Most studies involving imaging AI and patient race/ethnicity warn that AI might exacerbate healthcare inequalities, but a new JACR study outlines one way that imaging AI could actually improve care for typically underserved patients.
The AI vs. EHR Disparity Problem – The researchers used a deep learning model to detect atherosclerotic disease in CXRs from two cohorts of COVID-positive patients: 814 patients from a suburban ambulatory center (largely White, higher-income) and 485 patients admitted at an inner-city hospital (largely minority, lower-income).
When they validated the AI predictions versus the patients’ EHR codes they found that:
- The AI predictions were far more likely to match the suburban patients’ EHR codes than the inner-city patients’ EHR codes (0.85 vs. 0.69 AUCs)
- AI/EHR discrepancies were far more common among patients who were Black or Hispanic, prefer a non-English language, and live in disadvantaged zip codes
The Imaging AI Solution – This study suggests healthcare systems could use imaging AI-based biomarkers and EHR data to flag patients that might have undiagnosed conditions, allowing them to get these patients into care and identify/address larger systemic barriers.
The Value-Based Care Justification – In addition to healthcare ethics reasons, the authors noted that imaging/EHR discrepancy detection could become increasingly financially important as we transition to more value-based models. AI/EHR analytics like this could be used to ensure at-risk patients are cared for as early as possible, healthcare disparities are addressed, and value-based metrics are achieved.
The Takeaway – Over the last year we’ve seen population health incidental detection emerge as one of the most exciting imaging AI use cases, while racial/demographic bias emerged as one of imaging AI’s most troubling challenges. This study managed to combine these two topics to potentially create a new way to address barriers to care, while giving health systems another tool to ensure they’re delivering value-based care.