Langate created a data ETL platform that streamlines extraction, transformation, and loading of patient data from a variety of sources.
By creating a data ETL platform that manages data from more than 300 nursing facilities, we have reduced the time spent on routine manual work by streamlining their data operations which lets them focus on essential business tasks rather than retyping and fixing incorrect data.
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 would facilitate fast, accurate, and secure data exchange.
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 the stress from finances, purchasing, HR, pharmacy cost, revenue, and technology.
In order to successfully provide services to more than 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 the data, and data inconsistency. They wanted to solve these issues within one IT solution and, most importantly, automate the process of fixing typos and incorrect values in key identifiers.
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 are tolerable towards data variance. This feature will help them avoid duplicates and reduce the need for manual searches.
There is a large amount of patient data to download and a very limited time to do so. The diversity of systems contributed to the challenge as well since it made the process longer. The client wanted to speed up the downloading process in order to serve more patients.
Langate has successfully completed such a complex task. We have developed a Data ETL Platform that overcomes major challenges in healthcare data exchange and enables the processes of extraction, transformation, and loading of patient data into the central data warehouse.
To help the client reach the business objectives, we offered the following solutions:
The problem of slow downloading was addressed by enabling the solution to run scalable data processing workflows on multiple servers in parallel.
In order to have consistent data without duplicates and typos, we have implemented data normalization. It collects data from different sources, links them to the master source, and creates one full entry.
If there are any typos or mistakes in key identifiers – social security number, DOB, HICN, etc., the users receive an alert instead of having to double-check all of the data entries manually.
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