Predictabiledi – The only predictive analytic data designed to position our clients in the forefront of ICD-10 implementation. Used in combination with Authenticd we can deliver highly relevant test data for statistical analysis in identifying trends and patterns against current and proposed ICD-10 business rules.

The core of predictive analytics relies on capturing relationships between known variables from historical healthcare data analytics and using it for predicting future outcomes.

Predictabiledi is specifically designed to supply our clients with the large amounts of ICD-10 transactional data elements they will require for testing vendor’s ICD-9 to ICD-10 products, make accurate decisioning on system remediation and for determination of the all-important revenue impacts ICD-10 places on all covered entities.

Prior to starting remediation, organizations will need to know how the change and adapt to the new world that is ICD-10. Predictive analysis will be highly critical in assisting executives and stakeholders alike in developing their ICD-10 roadmap. Likewise in the testing arena, it isn't the right approach to just test a bunch of codes under each DRG and call it good. A development and testing approach needs to test the exact code representations that will become reality in 2013.

Predictabiledi portrays the history of healthcare events and how they will be coded in ICD-10 going forward. Through our expansive clinical records database, we can determine the frequency distribution and analysis of the correct ICD-10 codes for both diagnosis and procedures. Only Predictabiledi delivers the end-to-end Provider to Payer experience and shows our clients a statisical accurate respresentation of what ICD-10 coding will occur once ICD-10 goes live.

The future of data analytics within the development and testing lifecycles requires the power of Predictabiledi. Contact us for easily understandable examples of our capability and our managed services and why using a tool based approach that only groups DRG's will leave teams highly exposed to incorrect implementation approaches and searching for better, more authentic ways to empower their organizations.