How SAS Helps Improve Population Health
Integrate health and nonhealth data to guide whole person care, as well as community programs that reduce health disparities. Improve quality of care and health outcomes by integrating analytics within your 360-degree view of patients, members and clients. Gain insights at all levels – from individual care, to cohorts, to full populations – to inform policy for better communities. SAS enables you to expand access to care, improve health outcomes and increase patient safety.
- Analyze structured and unstructured clinical and operational data – including freeform notes and focus group transcripts – 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.
- Understand the clinical and nonclinical factors that affect readmissions.
- Avoid medication, surgical and other interaction errors through increased data sharing.
- Analyze diverse data sources to predict and medically investigate patient safety signals.
- Identify patients that have higher risk of infection to optimize discharge planning.
- Predict and prevent avoidable readmissions.
Whole person care
- Provide a more complete, accurate picture of client services and the impact on human and financial outcomes across health and nonhealth services.
- Understand overall community needs, as well as contextual factors that can become barriers to care.
- Forecast demand for services needed by high-risk populations, and measure program effectiveness
The aim is to develop multiple models in SAS in order to better inform parents, provide the best possible healthcare for babies for a better long-term neurodevelopmental outcome and to eventually apply it to other intensive care departments. Manon Benders Professor & Head of Neonatology University Medical Center Utrecht
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.
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.