LAUSR

INTRODUCTION AND OBJECTIVE: Referring patients to specialty care is an inefficient and error prone process. Gaps in the referral process lead to longer patient wait times, increased patient anxiety, worse health outcomes, and rising healthcare costs. We developed and implemented referral automation software to improve the efficiency of the referrals process using a combination of novel and off-the-shelf technologies. METHODS: Referrals Automation is an application designed by the Center for Digital Health Innovation at UCSF that automates several manual steps in referral processing. The application automates the receipt and digitization of faxed referrals, parses data elements and creates structured data in the electronic health record (EHR). Referrals are organized into a single window for final human review. The program was first deployed at our institution in July 2018 and expanded to the Urology practice in July 2019. The pre-intervention group data were obtained before April 2019 and the post-intervention group data were obtained from July to October 2019. Data were extracted from the cloud-based Referrals Automation software and from the EHR. RESULTS: Overall, there were 12,630 referrals processed in the pre-launch period (start to end) and 1,505 in the post-launch period. After implementation, patients were called sooner (3.9 days vs. 11.1 days, p< 0.01), appointments were scheduled sooner (6.3 days vs. 12.3 days, p< 0.01), and patients saw the urologist sooner (25.0 days vs. 36.6 days, p< 0.01). After implementing the Referrals Automation application, we observed a 2-5% improvement on our ability to meet the milestones set forth by the organization across all referral process steps. CONCLUSIONS: Automated referral processing software can improve the efficiency of the specialist referral process, leading to quicker appointment scheduling, earlier appointment dates, and improved access to urologic care.