Perspectives from a pandemic: COVID-19 magnified challenges to data access and interoperability


The emergence of coronavirus disease 2019 (COVID-19) in the U.S. set off a race to “flatten the curve” — to slow the spread of the virus to give the health system time to prepare to meet a surge in patients requiring critical care — and thus save lives.

Policymakers, health systems, communities and individuals continue to rally around this common goal. Planning efforts demand consistent and timely data about hospital capacity and utilization, as well as the existing inventory of ventilators, personal protective equipment (PPE) and other materials. This information is essential to monitoring disease spread and health system capacity, actuarial and financial management, supply chain management, the prioritization of patient intervention, and the safety of face-to-face visits.

At the hospital or health system level, these questions can be more quickly answered by integrating across IT systems that track clinical data from electronic medical records (EMRs), inventory, bed capacity and more. But at the state and national levels, where greater coordination is required, tracking is often manual and much guesswork remains. Information may either be too sparse, too decentralized or too out of date to provide an accurate view during a rapidly evolving crisis.

Real-time data and the case for interoperability

Over the long term, lack of cross-system interoperability and communication poses barriers to care coordination, chronic condition and complex care management, and real-time clinical decision support. These deficiencies contribute to increasing the overall cost of care and of supporting IT infrastructure. Responding to this crisis has meant that hospitals, states and federal agencies have had to manually compile spreadsheets and faxes to get a clearer picture for COVID-19 monitoring and response.

There are two key regulatory initiatives to help facilitate medical data collection and accessibility while addressing security and patient privacy concerns. First we had the 2009 HITECH Act, which incentivized the adoption of EMRs. More recently, the 2016 21st Century Cures Act included provisions to promote greater transparency between patients, providers and health IT developers by modernizing how health data should be shared.* These laws and regulations will enable interoperability and put us in a better position to not only respond to COVID-19, but to better manage patient health. The data they unlock are essential components of a more responsive and agile health system.

While the immediate challenge is responding to COVID-19, by giving form to the once abstract benefits of real-time data sharing and interoperability, the pandemic is driving positive digital transformation that will inevitably continue to shape the future of health care.

COVID-19 and digital transformation

The idea of “digital transformation” has been part of most organizations’ strategic plans for years. But suddenly, with many employees working from home and conducting meetings virtually, achieving that transformation has become paramount. Similarly, in health care, the pandemic crystalized demand for new ways to collect, analyze and share information, and at the same time exposed just how far our industry must go to provide the processes, tools and infrastructure to make it possible. Advances in areas like cloud migration, automation, systems integration, and yes, interoperability, had all been progressing slowly before the pandemic. Now those efforts have been fast-tracked.

With an energized sense of purpose, we’ve all made progress. For example, hospitals and other providers are centralizing their collection and communication of admission, discharge and transfer data, current inventory and bed capacity. Other data collection processes already on the path to gain speed — including pharmacy and lab data — were also hastened during the pandemic. In turn, health plans have sped up prior authorizations to expedite payment for COVID-19-related diagnoses and treatments. These stakeholders have also accelerated their ability to share deidentified and aggregated information with public health leaders for government response coordination.

In addition to helping to improve communication and coordination, this progress is helping to drive innovation around the use of real-time data and emerging technology applications for COVID-19 forecasting. As we look ahead, there will be many opportunities to leverage more timely, real-time data.

There’s no going back

COVID-19 quickly created enormous health and economic impacts. Through this devastating public health crisis, we’ve glimpsed how real-time data and interoperability can positively improve our health system. With access to the most comprehensive set of information, better decisions about care delivery, population health and patient experiences become possible.

Our health system can expect challenging months ahead as we navigate regional surges, and if an anticipated wave of COVID-19 cases materializes during the annual flu season. Imagine how useful real-time data would be for tracking ICU capacity, predicting hotspots of new outbreaks or a host of other things we can’t do today because of the latency or unavailability of data.

There’s no going back. We must sustain the advances we’ve made in 2020, and we must find ways to continue to improve real-time data collection and interoperability.

Additional stories around the industry response to COVID-19 and our efforts to confront current challenges can be found on Optum® Community Circle. You can also find more perspectives on enabling health care innovation on our data, analytics and technology blog.

*Because of the pandemic, the rules governing data sharing are now due to be implemented and enforced beginning in 2021.


About the Author

Sridhara_Srinivas_080916_ppt (1)Srinivas Sridhara, PhD
Vice President, Enterprise Data Strategy and Data Science
Optum Enterprise Analytics

Srinivas Sridhara is Vice President of Enterprise Data Strategy and Data Science within the Optum® Enterprise Analytics organization, where he heads central teams focused on machine learning and artificial intelligence, data strategy, and research. These teams provide services across Optum business units, including payer (Medical and Rx), provider, life sciences, and government sectors, with use cases and problem areas varying from automation and policy analysis to clinical decision support and consumer analytics.

Srinivas has twenty years of experience across machine learning and artificial intelligence, data strategy, performance measurement, health services research, healthcare planning, and health policy analysis. He serves on national policy committees, including the National Quality Forum’s Cost and Resource Use Committee, which reviews and endorses measures to be included in MACRA programs (e.g. MIPS, APM’s). Prior to Optum, Srinivas was the Managing Director for Data Science and Advanced Analytics at Advisory Board, heading its data science practice and leading IP development to support technology, consulting, and research engagements. Srinivas previously worked in government and academia leading policy analysis, healthcare planning, and health services research programs. Srinivas has a PhD in health services research from the Johns Hopkins Bloomberg School of Public Health.

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