When Data Integration Saves Lives

When Data Integration Saves Lives
When Data Integration Saves Lives
In an article published in Health Informatics, its author, Gabriel Perna, claims that data integration could save lives, as we learn more about illnesses and causal relationships.

According to the article, in Hamilton County Ohio, it’s not unusual to see kids from the same neighborhoods coming to the hospital for asthma attacks.  Thus, researchers wanted to know if it was fact or mistaken perception that an unusually high number of children in the same neighborhood were experiencing asthma attacks.  The next step was to review existing data to determine the extent of the issues, and perhaps how to solve the problem altogether.

“The researchers studied 4,355 children between the ages of 1 and 16 who visited the emergency department or were hospitalized for asthma at Cincinnati Children’s between January 2009 and December 2012. They tracked those kids for 12 months to see if they returned to the ED or were readmitted for asthma.”

Not only were the researchers able to determine a sound correlation between the two data sets, but they were able to advance the research to predict which kids were at high-risk based upon where they live.  Thus, some of the cause and the effects have been determined.

This came about when researchers began thinking out of the box, when it comes to dealing with traditional and non-traditional medical data.  They integrated housing and census data, in this case, with that of the data from the diagnosis and treatment of the patients.  These are data sets unlikely to find their way to each other, but together they have a meaning that is much more valuable than if they just stayed in their respective silos.

“Non-traditional medical data integration has begun to take place in some medical collaborative environments already. The New York-Presbyterian Regional Health Collaborative created a medical village, which ‘goes beyond the established patient-centered medical home mode.’ It not only connects an academic medical center with a large ambulatory network, medical homes, and other providers with each other, but community resources such as school-based clinics and specialty-care centers (the ones that are a part of NYP’s network).”

The fact of the matter is that data is the key to understanding what the heck is going on when cells of sick people begin to emerge.  While researchers and doctors can treat the individual patients there is not a good understanding of the larger issues that may be at play.  In this case, poor air quality in poor neighborhoods.  Thus, they understand what problem needs to be corrected.

The universal sharing of data is really the larger solution here, but one that won’t be approached without a common understanding of the value, and funding.  As we pass laws around the administration of health care, as well as how data is to be handled, perhaps it’s time we look at what the data actually means.  This requires a massive deployment of data integration technology, and the fundamental push to share data with a central data repository, as well as with health care providers.

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