Patient Data Sharing: Lessons Learned and Opportunities Ahead

As we enter a new year, it important to reflect on some of the lessons the healthcare industry has learned from the previous challenging year, and how we can move forward in the future. The pandemic has emphasized the need for patient data to be easily shared and has revealed gaps in the way data is recorded, aggregated, normalized and interpreted. This has again become clear as we determine how, when and to whom the COVID-19 vaccines will be administered. To do so quickly and accurately requires automated tools to normalize the data and identify specific cohorts.

The 21^st Century Cures Act, is intended to improve interoperability, data sharing, and patient engagement. When the rules were finalized, we expected big changes on the horizon for how healthcare could share data and information more seamlessly. However, the COVID-19 pandemic brought these issues into immediate relief. As we now attempt to vaccinate the globe, we have learned that the healthcare industry was underprepared to aggregate, normalize and share information that was critical to containing and managing the disease.

Learn how one state's immunization database is supporting the COVID-19 vaccine rollout in this HIMSSCast episode on HIMSS TV.

Clinicians, despite being under tremendous pressure, must be able to record sufficient details about their patient’s conditions, treatments, and outcomes so that the resulting data can help manage the pandemic going forward. The unprecedented global collaborations that arose to research this novel disease, as well as help healthcare systems respond to the pandemic, relied heavily on data, much of which was incomplete or of undetermined quality. This made it hard to draw firm conclusions about which treatments were working and which were not. We did not understand how many people were infected, nor did we know what the priority areas would be. It took much more effort and time to extract value from the data than was necessary. True, some of the issues were wrapped up in politics and technological challenges such as the availability of testing, but many issues were related to how we record, aggregate, normalize and interpret the data we did collect. 

Some of issues that arose included the lack of standardized terminology to capture details of COVID-19 infections and conditions. Rapidly changing coding recommendations put burdens on health information systems and created analytics challenges, particularly during the start of the pandemic. Large-scale observational research undertaken concurrently with controlled trials relies on existing health system data. Unfortunately, much of the data needed to understand the severity of illness or likely comorbid factors were either not captured or captured in heterogeneous ways. A great deal of iteration and collaboration was required to determine the best ways to classify patients and extract data for analysis.

There are many reasons for these gaps, including incentives for electronic documentation remaining tied to financial and quality reporting metrics. EHRs differ in their ways of collecting and storing information. Additionally, new interoperability standards, such as FHIR and the U.S. Core Data for Interoperability, have not been fully implemented and do not yet have the necessary specificity to address these important research questions. 

Other parts of the health information ecosystem required to respond to the pandemic would benefit from improved interoperability and strong data normalization. These include integrated delivery networks, enterprise data warehouses, Health Information Exchanges (HIEs), and health analytics vendors. HIEs and immunization registries, for example, manage vast amounts of patient data which could be used to understand the pandemic, help with prioritizing/administering vaccines, and over the long term, address population health challenges.

The COVID-19 vaccine rollout is just the latest example of our problematic response to the pandemic and has raised concerns about maintaining an equitable distribution of vaccine. Health IT has a major role to play in the dissemination and evaluation of the coronavirus vaccine. There is a need to prioritize who should receive the vaccine, report on who was missed, and follow up. This requires data from disparate sources (e.g., EHRs, facility-based registries, state and jurisdictional immunization information systems, etc.) that will need to be standardized and integrated into a single system. To do so quickly and accurately will require automated tools to normalize the data and identify specific target populations. Well-defined terminology value sets can be designed to stratify the population based on risk, likelihood of benefit, and need. These cohort definitions could then be used prospectively by decision support algorithms to help in the implementation of best practice guidelines for vaccination as well as for population health reporting, ensuring that the correct target populations receive the vaccines as intended.

Unfortunately, the likelihood that all this will be accomplished without a significant investment in data systems is low. It requires the proper capture, aggregation, normalization and summarization of patient data in a manner that does not add further burden to the providers and the delivery networks. The confusion so far about how to equitably administer 330 million doses of different COVID-19 vaccines has raised serious concerns about whether we are up to the task. There is plenty reason to be optimistic that sometime this year we will see an end to quarantines and stay-at-home advisories. But the bigger lesson of the past year is the need for widely deployed health IT tools that will help the industry leverage the massive amounts of data available to address the new challenges that the future will inevitably bring.

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