Using Predictive Analytics in a Healthcare Enterprise
Medicine focuses on anticipating and reducing risk by looking at current and historical data. Making medical decisions with certainty has become easier with the advance of predictive analytics in healthcare. Predictive analytics show healthcare providers the likelihood of events and outcomes and prevent and treat health issues. Advances in Artificial Intelligence and the Internet of Things (IoT) have given rise to algorithms and predictive models that make meaningful predictions based on historical and real-time data. Predictive algorithms can support patient care decisions, make population-based health interventions, and support operational and administrative challenges.
Rather than relying on a patient’s traditional medical history, predictive analytics estimate future health outcomes based on historical data patterns. Predictive analytics models alert business users to potential events to make informed decisions about how to proceed. Predictive analytics applications are especially useful in intensive care, surgery, or emergency care, where the ability to make the right medical decision quickly affects a patient’s life.
What is predictive analytics? Predictive analytics is a branch of advanced analytics that uses big data to create predictive models that forecast future outcomes. The predictive analytics process combines historical and current data with advanced statistics and machine learning to model future events. Learning from past data results in better business intelligence, better decisions, and better customer service. Predictive analytics tools rely on deep learning from machine learning to create algorithms that detect data trends and patterns. This predictive analysis helps build models that identify and solve business problems and make predictions.
There are several use cases of predictive analytics throughout the healthcare industry. Healthcare providers and insurance companies can apply predictive analytics tools to financial services, business processes, and data security challenges in addition to patient care to see gains in operational efficiency and overall customer experience.
Analytics techniques allow healthcare professionals to risk chronic diseases through lab testing, biometric data, claims, patient data, and population health factors. Such insights help healthcare professionals identify patients with higher risks of developing chronic conditions to be treated as early as possible to ensure the best possible outcome.
Predictive models help hospitals avoid 30-day hospital readmissions by alerting medical professionals when a patient’s risk factors predict a high likelihood for readmission. Patient readmissions cost hospitals expensive penalties under Medicare’s Hospital Readmissions Reduction Program, which financially incentivizes healthcare providers to prevent unplanned returns.
Data modeling helps healthcare providers get ahead of patient deterioration during a hospital stay. Patients are exposed to the threat of sepsis, acquiring infections, or deteriorating due to their existing medical condition. Analytics help medical professionals act quickly if a patient’s vitals worsen and help predict deterioration before symptoms present.
When patients fail to arrive for appointments, it causes unexpected scheduling gaps that cost money. Predictive analytics can forestall appointment no-shows by identifying patients who are likely to miss an appointment without warning. Preventing no-shows improves provider satisfaction, reduces revenue losses, and allows healthcare centers to give open appointments to other patients in need.
Properly managing the supply chain is the best way for a healthcare provider to manage costs. The supply chain is a major cost source and one of the most significant areas healthcare organizations can cut unnecessary spending and improve operational efficiency. Hospital executives turn to predictive analytics tools to gain actionable insights into ordering patterns and supply use to reduce variation.
Predictive analytics improves healthcare management decisions and patient care, resulting in more meaningful patient-care and provider relationships. The more that automation and machine learning tools advance, the more important large data sets become, and the more predictive modeling will improve healthcare.