Patient Retention Metrics for Digital Health Apps

The utilisation of generic customer metrics in digital health apps may prove misleading. For example, on TikTok or Instagram, the user’s primary goal is entertainment. Therefore,  feed scrolling, return to the app, and session length are great proxies for retention. However,  unlike consumer apps, patient retention metrics for digital health apps must show a direct connection to health or clinical outcomes. 

In 2022, the National Institute of Mental Health financed research that showed: 

“digital health technologists and researchers agree that assessing “generic” measures of engagement (e.g., number of sessions, weekly active usage, or program completion) may not be sufficient if they are not strong mediators of outcomes. Consequently, there is a growing call to follow a clinically informed and data-driven approach to identify specific engagement metrics that uniquely predict the long-term value for a digital therapeutic…”

For clinically-oriented solutions, the focus is on consistent behavior expressed in patient retention metrics rather than on surface-level engagement. In cases where consumer apps optimize for higher usage numbers, digital health apps strive to achieve a reduction in outcomes and consistency in usage. For instance, a ridesharing app optimizes for a higher number of completed rides. In contrast, a therapeutic app prioritizes a reduction in hospital readmissions through regular usage. Drops or spikes in usage often signal an issue, and for a clinical app, it can escalate to adverse effects and regulatory exposure.

In this blog post, we’ll discuss how meaningful patient retention metrics impact healthtech startups’ business model and pricing. Then, we’ll break down the process of selecting retention metrics linked to health/clinical outcomes and economic value.

What is a Meaningful Patient Retention Metric in Digital Health Apps?

Similar to the trend in consumer apps to define a unique North Star Metric, digital health apps also need to begin by stepping away from generic metrics. A foundational step in choosing the right patient retention metrics is to determine the product’s measure of value. While an e-commerce app’s value lies in the growing number of transactions, digital health apps focus on improving health/clinical outcomes. Consequently, the North Star metric in healthtech is only meaningful if it predicts this improvement. For instance, Omada Health measures the following clinical outcomes:

• Weight Loss for its Prevention & Weight Health;
• Reduction in glycated hemoglobin for its Diabetes Program;
• Reduction in blood pressure for its Hypertension Program.

To achieve those clinical outcomes, certain user behaviors matter much more than others. For instance, app opens do not matter if there is no meal logging or BP wearable syncing. Omada’s diabetes program has in-app one-to-one coaching, so coaching participation is meaningful, while screen time is not. Therefore, to correctly define retention metrics, one has to start by defining the desired outcomes. 

Digital health apps are inherently different from other consumer apps in how they generate revenue. First, patient retention metrics predict clinical outcomes. In their turn, clinical outcomes predict economic value. Based on this economic value, healthtech startups get enterprise contract renewals and become more desirable for insurers, commercial health plans, Medicare/Medicaid, and other payers. 

For instance, Omada’s ability to prove its clinical outcomes allows it to land new and retain existing contracts with employers and health plans that cover employees’ spending on the Omada Diabetes Program. By measuring retention metrics that determine patient behavior leading to those clinical outcomes, Omada can directly impact its bottom line. 

This connection is shown in the graph below.

Digital Health Apps: Clinical Return on Investment

The economic value largely stems from the AMA framework. The AMA, American Medical Association, developed its Return on Health framework back in 2021. It defined 7 different value streams for telehealth and digital health apps, both monetary (like cost-savings) and non-financial (such as patient satisfaction). This framework forms the basis for adapting business models and pricing in digital health apps. Headspace for Work is one of the most prominent examples of this.

Case Study: Headspace For Work

According to Megan Jones Bell, chief strategy and science officer at Headspace:

“…an evidence-based digital therapeutic is to align your pricing model with the value you’re delivering across two areas: usage and clinical outcomes. For us, that often means a monthly active use metric and an agreed-upon reduction in stress or anxiety or depressive symptoms…our pricing includes an enrollment fee and then monthly billing based on that combination of usage and outcomes.”

Headspace originally started out in the wellness sector by providing meditations alongside the growth in mindfulness popularity. Its consumer wellness product operates on a freemium subscription. To expand its business model, Headspace launched its Headspace for Work. This entered the field of evidence-based medicine.

Its clinical outcomes are:

• Reduction in stress or anxiety;
• Reduction in depressive symptoms. 

These clinical outcomes, in turn, ensure the following economic value:

• Reduction in employee absenteeism and increase in productivity;
• Cost-savings on healthcare: an investment of $1 in mental health preventative care generates savings from $2 to $10;
• Increased accessibility, as face-to-face mental health services provided through work are still stigmatized, unlike those accessed via the Headspace app.

The economic value Headspace for Work provides enables it to move from a self-pay wellness solution to an evidence-based one that can secure 2-3-year employer contracts, be eligible for FSA/HSA deductions, and now have access to Medicare/Medicaid in certain states. 

However, when it comes to the app’s day-to-day retention metrics, they worked out that all the above depend on: 

• Users visiting an app 3-4 times per week, 
• over a course of several weeks to see measurable outcomes, 
• with 10+ minutes of meditation per app visit. 

Framework For Selecting Patient Retention Metrics in Digital Health Apps

There is a challenge of working out meaningful patient retention metrics. In traditional healthcare, it’s often clear-cut. The link between treatment and outcomes is well-studied. However, in digital health apps, users or patients interact with many different elements inside and outside the app. These include educational content, reminders, coaching, primary features, and its connection to off-app services. Each of these interactions has a different impact on health/clinical outcomes. As such, it often takes time to determine the most impactful ‘active ingredients’.

Therefore, as with MVP development, best practice suggests viewing the choice of patient retention metrics in healthcare app development as an iterative process. The Lean Build-Measure-Learn cycle provides a foundation for the 4-step HealthTech Retention Metrics Framework.

