Healthcare process improvement is the methods toolkit: Lean (waste), Six Sigma (defects / variation), and PDSA (small tests). It is how you find a broken step and change it on purpose. It is not the outcome scorecard, and it is not buying automation software.
If you meant the outcome (how the hospital runs — ALOS, OR, discharge) → operational efficiency in healthcare. If you meant automation software (what to automate, vendors, buy-in) → healthcare workflow automation.
This page is the named methods, a traditional-vs-AI comparison, and one imaging example. PYCAD is not a process-improvement or hospital-ops platform.
Lean, Six Sigma, PDSA
Three lenses. Use the one that matches the problem. Do not run all three as a slogan.
- Lean — remove waste: time (waiting for a result), motion (a nurse crossing the unit three times for one tray), resources (expired stock, a redundant lab). Map admission → discharge and the waste is visible.
- Six Sigma — kill defects and variation. A defect here is a medication error, a misread scan, a post-surgical infection. Collect the data (hand hygiene, sterilisation, airflow), find the root cause, fix the system, not the person.
- PDSA — Plan a small change (rewrite the intake form), Do it on the next 10 patients, Study whether check-in got faster or cleaner, Act: keep, tweak, or scrap. Frontline experiments, not a six-month programme.
Lean declutters. Six Sigma is the detective for high-risk variation. PDSA is how a unit tests an idea this week. Sponsor + a cross-functional team (nurses, techs, admin, not just managers) + one visible problem is the launch kit. Track wait, error, and overtime before and after.
Traditional vs AI-powered
The methods do not go away when you add a model. AI changes the speed and the question — from “what just happened?” to “what is likely next?”
| Aspect | Traditional (manual Lean / Six Sigma) | AI-assisted |
|---|---|---|
| Data | Small samples, spreadsheets, weeks of observation | Large, messy sets (EHR, labs, imaging metadata) in near real time |
| Focus | Reactive: find the bottleneck after it hurts | Predictive: staffing, supply, readmission risk before the surge |
| Scale | One process, one team | Several processes watched at once — if the data is clean |
| Risk | Human bias, thin samples | Garbage in, confident garbage out. Still needs a steward |
NLP that drafts a note, or a model that flags sepsis risk, is a tool inside a PDSA loop. It is not a substitute for naming the waste.
One imaging case
A mid-size radiology department: report TAT at 48 hours, scanners idle ~30% of the time, techs spending up to 15 minutes per patient on disconnected data entry. Lean mapping showed the waste. The fixes were specific: automated scheduling and reminders (no-shows and idle slots), a secure line to the referring physician (stop the phone tag), and an imaging-AI first-read in PACS that flags likely findings for the radiologist. TAT moved to about 12 hours; utilisation climbed because cancelled slots were backfilled. That is process improvement with an imaging tool in it — not an “ops platform.”
PYCAD builds custom web DICOM viewers and medical-imaging AI (annotation → model deployment). That is a connector / imaging stack, not a hospital-ops platform. The imaging-AI example above is one honest use of that stack inside a process-improvement case. Case studies.