ArticleJAMA dermatology2020
Evaluation of Point-of-Care Decision Support for Adult Acne Treatment by Primary Care Clinicians.
Article in JAMA dermatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Preferences for Personalized Text Message Appointment Reminders Among Outpatients in a Universal Health System: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Single-cell transcriptomics and spatial validation reveal dysfunction of keratinocytes and NK cells driving acne development.Scientific reports · 2025Article
- Prescribing Trends for Acne Vulgaris Visits in the United States.Antibiotics (Basel, Switzerland) · 2023Article
- Assessing the impact of a gynaecomastia assessment and treatment infographic in primary care in Greater Manchester.Annals of the Royal College of Surgeons of England · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Importance: Acne is a common reason for referral to dermatologists from primary care clinicians. We previously modeled the impact of algorithm-based acne care in reducing dermatology referrals, missed appointments, and treatment delays. Objective: To prospectively evaluate the downstream outcomes following a real-time, algorithm-based electronic decision-support tool on the treatment of patients referred for acne. Design, Setting, and Participants: This prospective cohort study included 260 treatment-naive patients referred to a dermatologist for the chief concern of acne, as well as the referring primary care clinicians, at 33 primary care sites affiliated with Brigham and Women's Hospital from March 2017 to March 2018. Interventions: We developed and implemented a decision-support tool into the electronic medical record system at an academic medical center. The algorithm identified patients referred to a dermatologist who had not previously been treated for acne and offered guideline-based recommendations for treatment via a real-time notification. Main Outcomes and Measures: Treatment modification by referring clinicians. Results: Of 260 patients referred for acne, 209 (80.4%) were women, 146 (56.1%) were non-Hispanic white, and 236 (90.8%) listed English as the preferred language. Patients had a median (quartile 1-quartile 3) age of 28.8 years (24.4-35.1 years) and 185 of 260 had private insurance (71.1%). In total, the algorithm was associated with cancellation of the initial referral in 35 of 260 (13.5%) instances and treatment initiation by the referring clinician in 51 of 260 (19.6%) instances. Conclusions and Relevance: This decision-support algorithm was associated with a modest reduction in rates of acne-related referrals to dermatologists, and an increased likelihood of treatment initiation by the referring clinician.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.