Big Data for Small Communities

An in-depth look into how Geo-personalized care for infectious disease management is catching on.

The relationship between population health and personalized medicine is clearly evident in infectious disease management. One clinician’s antibiotic choices for a patient in Florence, Kentucky can result in spread of infection-and resistance-reaching far beyond the exam room.

The concern is palpable now that antibiotic-resistant bacteria, or superbugs, are becoming increasingly impervious to typical antibiotics. The CDC reports that more than two million people in the U.S. contract antibiotic resistant infections annually, resulting in at least 23,000 deaths.

Knowledge of local resistance trends is key to managing infections. This insight can support more targeted antibiotic prescribing, which is vital to curbing the spread of superbugs. Hospitals already track in-patient resistance trends and provide data to their doctors in the form of biograms-but their knowledge is limited to the data within their walls. And what of infections managed outside of hospitals? Too often clinicians in outpatient settings are in the dark; they are unaware of bacteria prevalent in their communities and lack data about which bacteria are resistant to specific antibiotics.

Translation: Little real-time data are available to support decision-making against the growing threat of superbugs. This is a big problem that calls for a big data solution.

Big Data and Mobile Innovation

The concept of big data-particularly in health care-is well documented and continues to have enormous potential. Data, and the analysis that goes with it, can provide clinicians with valuable, real-time information around a specific patient population to support and inform a clinician’s decision process.

Today, it’s equally as important to consider how information is delivered. For physicians at the point of care, mobile is the optimum modality. Given the burdens of patient consultations, documentation, and administrative work, clinicians have little time to be running back to their computers to access timely epidemiological information. Relevant information must be accessible and assimilated within seconds, while the patient is present. With updated analyses delivered in real time, mobile platforms provide clinicians with constant access to vital information – without disrupting care delivery.

An Example of Big Data at Work

Epocrates Bugs + Drugs, a community-based antimicrobial susceptibility reference app, is a big data solution sized for a single caregiver treating a single patient in one moment of care. Through the synergy of data and mobile innovation, the app gives ambulatory clinicians access to continually updated information about bacteria and resistance patterns in their patient’s local community-something that has never before been available. Geotargeted data is right at your fingertips.

With Epocrates Bugs + Drugs, the big data insight is what fuels the application. More than 6000 bug-drug sensitivity data points, from over 300 microbiology labs nationwide, flow into the athenahealth’s cloud-based EHR each workday. Information is de-identified, aggregated, and tied back to patient residential ZIP codes for use in the app. A dynamic radius is configured based on data density to deliver meaningful results for each geographic setting. This means the app can zero in tightly on some communities, but needs to zoom out to a larger geographic radius in areas where there are fewer data points.

Epocrates Bugs + Drugs in Action

In the modern clinical setting, quantitative information is an increasingly crucial part of the equation that drives decision-making at the point of care. A century ago, diagnostics and therapeutics were few in number, and data to drive evidence-based decisions were largely lacking. Over the past decade, resources such as the Epocrates application, have become ubiquitous in clinical practice. Now, Epocrates Bugs + Drugs delivers relevant, quantified geotargeted data to clinicians for use in the complex decisions around infection management.

For example, if a patient presents with a complicated urinary tract infection, waiting for lab tests to confirm the specific bug involved could take days. In the interim, without a reference or any other data, a physician might decide to prescribe an antibiotic effective for the most common bacteria, E. coli, and a few other common bugs. In a typical outpatient setting, clinicians previously had no information about local resistance patterns and superbugs. Now, with geotargeted susceptibility data, clinicians have access to an additional-potentially-vital-resource to factor into clinical decision-making. No data can replace clinical judgment; clearly there are a myriad of patient-specific factors to consider in managing infections. Yet, the data in this pioneering app represent a new frontier in the direction of more personalized medicine, supporting the chances of successful empiric and specific therapy.

Looking Ahead

Big data offers a reservoir of possibilities-the challenge is to harness that data in a convenient mobile format for health care professionals to use at the point of care within seconds. When content and insight are brought together, providers receive a more meaningful picture of an individual patient’s condition. The more high-quality, unbiased information a physician has at his or her disposal, the better.

As we move ahead, we look forward to a world wherein certain data are aggregated and shared – with appropriate privacy safeguards – for the benefit of all patients. Dr. Amy Abernathy, in her TEDMED talk, encouraged people and organizations to consider donating their data – just like people donate blood – for public good. Big data is here. Now, it is a matter of using the information to solve real-world clinical problems, and expand its benefits. Through the lens of big data, we can continue to support population health management while providing personalized, high-quality patient care.

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