Clinical Variation Management

The Journey from Volume to Value-Based Care Starts Here

Understanding and managing clinical variation helps you drive high-quality care at lower costs.

As the healthcare industry transitions from a fee-for-service to a fee-for-value payment model, providers are increasingly taking on the financial risk of poor patient outcomes and are incented to deliver higher quality care at lower costs. Programs like the Center for Medicare and Medicaid Services (CMS) Bundled Payment for Care Improvement Initiative reward care quality instead of quantity. Healthcare providers are under immense pressure to identify and enforce best practices that efficiently deliver high-quality care across an entire patient episode.

A Quick Tour of CVM

Watch this short tour of our Clinical Variation Management application using 1315 total knee replacement patients.

Learn How a Tiny Community Hospital Deployed AI to Reduce Costs and Enhance Patient Outcomes

Watch this informative webinar featuring Dr. Michael Sanders, Chief Medical Information Officer of Flagler Hospital.
Click below to access the recording.

Discover

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.

Predict

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.

Justify

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.

Act

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.

Learn

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.  

A Small Hospital with an Ambitious Plan for AI

Ayasdi’s Clinical Variation Management 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.  

A 2018 winner of Best Hospitals by Healthgrades, Flagler Hospital of St. Augustine has always delivered care that belies its 335-bed size. Still, the decision to embark on a program to use artificial intelligence to reduce clinical variation was ambitious even for them.

The results were extraordinary. For pneumonia alone they were able to:

  • Save more than 35% per patient episode
  • Reduced length of stay by two days
  • Reduced readmissions by more than 7X

Further, their operationalization of the solution enabled them to increase their expected care process model throughput by 50% – meaning better care for everything from delivering babies to replacing joints.