Ayasdi for Clinical Variation Management draws on the power of machine intelligence to rapidly analyze all your electronic medical record (EMR) and financial data, representing thousands of patient procedures and millions of individual events. Using unsupervised and semi-supervised learners it automatically surfaces groups of similar patient procedures and generates clinical pathways that result in the best patient outcomes at the lowest costs for your local patient population – in a fraction of the time associated with traditional carepath generation methods.
Powerful prediction draws on discovery and in the case of clinical variation management allows healthcare organizations to accurately predict the quality and cost for the desired treatment outcomes. Understanding what to expect for a certain surgical procedure is a function of understanding the patterns and groups associated with the previous treatment of that procedure.
For AI to deliver on its promise in healthcare it must be able to justify and document its recommendations. Solutions that deliver a blackbox cannot be deployed in environments where patient lives are at risk. Ayasdi’s CVM solution details each of the inputs to its recommended pathways and facilitates comparison to existing guidelines.
Intelligence needs to be activated. Ayasdi is integrated with EMR systems to facilitate the rapid deployment of intelligence across the organization. Further, Ayasdi’s CVM application provides healthcare organizations with intuitive dashboards that let you objectively monitor adoption and adherence to standardized clinical pathways. The adherence analytics allow you to engage in data-driven conversations about care variation, capture the collective voice of your physician community, and continuously gather feedback to improve existing clinical pathways.
The Ayasdi application comprehensively analyzes your hospital’s data and captures all clinical variation – both good and bad. It reflects the collective experience and expertise of your own physicians and ensures that you do not miss out on good variations that result in better patient outcomes. Furthermore, the application is always looking at new data as it comes in, finding emerging patterns that reflect the current practice within the organization.