An Electronic Health Record–Based Automated Self-Rescheduling Tool to Improve Patient Access: Retrospective Cohort Study (Preprint)
Authors: Ganeshan Smitha , Liu Andrew W , Kroeger Anne , Anand Prerna , Seefeldt Richard , Regner Alexis , Vaughn Diana , Odisho Anobel Y , Mourad Michelle 
Published: August 2023

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 the 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 electronic health record (EHR)–based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled.

METHODS

In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer.

RESULTS

From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) 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 aged 65 years or older (vs 20-40 years; <i>P</i>=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; <i>P</i>&lt;.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (<i>P</i>&lt;.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; <i>P</i>=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million.

CONCLUSIONS

Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.