Using analytics to determine competitive rates for Insurance products: Insurance companies can run predictive analytics on the insured patients health data and claims data to predict future costs for next policy renewals or use the trends for new business offers, and at the same time may want to offer Insurance plans on competitive rates.
Using analytics to increase the accuracy of diagnoses: Physicians can use predictive algorithms to help them make more accurate diagnoses. For example, when patients come to the ER with chest pain, it is often difficult to know whether the patient should be hospitalized. If the doctors were able to answers questions about the patient and his condition into a system with a tested and accurate predictive algorithm that would assess the likelihood that the patient could be sent home safely, then their own clinical judgments would be aided. The prediction would not replace their judgments but rather would assist.
Analyzing Electronic Health Records (EHR) - Using analytics we can aggregate and analyze patient's Electronic Health Records (EHR) from hospitals and other healthcare providers and make them available online at blazing speed. Analytics can bring down the cost of providing healthcare by sharing patient information to doctors and providers so as to reduce ordering duplicate tests and cut down on time taken to provide patient care. Current solutions have only a few months of historical patient information available online, plus the search is very slow
Big Data in Hospital Network - Instead of taking readings every few hours, a provider can record data from all the medical instruments in a pediatrics ward. By deploying analytics on the captured data we can look at it from different points of view. This can help physicians spot infection trends 12 to 24 hours in advance by running predictive analytics based on data from previous trends and the patient in hand. This can help start a course of treatment earlier, save the lives or shorten stays.
"Predictive analytics allow researchers to develop prediction models that do not require thousands of cases and that can become more accurate over time."
Reducing preventable hospital readmissions using analytics - Hospital can reduce high 30-day readmission rates, using predictive analytics to keep patients at home. Real-time EHR data analytics can help a hospital cut readmissions based on data elements included in the patient's chart. This kind of analytics helps hospital direct more resources towards the patients with the highest risks who needed the most care, so they will do well when they leave the hospital.
Using analytics for preventive healthcare to cut down patient/provider expenses - Healthcare predictive analytics enables providers with tools they need, to proactively act on their patients' needs. By using patients' past behavior, analytics can help predict future events, such as a diabetic ending up in the emergency room since he did not refill his medication or a child with asthma requiring a hospital admission due to environmental triggers of her disease.
Hospital quality and patient safety in the ICU - The ICU is another area where predictive analytics is becoming crucial for patient safety and quality care. The most vulnerable patients are prone to sudden downturns due to infection, sepsis, and other crisis events which are often difficult for busy staff to predict. Integrating bedside medical device data into sensitive analytics algorithms can detect vital signs hours before humans have a clue.
Determining patients at high risk using analytics - EHR data fed to a predictive analytics algorithm can give clinicians an early warning about sepsis, which has a 40 percent mortality rate and is difficult to detect until it's too late. Running analytics on EHRs can provide us information, as to when aggressive diagnosis and treatment are needed and when they can be avoided.