How SAS Helps You Improve Population Health
High-quality health care is both safer and more cost-efficient. It saves patient lives, reduces the burden on hospitals and enables more patients to receive treatment. SAS helps you deliver better overall quality of care by reducing readmissions, improving health outcomes and increasing patient safety.
- Understand the clinical and nonclinical factors that affect readmissions.
- Predict and prevent avoidable readmissions.
- Identify patients that have higher risk of infection to optimize discharge planning.
- Analyze structured and unstructured clinical and operational data to uncover hidden insights on indications.
- Turn insight into evidence-based knowledge that can help you predict and improve outcomes.
- Use all data available to determine optimal treatment, focused on value-based care.
- Avoid medication, surgical and other interaction errors through increased data sharing.
- Analyze diverse data sources to predict and medically investigate patient safety signals.
- Identify potential issues before they become a reality.
How does a Dutch hospital proactively treat or even prevent sepsis infection in premature infants?
SAS helped Universitair Medisch Centrum (UMC) Utrecht:
- Develop a statistical model that can support or deny the presence of the bacteria that causes sepsis in premature babies, with 90 percent accuracy.
- Reduce the unnecessary use of antibiotics, and all the consequences that such treatment entails. Prior to the statistical model, 60 percent of all premature babies received antibiotics.
- Show that analytically driven solutions are capable of solving complex problems in health care.
How is a landmark population health study helping the state of Nevada address some of its most complex health problems?
SAS is working with Renown Institute for Health Innovation on the Healthy Nevada Project, analyzing citizens' genetic, clinical, environmental and socioeconomic data to:
- Develop a health determinants platform that will surface population health risks from patient variables, such as gender, age, and personal or family health history.
- Analyze population health outcomes and their correlations to participant genetic information and varying environmental factors such as air and water quality.
- Understand how environmental factors can help predict who may be at risk, allow for quicker diagnoses and encourage the development of more precise treatments.