Schedule-based metrics for the evaluation of clinic performance and potential recovery of cancelled appointments.

BACKGROUND

Assessment of outpatient clinic performance is important to optimize patient access. Metrics based on schedule data may assist with assessment of operational efficiency and recovering cancelled appointments.

OBJECTIVES

To define schedule-based characteristics of clinic operations and to evaluate potential for recovery of cancelled appointments.

METHODS

Sixty-seven weekly cardiology clinics from a single provider over 18 months at an academic medical center were analyzed. Parameters included clinic slots eligible to have patients scheduled (available), slots occupied by appointments (scheduled), and slots for which patients attended the associated visit (appeared). Rates of usage (scheduled/available), appearance (appeared/scheduled), and utilization (appeared/available=usage rate*appearance rate) were calculated. Surplus slots were defined as the difference between available slots and slots occupied by patients that appeared. Cancellation lag-time was defined as the interval between a cancellation and the appointment time. If a patient did not notify the clinic regarding a non-appearance, cancellation lag-time was set to zero. To quantify the impact of a change in clinic operations on efficiency, these metrics were used to evaluate a different cardiologist's clinic before and after its physical location moved.

RESULTS

For approximately 900 patient visits, usage and appearance rates were∼80%, yielding a utilization rate of ∼2/3. On average, there were nearly 8 surplus slots per clinic. Approximately 30% of cancellation lag-times had positive values and nearly half of positive cancellation lag-times were >3h, indicating potential for recovery of those appointments. The intervention analysis showed that usage rate decreased and surplus slots per clinic increased significantly after a change in clinic location.

CONCLUSIONS

Schedule-based analysis provides a framework to assess changes to clinic operations, identify mechanisms underlying inefficiency, and suggest solutions for improving clinic performance (i.e. more schedulers in response to low usage rates). Cancellation lag-time analysis suggests recovering a portion of same-day cancellations is plausible.