qikit

SPC charts and quality improvement tools for healthcare, in Python.

One function. One result object. One .plot() call.

Install

pip install clinical-qikit

Quickstart

from qikit import qic

result = qic(y=values, chart="i")
result.plot()   # returns a Plotly figure

qic() mirrors R's qicharts2, the package most healthcare QI practitioners already know — same chart types, same run-chart signal detection, same mental model, now in Python.

Chart types

All chart types are computed by the same qic() entry point via the chart= argument:

qic(..., funnel=True) renders a funnel plot instead, for cross-sectional comparison across p/pp/u/up charts sorted by denominator.

Beyond qic(), the package also provides:

• paretochart(x, data=...) — Pareto chart of categorical frequencies.
• bchart(...) — Bernoulli CUSUM chart for early detection of shifts in binary (pass/fail) data.
• design(factors, ...) / analyze(design_obj, response, ...) — full-factorial design of experiments (DOE) and effect analysis.

Excel add-in

A Microsoft Excel task-pane add-in built on the same charting engine (ported to TypeScript) lives in excel-addin/ — see its README for dev-harness and sideload instructions, and docs/excel-addin-roadmap.md for the roadmap.

Web app

app.py is a Streamlit front end over the same engine:

pip install -e .[app]
streamlit run app.py

Development

uv sync --extra dev
uv run ruff check .        # lint (CI-enforced)
uv run pytest tests/       # 176 tests incl. cross-language fixture conformance

The Python engine is authoritative; the TypeScript port in excel-addin/packages/engine conforms to it via the shared JSON fixtures in fixtures/. After intentional engine changes, regenerate the fixture snapshots with uv run python scripts/update_snapshots.py.

References

1. Montgomery DC. Introduction to Statistical Quality Control, 8th ed. Wiley, 2019.
2. Provost LP, Murray SK. The Health Care Data Guide, 2nd ed. Jossey-Bass, 2022.

License

MIT