Streamlining Data Operations in Healthcare
Langate created a data ETL platform that streamlines extraction, transformation, and loading of patient data from a variety of sources.
Work
- Creating an ETL platform for our client
- Speeding up data downloads with multi-server processing
- Data normalization to prevent duplicates and manual searches
- Automatic error alerts
Summary
By creating a data ETL platform that manages data from over 300 nursing facilities, we reduced the time spent on routine manual work by streamlining data operations. This lets staff focus on essential business tasks rather than retyping and fixing incorrect data.
Learn how Langate created a solution for smooth healthcare data extraction, transformation, and analysis
In order to provide high-quality care, healthcare providers need to retrieve patient-related data from various external sources that may have different storage formats. So, facilities need a tool that facilitates fast, accurate, and secure data exchange.
The Client
Our client is the premier provider of IT and financial services in the healthcare industry. They have been servicing skilled nursing facilities and rehab centers nationwide for over a decade, helping them grow without experiencing business challenges related to finances, purchasing, HR, pharmacy costs, revenue, and technology.
The Problem
To provide services to over 300 nursing facilities, our client needs to extract thousands of patient records from various sources daily.
This results in duplicates, a large amount of time spent downloading data, and data inconsistency. They wanted to solve these issues with one IT solution and, most importantly, automate the process of fixing typos and incorrect values in key identifiers.
Reach out to Langate to develop a solution that will simplify data management and automate routine processes.
Primary Challenges
Data inconsistency and duplicates were the challenges we focused on first.
Data Variance Tolerance
Since the data coming in is in different formats and from different patient data records, the client needs matching algorithms that tolerate data variance. This feature will help the client avoid duplicates and reduce the need for manual searches.
Fast Downloading
There is a large amount of patient data to download and a very limited time to do so. The diversity of systems also contributed to the challenge since it made the process longer. The client wanted to speed up the download process in order to serve more patients.
The Process
We developed a data ETL platform that overcomes major challenges in healthcare data exchange and enables patient data to be extracted, transformed, and loaded into the central data warehouse.
The Solution
To help the client reach their business objectives, we offered the following solutions:
Achievements and Future Plans
- Our client is now able to extract data from different systems without performing as much manual work as they did before.
- All mistakes are found and reported automatically, which reduces the resources required.
- Data transfer has never been as automated and flawless as it is now.
- We see it as a step toward better data exchange that eliminates burnout caused by having to organize, find, and fix data in healthcare. It also improves the quality of data entries and provides room for more accurate data analysis in the industry.
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