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Clinician-Researcher

🩺 Clinical Audit · R/Python · Shiny/Dash · Reproducible Reporting

🎯 Why this matters now

The 10-Year Health Plan highlights three shifts — Hospital → Community, Analogue → Digital, Sickness → Prevention — powered by Data, AI, Genomics, Wearables, Robotics. This path focuses on data + AI foundations you can ship now: clean audit datasets, reproducible analyses, and simple interactive reports that inform service improvement and research.

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👤 Role snapshot

You combine clinical expertise with data to investigate outcomes, service effectiveness, and patient pathways. Typical inputs: clinical audit extracts, registries, spreadsheets, and published evidence — all within IG/ethics boundaries.

See also: R · Python · Shiny · Dash · Secrets & .env

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🎯 Outcomes to target (aligned to the Plan)

Clinical outcomesTimelinessAdoptionPreventionReproducibility

• Clinical outcomes: mortality/complications, readmissions, PROMs where available
• Timeliness: time from data cut → report publication
• Adoption: teams using the report, actions logged, follow-up audits completed
• Prevention: recall completeness for LTCs; proactive outreach triggered
• Reproducibility: one-command re-run; definitions/version recorded in the report

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⚙️ 90-minute quickstart

Goal: clean a small audit extract, run a simple comparison, and publish a minimal interactive view.

1) Clean & summarise (choose R or Python)

R (tidyverse)

analysis.R

# packages: install.packages(c("tidyverse","broom"))
library(tidyverse)
library(broom)

df <- read_csv("data/audit_sample.csv")  # de-identified rows
df <- df |>
  mutate(group = if_else(pathway == "intervention","Intervention","Control"))

summary_tbl <- df |>
  group_by(group) |>
  summarise(n = n(),
            age_mean = mean(age, na.rm=TRUE),
            outcome_rate = mean(outcome_success == 1, na.rm=TRUE)) |>
  arrange(desc(n))

print(summary_tbl)

# Two-sample proportion test (success rate)
tab <- xtabs(~ group + outcome_success, df)
pt <- prop.test(tab[,"1"])  # assuming 1 = success
tidy(pt)

Python (pandas + scipy)

analysis.py

# pip install pandas scipy
import pandas as pd
from scipy import stats

df = pd.read_csv("data/audit_sample.csv")   # de-identified rows
df["group"] = df["pathway"].apply(lambda x: "Intervention" if x=="intervention" else "Control")

summary = (df.groupby("group")
             .agg(n=("group","size"),
                  age_mean=("age","mean"),
                  outcome_rate=("outcome_success","mean"))
             .reset_index())

print(summary)

# Two-proportion z-test (success rate)
interv = df[df.group=="Intervention"]["outcome_success"].astype(int)
control = df[df.group=="Control"]["outcome_success"].astype(int)
zstat, pval = stats.proportions_ztest([interv.sum(), control.sum()],
                                     [len(interv), len(control)])
print({"z": zstat, "p": pval})

2) Publish an interactive view (pick one)

Shiny (R)

app.R

# packages: install.packages(c("shiny","plotly","readr","dplyr"))
library(shiny); library(plotly); library(readr); library(dplyr)

ui <- fluidPage(
  h3("Audit outcomes by pathway"),
  selectInput("path", "Pathway:", choices = c("Intervention","Control")),
  plotlyOutput("fig")
)

server <- function(input, output, session){
  df <- read_csv("data/audit_sample.csv") |>
        mutate(group = if_else(pathway=="intervention","Intervention","Control"))
  output$fig <- renderPlotly({
    data <- df |> filter(group == input$path)
    fig <- plot_ly(data, x=~age, y=~as.numeric(outcome_success), type="scatter", mode="markers")
    fig <- fig %>% layout(yaxis=list(title="Outcome (1=success)"))
    fig
  })
}

shinyApp(ui, server)

Dash (Python)

app.py

# pip install dash plotly pandas
import dash
from dash import html, dcc
import plotly.express as px
import pandas as pd

df = pd.read_csv("data/audit_sample.csv")
df["group"] = df["pathway"].apply(lambda x: "Intervention" if x=="intervention" else "Control")

app = dash.Dash(__name__)
app.layout = html.Div([
  html.H3("Audit outcomes by pathway"),
  dcc.Dropdown(["Intervention","Control"], "Intervention", id="path"),
  dcc.Graph(id="fig")
])

@app.callback(
  dash.Output("fig","figure"),
  dash.Input("path","value")
)
def update(path):
  data = df[df.group==path]
  return px.scatter(data, x="age", y="outcome_success", title=f"{path} cohort" )

if __name__ == "__main__":
  app.run_server(debug=True)

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▶️ Run

# R path
Rscript analysis.R
Rscript app.R   # or click "Run App" in RStudio

# Python path
python analysis.py
python app.py

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🗓️ Week-one build (repeatable, safe)

Day 1 — Protocol & data contract

• Define primary outcome(s), inclusion/exclusion, covariates.
• Create a data dictionary (variable name, type, definition, source).

Day 2 — Reproducible project

• R: initialise an R Project and renv; Python: venv + requirements.txt.
• Store raw data separately; write cleaned outputs to out/ (CSV/Parquet).

Day 3 — Analysis plan & report

• R Markdown/Quarto or Jupyter notebook that runs end-to-end.
• Add interpretation text next to stats output (not just p-values).

Day 4 — Interactive view

• Shiny/Dash page with filters for cohort, timeframe, and key subgroups.
• Show “Data last updated”, sample size, and definition tooltips.

Day 5 — Governance & sharing

• DPIA/Caldicott checks; small-number suppression; pseudonymisation.
• Share report internally; gather clinician feedback and iterate.

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🧰 Open-source augmentations (pick 2)

Quarto / R Markdown
Single-source, reproducible reports.

Evidence.dev
SQL + Markdown → static site; auditable.

FastAPI
Expose a single outcome metric as an API.

Git + GitHub
Track change; PR review; simple CI.

See also: R · Python · Shiny · Dash · Git · GitHub

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🛡️ IG & safety checklist

• Use de-identified/synthetic data for development examples.
• Keep secrets out of code and git; use a secret store in production.
• Apply small-number suppression and aggregation before export.
• Record approvals/ethics IDs in the README and report header.
• Keep a clear data lineage: source → transform → output.

See also: Secrets & .env

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📏 Measuring impact

• Clinical relevance: does the analysis answer the service question?
• Timeliness: time from data cut to report (target: ≤ 1–2 days for routine audits).
• Reproducibility: one-command re-run; commit hash recorded in the report.
• Adoption: number of teams using the report; decisions logged.

What’s next?

You’ve completed the Persona — Clinician-Researcher stage. Keep momentum:

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