2025-02-13
The task: Debug code processing medical records in CSV format. Simple enough, until it wasn’t.
Two major red flags emerged:
🚩 First: Zero documentation
• No input/output examples
• No acceptance criteria
• No test cases
• No data structure specs
🚩 Second (the deal-breaker): They insisted on column-wise processing of patient records.
Let me show you why this matters:
Good (standard approach):
for patient_record in records: validate_patient_data(patient_record) process_single_patient(patient_record)
Bad (what they wanted):
columns = transpose_medical_records(data) for column in columns: process_column(column) # Pray nothing breaks
The dangers:
• Patient data misalignment
• Wrong SSNs matching wrong histories
• HIPAA compliance nightmares
• Impossible record atomicity
• Error handling complexity 📈
Tech interviews should evaluate:
✅ Clean code writing
✅ Data safety understanding
✅ Best practices knowledge
Not:
❌ Implementing known-bad solutions
❌ Compromising data integrity
I ended it because implementing their solution would be professionally irresponsible.
Sometimes walking away isn’t failure—it’s integrity.
What Undercode Say
Data integrity is a cornerstone of software engineering, especially when dealing with sensitive information like medical records. The example above highlights the importance of adhering to best practices, even under pressure. Below are some Linux and IT-related commands and practices that can help ensure data integrity and security in your workflows:
1. Data Validation with Python:
Use Python libraries like `pandas` for robust CSV processing. Always validate data before processing:
import pandas as pd
df = pd.read_csv('medical_records.csv')
df.dropna(inplace=True) # Remove incomplete records
df.to_csv('cleaned_records.csv', index=False)
2. File Integrity Checks:
Use `sha256sum` to verify file integrity:
sha256sum medical_records.csv
3. Automated Testing:
Write unit tests using `pytest` to ensure your code handles edge cases:
def test_patient_validation():
assert validate_patient_data({"name": "John Doe", "SSN": "123-45-6789"}) == True
4. Logging for Debugging:
Use Python’s `logging` module to track errors and data flow:
import logging
logging.basicConfig(filename='app.log', level=logging.INFO)
logging.info('Processing patient records...')
5. HIPAA Compliance:
Encrypt sensitive data using `openssl`:
openssl enc -aes-256-cbc -salt -in medical_records.csv -out encrypted_records.enc
6. Version Control:
Use `git` to track changes and maintain accountability:
git init git add medical_records.csv git commit -m "Initial commit of medical records"
7. Data Backup:
Automate backups using `cron` and `rsync`:
crontab -e <h1>Add this line to backup daily at 2 AM</h1> 0 2 * * * rsync -avz /path/to/medical_records /backup/location/
8. Error Handling in Bash:
Use `trap` to handle errors in shell scripts:
trap "echo 'Error encountered. Exiting...'; exit 1" ERR
9. Secure File Transfers:
Use `scp` for secure file transfers:
scp medical_records.csv user@remote:/path/to/destination
10. Database Integrity:
Use `pg_dump` for PostgreSQL backups:
pg_dump -U username -h localhost dbname > backup.sql
By integrating these practices into your workflow, you can ensure data integrity, security, and compliance with industry standards. Always prioritize ethical and responsible coding, especially when handling sensitive information.
For further reading on HIPAA compliance and data security, visit:
– HIPAA Compliance Guide
– OWASP Data Integrity
Remember, walking away from a bad solution is not a failure—it’s a commitment to professionalism and integrity.
References:
Hackers Feeds, Undercode AI
