Project Overview

The project aimed to develop a Python module for a Medical Communications Group. The module was designed to efficiently scrape, normalize, and validate big data medical records. The objective was to create a solution that could scrape publicly available medical big data, match the records against the client's database, and perform basic data normalizations using artificial intelligence (AI) techniques such as data scraping, matching, and natural language processing (NLP). The implementation of AI enabled the automation and optimization of the entire process, ensuring accurate and reliable medical data management.

The Problem

Our client, a medical communications group, faced challenges in managing and validating a large volume of medical records. With over 1.2 million entries, the manual validation process was not feasible, time-consuming, error-prone, and inefficient. Additionally, the availability of publicly accessible medical big data required an automated solution to gather and integrate the relevant information seamlessly.

The Goal

The goal of this project was to address the challenges faced by our client by developing a Python module that would scrape publicly available medical big data, normalize the records, and validate the data using a matching score based on data normalizations and NLP techniques. The objectives included:

• Develop a data scraping module to extract medical records from the web.
• Normalize the extracted data to ensure consistency and accuracy.
• Match the scraped data with the client's database to validate the records.
• Assign a matching score to each entry based on data normalizations and NLP.

User Research

• Conducted user interviews with our client company's staff to understand the existing challenges in managing and validating medical records.
• Analyzed the client's database and identified the data normalization requirements.
• Explored publicly available medical big data sources to determine the feasibility of scraping relevant information.

Through user research, it was found that the client faced difficulties in efficiently managing and validating a large volume of medical records. Manual validation processes were unfeasible, time-consuming, and error-prone, necessitating an automated solution that could gather, normalize, and validate data from publicly accessible sources.

Pain Points

The main pain point experienced by our client was the inefficiency of the manual validation process due to the large volume of medical records. Additionally, the lack of automation for data scraping and normalization resulted in inconsistencies and inaccuracies in the records.

Solution Brief

To address the challenges faced, the following solution was implemented:

• Data Scraping: A custom Python module was developed to scrape publicly available medical big data from the web. The module utilized web scraping techniques to extract relevant records efficiently.
• Data Normalization: The extracted data was subjected to basic data normalizations to ensure consistency and accuracy. This step involved cleaning and standardizing the data to match the format of the client's database.
• Data Matching: The scraped data was matched against the client's database to validate the records. The module employed matching algorithms and NLP techniques to assign a matching score to each entry, enabling the identification of potential matches.
• Matching Score Calculation: The matching score was calculated based on data normalizations and NLP analysis. This process involved comparing the extracted data with the client's data and determining the level of similarity and relevance.

Impact and Lessons Learned

The implementation of the Python module for scraping, normalizing, and validating big data medical records had a significant impact on the client's data management processes. The key outcomes of the project were:

• Automation and Efficiency: The automated data scraping and normalization processes significantly reduced manual effort and improved the efficiency of managing medical records.
• Improved Data Accuracy: The data normalization techniques ensured consistency and accuracy in the records, reducing errors and enhancing data quality.
• Time Savings: The automated validation process saved time by identifying potential matches and assigning matching scores, eliminating the need for manual validation.
• Enhanced Decision Making: The matching scores provided insights into the quality and relevance of medical records, enabling informed decision making for medical communications.

Through this project, we learned the importance of leveraging automation and AI techniques to streamline data management processes in the medical communications industry. The successful implementation of the Python module showcased the impact of our services in improving efficiency, accuracy, and decision making for the client's medical data operations.