Hitting Your Targets: A Check-up on Data

Aug 02, 2018

Big data brings big opportunities for life sciences sales and marketing teams. Specifically, provider data delivers valuable insights into customers and drives decisions about where to focus resources. A company’s performance, productivity, and profitability are heavily dependent on the quality of provider data powering its operations. Yet, many organizations do not realize how quickly provider data erodes and relies on outdated, incomplete, or simply wrong information. With physicians and other providers moving and working from multiple locations, more than 25% of provider demographic information changes each year.1 As a result, a typical provider data set has more than 40% of outdated or missing information.1

When sales reps use inaccurate data from the company’s database to determine which prescribers to call upon, they waste valuable time. In a recent study, inaccurate CRM prospect data cost on average 550 hours (27.3% of seller time) per rep year. At an average annual salary of $157,000, the cost can be upwards of $43,000 per year in wasted time and effort!2

Sales reps make on average seven prescriber visits per day,3 and today it is harder than ever to secure meetings with physicians. Time constraints and the sheer number of brands vying for prescribers’ attention have led many offices to limit or even refuse time with the industry reps. The result is often lost opportunities and market share.

In addition to obtaining accurate information on an individual provider, it is equally important to have accurate information on the organizations called Integrated Delivery Networks, or IDNs. As account-based selling increases, organizations need to ensure they have accurate connections among providers and facilities and understand the relationships and hierarchies between healthcare organizations. 

As the sales force accounts for 88% of pharmaceutical companies’ expenditures, planning and executing provider visits based on bad information also leads to wasted financial resources.4

In addition to sales and marketing inefficiencies, bad provider data impacts other business areas. Life sciences organizations must comply with specific federal and state reporting and compliance requirements, such as promotional spend reporting, which require regular data remediation to avoid penalties.

Cleaning the house

According to LexisNexis Health Care, the typical number of changes in the national practitioner database in one week is as follows:

 

Figure 1.

It’s no wonder that a typical life sciences provider database often contains errors, duplications, and inconsistences. A data health check identifies providers who should not be in the database to begin with, including unlicensed providers, providers who have moved, or sales force contacts who do not have prescribing authority such as office administrators. A database often contains inactive and deceased practitioners, practitioners with sanctions against them, and duplicate record entries.

Leveraging a source of the “golden record”—a singular provider record version with all preferred attributes—in conjunction with deduping, cleansing, and augmenting in-house provider data allows pharmaceutical companies to meet compliance requirements and ensure sales force effectiveness.

Enhanced data equals added value

When provider data has gaps, sales reps have to fend for themselves in researching the missing information. Incomplete records often lack phone numbers, physical email addresses, and other essential provider information. In addition to filling the gaps, a life sciences organization can take a step further and enhance it to add valuable insights, such as identifying the best address, provider specialty, group, hospital, and delivery network affiliations.

Additionally, many providers who should be in the database may not be there, representing missed opportunities. In-house databases often miss providers of a given specialty, NPs and PAs with prescribing authority as well as new and recently moved physicians.

Advanced analytics combining medical claims with provider data yield much more comprehensive insights than either of the two datasets alone, allowing life sciences organizations to uncover hidden markets or find additional practitioners with patients who might be a fit for a specific therapy. Understanding the strength of affiliations between healthcare providers and organizations is key to finding high-value prescribers. Firstly, providers need to be connected to healthcare organizations with a high degree of accuracy. Secondly, claims should be used to reveal the strength of those connections, defining an actual workload and location. This holistic view of specialties, or providers, and settings (organizations) can help optimize outreach, translating into more opportunities.

Data needs good hygiene

An occasional data hygiene attempt will not keep provider data clean any more than going to the gym once a year will keep one in shape. To keep provider data quality high, the best practice is to implement routine data fitness, through data governance and stewardship programs, with the goal of quarterly updates. A data governance program includes standard policies, procedures, processes, and rules established for accessing, ingesting, augmenting, and maintaining optimum data quality. Examples of data governance standards are data definitions; identifying and sourcing master/reference data and enterprise data; influencing technology standards to enable access, maintenance, and enhancement. Examples of policies and processes include monitoring and measurement, data access and delivery and data change management. Within the organization itself, a data governance program will define roles and responsibilities (as they relate to data flowing within the organization), organizational structure, planning and prioritization, and change management.

A data stewardship program encompasses a wide range of specific tactical activities to achieve these goals. These activities include the following:

  • Bringing together two or more master data entities
  • Separating an entity or parts of an entity into existing or new entity
  • Associating the organization’s records with a master data management entity record, and
  • Replacing the value of a specific attribute within the profile. This includes designating a target address that should be used regardless of predefined address logic and freezing accounts that should not be updated without a manual review, regardless of predefined rules.

The bottom line

No matter what sophisticated technologies a life sciences organization uses, and how smart its sales and marketing strategy is, if there are flaws and gaps in foundational provider data, the company will end up with wasted resources and lost market share. Implementing ongoing data governance and stewardship programs will help improve efficiencies, allocate resources, and target customers with increased precision.

 

Dara Price-Olsen, Sr. Director, Product Management & Market Planning,

Casey Hibbs, Director, Market Planning at LexisNexis Risk Solutions—Health Care

 

 

References

  1. “Provider Data Management Solutions” LexisNexis 2014.
  2. Carevoyance. “All About Provider Directories, Part I: The Who of Healthcare”. March 13, 2018. https://www.carevoyance.com/blog-all/all-about-provider-directories-part-i-the-who-of-healthcare
  3. Repsly. “Field Data Insight: Average Number of Visits Per Day By Industry.” December 2015. https://www.repsly.com/blog/field-team-management/field-data-insight
  4. ZS. “Want Better Access to Physicians? Understand What’s Top of Mind.” 2016.

https://www.zs.com/Publications/Whitepapers/Want-Better-Access-to-Physicians-Understand-What%E2%80%99s-Top-of-Mind.aspx

 

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