By Jaimes Blunt

Healthcare leaders have high expectations that AI will pay off in improved patient outcomes and customer experiences relatively soon – even as most respondents to a recent survey on AI in healthcare say their existing AI tools are flawed.

The survey, conducted online in late 2024 by emtelligent and including feedback from 250 healthcare leaders using or considering using AI tools to extract insights from unstructured clinical data, shows that nearly half (45%) of healthcare organizations have been using AI or natural language processing (NLP) technologies for three or more years. However, fewer than four in 10 respondents (38%) said they currently have a “perfect” solution in place for extracting insights from unstructured data (such as clinician notes, PDFs, and faxes), which comprises 80% of all electronic health records (EHR) data. This suggests general dissatisfaction with existing tools for extracting unstructured data.
Nonetheless, nearly all respondents (95%) said they expect to see “measurable outcomes or improvements from AI or NLP implementations,” while slightly more than half (52%) anticipate a similar return on investment (ROI) in less than a year.

Such widespread optimism reflects the justifiable belief that AI can have a transformative effect across the healthcare continuum based on the technology’s ability to convert unstructured data into actionable clinical insights, medical research breakthroughs, and greater operational efficiency. In particular, respondents see significant opportunities in using unstructured data for predictive modeling and to drive innovation.

Survey respondents who are familiar with or already deploying AI to extract unstructured data cited multiple top use cases, depending on their organizations’ role in the healthcare ecosystem. For health systems, patient summaries (79%), care management (64%), quality improvement (64%) and data analysis (64%) are top priorities. Other top use cases include:

• Data abstraction (59%)
• Thorough clinical data access (54%)
• Complex data analytics (49%)

Organizational barriers and the path to AI maturity
The barriers to implementing AI solutions most frequently mentioned by survey respondents are data privacy and security concerns (39%) in addition to skepticism regarding outcomes and value (33%). The latter percentage indicates AI champions have their work cut out for them in selling internal decision-makers on how to leverage AI and unstructured data to reduce operating costs, improve care delivery, and drive new innovation.

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