3 Keys to Meeting Pharma Regulators’ Data Expectations

Regulatory authorities are scrutinizing pharmaceutical companies’ data control practices with more rigor than ever before. Case in point: The U.S. Food and Drug Administration (FDA) issued a record number (57) of Form 483 Inspectional Observations for inadequate controls of digital systems and data to pharma manufacturers last fiscal year despite an overall pandemic-caused decrease in the number of on-site visits from inspectors. ⁽¹⁾ Beyond the surge in data-related Form 483s, the FDA’s enhanced focus on clinical organizations’ data integrity issues has even gotten to the point of the agency demanding some studies to be repeated. ⁽²⁾

Why Is the Focus on Data Integrity Intensifying?

The uptick in data integrity reprimands shouldn’t come as a surprise to anyone in the pharma industry, given regulators’ continually increasing (and well-warranted) concerns about data management matters in general. To understand the factors contributing to the spike, look no further than the four most common data-related infractions that trigger FDA warning letters as cited in a recent Deloitte study:

1. Data not fully and accurately documented.
2. Critical deviations not investigated.
3. Data not recorded contemporaneously.
4. Lack of adequate access controls. ⁽³⁾

As shortcomings like these continue to be pervasive across the industry, the FDA and other regulatory agencies around the world will continue to ramp up their prioritization of issues recognized as being related to data control. Trends indicate that the veracity of laboratory instrumentation and digital manufacturing batch record data will be even more deeply scrutinized by the FDA moving forward. ⁽⁴⁾

Improving the Reliability of Your Quality Data

Quality data is the currency of compliance in the pharma industry. It’s also the primary mechanism for driving continuous improvement and achieving a state of proactive quality management. To reach lofty performance goals, however, data must be readily accessible and capable of being easily contextualized. After all, the systems, processes, and personnel that pharma organizations rely on to manage quality are only as effective as the usefulness of the data with which they’re provided. In other words, only accurate data translates to meaningful outcomes.

“In order to achieve an environment where compliance is the de facto outcome, not the focus, organizations must establish the data fabric and create a data-centric culture where the ultimate value of data can be realized,” said Accenture Life Sciences R&D Operations Senior Manager Neil Fausz. “That means structuring and understanding your data and how you can use it to build predictable quality into the end-to-end process.” ⁽⁵⁾

Fausz rightly points out that “data is meaningless until it is connected, becomes informational, and is actionable.” To improve the actionability of data, pharma organizations can take three fundamental steps that will enhance their ability to leverage it to achieve quality and compliance goals.

3 Steps to Realizing Data Interoperability

Step 1: Make Quality Data Your Cornerstone:

The first step in any effort that’s applicable across the entire enterprise is ensuring everyone is on the same page. Unifying data within a natively connected architecture enables companies to bridge the gaps between quality and other business processes. A unified platform structure that links data will break down the data-isolating silos that tend to arise in pharma organizations, such as the rifts that frequently exist between R&D and quality control teams. Additionally, if your system integrates quality data with training processes, it will be much easier to maintain compliance and keep personnel in sync with current procedures, up-to-date information, and each other. The efforts of organizations that have established digitally connected quality data as their anchor are paying off, according to Accenture surveys showing that 70% of the life sciences companies that have digitized data management are achieving or exceeding expected business value. ⁽⁶⁾

Step 2: Identify Inefficient and Error-Prone Processes:

Every pharma company has quality processes that could be more efficient. Making improvements to your quality data management capabilities will help you address mistakes faster and greatly reduce the likelihood of their recurrence. You can start making improvements by first identifying any opportunities to advance the connectivity of your quality data and then prioritizing the areas where optimization is needed most.

Step 3: Establish a Formalized Structure With Purpose-Built Tools:

Manual data management practices aren’t just inefficient — they’re the main culprit behind the introduction of errors into pharma companies’ critical business information and processes. Automating documentation practices is the answer. The strongest argument for automation, according to empirical studies cited by McKinsey & Company, is that accuracy only reaches 91% when documentation tasks are performed manually. ⁽⁷⁾ Modern quality management software solutions are specifically designed to eliminate human errors in documentation while also enhancing quality professionals’ ability to manage, report on, and analyze accurate and up-to-the-second data. Not only can a proven solution digitize documentation processes and help you make better decisions sooner, but it can also dramatically increase operational efficiency and flexibility.

To learn more about digital tools that can optimize the way you manage quality data, visit MasterControl’s quality management solutions page. For more details on regulators’ increasing focus on quality data and other notable trends shaping the future of pharmaceutical and biotech industries, download MasterControl’s 2022 pharma quality trends brief.

References:

1. “FDA FY2020 Drug Inspection Observations and Trends,” by Barbara Unger, Pharmaceutical Online, Dec. 9, 2020.
2. “FDA tells drug makers to redo studies run by two contract research firms due to data integrity issues,” by Ed Silverman, STAT+, Sept. 28, 2021.
3. “Under the Spotlight: Data Integrity in Life Sciences,” Deloitte LLP, 2017.
4. Supra note 1.
5. “Today’s Digital Agenda: Intelligent Quality to Transcend Compliance,” by Neil Fausz, Accenture Life Sciences blog, Aug. 12, 2021.
6. “Controlling Quality by Managing Data,” by Barry Heavy, Accenture Life Sciences blog, Dec. 7, 2020.
7. “Operations can launch the next blockbuster in pharma,” by Ulf Schrader, McKinsey & Company Insights, Feb. 16, 2021.