Machine learning for clinical operations improvement via case triaging

In recent years, an increasing number of machine learning (ML) models have been developed for interpreting images of skin conditions and for risk stratification. Beyond accurate image interpretation, one potential application of these interpretations may be triaging systems to help direct care to the right care provider at the right time. This is a critical need because dermatologist appointment wait times exceed a month in many regions, a trend that can potentially be alleviated by rapidly stratifying patients to clinicians with the appropriate level of training (e.g., board-certified dermatologist, advanced practice provider under dermatologist supervision, non-dermatologist) and the appropriate urgency. To help understand ML's potential for this triaging, we analysed a previously-described deep learning system (DLS) that provides a differential diagnosis of teledermatology cases and that improved the diagnostic accuracy of primary care physicians and nurse practitioners in a randomized study. We reordered the cases within each ‘review batch’ of 500 based on the urgency category of the DLS-predicted skin condition (which is an automated process requiring no human intervention). On average, this caused the review order of urgent cases to be prioritised substantially sooner than that of less urgent cases, with the average rank of ‘immediate intervention cases’ being about 100 (vs. 253 without reordering, p < 0.001), and that of ‘no need to see a doctor’ cases being close to 400 (vs. 252 without reordering, p < 0.001). Our approach has the potential to accelerate triaging and reduce the burden on the limited dermatology workforce to focus on patient management.

• Health & Bioscience