Rapid growth of medical artificial intelligence technology usage identified from insurance claims analysis, yet major barriers to widespread adoption remain

1. The adoption of medical artificial intelligence (AI) technologies has increased exponentially over the past few years, both in terms of total number of claims and the development of new current procedural terminology (CPT) medical AI procedures.

2. Medical AI technology usage was shown to be largely restricted to metropolitan areas and/or those with an academic hospital and is enriched in areas with higher incomes.

Evidence Rating Level: 2 (Good)

Study Rundown: Advancements in medical AI technologies have increased rapidly, especially with the development of new large language models that provide generative capabilities. Before approval, device makers are required to provide detailed safety and efficacy data to the Food and Drug Administration (FDA). Yet, after approval, there is scant evidence for how widely these technologies are used and what factors drive certain technologies to be adopted by specific communities.

This study aimed to characterize the adoption of medical AI devices by using a dataset of medical and pharmacy claims submitted to large commercial health insurance plans. The authors identified 16 medical AI procedures billable under a CPT code spanning a wide range of technologies in various healthcare specialties. Amongst all medical AI procedures, just a handful made up most claims data.

In addition to examining adoption metrics over time, the authors used zip codes linked to the claims dataset to determine associations between medical AI product usage and demography. Not surprisingly, there was a strong association between the presence of an academic hospital in a zip code and the odds of using a medical AI procedure. Other factors that strongly predicted medical AI usage included the median income of the zip code and whether the zip code was classified as metropolitan. These analyses suggest limitations to the extent of AI adoption in health care, perhaps primarily driven by structural and societal factors.

Click here to read the study in NEJM AI.

Relevant Reading: How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals

In-Depth [cross-sectional study]: This study used a large insurance claims dataset to determine the time variability and socioeconomic factors associated with adopting medical AI technologies. The authors examined the CPT codes from the IQVIA PharMetrics Plus for MedTech dataset consisting of approximately 11 billion claims from January 1, 2018, to June 1, 2023. The authors first examined 16 medical AI procedures based on 32 unique CPT codes and found a generally exponentially increasing utilization of those codes across the dates they surveyed.

The authors then performed a multivariate logistic regression to determine whether at least one occurrence of billing of an AI CPT code was associated with certain factors that could be extracted from the zip code. They categorized zip codes into having a high median income (>$100,000 median annual household income) vs. not high, whether the zip code was metropolitan (by the U.S. Department of Agriculture), and whether it had at least one academic hospital center (determined from the American Association of Medical Colleges). Indeed, the authors found strong statistical significance (p < 0.001) across all three predictors wherein those zip codes with an academic hospital were 17 times more likely to have had a medical AI procedure billed in the epoch they examined.

In conclusion, this study carefully evaluates the temporal changes and geographic distribution of the use of medical AI technologies and highlights demographic barriers to more widespread adoption.

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