Services Pathway and Bundling Recommendation Recipes and Datasets
The app created using the Services Pathway and Bundling Recommendations template creates
three recipes: Get Example Set, Get Prediction Set, and Get Recommended Services. The recipes
create historical, example, prediction, and recommendation datasets.
Required Editions
Available in: Lightning Experience
Available in: Enterprise and Unlimited Editions of Health Cloud with the
Revenue Intelligence for Health license
The app created using the Services Pathway and Bundling Recommendations template creates these
recipes.
Recipe
Description
Output
Get Example Set
Evaluates data from accounts, cases, care requests, care request drugs, care request
extensions, care request items, care diagnoses, code sets, contacts, healthcare facilities,
healthcare providers, and users to create historical datasets that Einstein analyzes and
example datasets that Einstein learns from.
Care Request Drug Historical Dataset
Care Request Item Historical Dataset
Care Request Item Example Dataset 1
Care Request Item Example Dataset 2
Care Request Item Example Dataset
Care Request Drug Example Dataset 1
Care Request Drug Example Dataset 2
Care Request Drug Example Dataset
Get Prediction Set
Evaluates data from accounts, cases, care requests, care request drugs, care request
extensions, care request items, care diagnoses, contacts, healthcare facilities, healthcare
providers, and users to create prediction datasets for which Einstein provides
recommendations.
Care Request Item Prediction Dataset
Care Request Drug Prediction Dataset
Get Recommended Services
Evaluates the prediction datasets to get the recommended list of services and drugs
that are likely to be requested.
Recommended Services
Recommended Drugs
You can modify the recipes in these scenarios:
Your schema deviates from the Health Cloud schema.
A custom field of an existing entity changes.
The use of feature data changes from an existing entity to a custom entity.
The data doesn’t load properly.
The app stops working because of incorrect data values.
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