Why Data Quality is a Critical Concern for Life Science Compliance Programs
By Robert Zelinsky on September 20, 2019
Data governance is frequently left out of analytics discussions in favor of flashy visualizations and advanced metrics. However, companies must invest heavily in the tools and training necessary to ensure data quality prior to developing an analytics program that provides reliable insights for strategic decision-making.
Although frequently overlooked, data governance is a core piece of any investment into advanced analytics. This is especially true for life science compliance programs, who must rely on the quality of the data they utilize to provide accurate submissions to regulatory authorities, make informed decisions about compliance risk, and allocate limited resources.
What is data governance?
Data governance is a broad term used across different industries to reflect a set of leading practices meant to formally manage data assets within an organization. It is a concept that includes the “overall management of the availability, usability, integrity, and security of data used in an enterprise”.
Global aggregate spend reporting requirements have played a major role in forcing many life sciences companies to invest time and resources into improving internal data governance. However, many companies still rely on manual remediation of data to improve quality after collection, which diverts limited resources away from spending their time assessing the data for strategic insights.
Why is data governance important for life science compliance programs?
Data management is the foundation upon which advanced metrics like key risk indicators (KRIs), key performance indicators (KPIs), and dashboards rely. Protocols surrounding data management to promote data quality ensure that compliance programs can:
- Trust the data they are using to make strategic decisions
- Reduce manual labor associated with after-the-fact data cleaning exercises
- Feel confident investing further time and resources into analytic enhancements
- Meet legal and regulatory obligations (e.g., global aggregate spend reporting requirements like the US Sunshine Act or the accounting provisions of the Foreign Corrupt Practices Act (FCPA))
Deprioritizing data management can have compounding negative effects, including:
- Lack of organizational trust in compliance program analyses and risk assessments
- Reduced investment in the technology and tools to support a data-informed compliance program
- Increase in downstream work with respect to data cleaning and database management
- Increase in time taken to process and deliver requests requiring the aggregation and assessment of risk-related data
An emphasis on data quality is critical to data mastery
Growing a compliance program (and, more broadly, a company) that reliably utilizes data to inform operational decisions requires mastery of data management. This starts with a data mindset. Programs must view data and analytics as a core value and see data enablement as an investment and not an expense. This mindset constantly strives to break down data silos between departments through data sharing and centralization of data management (where possible).
A data-enabled compliance program requires an investment in the technical and human resources required to build and scale data infrastructure, which includes:
- Technological investments in:
- Key technologies and an analytics infrastructure, including, for example:
- Where possible, software to manage key engagement and internal review workflows (e.g., those related to payment/activity approvals, event planning, field interaction tracking, and etc.)
- Carefully mapped and managed APIs across integrated systems
- Database technologies and best practices
- A well-maintained master data management (MDM) system (see more below)
- Well-defined data governance protocols
- Integration of cross-department databases
- Human investments in:
- Change management to stress the importance of data collection and to ensure that new protocols and tools are being appropriately utilized
- Identification of full or part-time data stewards to maintain MDM data management protocols and align syndicated market with field data in MDM solutions
- Ongoing training on new technologies or changes to existing workflows to support improved data capture and aggregation
These investments will pay dividends over time as data management bears the fruit of better analytics and insights to improve decision-making and to help create a culture of evidence-based reasoning.
Data governance in practice: why it’s important
Data governance is critical for life science compliance to effectively understand compliance risk and to meet the exacting specifications of aggregate spend reporting requirements. One example is with respect to maintaining the accuracy and “currentness” of MDM records for healthcare providers (HCPs) and organizations (HCOs).
According to Gartner, MDM is “a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.” Typically, an MDM software solution is integrated with syndicated market data from one or more external data vendors and managed internally by one or more data stewards who follow strict data governance protocols.
In a life science compliance context, data governance plays a key role in ensuring that, for example, payments made to physicians are attributed to the right physician each time they are created. Historically, minor variation in identifying information could lead to the creation of multiple physicians who are actually the same individual (or the mistaken assignment of spend to the wrong physician). Each of these outcomes is associated with compliance risk, including misreporting spend data to regulatory authorities and potentially not capturing high levels of spend going to a single physician (potentially violating policy spend limits).
A key difficulty of MDM is the dynamic nature of data governance. A number of real-world scenarios can trigger an update to MDM records, including:
- A sales representative discovers that a physician’s office has recently moved
- This will impact both sales calling as well as aggregate spend reporting. For example, if the physician has started to practice in a new state, this could implicate new state reporting requirements, which will not be caught by reporting software if the address associated with the physician is still the old address
- A physician moves from a private practice group to a GPO
- This may impact on how company representatives may interact with the physician depending on the GPO’s HCP engagement policies
- A physician develops a new sub-specialty and is engaged by the company for expertise in that sub-specialty
- This may impact both the specialty category assigned to the physician for aggregate spend reporting purposes as well as their fair market value payment rate (which could be lower or higher depending on the sub-specialty). Without an MDM system used across the company, there is also a risk that different departments may categorize the physician according to different specialties, thereby providing the physician two different FMV rates
Most MDM solutions can be integrated with existing engagement and workflow technologies. This is one area where the “compounding” effect of data governance is felt throughout the organization and the compliance program. MDM technologies enable engagement and workflow technology users to specify precisely who the target of an interaction or payment was as a part of their day-to-day activities. Many engagement and workflow technologies also enforce strict protocols with respect to how data may be entered into the system (e.g., multiple choice options only) and prevent users from moving forward with an engagement without completing required fields. This not only helps to ensure compliance with company policies and procedures, it also simplifies (and encourages) data aggregation and utilization in advanced metrics like KRIs and KPIs.
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