Unfilled Slots: Tackling Non-Arrivals - Part 1

Slots that go unfilled are arguably the greatest challenge in patient access. They represent a failure to accommodate a patient despite unrelenting demand; they also equate to a financial loss, a result no health system can afford today. Our providers’ time is our system’s most important asset – and an unfilled slot represents our failure to be good stewards of that precious resource. Considering the importance of unfilled slots, let’s approach the problem of non-arrivals with three key tactics: gather and assess business intelligence, determine prevention strategies, and manage the templates.

Gather and Assess Business Intelligence. This may seem a simple step, but the nuances of identifying booked slots that don’t arrive are often overlooked. Leaders pull data from dashboards about no-shows, cancellations, etc., not realizing that most of the decision-making for the explanation of the non-arrival is a manual one. When a patient calls to cancel, doesn't show up for an appointment, or a provider requests an appointment to be rebooked, the person who is documenting that transaction is the one who selects the reason for it, typically from a selection set that displays as a drop-down menu. These staff positions - receptionists, call center agents, secretaries - suffer from high turnover. The topic of non-arrivals is just one of a multitude of subjects featured during training; for many health systems, it's one slide during a multi-day training session.

Further, there is a selection bias. A patient who no-shows, for example, may be charged, but a cancellation doesn't generate a charge. A provider who bumps patients may be reprimanded by their leader, but a cancellation will go undetected. The consequence of the selection biases the participant in their choice of the reason for non-arrival.

Further, there are layers of complexity based on the terminology. The term - "cancellation" - couldn't that mean a "canceled" clinic (v. "bump," which is the generally accepted term for a provider-directed cancellation)? There are rarely definitions for staff to reference. And, when is a cancellation really a no-show?; for example, what is the accurate reason for non-arrival when the patient calls five minutes before their appointment starts?

The workflow surrounding non-arrivals is remarkably complicated. If the patient is calling to reschedule, is the step to document the non-arrival of the originally booked appointment a required part of the workflow? How are non-arrivals handled and documented in the self-scheduling solution when the patient is driving the workflow? How and when is the information relayed to the scheduling team – and the clinic?

Finally, who is booked but not arriving? This is rarely part of the analysis but arguably should be. There is evidence in the literature that certain cohorts of patients - uninsured and unemployed - have higher no-show rates. From an equity lens, this represents a more vulnerable patient population that may be challenged by the social determinants of health. Each one of these issues must be considered before embarking on a strategy to tackle non-arrivals, as the business intelligence must be timely and accurate.

In Part 2 of this blog post, we'll discuss how to Determine Prevention Strategies.

A special thanks to Chris Profeta for his contributions to this article.

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