Because Candello cases are clinically coded to the event that led to the claim—instead of just the insurance information—it can correlate clinical system and process failures to financial outcomes (indemnity and expenses) that are accurate to the penny. These attributes make Candello data both evidence-based and actionable.
Data
Candello data is not only large in volume and nationally representative in breadth. It is also the only data that contains both open and closed MPL cases and cases with and without a paid indemnity.
The Data Collaborative
Candello’s national database of medical professional liability (MPL) cases is a robust patient safety learning engine, built for making better data-informed decisions that can help save lives.
Sourced from academic medical centers, community hospitals, other health care settings, and captive and commercial insurers nationwide, Candello enables comprehensive analyses compared with your peers.
The Coding Process
Candello utilizes experienced, highly-skilled and routinely audited nurse coders to transform Candello member source material into coded data for analysis and benchmarking. Recently, CRICO and Candello have developed native Artificial Intelligence (AI) tools to support and augment the long-standing clinical coding process when Candello members approve its use.