Access to dermatologists remains a nationwide challenge. Optimizing referrals to a dermatologist may reduce patient wait times.
To model the effect of algorithm-based acne treatment by primary care clinicians on referral patterns and costs.
Design, Setting, and Participants
Overall, 253 referrals from primary care clinicians to dermatologists for acne from January 2014 through March 2015 were reviewed at Brigham and Women’s Hospital. No-show rate, diagnostic concordance between primary care clinicians and dermatologists, treatment at the time of referral, and treatment by a dermatologist were ascertained, and we modeled 2 treatment algorithms—initiation of topical treatments by primary care clinicians (algorithm A) and initiation of topical treatments and oral antibiotics by primary care clinicians (algorithm B)—to identify the most effective referral patterns and costs.
Main Outcomes and Measures
The primary outcome was the elimination of unnecessary appointments with a dermatologist. Secondary outcomes included reduction in delay to treatment, health care cost savings, and decrease in no-show rate.
Overall, 150 of 253 referred patients were seen and treated by a dermatologist; 127 patients (50.2%) were not on prescription acne treatment at the time of dermatology referral. Model A reduced initial referrals in 72 of 150 cases (48.0%), eliminated referrals in 60 of 150 cases (40%), and reduced average delay-to-treatment by 28.6 days. This resulted in cost savings of $20.28 per patient, reduction of wait time by 5 days per patient, and decreased the no-show rate by 13%. Model B reduced initial referrals in 130 of 150 cases (86.7%), eliminated referrals in 108 of 150 cases (72%), and reduced average delay-to-treatment by 27.9 days. This resulted in cost savings of $35.68 per patient, shortened wait-time by 9 days per patient, and decreased the no-show rate by 24%.
Conclusions and Relevance
Algorithm-based treatment of acne by primary care clinicians may eliminate unnecessary appointments, reduce wait time for treatment, lower costs, and reduce patient no-shows.