How Predictive Analytics Is Used in Healthcare
The practice of medicine focuses on anticipating and reducing risk based on current and historical patient data. Medical professionals historically have made decisions that don’t always have clear, certain outcomes, but with advances in new technologies, this is beginning to change.
Predictive analytics helps clinicians and medical professionals determine the likelihood of events and outcomes before they happen so that health issues can be more effectively prevented and treated. The algorithms used in Artificial Intelligence (AI) and the Internet of Things (IoT) provide historical and real-time data that help users make meaningful predictions. Predictive algorithms support clinical decision making for individual patients, show interventions on a population level, and can be applied to operational and administrative challenges.
Predictive analytics is a branch of advanced analytics that uses current data and historical data to model forecasts about the likelihood of future outcomes in business processes. The best predictive analytics tools make it easy to gain predictive insights and actionable insights that improve business intelligence. Built-in data science with linked views and one-click predictions simplify the predictive analysis with custom visualizations and predictive models that give real-time results. Easy access to machine learning (ML) algorithms trains data to produce accurate predictions and insights for stronger forecasting. The best predictive analytics tools elevate a business’s analytics knowledge to aid in important decision making and future outcomes.
Risk Scoring for Chronic Diseases
There are many predictive analytics applications in healthcare. Prediction and prevention are essential to delivering quality patient care. Healthcare providers can pinpoint patients with higher risks of developing chronic conditions earlier in a disease’s progression than ever before. Such insights give patients the best chances of avoiding long-term, costly health problems. Predictive modeling is key to identifying and managing high-risk patients and improving quality and cost outcomes.
Identifying Equipment Maintenance Needs
Predictive analytics is useful for identifying equipment maintenance needs before they happen. Components of some medical equipment degrade over time with regular use. Being able to predict when equipment needs maintenance or replacement parts minimizes unplanned workflow disruptions that affect healthcare providers and patients. The more that new technologies and advanced equipment is used by healthcare providers, the more important predictive maintenance becomes.
Paraplegics confined to wheelchairs and patients with spinal cord injuries who need to learn how to rewalk can greatly benefit from a wearable robotic exoskeleton. Robot suits have proven useful as a means of reducing workplace injuries and aiding those who struggle to stand upright.
Ekso Bionics is the global leader in exoskeleton technology, providing disruptive clinical robotics for rehabilitation. Workplace injuries caused by repetitive motions, overexertion, and fatigue can be alleviated with the use of an exoskeleton. Their FDA-approved exoskeleton suit enhances the wearer’s natural abilities, endurance, and overall quality of life. Exoskeleton technology empowers human mobility and enhances strength and endurance with advanced robotics.
Predicting Patient Utilization Patterns
Another predictive analytics application in healthcare is the prediction of patient utilization patterns. Medical clinics that operate without fixed schedules have to vary their staffing levels in anticipation of fluctuations in patients. Analytics help predicts patterns in utilization so that clinics can have sufficient staff scheduled to manage the flow of patients while reducing wait times and improving patient satisfaction. Visualization tools and analytics help clinics make better decisions about workflow adjustments and scheduling changes.
Precision Medicine and New Therapies
The more that precision medicine and genomics evolve, the more analytics are used to supplement clinical trials and drug discovery techniques. Predictive modeling and simulation predict clinical outcomes, guide clinical trial designs, supports effectiveness, optimizes dosage, predict product safety, and evaluates potential adverse outcomes. Prescriptive analytics is especially useful in drug individualization, modeling, and simulation.
There are many predictive analytics applications in healthcare including risk scoring for chronic diseases, predictive equipment maintenance, predicting patient utilization patterns, and precision medicine and new therapies.
