FMLA Intermittent Leave Tracking Screener
8.00 in non-compliant intermittent FMLA leave episodes and recertification triggers identified across across 2 items.
Employee took 4 episodes in May against medical certification cap of 2 episodes/month. Exceeds certified frequency by 100%.
Method1Correlated electronic physical badge entry logs against claimed intermittent medical hours.
2Audited absence frequency against physician medical certification parameters under 29 CFR 825.
| Reference | Description | Leave_Episodes |
|---|---|---|
| EMP-401 | Certified Medical Frequency Exceeded | 4.00 |
| EMP-401 | Contradictory Badge Swipe While on Claimed Leave | 4.00 |
Employee badge access logs and HRIS intermittent leave absence requests, plus DoL 29 CFR Part 825 FMLA regulations
Intermittent FMLA compliance exception report and statutory leave balance entitlement schedule (Resolution Dossier, Findings Schedule)
Open FMLA Intermittent Leave Tracking Screener on sample data now with no sign-up, then run your own files free for 14 days. $2,500 a month after that, cancel any time. 30% goes to the referring partner.
Read in memory for the session, never stored, never used to train a model.
Outputs are computed from your inputs and the tool's rules. Check them before you rely on them.
14 days on your own files, then $2,500 a month.
Open FMLA Intermittent Leave Tracking Screener on sample data now with no sign-up, then run your own files free for 14 days. $2,500 a month after that, cancel any time.
On a sample file: 8.00 in non-compliant intermittent FMLA leave episodes and recertification triggers identified across across 2 items.
Why not just use ChatGPT?
Rules that stay current: the rate cards, tariffs, code sets and regulations it checks against are kept up to date for you; a one-off prompt starts from nothing each time. Evidence that stands up: every finding cites the line, the file and the rule, so it holds up with a vendor, a payer or an auditor. The same answer every time: the checks are written rules, not a fresh guess, so this month's result can be compared with last month's. Nobody has to own it: no one inside has to build, test and maintain a home-made tool, and files are not pasted into a public chatbot.
Start on your own files
If you would rather talk it through, write to dev.sathya@baseloom.app; that reaches Dev Sathya, founder.