The Data Extraction Agent reads, understands, and routes unstructured medical documents so your team never misses a lab result, referral, or imaging report again.
The agent parses every document—no more backlog or triage errors.
Data is flagged, extracted, and routed before it gets buried.
Clean, structured summaries sent directly to your EMR.
The agent understands intent and routes docs to the right person or system.
The AI can convert reports into clear, patient-facing explanations on demand.
- Reduce chart review and triage bottlenecks
- Eliminate manual retyping of diagnostic results
- No missed critical findings or overlooked labs
- Alerts when data is incomplete or inconsistent
- Ingests from any source (fax, email, external EMR)
- Sends clean summaries to your preferred systems or staff
- Works across specialties, departments, and locations
- Adaptable to urgent care, diagnostics, outpatient, and hospital
BitLab specializes in helping medical professionals convert medical insights into compliant, investor-ready healthtech platforms.
and 40+ others
A patient undergoing diagnostic workup for suspected colorectal cancer had a biopsy sent to a regional pathology lab. The resulting faxed report was 9 pages long, with several nested interpretations. The oncologist reviewed the summary but missed a crucial line noting lymphovascular invasion (LVI)—which should have prompted more aggressive staging and treatment planning.
The pathology report wasn’t parsed or flagged for critical content. Staff filed it in the EMR, but no one realized LVI was mentioned on page 7 of the narrative. As a result, the initial treatment plan lacked necessary escalation.
- Patient underwent conservative therapy and had to return for extended treatment
- Clinic faced reputational damage with the referring physician
- Internal review cited “documentation overload and missed insight”
The AI would have:
- Parsed the report and flagged the presence of “lymphovascular invasion”
- Summarized the finding in a structured field for provider review
- Triggered a routing alert to the lead oncologist
→ More accurate treatment planning
→ Reduced clinical risk and litigation exposure
→ Better documentation for tumor board discussions
A multi-location clinic received 80–100 external referrals daily via fax and email. Each document needed manual triage: was it a new patient, imaging follow-up, or re-referral? Referral coordinators struggled to keep up, leading to scheduling delays and missed SLAs.
Referrals were unstructured and arrived with inconsistent formatting. Critical info (reason for visit, preferred provider, urgency) was often buried or handwritten. Documents were manually reviewed, tagged, and uploaded—a process taking 2–3 minutes per referral.
- Referral backlog of 300+ cases
- 4–6 day average response time
- Lost revenue from referrals that went elsewhere due to delays
- Detected document type as referral
- Extracted key fields (reason, referring physician, urgency)
- Auto-routed based on provider, specialty, and insurance filters
→ Referral review time reduced from 3 minutes to <15 seconds
→ 48-hour SLA achieved across all clinics
→ 2x increase in referral conversion and throughput
A radiology center received hundreds of PDF lab results for patients undergoing contrast imaging. Lab values like eGFR and creatinine were manually entered into a form to assess kidney function before scan approval. In one case, a decimal point error in creatinine led to a scan being performed when it should have been postponed.
Lab data was being entered manually by front-line staff. Visual errors (1.9 entered as 0.19) caused downstream risk. Supervising techs weren’t always cross-checking labs against thresholds due to workflow pressure.
- Patient underwent contrast-enhanced imaging with impaired renal function
- Internal incident filed and patient required hydration + follow-up care
- Payer flagged the case during a random utilization review
- Parsed lab values directly from the PDF and normalized the units
- Flagged creatinine and eGFR outside threshold for renal clearance
- Sent an approval block to imaging staff with explanation and suggested actions
→ Clinical error prevented through automated verification
→ Protected patient safety and reimbursement eligibility
→ Reduced liability and strengthened compliance processes