What Does Success Look Like in Healthcare and Life Sciences Data Management?

There’s hardly been a more challenging time for data leaders in healthcare.

Even before the arrival of a once-in-a-century pandemic, which demanded new insights virtually overnight, healthcare organizations were already struggling to keep up with the demand for data and the analytics it delivers. A staggering volume of new digital health information— from electronic health records and IoT sensors to mobile apps and more — poses an unprecedented challenge (and opportunity) for those leading modern clinical and healthcare technology teams.

In addition, many organizations face skepticism around the quality and reliability of these data assets. But it’s never been more important to get these fundamentals right. A new generation of healthcare leaders are using trusted data to fuel predictive diagnosis, deliver personalized care, and provide mobile and remote video services. Pharmaceutical researchers and drug manufacturers are leveraging cloud technology and hyperautomation to improve every step of the value chain, from discovery to distribution.

Navigating all this disruption is hard enough when you’re not also responding to the daily alarms of a prolonged public health crisis. Fortunately, we can hear directly from leaders with first-hand experience steering large public and Fortune 500 healthcare organizations to learn how they effect change and deliver fast results using AI-powered data management.

Accelerated Drug Discovery: Eli Lilly Unlocks 20% Faster Clinical Trials with Trusted AI

R&D is the first bottleneck for any pioneering healthcare treatment. The sudden emergence of the COVID-19 virus only underscored the importance of agile and efficient drug discovery, a process that spans both laboratory research and patient testing to prove efficacy and safety.

Veteran data leaders know that operational questions are rarely solved with a single data domain. Instead, organizations need a unified view of governed data across departments, from clinical and commercial operations to manufacturing and research.

Having matured its data governance program in recent years, pharmaceutical giant Eli Lilly is now reaping the benefits of a trusted enterprise data foundation. The award-winning team uses trusted AI models to optimize its clinical study design, reduce discontinuation rates, and accelerate drug development timelines by up to 20%.

Stronger Supply Chains: Pfizer Automates 99% of Cloud Data Integration

As healthcare and life sciences organizations modernize their IT infrastructures in the cloud, they also need to grapple with the complex legacy footprints of their physical hospitals, labs, and manufacturing sites — all essential on-prem environments where uptime is crucial.

At Pfizer Digital, data and analytics leaders have the ambitious goal of digitizing supply chain, process and development across all areas of healthcare, from oncology to ophthalmology; this work starts with migrating the company’s siloed systems to a new cloud data warehouse.

Using a cloud-native integration between Snowflake and Informatica, Pfizer rapidly scales up data processing and sharing, automating 99% of mappings from on-prem to cloud and eliminating the need for laborious hand-coding or troubleshooting.

Simplified Data Onboarding: Anthem Saves $10M With AI-Powered Catalog

Healthcare leaders need well-functioning data architectures to succeed in their digital transformation programs. This includes a comprehensive, end-to-end view of how data is cataloged and transported throughout the enterprise, so that teams spend less time preparing data and more time executing with it.

Leading health insurance provider Anthem uses AI-powered metadata to enhance its central data catalog and support advanced programs around genomics and home and virtual health. Having a richer, user-friendly catalog improves data literacy across the organization and streamlines data onboarding for new projects from days to hours. This operational unlock has recouped $10 million annually in time and cost savings, improving care for over 200 million members.

Just-in-Time Analytics: UNC Health Builds Critical Dashboards in One Weekend

In any public health emergency, clinical leaders rely on analytics teams to quickly collect vast amounts of data, share new knowledge as it becomes vetted and available, and improve patient outcomes by arming decision-makers with the best insights at the right time.

Following the initial COVID-19 outbreak, UNC Health leveraged data management technologies to mobilize a coordinated response. Using a central data catalog and self-serve data governance portal, the team deployed up to 10 critical dashboards within a week of the outbreak. This approach helped reduce preparation time for new analytics assets by up to five hours, improving delivery cycles to business users and bolstering enterprisewide trust in data.

Enhanced Member Care: CVS Health Reduces Data Quality Issues by 99%

Maintaining data quality is an ongoing challenge for healthcare organizations. Not only is the volume of medical data increasing, providers are also servicing members through a growing number of remote and direct-to-consumer channels.

As a diversified health services company, CVS Health needs to keep patient data consistent across its integrated model of pharmacy benefits, health insurance and neighborhood clinics. To serve members faster and prepare its IT systems for migration to the cloud, CVS Health uses AI-powered data quality to automate file monitoring and data processing, delivering client reports in days instead of months.

Now, CVS Health processes millions of benefits updates each month, enabling the business to scale efficiently while reducing data errors by 99%.

Better Care for All: NYC Health + Hospitals Arms 50,000 with Trusted Analytics

Whether you’re in the public or private sphere of healthcare, rising costs are leading to an increased focus on efficiency. Hospitals and clinics strive to increase capacity through better resource utilization (while lowering costs to patients and insurers). But this transformation requires an all-hands-on-deck approach to democratize data use across functions.

As the largest public healthcare system in the U.S., NYC Health + Hospitals has always had an ambitious charter to deliver affordable care to all New Yorkers. In recent years, the organization’s data leaders sought to modernize its data and IT infrastructure, not only to sustain a vast network of over 70 locations and 1.4 million patients, but also to support smarter and more resilient operations.

The team partnered with Informatica and Snowflake to consolidate its siloed data sources and medical glossaries, and empower employees with a self-serve analytics dashboard called “DnA Dash,” where doctors and administrators alike can quickly find the data they need to make decisions.

As a result, data scientists can get started faster on building smarter predictive diagnosis tools, and providers are optimizing facility use and treatment to deliver higher-quality, lower-cost care.