Schedule Utilization: Patient-Centered Access Metric? PT 2

Schedule Density: A Better Way to Measure Good Access

Last week, we determined that good patient access must incorporate the ease of delivering it – a frictionless process, from the patient’s perspective. We also delved into schedule utilization, which may fall short as a value-added metric unless template integrity exists.

Once the hard work of defining clinical expectations, building the schedules, and securing them has been solidified, it then becomes possible to add features to schedule utilization to transform it from an operational metric to one that incorporates the patients’ experience. Combined, these measures offer valuable insight into patient access.

Schedule utilization as a static metric doesn’t provide insight about the patients’ experience in their journey – the key is to add a dynamic component. When the slots are filled is more critical than if the slots were filled. From the patient’s perspective, it matters not if Dr. Smith fills 100% or 50% of their schedule. The key is whether the patient was given the right care, at the right time, with the right provider, at the right place – in the right way. If a provider fills their schedule 100% and regularly needs to schedule three-month follow-ups, but is booked six months out, then the system is providing bad patient access by anyone’s definition – the health system and the patient.

To increase the value of the metric, add a time-based element by creating a sister “schedule density” metric that is reported in conjunction with the overall utilization. The schedule density score will capture the percentage of the schedule that is filled based on a forward-looking timeline. This can be particularly useful for measuring the success of follow-up windows or urgent appointment availability. Schedule density scores are similar to the new patient appointment lag inside a certain window (usually 14 days), but instead of measuring how many patients are scheduled in that time period, the density score measures the proportion of the schedule that was filled within that window. This metric can be designed for any patient population, new or established, making it more flexible and customizable to the practice needs. For example:

  • The Transplant team knows that their patient panel consists of 60% greater than 1-yearpost transplant patients (long-term) and 40% less than 1-year post transplant patients (short-term). The long-term patients are seen annually, and the short-term patients areseen every three months unless complications arise.
  • Based on historical data, the Transplant team estimates 10% of the schedule needs to be open for the next 10-day appointments to manage short-term patients with complications.This 10%-next-10-day access will be the Transplant team’s Schedule Density target. The calculation in this example would simply be the percentage of the scheduled appointments that were booked within 10 days of the visit.
  • The Transplant team’s Schedule Utilization and Density score might be reported as such:

    In this example, the data suggests that the Transplant program is providing good patient access. Even though more patients (12% inside 10 days) required complication visits than predicted, the Transplant team was able to accommodate the patients based on the 97% schedule utilization (if needed, the team could have converted more slots to urgent based on the less than 100% utilization). However, if this trend continues, then rebalancing the schedule might be necessary.

Schedule density targets such as this example require individualized goals – each specialty will have a different need for appointments at different intervals. The target window and percentage for an orthopedics practice might be different than a neurology practice, for example.

In the example above, schedule utilization, when paired with schedule density measurement, informs the system on the right time portion of the good access definition.

There are many ways to add a time-based component to schedule utilization. Many systems already report appointment lag, for example. Combining a dynamic element to measure how the schedule fills increases the educational value of the metric and gives the health system a better chance of improving patient access.

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