaging directly with patients.
With Great Power Comes Great Responsibility
Despite these advantages, key concerns remain. Can information and documentation produced by AI be trusted?
While AI systems can be highly accurate, they are not infallible. Risks include misdiagnosis, overlooked details, and overreliance on flawed outputs – all of which can lead to serious consequences.
Generative AI also introduces the risk of “hallucination” - producing content that sounds correct but is actually inaccurate. For example, lawsuits have alleged that healthcare insurers wrongly denied claims using AI without clinician review (Court Allows Lawsuit Over AI Use in Benefit Denials to Proceed, April 2025; Health Insurers Sued Over Use of Artificial Intelligence to Deny Medical Claims, December 2023).
These risks raise crucial questions about liability, accountability, and adoption:
- Who is responsible for AI-related mistakes?
- How do we ensure AI compliments - rather than replaces - human judgment and critical thinking?
- How do we prevent negative disruption from AI use?
Validating and Establishing Accountability
To deploy AI responsibly, healthcare organizations must build safeguards grounded in validation, education, and workflow integration. Clinicians and business owners must be involved in validating outputs and training efforts to ensure accuracy and appropriate adoption.
It is critical to establish and educate workflows and policy that ensures human oversight and validation of AI generated information.
Engaging clinicians early and equipping them with training will help reinforce adoption and ensure AI is enhancing human expertise.
Recommended approaches include:
- Workflow transparency: Clearly define and document where and how AI interacts with day-to-day processes, so staff know when outputs originate from AI.
- Rigorous testing: Use structured test scripts and validation processes to verify that AI-generated outputs are accurate and contextually appropriate.
- Confidence scoring: Display confidence scores alongside outputs, helping clinicians assess reliability.
- Chart linking: Ensure AI-generated content is traceable back to the specific patient data elements that informed it.
- Human review: Make clear that final decisions rest with clinicians and business owners.
- Training and education: Engage clinicians early in AI adoption, equipping them with the skills and awareness needed to critically validate AI outputs and understand AI limitations.
When AI is applied, even in seemingly simple functions like automated reminders, it must be validated for accuracy and understood within its workflow context.
Conclusion
AI is revolutionizing healthcare, but success requires safeguards that emphasize validation, transparency and education. To ensure this, organizations should:
- Track systems that are utilizing AI
- Document workflows - indicating where AI is integrated
- Conduct structured testing of AI with test scripts
- Provide comprehensive end-user training
With these practices organizations can rely on AI to enhance – not endanger - healthcare delivery.
How MAKE Solutions Can Help
MAKE Solution’s TransIT tool supports organizations in implementing AI responsibly. TransIT enables teams to:
- Track and maintain an inventory Plan of systems with AI capabilities
- Create and maintain Workflows outlining AI integration in daily operations
- Develop structured, repeatable Test Scripts for ongoing validation and user training
By combining visibility, accountability, and repeatable testing, TransIT helps ensure AI enhances care delivery safely and effectively. Visit makesolutionsinc.com to learn more.
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