Data quality management, like data governance discussed in the prior article in the series, is a core component of a successful enterprise information management strategy and should be an integral part of any medical device manufacturer’s approach to unique device identification compliance.

Lack of data quality management results in inconsistent and inaccurate data, which leads to poor decisions. Poor decisions, in turn, can lead to inefficiencies, errors, additional costs or loss of business. The inability to successfully manage data quality may also result in fines or other regulatory actions when compliance needs cannot be met within required time frames.

Data quality management involves ensuring that data is accurate, reliable, consistent and complete. It is a process that requires a strategy, and it involves ongoing monitoring and maintenance of data quality. It also provides a focus on continuous, measurable improvements in data quality.

For large companies manufacturing broad inventories of different medical devices, the need to proactively manage data quality takes on even greater importance. As part of their everyday operations, these entities are faced with large volumes of data and complexity, in addition to issues related to UDI compliance.

It is important to note that a data quality program supports and works with a data governance program. A data governance program ensures that the “definition of quality” will be agreed upon, understood and measured and that the appropriate policies and processes are in place to support it. It is also important that both business and IT share in the responsibility for data quality and data governance.

UDI Rule Highlights the Need for Data Quality Management in the Medical Devices Industry

One can readily maintain that the need for UDI regulation, itself, came about in large part due to a distinct lack of data quality management in the medical devices industry. Medical devices distributed in the United States, unlike pharmaceuticals, lacked a standard system of identification and tracking, and this resulted in device information that was fragmented, inaccurate and inconsistent, device recalls that were slow and inefficient, and adverse event reporting and tracking that was incomplete and unreliable. Ultimately, this could lead to poor decisions, injuries or even death.

UDI rule, with its core requirements governing device and package labeling, direct part marking in certain cases and submission of relevant pieces of information to the FDA’s Global Unique Device identification Database, directly addresses this prior data quality management deficiency and holds the promise of not only improved patient safety and care but also widespread business benefits to stakeholders throughout the health care value and supply chains.

Data Quality Management for UDI Compliance at the Organizational Level

For device manufacturers tasked with meeting UDI requirements, data quality management will be no less important to the successful implementation of their UDI programs. In an organization, data quality issues, such as data errors or information that is incomplete, inconsistent or duplicated, can arise from a variety of sources, including:

  • Manual data entry
  • Different data standards, policies, processes — or data that remains unsynchronized and fragmented — across different systems, departments and lines of business
  • Data flowing into the organization from external sources that is inconsistent with that of internal sources

Beyond these ongoing sources of quality issues, data quality challenges may also arise during initial gathering of device attribute data required for labeling and submission to the GUDID. Information may need to be compiled from numerous disparate systems or sources, with some data elements possibly still only be available in hardcopy form.
Much like poor data quality leads to poor decisions, errors, operational inefficiencies, additional costs and loss of business, it can also make meeting UDI compliance requirements challenging. There are seven primary dimensions of data quality management that an organization addressing UDI compliance should heed. The following table identifies these seven dimensions, key questions for each and the impact to compliance when they are not effectively addressed.

Once you’ve determined which dimensions are pertinent to your data issues, it is then time to “push” your data through those quality criteria and begin the improvement process:

  1. Profile the data to identify gaps and discrepancies.
  2. Cleanse the data according to your business requirement and the UDI guidelines.
  3. Validate the data to be certain it complies with the standards.
  4. Measure and monitor the data to ensure it adheres to the quality thresholds over time.

As a result of this profiling and cleansing process, it is inevitable that there will be some remediation necessary. Data stewards are excellent resources to leverage for this activity since they are likely already remediating data in other parts of the organization. Repurposing data stewards to address UDI data quality requirements is a good way to maintain consistency across your data management practice as well as take advantage of existing resources.
Ultimately, just as the overall success of the FDA’s UDI regulation will be contingent upon the accuracy, consistency, completeness, timeliness, etc. of the data submitted to and maintained in the GUDID, so too will the success of an individual device manufacturer’s ability to comply with this regulation depend upon the quality of that organization’s own data — device-related and otherwise — and, more precisely, on that organization’s ability to effectively manage all dimensions of data quality.

The next article in the series will address the role of master data management for product data in achieving UDI compliance. The article will also touch on the process involved in submitting data to the GUDID.

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