Improving patient experiences in the payer space through actionable insights and strategies

As customer expectations of healthcare companies continue to evolve, companies need to consider how data-driven strategies can enhance their patient experience and patient journeys.

Based on Credera’s experience in the data and healthcare space, we’ve seen healthcare companies benefit from following a three-step approach to a data-driven customer experience:

1. Understand existing patient experiences through data.

2. Build insights from data-driven patient experiences.

3. Continuously define and deliver improved patient experiences.

Through this multi-step approach, healthcare companies can validate whether they are meeting their patients’ most critical needs, while also gaining insight into how patients respond to changes. This article will explore each area of this approach and highlight how healthcare companies, specifically in the payer sector, can develop data-driven strategies to respond to patient asks and needs.

1. Understand existing patient experiences through data

Healthcare companies can leverage their existing data as a foundation for interpreting patient behavior. The company has an opportunity to create a unique experience for the patient from the moment a patient identifies the healthcare company’s services. By following a patient’s browsing or care delivery journey, we can create specific personas that aggregate the most common patient experiences. From there, we can create aggregated personas to identify specific high or low experiences in their journey.

For example, the following interactions are associated with frustrated patient behavior and are low moments in the patient journey when interacting with a payer:

• The patient signs in and searches for similar terms without engaging in chat.

• The patient connects to procedure cost estimators and does not complete the information necessary to receive an estimate. They then report a negative experience at time of billing.

• The patient reviews care options available and doesn’t have a clear understanding of coverage related to telehealth.

By exploring these interactions, healthcare companies can reflect on their understanding of the high and low experiences throughout the patient journey and work to improve the experience.

2. Build insights from data-driven patient experiences

Once healthcare companies have a deep understanding of the patient journey, they can further their insights by collecting feedback at these important moments.

Building on the low moment of the patient journey, the poor experience of the patient who connected to but didn’t complete the procedure cost estimate page can be modified via data collection through a variety of methods.

For instance, using our previous example:

• The company can add a new field option, “I need assistance,” to the calculator that would connect them to chat with someone to help define the necessary inputs.

• The company can collect data on which fields need description modifications or clarification.

• The company can request feedback through a quick survey in a pop-up module.

• The company can collect data on fields that are left blank most often when the form is submitted.

These low points in the patient experience are data points that provide clear opportunities to test modifications, collect data (again), and learn through the change. Healthcare companies can then use these insights to drive strategies for improving patient journey lows and/or repeating and reinforcing highs.

3. Define and deliver improved patient experiences—iteratively

Through regular reflection of patient journeys and data insights, healthcare companies can create agile strategies to improve their patient experience incrementally and repeatedly. In doing so, healthcare companies align their business goals with the real needs of users, informing strategic decision-making, and effective priority setting—critical elements to driving accelerated impact.

Sample strategies healthcare companies can consider include the following:

Managing data capabilities through machine learning

Machine learning (ML) is a powerful tool for gathering first-party data, and while healthcare companies may implement ML to curate the patient experience, it requires intentional management.

Intentional management, through machine learning operations (MLOps), ensures model flexibility and efficiency, even enabling automated relearning to develop processes and enhanced digital offerings. For application within the payer sector, a ML model could use past appointment and claim information to recommend a next appointment or test for the patient. MLOps can then ensure there is no model degradation or data drift over time that is impacting these recommendations, specifically by tying performance metrics back to the business metrics or patient experiences they are intended to drive.

Strategically implemented ML models can be taught to leverage every customer interaction (e.g., hover time, click throughs, etc.) to predict behavior, personalize the user interface, and optimize administrative tasks, creating novel, individualized customer experiences every time they interact with the company.

Developing a robust test and learn agenda to build a patient experience rhythm

The patient experience is dynamic. As healthcare evolves due to innovation and shifting demand, patient personas and their journeys evolve as well. To continually and effectively understand the patient experience, healthcare companies need to develop a test and learn cadence centered around improving the customer experience and journey. Specifically, through regularly scheduled cohort testing (such as A/B testing), focus groups, and patient research, healthcare companies can test their different hypotheses and results on the patient experience. Mathematically, this can be described as:

This may look like running an A/B test to identify which page facilitates more completion rates. To expand this into a patient experience rhythm, healthcare companies can schedule regular experiments to test the compound impact of the results on the patient experience over time. This can look like regular A/B tests, coupled with quarterly patient focus groups and allow the results (and what they say about the patient experience) to influence the overall prioritization of technical work for the next period.

These sample strategies can help ensure that healthcare companies are able to gather and interpret essential patient experiences to create more meaningful interactions.

Tying the three-step approach together

Credera has used this approach with an insurance client that was experiencing high traffic to their paid web ad campaigns but low conversion rates to their services. The client engaged Credera to redesign their website using a data-driven, user-centric approach that optimized for SEO and conversion.

The Credera team began by conducting client workshops to understand the current state, document the future state, and ran usability tests to gain insight into user pain points. The team then completed funnel analysis on visitor engagement to further refine their insights.

As a result, the team developed a customer journey and personas to inform the creation of user-friendly and engaging content to display to website and mobile visitors. The team also provided new key performance indicators (KPIs) to better measure the site’s success and an A/B testing backlog with additional ideas to further optimize the site after the initial enhancements.

Moving forward

As healthcare companies address increased demand and changing patient preferences, it is critical each healthcare company understands its patients through its data, builds actionable insights from this data, and uses this data to repeatedly define and test improvements for their patient experiences.

Credera has helped our healthcare clients to define actionable insights and build strategies to ultimately improve patient care. If you are interested in exploring how to implement the three-stage approach described above, we would love to help. Please reach out to us at findoutmore@credera.com

• Customer Experience
• Data Science, Artificial Intelligence & Machine Learning
• Data Strategy
• Healthcare
• Insurance
• Machine Learning
• Payer