Using HEART framework to measure task success in healthcare products


Healthcare products are special. The users – payers, providers, or patients — need the same delightful experience of a consumer product as well as all the compliance related things that are part of our highly regulated healthcare ecosystem.

The result? It becomes a hard choice for the product team to balance the regulatory needs with building a delightful experience. Even if a product team can pull off such a balance, they need reassurance that they made the right choice. The last thing you want to know is that doctors and clinicians were confused by the experience and ended up making poor choices while diagnosing a patient.

If you are in such a situation, like I’ve been multiple times, then you could leverage Google’s HEART framework to understand the impact of your product and its experience choices.

The HEART framework helps measure the effectiveness of your product and bring some science in the art of ‘what user’s want’. It’s an acronym that stands for-


Happiness: The happiness or satisfaction users feel while using your product/feature. It is measured by metrics like how easy it is for users to discover what they want to achieve (ease of use), if they are willing to refer the service/product to someone else (referral likelihood) or not.

Engagement: This measures how engaged users are with your product. Engagement is calculated by metrics like how long the users stay in the application (session length), how often they return (frequency), how habituated they are to use the product, and likewise.

Adoption: It defines the popularity metric of your product i.e. measure of market adoption. It is often measured by the number of app users, purchases, subscriptions, downloads, etc.

Retention: It measures the count of returning users of your product. Or, sometimes indirectly by measuring user churn.

Task success: It measures how successful users were in completing the tasks they set out to do in a product. This is often measured by how many users erred while performing a task on a product/feature. Measuring this is crucial as it helps you improve areas where you lack.

For me, measuring task success in healthcare products has been one of the most important metrics to measure efficacy of user experience decisions.

I lean towards Task Success because I want to know if the users are able to complete the task swiftly — and with accuracy, especially in scenarios like patient check-in, claims processing, wellness visits. In fact, measuring task success in healthcare products is beneficial in any scenario where there is a race against time.

Tracking task success efficacy in healthcare products has multifold benefits

  1. You can measure the increase in users’ productivity.
  2. You can see if it has led to a lesser number of support calls, or messages.
  3. It is directly tied to the training. You can cut down on the training spend if the task success metric signals that users are flying through the experience.
  4. You can also track the quality of the task completion. If the user knows what they need to do and how to do, the outcome will have lesser errors (or none at all), increasing the quality of the outcome expected.

Enough preamble. Let’s see how this works.

Measuring task success isn’t any rocket science, as you will see shortly. All it takes is breakdown of what you want to measure in Goals, Signal and its Metrics.

Goals a.k.a “What you want to achieve?”: This helps set how you would define success i.e. the north star for a given feature or the entire product. If we take the example of wellness visit, the goal could be to reduce the time taken by users to create and submit a new wellness visit.

Signals a.k.a “How would we know?”: Signals act as indicators to determine if the goal that you’ve defined is accomplished or not. Continuing our wellness visit example, the goal to reduce the time for creating and submitting a wellness visit can be divided into the following signals –

  • Identification of the workflow/path to initiate the process.
  • Quick completion of required documentation.
  • Accuracy of documentation.

Metrics a.k.a “Statistics”: The stats are needed to infer signals to see if you have made the right decision. Of course, your product has to be instrumented to collect the data to generate these stats. In our wellness visit example, here are the possible metrics for the signals mentioned above.

  • How quickly were they able to complete the task? Has the average time taken for visit creation reduced? If yes, by how much?
  • How many times did they err?
  • How many support calls were received for this process?
  • How many times was the ‘Help’ section for this process referred to by users?

Let’s unpack this example to see how you would lay out the metrics, and track the efficacy of your product’s user experience-


The last problem to solve here would be how to capture these metrics. There are many tools and techniques, and for the metrics above, some simple ones are-

For finding the speed of task completion

  1. This can be determined by running simple analytics on the time taken from initiation of process to submission of documentation.
  2. Another measure could be to see if there has been an increase in the number of visits created each day during a defined period.

For finding the error rate

  1. An easy measure could be the number of wellness visits that were deleted (if the application allows) very soon after they were created.
  2. Another way to find out the error rate in the process of creation can be calculated by recording the number of support calls that come to change the data from the backend.
  3. Error rate can also be calculated by the number of visits rejected in a review process due to incorrect documentation.

While the remaining metrics are self explanatory, an additional technique could be to measure the training effort of existing users for this process.

That’s all. Seriously. The most common advice for seeking happiness, and living a good life is — follow your HEART. Perhaps Google took a leaf from this advice and created a framework to measure all the facets that make a great product.

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