The Overarching Covid-19 Challenge in Healthcare: Stronger Data Integration

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The Covid-19 pandemic has hit all areas of our society and requires the coordination of a broad coalition of assets to contain it. The answer includes multiple federal and state government agencies, thousands of hospitals, and a wide range of commercial manufacturing capabilities and supply chains. We saw this coordination and collaboration with personal protective equipment and ventilators early on and will see it again as we speed up vaccine distribution.

To coordinate an effective response, it is important to incorporate different types of data from multiple domains and sources, which has long been a challenge in healthcare. Barriers include government budget structures that do not encourage data sharing and legacy databases that create barriers to data integration. The commercial sector also brings with it the challenges of competition and proprietary systems. Even seemingly simple questions, such as how many ICU beds are available in a ward, are incredibly difficult to answer in near real time. While well-tagged and easy-to-use websites offer impressive updates to case numbers, emergency response centers have found it difficult to incorporate this data into bed availability, hospital stay projections, workforce data, supply chain data, harm reduction measures, and social determinants of health . and other key data elements that enable effective planning and response.

In addition to public health challenges, new models of care have highlighted the need to better integrate data to ensure care for chronic diseases. The pandemic has accelerated the adoption and use of telemedicine. But here too, tools, sensors, apps and devices are often provided on different data platforms, which makes it difficult for patients and providers to integrate data in a meaningful way.

The pandemic shows the need for better data integration to improve management of this crisis as it continues to hamper the daily care of patients.

Lessons learned from defense and intelligence communities
The healthcare industry is lagging behind other industries in adopting data management and open source innovations. The health community can learn a lot about data management from defense and intelligence agencies. They have to integrate large amounts of data from different systems in order to create a common operational picture that supports decisions about the life and death of war fighters.

The 9/11 attacks showed that data gaps can be fatal. The 9/11 Commission’s report found that information that could have prevented this tragedy was scattered across the databases of several different intelligence agencies. Following criticism by the Commission, the secret services adopted “lakes” with little schema data that could accommodate several “streams and rivers” of different data and enable easier integration. Think of these data systems as huge tables, with each cell containing an entry element. With automated meta-tagging, every information cell can be correlated with every other data element in order to reveal patterns that would otherwise have remained undetected. These data platforms also enabled the accumulation of massive data stores that optimize advanced analytics and artificial intelligence. Intelligence agencies also benefited from security measures at the individual cell level, which improved data security. This is an important feature to consider as health information comes under increasing cyberattack.

The intelligence community also advocated open source tools and open architectures for these data systems. Open source enables the rapid development of new tools at a lower cost. Open architectures avoid costly and stagnant vendor lockdowns and enable the introduction of new world-class tools and features such as are often developed by small niche companies and startups

Many of these innovations were introduced 15 years ago and eagerly adopted in the commercial arena. However, this does not apply to the health sector. However, there are notable exceptions that represent bright spots in the health landscape.

Advana: Association of different systems and users on a common platform
Advana, a Department of Defense (DoD) data platform, consolidates more than 200 business systems across the DoD and makes data discoverable, understandable, accessible and usable for advanced analysis by more than 17,000 users in the Army, Navy and Air Force about operational readiness, contracts, supply chain logistics and more. The platform has helped the Department of Defense coordinate its Covid-19 response by enabling the easy integration of a wide variety of data, including case, bed, supply chain, readiness and financial data, to make key healthcare decisions to meet. The open architecture platform supports multiple projection models and analysis tools that allow the DoD to validate the results in ways that would not have been possible with a single approach.

Advana faced many of the data integration obstacles that healthcare IT leaders are aware of: non-standard interfaces, duplicate data and systems, legacy technology, and a history of different entities pulling their own data for decision making. In order to integrate different data from spreadsheets, APIs (Application Programming Interface), database dumps and data warehouses from across the company, Advana transfers data feeds, automatically categorizes them, marks them and converts them into a common data model for analysis at the company level to improve.

Prepare for the next health crisis
The value of big data in healthcare is clear, but unless we can integrate and correlate different types of data, we cannot realize the benefits. The data challenges of the Covid-19 response illustrate this problem. The seams between government agencies, health systems, and departments within the same organization create chronic barriers to data sharing. Few organizations manage more data than defense and intelligence services, and like in healthcare, their decisions often have life and death consequences. For critical decisions, they have developed effective strategies in order to create a common operational picture through robust data integration. As we continue to respond to this pandemic and prepare for the next crisis, the health community should learn from these mission-critical organizations.

Editor’s Note – The author is an advisor to the Department of Defense.

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