Why Gaps in Patient Data Leave Us Exposed During a Pandemic

Featured in MedCity News

In the fight to contain the spread of Covid-19, two important things are becoming clear. First, fighting pandemics depends on large volumes of data that are complete, and accurate and available fast. Second, public health emergencies are a people problem, not a patient problem.

In the traditional patient-centric world, a primary care physician orders a test that gets sent off to a lab. Then, the physician is notified when Patient ID No. 12345678’s results are back. When a situation grows into a public health issue, we need to know the person behind the ID number—how to contact the individual, where they live, who they live with, where they have been, who they have seen, and more. This type of information is vital to properly managing a pandemic and allowing us to safely move out of a shelter-in-place mode. 

From doctors and care providers to patients, partners, payers, and everyone in between, we are elevating healthcare management in ways that were previously not possible. 

Unfortunately, there is a disconnect between the patient data collected by a health system and the person data needed for public health. This disconnect is exacerbated when testing occurs at a location where the sick person has no prior relationship or, as is often the case, no patient record, as is the case for drive-through testing locations. There are defensible reasons for some separation of these types of data, specifically for the protection of patient privacy. However, there has also been some neglect in our health data management processes and systems.

To slow the spread of Covid-19 and support eventual economic recovery, we need to confidently know who is sick and how to contact them so public health workers can better understand and manage the spread of the disease. Getting ahead of the spread is more complicated than knowing Patient ID No. 12345678 tested positive and passing that information back to the physician or organization that ordered the test. As a country, gaps in patient data leave us exposed—and when it comes to the coronavirus, we’re losing valuable time:

Eliminating Patient Data Gaps to Reduce Risk

Many progressive organizations have championed the need for a person-centric understanding of health and health risk, and the push for data interoperability and initiatives around population health and health information exchange target this concept. However, such efforts have been stymied by lack of incentives for providers, funding issues, and complex information-sharing regulations at the local and federal level. While the COVID-19 crisis has quickly provided the incentives to collect and share the required person-centric data to aid in the fight against disease, regulations and a lack of technical infrastructure still stand in the way.

How can healthcare organizations, government agencies, and others more effectively connect complex patient information without changing ownership or implementing new core applications? One way is by adding technical infrastructure that enables confident connection of person identity data and information sharing and can operate independent of any application. Augmenting an existing technical stack or data warehouse with a master person index (MPI) that leverages referential matching or is reference data aware can quickly bridge application and infrastructure gaps while laying a foundation for the future.

Innovative health information exchanges and health systems already leverage solutions like this to connect patient information across traditionally disconnected sources and regions—especially useful when the work of healthcare systems and teams suddenly needs to be coordinated, such as during a public health emergency. An MPI with referential matching matches or links together person records after enriching them with missing demographic data such as phone numbers and addresses. This eliminates time spent hunting and pecking for information that is often locked on an island of automation. At Northwell Health, a next-generation MPI helped the health system successfully resolve 87 percent of mismatched claims.

Taking a proactive, data-driven approach to identity matching and resolution would help prepare organizations to act with confidence during public health emergencies and remove the guesswork from response efforts, saving time while strengthening the ability to control the spread of disease.