In a typical hospital, nurses manually measure patient vital signs every few hours. This means that the health of their patients may change in the hours between two vital sign measurements.
Even if you brought up Hadoop for the single purpose of eliminating view-only legacy systems, it would be well worth your while.
In January, the medical center will start piloting a new technology called SensiumVitals® to monitor and transmit patient vital signs every minute. Patients in the pilot will wear a SensiumVitals patch that will monitor and wirelessly transmit heart rate, respiratory rate, and temperature. Nurses will be alerted if any of a patient’s vital signs cross certain risk thresholds, so the staff can attend to the patient immediately.
But from a long-term perspective, this sensor data enables something much more profound: predictive analytics that can allow caregivers to respond before a patient’s vital signs ever cross a dangerous threshold.
Most of those minute-by-minute snapshots of vital signs will be unremarkable, but the data points they generate (4,320 per patient, per day) are the building blocks for algorithms that can predict near-term outcomes with an ever-increasing degree of certainty. Like the previous example with heart patients, this data will reduce average time to insight for important medical decisions the staff needs to make.
This is because an increased temperature, heart rate or respiratory rate in isolation of other data may not be cause for concern. But those same vitals, combined with all of the prior data on that patient, combined with years of data on other patients with similar risk factors, combined with unique characteristics of that patient’s medical history, physical characteristics, gender and age—all of that will eventually paint a far more detailed picture, with more predictive power.
“For healthcare, we have never had the ability to do this. We have always taken the approach that we think we know what data elements are important. Now with all the data, we let the data determine what is important for predictive analysis. Yogi Berra might have said it like this: we are now able to capture the data that we know that we need as well as the data that someday we will know that we needed.”
Then researchers can easily present the anonymous sample cohort to their Internal Review Board for approval, without ever having seen uniquely identifiable information. This speeds the process of preparing and approving a study, while assuring patient confidentiality.
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