Data Analytics to Achieve Productive Outcomes

Data Analytics to Achieve Productive Outcomes

By Todd Simpson, CIO, FDA

Todd Simpson, CIO, FDA

The U.S. Food and Drug Administration (FDA) regulates foods, drugs, biologics, medical devices, cosmetics, dietary supplements, tobacco products, electronic products and more. Between 2009 and 2013, the FDA’s roles and responsibilities increased considerably as a result of five significant pieces of legislation, leading to an even larger amount of data influx from multiple sources. Analyzing this vast amount of data created a tremendous challenge, especially when scrutinizing data around adverse events, which could mean the difference between life and death.

Last year, in order to gain a more complete understanding of what was needed to utilize this data to make real-time decisions, the FDA conducted a business intelligence (BI) assessment to determine the best technology to help meet the FDA’s growing analytics needs. The assessment uncovered system-wide bottlenecks, constrained resources, and delayed time to value with key data sets across the roughly 114,000 reports and more than 145 data sets that currently only leverage the web intelligence feature of the SAP BusinessObject Suite.

New Year. New Technology

Because of the assessment, the FDA decided to enhance the BI technology to meet growing analytics needs, update technical and architectural components that were causing latency and reporting issues, improve data sharing abilities, provide better end-to-end visibility, and expand access to end-user training.

Real-time Insights for Real-time Decisions

The FDA’s strategic goals are dependent on accessing data efficiently and analyzing it effectively. The organization is focused on making sure we have the "right tool for the right job," which means real-time dashboards to track signals and occurrences around outbreaks and adverse events that do not require formatting and manipulation.

These new capabilities will provide for better reporting on post-market drug safety as well as improved real-time insights when identifying adverse effects tied to a product or group of products. This will allow the FDA to make decisions quickly that would have previously taken much longer regarding the safety of products being used by the public. The improved exploration capabilities will also allow the agency to analyze data sets to uncover trends in products that are out of compliance.

Moving Forward with Globalization

With globalization it is imperative to create an environment where information sharing, data-driven risk scoring, assessment and analysis to provide enhanced intelligence are critical. Data gathered from multiple sources need to be searchable in order to obtain a comprehensive picture of how a product or group of products is performing. This will also give a more complete view of medical events worldwide, so that we can better plan and address them, before they escalate.

In addition, integrating data across the organization will allow teams to share best practices and recognize great work that can be leveraged in new ways to protect public health.

Solving Challenges in Adverse Event Reporting

The FDA has collected adverse event data through many sources—internal to FDA and external. Once this data was collected, complex statistical algorithms are applied to see if the information collected was alarming to determine if the FDA should do further investigation. The process of integrating data, co-relating, processing, visualizing and making statistical sense of the adverse events data to issue timely and lifesaving warnings is a time consuming task. The BI tools that we are implementing this year will allow the FDA to better analyze the data to make quicker, better decisions for food and product safety.

The impact of realizing this vision is profound for the FDA as all of the agency’s strategic goals are dependent on accessing data efficiently and effectively. The exponential growth of data–structured, unstructured, social media, predictive, and more– requires an enterprise-level BI strategy for predictive and advanced analytics.

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