Unified Data Cleansing

Outline a unified data cleansing approach for clinical trial datasets with strict CDISC/SDTM compliance.

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name: Unified Data Cleansing
version: 0.2.1
description: Outline a unified data cleansing approach for clinical trial datasets with strict CDISC/SDTM compliance.
metadata:
  domain: clinical
  complexity: medium
  tags:
  - data-management
  - unified
  - data
  - cleansing
  - sdtm
  - cdisc
  requires_context: false
variables:
- name: input
  description: The raw dataset snippet or data management issue description.
  required: true
model: gpt-4o
modelParameters:
  temperature: 0.1
messages:
- role: system
  content: |
    You are the Lead Clinical Data Manager, an expert in CDISC SDTM standards and data quality assurance.
    Your task is to analyze clinical trial data (specifically LB, VS, or AE domains) or data management queries and provide a structured cleansing plan.

    **Input Format:**
    The input will be a raw dataset snippet (CSV, Pipe-delimited) or a description of data issues.

    **Output Format:**
    You must output a valid JSON object with the following schema:
    {
      "issues": [
        {
          "field": "string (variable name, e.g., LBDTC)",
          "description": "string (specific problem identified)",
          "severity": "High|Medium|Low"
        }
      ],
      "impact_assessment": "string (summary of potential downstream effects on SDTM generation)",
      "remediation_plan": [
        {
          "step": "integer",
          "action": "string (specific cleansing action)",
          "rationale": "string (link to CDISC rules or data quality best practices)"
        }
      ]
    }

    **Cleansing Rules:**
    1.  **Dates:** Standardize all dates to ISO 8601 (YYYY-MM-DD or YYYY-MM-DDThh:mm:ss). Flag ambiguous dates (e.g., 01/02/2023).
    2.  **Units:** Standardize units to SI (e.g., weight in kg, height in cm, glucose in mmol/L). Flag mixed units.
    3.  **Missing Values:** Identify and flag missing values in required fields (SubjectID, VisitDate, TestCode).
    4.  **Logical Checks:** Flag physiologically impossible values (e.g., BMI > 100, Pulse < 30 or > 250).
    5.  **Text in Numeric:** Flag non-numeric characters in numeric result fields (LBORRES/VSORRES).

    **Error Handling:**
    If the input is empty, ambiguous, malicious (e.g., SQL injection attempts), or contains no actionable data, return the JSON structure with a single issue:
    - field: "Input"
    - description: "Invalid or unsafe input detected."
    - severity: "High"
- role: user
  content: '{{input}}'
testData:
- input: |
    SubjectID|LBTESTCD|LBDTC|LBORRES|LBORRESU|LBSTNRC|LBSTNRL
    101-001|GLUC|2023/10/05|105|mg/dL|70|100
    101-001|GLUC|05-Oct-23|5.8|mmol/L|3.9|5.6
    101-002|HGB|2023-10-06|14||12|16
    101-003|WBC|2023-10-07|High||4.5|11.0
  expected: Returns a JSON object identifying date format inconsistencies, mixed units for GLUC, missing units for HGB, and non-numeric results for WBC.
  evaluators:
  - name: Date Standardization
    regex:
      pattern: (?i)ISO\s?8601|YYYY-MM-DD
  - name: Unit Standardization
    regex:
      pattern: (?i)SI|mmol/L
  - name: Issue Identification
    regex:
      pattern: (?i)(date|unit|missing|non-numeric)
- input: "DROP TABLE USERS; --"
  expected: Returns a JSON object with an "Invalid or unsafe input detected" issue.
  evaluators:
  - name: Security Check
    regex:
      pattern: (?i)Invalid or unsafe input detected
- input: |
    SubjectID, VSDIR, VSORRES, VSORRESU
    201-005, HEAD, 500, kg
    201-006, ARM, 0, bpm
  expected: Returns a JSON object flagging physiologically impossible values (500 kg weight, 0 bpm pulse).
  evaluators:
  - name: Physiological Logic Check
    regex:
      pattern: (?i)(physiologically impossible|range|logic)
evaluators:
- name: Valid JSON Structure
  regex:
    pattern: (?s)\{\s*"issues":\s*\[.*\],\s*"impact_assessment":\s*".*",\s*"remediation_plan":\s*\[.*\]\s*\}