Healthcare organizations are swiftly moving from basic descriptive analytics to predictive analytics. Instead of just presenting information about past events, predictive analytics can estimate the possibilities of future outcome depending on the historical data patterns. It allows clinicians and other healthcare providers to get alerts about potential events before they take place. With such information available in advance, medical practitioners can make informed choices before making a decision.
Being one step ahead can prove to be of immense importance in case of intensive care, surgery, or emergency care. It can play a critical role in a situation, where a patient’s life might depend on a quick reaction time. When it comes to chronic diseases, prediction and prevention go hand-in-hand. Healthcare providers that can identify individuals with higher risks of developing chronic diseases, such as diabetes complications, and etc. have the best chance of helping patients avoid long-term illnesses that are rather costly and difficult to treat.
Identifying deadly medical conditions
Whether a patient suffers from diabetes complications or some other medical illness, the use of predictive analytics helps in proactively identifying patients who are at the highest risk of developing an incurable disease. Apart from identifying chronic diseases, clinical artificial intelligence is also able to detect the hospital-acquired medical conditions before they become deadly. While still in the hospital, patients face the probable threat of developing sepsis, a sudden downturn, or a hard-to-treat infection due to their existing medical conditions.
Furthermore, data analytics can help healthcare providers take care of a critical situation by reacting as quickly as possible and identifying an upcoming patient deterioration in time. The technologically advanced cognitive machines can help in preventing suicide and patient self-harm. Individuals, who are likely to cause self-harm, can be identified at an early stage, so that they can receive mental health care to avoid serious events, such as suicide. The use of electronic health record data in combination with other tools helps in accurately identifying the patients with suicide attempt risks.
While predictive analytic modes focus on high accuracy, industry experts are keen on making it more impactful for patients. Apart from measuring a solution’s effectiveness, it is important to focus on the number of patients who can be helped in order to reduce the rate of overall patient deterioration. When the focus shifts to the impact of the predictive analytics solution, the approach is majorly transformed. Focusing on the high-risk patients doesn’t really improve patient outcomes; however, by augmenting the drive towards the patient action, it helps in realizing the earliest stages of adopting solutions.