Predictive analytics uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for patients. This information can include data from past treatment outcomes as well as the latest medical research.Predictive analytics increases the accuracy of diagnoses thereby helping in preventive medicine and improving public health.It provides employers and hospitals with predictions concerning insurance costs,allows researchers to develop prediction models,benefit patients for better outcomes.We apply the data collected by our ERP to predictive analytics for responses to medications, hospital readmission rates, time of recovering of patients.Examples are predicting surgery success rates, determining the likelihood of disease, helping a physician with a diagnosis, and even predicting the future wellness.Analytics help healthcare organizations remind patients to keep up with a healthy lifestyle, as well as keep track of a patients lifestyle choices.
To aid the CDH- Kuwait project in their clinical decision support (CDS) system we have provided BI foundation to correlate, analyze, and gather insight from Clinical data. Our BI tools are providing CDH with various predictive analytics, data modeling, forecasting, and trending for clinical data. For example we are providing R-reporting for : post-op patient stay, predictions for bleeding during surgery, success ratio of surgery for patient.The Cardiac EMR tracks and monitors all clinical activities to help doctors identify medical & surgery trends, future risks and to improve patient outcomes. The analytical data is needed to implement effective ways to identify, measure, and monitor quality of care. The reporting capabilities assist with complying with industry standards as well as meaningful use, ICD-10.