A one‐stop shop model for improved efficiency of pre‐exposure prophylaxis delivery in public clinics in western Kenya: a mixed methods implementation science study

INTRODUCTION: In public clinics in Kenya, separate, sequential delivery of the component services of pre‐exposure prophylaxis (PrEP) (e.g. HIV testing, counselling, and dispensing) creates long wait times that hinder clients’ ability and desire to access and continue PrEP. We conducted a mixed methods study in four public clinics in western Kenya to identify strategies for operationalizing a one‐stop shop (OSS) model and evaluate whether this model could improve client wait time and care acceptability among clients and providers without negatively impacting uptake or continuation. METHODS: From January 2020 through November 2020, we collected and analysed 47 time‐and‐motion observations using Mann–Whitney U tests, 29 provider and client interviews, 68 technical assistance reports, and clinic flow maps from intervention clinics. We used controlled interrupted time series (cITS) to compare trends in PrEP initiation and on‐time returns from a 12‐month pre‐intervention period (January–December 2019) to an 8‐month post‐period (January–November 2020, excluding a 3‐month COVID‐19 wash‐out period) at intervention and control clinics. RESULTS: From the pre‐ to post‐period, median client wait time at intervention clinics dropped significantly from 31 to 6 minutes (p = 0.02), while median provider contact time remained around 23 minutes (p = 0.4). Intervention clinics achieved efficiency gains by moving PrEP delivery to lower volume departments, moving steps closer together (e.g. relocating supplies; cross‐training and task‐shifting), and differentiating clients based on the subset of services needed. Clients and providers found the OSS model highly acceptable and additionally identified increased privacy, reduced stigma, and higher quality client–provider interactions as benefits of the model. From the pre‐ to post‐period, average monthly initiations at intervention and control clinics increased by 6 and 2.3, respectively, and percent of expected follow‐up visits occurring on time decreased by 18% and 26%, respectively; cITS analysis of PrEP initiations (n = 1227) and follow‐up visits (n = 2696) revealed no significant difference between intervention and control clinics in terms of trends in PrEP initiation and on‐time returns (all p>0.05). CONCLUSIONS: An OSS model significantly improved client wait time and care acceptability without negatively impacting initiations or continuations, thus highlighting opportunities to improve the efficiency of PrEP delivery efficiency and client‐centredness.