EHR Data Consolidation & Transformation
Budget: ₹12,500 – ₹37,500 INR
The objective of this project is to streamline and standardize Electronic Health Record (EHR) data received from multiple U.S. hospitals into a consolidated, analytics-ready format. The goal is to enhance data usability, integrity, and accessibility for downstream analytical, clinical, and operational processes. This initiative aims to reduce data redundancy, improve consistency, and establish a unified data structure that supports informed decision-making and regulatory compliance.
In-Scope Activities
• 1. Data Extraction:
1. Managing diverse input formats including CSV, XML, Excel, and unstructured text documents.
• 2. Data Cleaning & Standardization:
2. Identifying and rectifying inconsistencies, duplicates, and incomplete data entries.
3. Standardizing formats for patient demographics, visit details, diagnosis codes, and procedures.
4. Applying data validation rules to ensure accuracy and reliability.
• 3. Data Mapping & Transformation:
5. Defining and implementing field-level data mapping to align hospital data with the standardized model.
6. Transforming data into discrete (structured) and non-discrete (unstructured) formats using SQL-based ETL processes.
7. Creating automated workflows to ensure repeatability and consistency across data loads.
• 4. Data Validation & Quality Assurance:
8. Conducting reconciliation checks to ensure data completeness and correctness post-transformation.
9. Maintaining logs and validation reports for audit and compliance purposes.
• 5. Documentation & Handover:
10. Preparing detailed documentation for data flow, mapping logic, transformation steps, and validation criteria.
11. Delivering final, cleaned, and structured datasets along with metadata and data dictionaries.
In-Scope Activities
• 1. Data Extraction:
1. Managing diverse input formats including CSV, XML, Excel, and unstructured text documents.
• 2. Data Cleaning & Standardization:
2. Identifying and rectifying inconsistencies, duplicates, and incomplete data entries.
3. Standardizing formats for patient demographics, visit details, diagnosis codes, and procedures.
4. Applying data validation rules to ensure accuracy and reliability.
• 3. Data Mapping & Transformation:
5. Defining and implementing field-level data mapping to align hospital data with the standardized model.
6. Transforming data into discrete (structured) and non-discrete (unstructured) formats using SQL-based ETL processes.
7. Creating automated workflows to ensure repeatability and consistency across data loads.
• 4. Data Validation & Quality Assurance:
8. Conducting reconciliation checks to ensure data completeness and correctness post-transformation.
9. Maintaining logs and validation reports for audit and compliance purposes.
• 5. Documentation & Handover:
10. Preparing detailed documentation for data flow, mapping logic, transformation steps, and validation criteria.
11. Delivering final, cleaned, and structured datasets along with metadata and data dictionaries.
Related categories:
Python
Data Processing
SQL
Medical
Statistical Analysis
Data Analysis
Data Integration
ETL
Data Management