Big Data: Transforming Medicine
The intensive care unit (ICU) in every hospital contains a bewildering array of sophisticated devices that track patients’ bodily functions. The bleeping and blipping monitors let doctors and nurses know what’s happening in the bodies of very sick patients whose conditions can turn on a dime.
Each of these devices constantly generates data, and some readings, such as blood pressure and heart rate, for example, are recorded in the patient’s EMR. Traditionally, however, most of these data have been discarded.
Thanks to the work of researchers like David Maslove, this situation is starting to change. Maslove is a clinician scientist in the Queen’s Department of Medicine and Critical Care Program and a critical care physician at Kingston General Hospital. His work involves capturing and analyzing massive volumes of detailed electronic data derived from patients in the hospital’s ICU to understand more about the nature and progression of acute illnesses.
Much of these data come from blood samples, which Maslove has been collecting for whole-genome transcription profiling for about a year. What sets this project apart from other genomics projects is the sheer number of samples collected over the course of a patient’s stay in the ICU. Starting this year, high-frequency waveforms from bedside monitors and ventilators will be added to the mix, generating gigabyte-scale data that must be assembled and analyzed using novel computational approaches.
The data-intensive work in Maslove’s field has the potential to transform critical care medicine. For decades, diagnoses in emergency and ICU medicine have been syndromic – that is, patients are assessed according to a set of criteria, and if their conditions meet those criteria, the doctors and nurses follow a certain treatment course. As with all treatments used in hospitals, the utility of that treatment will have been previously verified via a highly-regulated randomized clinical trial. In other words, the treatment used in the ICU is based on aggregated similarities exhibited by the group of patients in the trial. The physiologic individuality of each patient is largely lost.
In contrast, by analyzing detailed genomic data from the ICU, Maslove hopes to identify the differences, rather than the similarities, between critically ill patients who may have been categorized as having the same condition (such as sepsis, which is Maslove’s particular interest).
The Holy Grail for Maslove and others is a database of genomic and other ICU patient data that can be used by physicians and nurses at the bedside to make better treatment decisions in real time.
“From a scientific standpoint, it’s a very exciting cross-disciplinary endeavour that involves bringing together expertise from clinical medicine, computer science, signal processing, epidemiology, genomics,” says Maslove. “We’re trying to find a way to bring all those data under the same roof so that they can be made available to clinicians at the bedside who are treating patients with rapidly evolving illnesses.”
(e)Affect Issue 7 Spring 2015