HiPaaS Enrollment AI

The best candidate in the healthcare x12 transaction set that could benefit the most from Artificial Intelligence is the 834 enrollment and benefits transaction.
The quality of data submitted on commercial 834 and non-standard files is historically poor. The impact to health plans is great, requiring them to expend resources and funds to "fix" the data prior to loading. There is also an impact to the member, in some cases, the data from the submitter is so poor the member may never have the correct benefit. This is due to the cycle time for payers to manually fix data and by the time it is fixed the new file overwrites the data and the process must be repeated.

The use of AI or machine learning is ideal to improve this process. AI can learn the common/recurring errors for each submitter and taking the data from the manual input fill in the data to resolve future errors.

AI can be used to look at the various member situations and then fill in the most likely data. This is based on the groups benefit options and using the other family members and partial selections update missing or incorrect data. Examples are the adding of vision plan information if incomplete using other family members selections. AI can correct if a member has selected PPO plans for all benefits except medical. AI can update to the correct benefits reducing manual correction.

The sky is the limit for the use of AI in this area, the ROI in staff saving and brand quality is significant. The next chapter in the use of AI in healthcare transactions is to solve authorization processing.

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