JMIR Publications

ABSTRACT

Background:

In many large health centers, patients face long appointment wait times and difficulties accessing care. Last minute cancellations and patient no-shows leave unfilled slots in a clinician’s schedule, exacerbating delays in care from poor access. The mismatch between supply of outpatient appointments and patient demand has led health systems to adopt many tools and strategies to minimize appointment no-show rates and fill open slots left by patient cancellations.

Objective:

We evaluated an EHR-based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on patient access and organizational revenue.

Methods:

Fast Pass appointment offers and scheduling data, including patient demographics was extracted from the electronic health record (EHR) from 6/18/2022 - 3/9/2023. Outcomes of Fast Pass offers (accepted, declined, expired, unavailable), and outcomes of Fast Pass scheduled appointments (completed, canceled, no-show) were stratified based on appointment specialty. For each specialty, the patient service revenue from Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer.

Results:

From 6/18/2022 - 3/9/2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6,603 (11.0%) were accepted across all departments and 5,399 (8.9%) visits were completed. Patients were seen a median (SD) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were 65+ of age (vs 20-40), other ethnicity (vs White), primarily Chinese speakers and other language speakers (vs English speaking) were less likely to accept an offer. Fast Pass added 2,576 patient service hours to the clinical schedule, with a median (IQR) of 251 hours (216 - 322) per month. The estimated dollar value of physician fees from these visits scheduled through nine months of Fast Pass scheduling in professional fees at our institution was $2,931,968.

Conclusions:

Self-scheduling tools that provide patients an opportunity to schedule into canceled appointments have the potential to both improve patient access and efficiently capture lost revenue from visit cancelations. The demographics of the patients accepting these offers suggests that such digital tools may exacerbate inequities in access.