The Costs of Operating a Student-run Free Clinic.

INTRODUCTION: Free clinics provide care for those who may otherwise not have access. While this care is often free for patients, it is not free to operate such clinics. This review will provide a budget and breakdown of all expenditures at a student-run free clinic along with average costs of services provided to patients. METHODS: Accounting data was used to categorize all expenses and generate an annual budget. An inventory tracking system was developed to measure the costs of all medical supplies and services accurately, providing information on costs per clinic and costs per patient for each provided service. RESULTS: The average cost per clinic was $53.55 (per patient: $2.14) for general clinic supplies, $43.74 (per patient: $7.29) for telehealth, $278.47 (per patient: $12.66) for laboratory services, $247.25 (per patient: $10.75) for pharmacy services, and $8.30 (per patient: $1.19) for social work. These costs contributed to a relative minority (< 33%) of the total costs to run a free clinic, where the highest costs were for volunteer appreciation and administrative overhead. Twelve categories of expenditures (administrative overhead, volunteer appreciation, medical and lab supplies, conferences and special projects, advertising and marketing, telehealth, pharmacy, specialty clinics, chronic care, patient transportation, social work, and accounting services) were ranked in order of necessity, and methods for cost reduction were discussed for each category. CONCLUSIONS: Categorizing costs can show where cost savings and cost-effective additions may be implemented. This study may serve as a financial and budgeting reference for other clinics.

Factors that Affect Patient Wait Times at a Free Clinic.

Free clinics may present long wait times. A retrospective chart review was conducted at a free clinic to understand contributing factors. Three wait times (total visit time, lobby wait time, and triage time) were analyzed across 349 patients. Variables included in the models were the total number of patients, providers, and volunteers; interpreter services; social work involvement; medical complexity; new vs. returning patient; scheduled vs. walk-in appointment; transportation provision; medical volunteer training level; and on-site medications and labs. Data analysis with multiple regressions was conducted. Factors that significantly affected wait times included the level of medical complexity (p<.001), medical volunteer training levels (p<.001), in-house labs (p<.001), in-house medications (p=.04), and new patients (p=.01). An intervention involving time benchmarks at the beginning of clinics reduced first-wave lobby wait times (p<.001). Future interventions addressing these factors may reduce wait times at other clinics.