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PHS Patient No-Show Predictor

Patient no-shows continue to be one of the most difficult challenges faced by healthcare providers. While the financial loss is significant (one study estimates that no-shows cost US healthcare providers as much as $1.5 billion annually), the most devastating impact is to patient care. Not only do missed appointments affect those patients who fail to show up to their appointment – often resulting in those patients not receiving the care they need, missed appointments also have a devastating impact on the patients who are denied access to care when already limited resources are squandered on unused appointments.

Throughout the years, providers have leveraged a number of methods to address the no-show problem (blind overbooking, automated text, email and phone reminders, etc.) but none of these strategies have made a significant, lasting impact on the problem.

These methods lack the guiding power of predictive analytics.

While the intelligence gained through predictive analytics can help improve and optimize traditional reminder models, our Patient No-Show Predictor is designed to go beyond simple reminders and even blind overbooking to enable strategic scheduling. The Predictive Health Solutions Patient No-Show Predictor actually calculates the likelihood of a patient missing their appointment, enabling staff to more effectively schedule same-day appointments, identify factors affecting access to care and minimize patient backlogs – ensuring that all patients receive the care they need.

Informed Scheduling Solutions

What if you could see, at a glance, which patients in your daily schedule are most likely to no-show, allowing staff to employ additional reminder protocols to improve patient attendance. Even better, what if you knew the optimal reminder protocol to employ to maximize the likelihood that your patient would show up? Our solution can leverage predictive analytics to assess which reminder protocol should be leveraged for maximum impact. While one patient may respond best to daily text reminders, another may need combination of email and personal phone calls to ensure they do not miss their appointment. Protocols can be customized per patient, based on historical information about what worked and what did not in the past, constantly evolving for future appointment.

But even with the most data driven efforts to minimize no-shows, there will always be those who miss their appointments. The question then becomes, how can we ensure those valuable appointment slots do not go unused, especially when there are patients desperately waiting to be seen. In those cases, providers may choose to leverage a strategic overbooking approach which enables staff to easily determine the ideal time to same-day or even next-day patients, based on those in the current schedule with the highest no-show likelihood.

Understanding Factors Affecting Access to Care

Our solution also helps to improve the likelihood that ‘at-risk’ patients will attend their scheduled appointments by understanding why an individual patient may miss their appointment. What factors are affecting that patient’s access to care? Do they lack adequate transportation? Will the traffic keep them from arriving on time? Could weather inhibit travel? Armed with this knowledge, schedulers can more intelligently identify issues that may impact patient compliance and implement solutions to address the issue – when the appointment is scheduled. For example, schedulers may avoid 8:00 am appointments for patients who live more than 30 minutes away and would have to traverse the city’s morning gridlock to arrive on time. For those patients with unreliable transportation, staff may offer to schedule transportation on behalf of the patients (e.g., Uber Health). The system could even suggest alternate travel routes as part of the reminder protocol to help ensure a patient is able to attend their appointment and receive the care they need.

Patient Back-Logs

Our solution also helps to address the backlog of patients waiting to be seen. Time and again, we hear stories of patients being denied access to care because the provider simply does not have the available appointments to see them. Our solution helps to ensure your daily schedule is fully utilized so that appointments no longer go unused while a backlog of patients languish on long waitlists hoping for an appointment to become available.

What are you waiting for?

Your no-show problem is not going away. By leveraging the power of analytics, Predictive Health Solutions can help you minimize no-shows and maximize patient access to care. Your patients are counting on you. And you can count on Predictive Health Solutions.

How it Works

Predict Patient No-Show Probability

By using historical scheduling information, mathematical models can be created which will then be used to score future appointment probabilities of the patients. Additional information like previous appointments, diagnosis codes, demographic characteristics, distance away from the practice, and many other attributes can be utilized which will further improve the model results. Need to check the probability of a single patient appointment or see probabilities for an entire day? No problem. The solution can provide you with real-time or batched based results.


Take Appropriate Patient Reminder And/Or Scheduling Actions

Staff can use the insight on patient no-show probability scores and same-day booking opportunities to take appropriate action – whether that is a customized and targeted reminder protocol, booking same-day appointments during time slots with a high no-show probability, or some combination of strategies.

Solve Your No-Show Problem

See increased revenue. Maximize the time available for patient care. Deliver better care for more patients, improve satisfaction and help more people in need.


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