Step 1 – Defining Health/Clinical Outcomes

Achieving greater user and patient retention has a two-way connection in digital health apps. On the one hand, retention is a precondition for reaching positive outcomes. On the other hand, one cannot drive clinical outcomes without controlling what leads to them. 

Moreover, clinical outcomes are proof of economic value. For instance, for a sleep app, a health outcome is improved quality of sleep. What economic value does it have? It directly reduces loss of productivity due to fatigue. In addition, it has a non-monetary value – improved well-being. 

For every kind of app, wellness or clinical, user and patient retention profoundly depend on correctly identified health/clinical outcomes with the economic value they deliver, both monetary and non-monetary. This is the foundation for marketing. In addition, it actively impacts the business model and pricing, as in the cases of Omada Health or Headspace for Work.

Step 2 – Selecting User or Patient Retention Behaviors

This is the most challenging part. The first place to start is to look into face-to-face equivalents. For instance, let’s take the app focused on providing cognitive behavioral therapy (CBT) for reducing depression symptoms. Instead of applying a generic metric of weekly active users, the patient retention metric should be the completion of in-app CBT exercises. For an app that aims to achieve a clinical outcome of lower blood pressure, instead of tracking app opens, the patient retention metric should be regular blood pressure readings. 

The medical literature on face-to-face drivers of clinical outcomes is vast and evolving. Many behaviors lend themselves well to digital health settings.

Face-to-face healthcare behavior for clinical outcomes	Digital Analogue for Patient Retention
In-person therapy	Weekly in-app session
CBT homework	Completion of in-app CBT exercises
In-person visit to discuss symptoms	Regular user input for symptom check-in
Sending records of food intake to the nutritionist	Logging meals into the app
Measuring blood pressure	Regular BP readings

However, digital health apps often do more than simply digitize what is available face-to-face. For instance, a nutritionist often reviews food logs weekly. However, an app can provide instant feedback, recalculate the remaining food intake for the day, and provide positive reinforcement upon every successful day. 

In driving retention metrics and creating product-related habits, digital tools offer things that often cannot be provided or efficiently scaled offline. For instance, push notifications or adaptive reminders. It is hardly imaginable that a nurse or a doctor will be calling up a patient to check if they’ve taken their morning meds. Gamification, social signals, real-time feedback, AI coaching, passive monitoring, and personalized algorithms can achieve things that traditional interventions cannot. As such, interaction with these elements contributes to patient retention and clinical outcomes. In addition, interaction with these features is measurable.  

Patient retention native to digital health apps

Let’s discuss a treatment adherence app. The basic patient retention behavior is confirming the intake of prescribed meds or doing certain exercises. However, there are also possibilities for digital-native behaviors turned into patient retention metrics:

• Length of a medication streak;
• Responding to notifications/reminders within 30 minutes;
• Participation in peer challenges.

All of these would be predictors of clinical outcomes, and they are easily measurable within the app. 

Step 3 – Building/Improving Connected Functionality

So, once the team has landed on retention behavior, let’s say – regular BP readings for achieving the clinical outcome of lowering blood pressure, they can develop the following hypotheses:

• Users forget to measure their BP, so we might test it by implementing daily reminders.
• Users remember about measuring BP at an inconvenient time, so there is an opportunity for AI//ML personalization for reminder times.
• Users lack motivation, so maybe launching gamified streaks can improve consistency.
• Changes in BP might be small and, thus, imperceptible, so motivation to consistently work on lowering it might be achieved by visualizing improvements with progress charts.
• Some people can only be motivated by social support, so there can be added social features of either family accountability or peer support.
• Finally, what if there is friction in BP readings? In-app analytics might discover that there are friction points that prevent users from completing regular BP readings. 

So, one user or patient retention behavior can lead to a variety of in-app solutions. The goal here is to use a variety of user retention strategies to discover the most impactful way to increase retention behavior. 

To determine the most impactful options, Lean Startup Services offer a rich toolkit:

• User interviews to simply ask users what is preventing them from completing BP readings regularly.
• Focus groups to test the wording of reminders that will motivate a higher number of people to complete the retention action.
• Usability testing to discover which prototype has a frictionless experience.
• In-product analytics to detect drop-off points and improve UX;
• Diary studies can reveal the user retention behavior over several weeks, and so on. 

The image below shows an example of integrating lean tools into the phases of development.

Step 4 – Measuring the Impact on Health/Clinical Outcomes

In consumer apps, continuous A/B testing is the norm. However, in digital health apps, a change in the app often leads to a change in health/clinical outcomes. Therefore, app changes may expose the digital health startup to regulatory requirements and clinical risks. Often, retention behaviors that directly affect health and clinical outcomes are first rolled out through small-scale studies, pilot programs, cohort analysis, observations, and even IRB-approved research. 

Then, if the tested change results in substantial improvement, the rollout is made for all users. If the change yields only a slight improvement or none at all, it is back to the drawing board.

Final Thoughts

Digital health apps are inherently different when it comes to the value users are seeking and the way revenues are generated. What is generally considered a good outcome in a consumer app can be a negative event in a clinical app. Thus, digital health apps need to follow a framework to ensure a straightforward connection:

• User behavior that leads to improvements in health or clinical outcomes should be expressed via patient retention metrics
• Health/clinical outcomes should generate economic value for a variety of stakeholders, including insurers, commercial health plans, and so on.
• Economic value expressed in monetary and non-monetary value streams becomes the basis for enterprise contracts, HSA/FSA eligibility, Medicaid/Medicare reimbursement, and the like.

When it comes to patient retention metrics in digital health apps, there are two major sources: equivalent of traditional healthcare and digitally native solutions.

FAQ: Patient Retention Metrics for Digital Health Apps