Healthcare payers and providers alike are challenged by the ongoing transition from fee-for-service payment models that incentivize high volumes of healthcare services to new, value-based purchasing systems that reward quality and efficiency.
At the same time as financial incentives and risks are changing, a wide range of new, clinically relevant data sources are now becoming available. Enterprise data warehouses are bursting with more data than ever, but most organizations are still struggling to learn how to mine these rich troves of data for insights that can transform the delivery of care.
The data scientists of Berg Analytics have the expertise in artificial intelligence and clinical informatics to leverage your rich data to build predictive models that will help you precisely target patient populations for the most effective interventions.
The Berg Artificial Intelligence Clinical Information System (bAIcis™) delivers actionable Patient Intelligence™ that can help your organization succeed in the transition to value-based care models.
Custom Risk Models
With Berg Analytics you can use all of your data to fully assess the risks and characteristics of each patient and personalize care plans to achieve optimal outcomes. Customized risk stratification models that leverage all of your behavioral and socio-economic data sources as well as your traditional clinical and financial data sets will outperform standard risk models that use a much narrower band of data points. With custom risk models, you can more effectively focus care management efforts to avoid emergent acute care episodes and precisely tailor clinical treatments to reflect individual patient risk profiles.
With Berg Analytics, automated data-driven discovery tools will uncover hidden cost drivers in your patient populations. With predictive analytics to support more effective population health management, your care managers can implement programs for community health, prevention and monitoring that effectively focus resources on members with the highest risks and apply the most appropriate interventions.
Fraud, Waste and Abuse
Data mining with Berg Analytics can identify new patterns that flag potential fraud, waste and abuse, enabling earlier intervention to limit losses. Berg’s clinically informed predictive analytics are especially valuable in uncovering patterns of waste due to overtreatment - instances where providers are consistently choosing to undertake inappropriate, unnecessary or harmful treatments that are not supported by clinical evidence. Predictive models can flag questionable medical and surgical practices for proactive review and help reduce unnecessary and excessive utilization of healthcare services.