Prototype development of dynamic predictive models for disease prevention

To provide better and improved patient care, it's important to know the driver of critical clinical processes and focus areas of improvement. For example, readmission to hospital and death after discharge are adverse patient outcomes that are serious, common and costly. Similarly, prolonged “Length of Stay” increases the possibility of nosocomial infections, introduces patient safety issues, increases the cost per discharge and consumes considerable hospital resources including beds and staff time. Being able to accurately predict the risk of such outcomes would allow health care professionals to focus on appropriate measures to reduce the risk. This paper demonstrates how, with the help of a predictive analytics tool, we can dynamically build models to predict any patient outcome of our choice. This prediction function calculates the probability of the outcome for a given set of patients and those probabilities are used to derive a risk score index for each patient. The result is finally presented in a dynamic business dashboard for further management actions.