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Part III: Why Mastering Provider Networks is Key To Reaching Decision Makers

In Part I and Part II of our Biopharma Customer Mastering Series, we covered in detail how machine learning is an essential tool in customer mastering and how you can use it to build flexible hierarchies to power your analytics. People are at the core of any go-to-market effort, and the target audience you want to reach includes key decision makers, such as physicians and providers. In addition to using customer mastering to create hierarchies in your marketing and customer engagement channels, you need accurate and fast tools to understand the provider networks within each channel. A human-guided machine learning approach to customer mastering lets you define those provider networks accurately, quickly and at any scale, so you can connect to decision makers.

Provider Networks Contain Dynamic Data

Healthcare markets continue to consolidate, and less than half of practicing physicians now work in private practice. . The data within provider networks is dynamic and can change in many ways. For example, physicians may enter or exit the network, change affiliations within a healthcare group, or move to a different site or facility as a health system expands. As these changes multiply across physician groups and health systems, the data underlying engagement strategies and analytics become out of date and inaccurate very quickly, making it harder to engage with providers. Engagement strategies need to adapt to these new and changing provider networks. Effectively reaching decision-makers in these networks requires understanding the hierarchy of each provider network at the group and institutional level and having accurate and up-to-date information on the people within the network and their affiliations.

Mastering Provider Network Data

Given the scale of provider network data, the complexity of provider affiliations, and the changing landscape of provider networks, a machine learning driven approach is an essential tool for understanding and reaching providers. When hospitals or physician practices merge (as we talked about in our previous post), or health systems acquire hospitals and physician practices, the provider networks get more complex and concentrated. At the practice level, a provider may be associated with one or more practices within a group. Each provider is also often associated with a larger group and site with facilities in multiple regions.

The role of customer mastering is to ensure accurate and up-to-date information is available for each provider in the network for each of these associations, as well as to weed out bad data and false connections. When providers have complex and changing associations in a network, it becomes difficult to manage and update using rules-based methods. With rules-based data mastering systems, the rules need to eliminate exceptions, express unique identity and pass the concept to each system. The number of rules needed to manage ever-growing sets of data can become unmanageable. Additionally, missing data, the temporality of data, and schema changes can easily break the system. The human-guided machine learning approach, however, is driven by a statistical model that takes into consideration the entire dataset. The data models are refined as more data is supplied, creating a system for constant improvements: the bigger the data sets, the more accurate the results.

Keeping Humans In The Loop To Verify Data Accuracy

In addition to managing providers in their healthcare hierarchy, customer mastering can also improve data quality and enrich the data with verified third-party sources associated with each provider to help rectify inaccurate or outdated information.

Keeping data accurate and up to date across complex provider networks that include large physician groups, multiple hospitals, ancillary providers, larger health systems and even payors, requires a human-guided approach with accuracy, speed, and scalability.

The key is to not only keep different source systems connected and match data across domains, but also having persistent ID across data domains and constant feedback from subject matter experts, the humans in the loop. Here is where machine learning comes in, to take that feedback and data stewardship and scale it out to the entire system. No matter how much provider networks grow or change, human-guided machine learning can synthesize that information into a full 360-degree view of each provider in their network.

Using Hierarchies To Find and Engage With Decision Makers Inside Complex Provider Networks

With a better understanding of your provider networks and their relationships, you can focus on where go-to-market efforts are successful and where they can improve by targeting decision-makers more efficiently and effectively. Healthcare decisions are increasingly being influenced by health plans and healthcare administrators at the institutional level as well, meaning engagement strategies must also include decision-makers beyond individual physicians. Understanding your provider network helps pinpoint where key decision-makers exist and craft a multi-level engagement strategy that can bring all these stakeholders together. Customer mastering creates a unified, accurate view of healthcare hierarchies and provider networks that makes it possible to shape both traditional and multi-level engagement strategies simultaneously.

Our next article on customer mastering in biopharma will focus on how biopharma companies can use customer mastering to support patients and improve the patient experience.