Medication Reconciliation

---

name: medication-reconciliation

description: Compare patient pre-admission medication lists with inpatient orders to automatically identify omitted or duplicated medications and improve medication safety.

license: MIT

skill-author: AIPOCH

---

# Medication Reconciliation

Compare patient pre-admission medication lists with inpatient orders to automatically identify omitted or duplicated medications and improve medication safety.

> **Medical Disclaimer:** This tool is for reference only. Final medication decisions must be confirmed by qualified medical staff. All patient data must comply with applicable data protection regulations (e.g., HIPAA).

Quick Check

python -m py_compile scripts/main.py
python scripts/main.py --help

When to Use

Workflow

1. **PHI Check:** Before processing, prompt the user to confirm data has been de-identified: "Please confirm that the input files have been de-identified or that you have authorization to process this patient data under applicable regulations (e.g., HIPAA) before proceeding."

2. Confirm patient ID, pre-admission medication list, and inpatient orders are available.

3. Validate that both input files are well-formed and patient IDs match.

4. Run the reconciliation script or apply the manual comparison path.

5. Return a structured report separating continued, discontinued, new, and duplicate medications.

6. **Dose-change detection:** When a drug appears in both lists with different dose strings, flag it as `dose_changed` with a warning: "Dose change detected — verify with prescribing physician before proceeding."

7. Flag warnings for critical drug classes (anticoagulants, hypoglycemics, antihypertensives, antiepileptics).

8. If inputs are incomplete, state exactly which fields are missing and request only the minimum additional information.

Usage

# Basic usage
python scripts/main.py --pre-admission pre_meds.json --inpatient orders.json --output report.json

# Use example data
python scripts/main.py --example

# Verbose output
python scripts/main.py --pre-admission pre_meds.json --inpatient orders.json --verbose

Parameters

| Parameter | Type | Required | Description |

|-----------|------|----------|-------------|

| `--pre-admission` | file path | Yes | JSON file of pre-admission medications |

| `--inpatient` | file path | Yes | JSON file of inpatient orders |

| `--output` | file path | No | Output report path (default: stdout) |

| `--example` | flag | No | Run with built-in example data |

| `--verbose` | flag | No | Include detailed matching rationale |

Output Format

The reconciliation report separates results into:

**Dose-change example:**

{
  "dose_changed": [
    {
      "drug": "Metformin",
      "pre_admission_dose": "500mg",
      "inpatient_dose": "1000mg",
      "warning": "Dose change detected — verify with prescribing physician before proceeding."
    }
  ]
}

Scope Boundaries

Stress-Case Rules

For complex multi-constraint requests, always include these explicit blocks:

1. Assumptions

2. Inputs Used

3. Reconciliation Result

4. Warnings and Critical Flags

5. Risks and Manual Checks

Error Handling

Input Validation

This skill accepts: pre-admission medication lists and inpatient order files (JSON format) for a single patient encounter.

If the request does not involve medication list comparison — for example, asking to prescribe medications, interpret drug interactions clinically, or access live EHR systems — do not proceed with the workflow. Instead respond:

> "medication-reconciliation is designed to compare pre-admission and inpatient medication lists to flag omissions and duplicates. Your request appears to be outside this scope. Please provide structured medication input files, or use a more appropriate clinical tool."

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective

2. Inputs Received

3. Assumptions

4. Workflow

5. Deliverable

6. Risks and Limits

7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.