Medical practice focuses on predicting and reducing risk based on current and historical patient data. In the past, medical professionals have made decisions that do not always have clear, definite results, but with the advancement of new technologies, this is beginning to change.
Predictive analytics helps clinicians and healthcare professionals determine the likelihood of events and outcomes before they occur so that health problems 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 decisions for individual patients, show population-level interventions, and can be applied to operational and administrative challenges.
Forecasting analytics is a branch of advanced analysis that uses current data and historical data to model predictions of the probability of future results in business processes. The best predictive analysis tools make it easier to obtain predictive insights and practical insights that improve business intelligence. Built-in one-click data science with related views and predictions simplifies predictive analysis with custom visualizations and predictive models that deliver real-time results. Easy access to machine learning (ML) algorithms trains data to produce accurate predictions and insights for stronger prediction. The best predictive analysis tools analyze business analytics to help make important decisions and future results.
Risk scoring for chronic diseases
There are many predictive analysis applications in healthcare. Prognosis and prevention are essential to ensure quality patient care. Healthcare providers can identify patients at higher risk of developing chronic disease earlier in the disease’s progression than ever before. Such insights give patients the best chance of avoiding long-term and costly health problems. Forecasting modeling is key to identifying and managing high-risk patients and improving quality and cost outcomes.
Identification of equipment maintenance needs
Forecasting analysis is useful for identifying equipment maintenance needs before they occur. The components of some medical devices degrade over time with regular use. The ability to predict when equipment needs maintenance or spare parts minimizes unplanned work process disruptions that affect healthcare providers and patients. The more new technologies and modern equipment are used by healthcare providers, the more important the predicted support becomes.
Paraplegics attached to wheelchairs and patients with spinal cord injuries who need to learn how to redirect can benefit significantly from the floating robotic exoskeleton. Work suits have proven to be useful as a means of reducing injuries in the workplace and helping those struggling to stand up.
Ekso Bionics is the global leader in exoskeleton technology, providing destructive clinical robotics for rehabilitation. Workplace injuries caused by repetitive movements, overexertion and fatigue can be alleviated with the use of an exoskeleton. Their FDA approved exoskeletal suit improves the natural abilities, endurance and overall quality of life of the wearer. Exoskeleton technology enables human mobility and improves strength and endurance with modern robotics.
Prediction of patient use patterns
Another application for forecasting analytics in healthcare is forecasting patient use patterns. Medical clinics that operate without fixed schedules need to change their staff in anticipation of patient fluctuations. Google Analytics assistance predicts usage patterns so that clinics can have enough staff to manage the flow of patients, while reducing waiting times and improving patient satisfaction. Visualization and analysis tools help clinics make better decisions about workflow adjustments and schedule changes.
Precision medicine and new therapies
The more precision medicine and genomics develop, the more assays are used to complement clinical trials and drug detection techniques. Predictable modeling and simulation predict clinical outcomes, guide the design of clinical trials, support efficacy, optimize dosage, predict product safety, and evaluate potential adverse outcomes. Prescription analysis is especially useful in drug individualization, modeling, and simulation.
In healthcare, there are many applications for predictive analysis, including risk assessment for chronic diseases, maintenance of predictive equipment, prediction of patient use patterns, and precision medicine and new therapies.