iRefer: A Novel Application for Making Referrals at a Student-Run Free Clinic
Abstract
Background: Referring patients to specialty care providers is an important function of a student-run free clinic. Selecting the appropriate referral site is a complex problem, as the criteria for eligibility are often very specific and frequently change. Mobile Clinic Project, a free clinic at University of California, Los Angeles, previously managed referral decisions by training a “referrals committee.†This committee maintained paper documentation on 140+ referral sites that was used to make on-site referral decisions. This approach was time consuming and subject to error. Our team aimed to build a web app to streamline the process of searching for the optimal referral site.
Methods: A web app was created, and pilot testing was conducted to determine its efficacy in the hands of referrals committee members versus untrained clinic volunteers. After pilot testing, the app was released for use in the clinic, and a Likert survey was done at three months to assess its utility and usability.
Results: The app, iRefer, allows users to electronically search information about referral sites and provides recommendations for the most appropriate sites based on patient information. Patient feedback about their visits to off-site providers can be incorporated into iRefer so that well-reviewed sites will be more highly recommended in future patient encounters. Additional features include the ability to print directions to the site directly from a volunteer’s phone and to track the number of times a particular referral site is recommended. Pilot testing of the app demonstrated that it allows users to make appropriate referral decisions with little training time required. Users viewed iRefer as an effective tool to improve the quality and efficiency of referrals at Mobile Clinic.
Conclusions: A web app may be a technology-enabled tool for facilitating referrals at a student-run free clinic.
Copyright (c) 2017 Alexander N Goel, Joaquin Michel, Shyama Sathianathan, Neveen Youssef
This work is licensed under a Creative Commons Attribution 4.0 International License.