Health Care’s Data Problem: The Real Obstacle to AI Success

The health care technology landscape is abuzz with large language models (LLMs) and conversational AI applications that show remarkable capabilities in synthesizing patient information and streamlining clinical workflows. Clinicians are hopeful that these tools can reduce documentation burden and enhance decision support.However, as implementations multiply across health systems, a fundamental challenge is becoming increasingly apparent: AI-assisted documentation tools can produce impressive outputs, but these are only as reliable as the data fed into these systems. This reality is becoming a central concern for health care technology leaders.Many health care organizations are building AI initiatives on data repositories filled with inconsistencies, errors, and gaps.