Real-time cost transparency
The Worklist app powered by VigiLanz, provides strategic awareness of clinical opportunities directly in a clincian’s workflow.
We ask for “value-based care” but how are providers supposed to make value judgments if they don’t know what things cost? The Stewardship app teaches and reinforces a value mindset by exposing real hospital costs to providers and nudging in their clinical workflow when lower cost opportunities exist, supplemented with clinical guidance and citations. The result is $100+ per admission in hard-dollar savings, adding up to millions of dollars.
It provides a summary of VigiLanz activations across many different patients at different facilities. Activations displayed are filterable by many different patient and activation attributes.
• Your hospital’s true costs (not charges)
• Calculated on every med, lab and radiology order
• In provider workflow
• No extra clicks or other places to look
• 70k+ current physician users
• $100+ per admission savings
What is the Worklist App?
The Worklist app powered by VigiLanz utilizes the power of VigiLanz's rule engine and activations by incorporating them into the clinical workflow giving clinicians the ability to filter targeted views of triaged activations across the facility to ensure clinical and workflow efficiency.
What is a filter?
Filters allow users to drill down further into what activations they want to see. Users can filter activations based on: unit, status, custom rule group, priority, time-frame, and encounter status.
What is a favorite?
User's have the ability to create, remove, and edit favorites! Favorites allow users to save filtered views they will use often. A user can select a favorite and it will load activations based on the filters within the favorite.
ATTRIBUTED TO THE RESPONSIBLE PROVIDER
The app uses your own wholesale acquisition cost for medicines and your direct, variable cost accounting data for labs to give a true representation to providers of what things cost. More importantly, we know the cost of every order, by every provider, the provider specialty and the patient’s acuity. From that, we understand variation in practice patterns among similar providers.
We use machine learning to turn our understanding of each provider’s practice tendencies into personalized contextual education. Content is delivered to the right provider at the right time…and not too often.