Line illustration reading left to right: EEG traces from a brain feed into a processor holding a neural network, which produces a draft report, and a clinician signs the finished document.

Healthcare. AI Systems, Automation.

A hospital network clears a three-week EEG backlog.

70%
less drafting time: 0
weeks of backlog, from three

70%
less drafting time: 0
weeks of backlog, from three

Client: Hospital network (under NDA), Pakistan
Industry: Healthcare
What we built: AI Systems, Automation
Timeline: 10 weeks, 2025

Before

An AI reporting pipeline that drafts EEG reports for neurologists to review and sign, cutting drafting time by 70%.

Neurologists were spending 45 to 90 minutes writing up every EEG. The queue grew faster than it could be cleared, and a three-week reporting backlog had built up behind it. Hiring more specialists was not an option at the speed the backlog was growing.

What we built

01
The decision that shaped it
We did not try to automate the diagnosis. The clinical judgement had to stay with the specialist, both for safety and for the hospital to accept the system at all. So we moved the automation one step earlier, to the blank page. If a neurologist opens a draft instead of an empty document, most of the time cost disappears without touching the decision itself.
02
What we engineered
A reporting pipeline that produces a draft report per study, structured the way the department already writes them. Clinicians review, correct and sign off, and the signed report is what leaves the system. Every draft is attributable and every edit is retained.
03
Automated draft generation per study
Clinician review and sign-off workflow. Department-specific report structure.
04
What leaves the system
Reporting moved from a bottleneck to a routine step. The existing backlog was cleared without adding specialist headcount, and the department kept full clinical control of what it signs.

01
The decision that shaped it
We did not try to automate the diagnosis. The clinical judgement had to stay with the specialist, both for safety and for the hospital to accept the system at all. So we moved the automation one step earlier, to the blank page. If a neurologist opens a draft instead of an empty document, most of the time cost disappears without touching the decision itself.
02
What we engineered
A reporting pipeline that produces a draft report per study, structured the way the department already writes them. Clinicians review, correct and sign off, and the signed report is what leaves the system. Every draft is attributable and every edit is retained.
03
Automated draft generation per study
Clinician review and sign-off workflow. Department-specific report structure.
04
What leaves the system
Reporting moved from a bottleneck to a routine step. The existing backlog was cleared without adding specialist headcount, and the department kept full clinical control of what it signs.

Automated draft generation per study
Clinician review and sign-off workflow
Department-specific report structure
Backlog clearance across the existing queue

What changed

EEG report drafting time cut by 70%, and a three-week backlog cleared.

Reporting moved from a bottleneck to a routine step. The existing backlog was cleared without adding specialist headcount, and the department kept full clinical control of what it signs.