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Automating Clinical Data Analysis: The Pipeline From Hospital Exports to Paper Drafts

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Automating Clinical Data Analysis: The Pipeline From Hospital Exports to Paper Drafts

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I've been building Data2Paper — a tool that turns research data into complete paper drafts. The latest challenge: handling clinical datasets from hospital systems.

If you've never worked with hospital data exports, here's what makes them... fun.

The input problem

A typical clinical data export looks like this:

PatientID | Age | Sex | HbA1c | SBP | DBP | eGFR | Dx | AdmDate | DisDate | Status
001       | 67  | M   | 8.2   | 145 | 92  |      | T2DM | 2024-01-15 | 01/25/2024 | alive
002       | 54  | F   |       | 128 | 78  | 85   | 2型糖尿病 | 20240203 | 2024-02-10 | 
003       | -5  | M   | 7.1   | 300 | 85  | 92   | type 2 DM | 2024-03-01 | 2024-03-08 | dead
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Notice: three different date formats in the same column, the same diagnosis coded three different ways, an obviously wrong age, a systolic BP that's probably a data entry error, missing values that could mean "not tested" or "not recorded," and mixed languages.

This is normal. Every clinical researcher I've talked to confirms: this is what the export looks like.

The analysis pipeline 

Raw export (CSV/XLSX)
│
├─ Structure detection
│   └─ row = patient? visit? wide? long?
│
├─ Data cleaning
│   ├─ Date format standardization
│   ├─ Coding unification ("T2DM" = "2型糖尿病" = "type 2 DM")
│   ├─ Outlier flagging (SBP=300, Age=-5)
│   └─ Missing value classification (not tested vs not recorded)
│
├─ Variable typing
│   ├─ Continuous (age, HbA1c, eGFR)
│   ├─ Categorical (sex, diagnosis, comorbidities)
│   └─ Time-to-event (survival time + censoring status)
│
├─ Statistical analysis (Python execution)
│   ├─ Baseline table with per-variable test selection
│   ├─ Regression (logistic / Cox / linear / Poisson)
│   ├─ Survival analysis (KM + log-rank)
│   └─ Diagnostic evaluation (ROC + AUC)
│
└─ Output generation
    ├─ Formatted tables (baseline, regression results)
    ├─ Figures (KM curves, ROC curves, forest plots)
    └─ Manuscript sections (methods + results)
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Key technical decisions

Python execution, not LLM computation. Statistics must be verifiable. The LLM writes the interpretation; scipy, statsmodels, and lifelines compute the numbers.

Clinical variable lookup. Recognizing "SBP" as systolic blood pressure enables domain-aware outlier detection (flag 300 mmHg as likely error) rather than purely statistical outlier methods.

Assumption checking. Every statistical test includes prerequisite verification — normality for parametric tests, events-per-variable for logistic regression, proportional hazards for Cox. Running analysis without assumption checks is the #1 reason clinical papers get sent back by reviewers.

The baseline table problem

Generating Table 1 (baseline characteristics) sounds simple but requires per-variable logic:

for variable in dataset:
    if is_categorical(variable):
        # n (%), chi-square or Fisher's exact
    elif is_normal(variable):
        # mean ± SD, t-test or ANOVA
    elif is_skewed(variable):
        # median (IQR), Mann-Whitney or Kruskal-Wallis
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The tricky part is automating the normality decision and handling the edge cases (small cell counts triggering Fisher's instead of chi-square, for instance).

Stack

  • Next.js + Vercel
  • Claude API for text generation
  • Python chain for statistical computation
  • Export: PDF / DOCX / LaTeX / ZIP
  • 7 output languages

What I'm still figuring out

  • Better heuristics for distinguishing "not tested" vs "not recorded" missing values
  • Automated detection of wide vs long format in longitudinal datasets
  • Handling mixed-language clinical notes in the same dataset

If you've worked on similar problems — clinical data pipelines, automated statistical analysis, or structured document generation from data — I'd love to compare notes.

datatopaper.com

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