How to Optimize Payer Mix with Data and Technology

To effectively meet the healthcare needs of the community, which has traditionally been the primary mission of a healthcare system, it’s crucial that hospitals and other healthcare organizations have positive sources of income. This helps to ensure that quality healthcare services can be extended to as many patients as possible.

As the shift toward value-based healthcare and choice/consumerism continues to impact the way consumers manage their healthcare, hospitals are facing the challenge of balancing – and optimizing – their payer mix (commercial, Medicare, etc.).

This issue is coming even more into focus as new healthcare options are available, especially for commercial payers. With more discretionary income, commercial payers are opting to seek out other healthcare alternatives beyond the traditional health system, such as walk-in clinics, telehealth, and alternative care. To compound the issue, traditional consumer brands (such as Amazon) are entering the healthcare space, leaving hospitals with new competitors and a host of new challenges in patient acquisition and retention.

Healthcare marketers have the ability to step in and strategically target healthcare patients and consumers to optimize payer mix. With the right data and technology, they can bring in the door a greater percentage of commercial payers than is typical for the hospital or service line – and, as a result, generate higher ROI for specific high-value service lines.

Optimizing Payer Mix for More Viable Income

With access to more consumer insights than ever before – thanks to data and technology – marketers are in a unique position to attract a greater percentage of commercial payers through targeted campaigns.

Data-Driven Targeting

Healthcare marketers are swimming in data – and it can be a powerful force to improve campaign performance and drive ROI, if leveraged correctly. Knowing this, let’s look at what type of data supports marketing’s efforts as they focus in on payer mix:

Third-Party Consumer Data paired with Propensity Models

Third-party consumer data provides marketers with a valuable starting point from which to target healthcare consumers. By purchasing data collected from aggregate sources, healthcare marketers are privy to a wide range of marketing insights, much more than relying on data generated in-house alone. For example, marketers can use third-party data to look at demographics, audience behavior, and contextual targeting. More importantly, the most sophisticated sources also include flags for indicators such as “likelihood to be insured” and “likelihood to have Medicare.” When propensity models are layered on top, marketers can begin to predict key areas of interest, healthcare needs, and relevant engagement opportunities for patients and healthcare consumers.

Claims Data

Claims data is typically anonymized but can include data points such as a patient’s payor category (i.e. commercial or Medicare), payer name (insurance company), and sometimes even payer plan. This can be used to better understand market areas with the most opportunity, whether or not patients of a specific payer are choosing out-of-network providers, which physicians are driving referral traffic that may correlate with payer mix, as well as opportunities for healthcare marketers to drive consumers to specific providers that match specific conditions.

Clinical Data

Clinical data from a hospital is often the most accurate and detailed set of data available to a health system. It’s not de-identified and often includes payer category, name, and plan. Marketers can combine this data with claims and consumer data to build a comprehensive profile of the characteristics of typical patients for specific services or procedures and use this information to improve audience targeting in service line marketing campaigns.

Together these three data sources offer directional insights into marketing’s targeting efforts. While each may not be perfect, when used together, they offer a more complete picture of the marketplace and uncover trends that can fuel effective campaigns. However, in order to take full advantage of the data, the right technologies need to be in place to help marketers make sense of it through analytics, visualization, and campaign execution.


While the surplus of healthcare data currently available to marketers may seem like the perfect opportunity to better engage audiences and improve targeting, without the appropriate technology, marketers will struggle to piece together the various congruities that translate into actionable insights.

With this in mind, let’s take a look at the technology healthcare marketers need to leverage in order to distill their data into actionable insights:


Using an HCRM, marketers can organize the various data sets they’ve collected into a clear picture of the marketplace, as well as specific areas of opportunity for marketers to focus their campaign efforts. Specifically, by utilizing an HCRM, marketers can piece together common patterns found throughout the data sets they’ve collected.

For instance, marketers can match and overlay different data like zip code, insurance type, procedural history, etc. together for a clearer visualization of target audiences.

Additionally, marketers can create propensity models for specific service line campaigns with an HCRM and overlay that data for a more complete visualization.


To assist with provider recruitment and physician outreach targeting, marketers can use a PRM to understand the payer mix associated with particular providers by taking into account the activities (types of conditions treated), as well as the inbound and outbound referrals that provider generates. From there, marketers can leverage those insights to create targeted outreach campaigns or recruitment campaigns that help bring providers that align with the organization’s goals into the fold of the healthcare system.

After marketers have gathered the appropriate claims, clinical, and third-party data, and accurately mapped out that data using HCRM and PRM technology, they can begin using the insights generated to optimize their campaign efforts.

Application of Payer Mix Optimization

Payer mix optimization can be especially effective for service lines where there is a more average mix of customers, e.g. a 50/50 split between commercial and government insured patients. We’ve seen it work especially well for orthopedic and bariatric service line campaigns.

With the data and targeting available, marketing is able to tip the scale for the type of patients they are bringing in the door – ultimately showing a higher return on investment. For example, with a targeted campaign, they could bring in a 70/30 split between commercial and government payers, as opposed to the 50/50 split with traditional marketing efforts.

In other words, marketing can show the C-suite that because of their targeted campaign efforts, they were able to bring in leads that are likely to generate more ROI than the average mix of patients to date.

Final Thoughts

In order to support a healthcare system’s mission goals, marketers need to be able to drive a positive revenue stream that facilitates their efforts to provide care for all patients. Optimizing efforts to drive a balanced payer mix can play a significant role in facilitating this objective. By leveraging data and technology together, marketers can reach more granular target audiences that help drive consumers through the doors of the healthcare system—helping provide better healthcare outcomes for consumers and patients alike.

Jessica Friedeman

Jessica Friedeman

Jessica Friedeman serves Evariant as Vice President, Product Marketing. Leveraging over a decade of experience in the healthcare industry, Jessica provides product, strategy and industry support so as to deliver solutions that maximize client’s marketing and organizational strategy. Jessica additionally served Evariant as Vice President, Solutions Engineering, lending technical support to the sales process. Prior to Evariant, she served as Director, Solutions Support at Truven Health Analytics (now IBM Watson Health), where she was responsible for ensuring the successful positioning and growth of the Marketing & Planning business lines.