What if you could know in advance which patients would benefit from certain therapies? Or identify patients approaching a medical crisis and intervene before it’s too late? While doctors have traditionally had to rely on instinct to make these calls, predictive analytics could be a game changer for hospitals, healthcare providers and patients.

The Power of Prediction

Medical sensors and data analytics can be used to power medical devices that can predict adverse outcomes before they occur. By analyzing very large data sets, researchers can identify subtle markers, such as small changes in vital signs or patient behaviors that can be correlated to development of serious conditions like heart failure or kidney failure. If we can learn to look for the right signs, we can develop an early warning system for imminent medical crises.

Combining data analytics with body-worn or implantable medical sensors will allow us to better monitor patient health. These sensors can pick up subtle changes in biometrics, biomarkers and other patient data over time. Using predictive analytics, smart sensors could use these readings to detect early warning signs of kidney failure, stroke, heart failure and other medical crises, alerting healthcare providers before adverse events occur. Data analytics could also be used to power smart apps or devices that provide ongoing guidance to patients in response to sensor data in order to help them better manage chronic conditions.

The Rise of Precision Medicine

Predictive analytics can also help make medicine more personal. Individual biochemistry, behaviors and genetic characteristics can all influence how diseases develop in patients and how patients respond to medical interventions. Analyzing population health data allows researchers to determine how these variables interact to impact health outcomes for individual patients. Smart devices can use this data to predict how an individual patient will respond to specific courses of action so healthcare providers can make smarter, more personalized recommendations.

This type of data analytics can also help manufacturers go beyond the general results of clinical trials to better understand the value their devices bring to specific groups of patients. For example, delving into the data may demonstrate that a device or therapy with unimpressive results on average in clinical trials is invaluable for certain subsets of patients based on genetic profiles, behaviors or disease subtypes. Conversely, data analytics could help to determine which variables put patients at higher risk for rare but serious adverse outcomes.

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