Weeks-Long Diagnosis Turnaround
The average time from HST order to interpreted results was 3–4 weeks, during which 35% of patients disengaged from the treatment process entirely.
Replacing a 3–4 week manual process of faxed results, manual data entry, and follow-up calls with an automated pipeline that delivers diagnosis-ready results in 72 hours — reducing patient drop-off from 35% to 5%.
Automate Your Diagnostic PipelineAn automated home sleep test pipeline that tracks every referral from order to diagnosis — with OCR-powered result extraction, auto-populated diagnostic fields, AI severity classification, and a streamlined doctor review queue.
The platform eliminates manual data entry from sleep study results, reduces diagnosis turnaround from weeks to hours, and prevents patient drop-off at the most critical stage of the treatment journey.
The gap between ordering a home sleep test and receiving and interpreting results was the single biggest patient drop-off point in dental sleep medicine — averaging 3–4 weeks with frequent lost results, manual data entry errors, and patients who simply gave up waiting.
A high-volume practice ordering 50+ HSTs per month was drowning in faxed results, manual data entry, and follow-up calls to labs. Staff spent hours each week chasing results, re-entering AHI scores, and calling patients who had fallen through the cracks.
The goal was to automate every step from HST order to diagnosis-ready results — lab routing, result tracking, data extraction, severity classification, and doctor review — turning the biggest bottleneck into the fastest workflow step.
The average time from HST order to interpreted results was 3–4 weeks, during which 35% of patients disengaged from the treatment process entirely.
Sleep study results arrived via fax, requiring staff to manually transcribe AHI, RDI, SpO2, and other metrics — introducing errors and consuming hours of staff time each week.
Without automated tracking, faxed results were frequently lost or misfiled, and patients were not notified when their results were ready — leading to abandoned cases.
Without standardized severity classification, different doctors interpreted the same results differently, leading to inconsistent treatment recommendations and documentation gaps.
We built an end-to-end automated pipeline that handles every step from HST referral to diagnosis — OCR extraction, AI classification, doctor review queues, and patient notification — eliminating manual work and patient drop-off.
Automatically extract AHI, RDI, SpO2 nadir, and sleep stage data from incoming sleep study reports — eliminating manual data entry entirely.
Machine learning models flag severity levels and prioritize the doctor review queue, reducing interpretation time from 20 minutes to 5 minutes per study.
Smart lab routing based on insurance, location, and preferred partnerships — with integrated fax for labs that haven't gone digital.
Every HST is tracked from order to diagnosis, with automated notifications ensuring no patient falls through the cracks during the diagnostic process.
