Can predictive analytics save lives?

High velocity and large volumes of patient data provide a unique opportunity to detect and predict life treating complications. The sheer quantity of data, coupled with limited critical care resources, makes data exploitation almost impossible. Yet this data holds the key to a myriad of clinical and resource allocation decisions.

Analytical tools can exploit this data to provide operational, financial and clinical benefits to all layers of high acuity management and staff. These platforms can harness the vast amount of available data to deliver highly accurate predictive clinical analytics that provide hospital management and medical personnel with the preemptive information they require to better manage their resources.

Correctly integrated into the clinical workflow, such platforms will deliver massive benefits, reducing the length of stay, lowering treatment costs and freeing up revenue-producing high acuity beds for other patients. Most importantly, these platforms can provide indications of life threatening complications. Without predictive analytics, floor management and discharge decisions are determined based on patients’ current state of health – decisions which may not be optimal for either the healthcare facility or the patient.

In short, YES, predictive analytics can indeed save lives.

The CLEW (previously known as Intensix) platform utilizes innovative prediction models derived from Big Data analysis and advanced high-dimensional analytics techniques. Deployed as a standalone app or integrated into an existing solution, the platform continuously processes a wide range of clinical data, including the patient’s medical background from the EHR, prior hospitalizations, vital signs, laboratory results, medications, procedures, etc. CLEW’s analytics engine identifies relationships between real-time physiological data and latent medical conditions, which is vital for the predictive early warnings generated by the system. The analytical engine identifies changes in the patient’s condition in real-time and indicates the possibility for life-threatening situations.