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Streamlining Data Operations in Healthcare

Healthcare

Langate created a data ETL platform that streamlines extraction, transformation, and loading of patient data from a variety of sources.

Streamlining Data Operations in Healthcare

Streamlining Data Operations in Healthcare

Work

  • healthcare data
  • extraction, transformation, and loading of data from different sources
  • data analysis
  • automated data fixing

Summary

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.

Project Scope

6

Month

2

Developers

1

Tech Lead

1

QA

1

Business Analyst

Tech Stack

Server-side
ASP.NET MVC 5
Entity Framework 6
SQL Server 2016
.NET Framework 4.6.1
Castle.Windsor
DotLiquid
Topshelf
Quartz.NET
Windows Services
Microservices
RabbitMQ
Redis
PostSharp
AutoMapper
Humanizer
Hangfire
SSRS Reports
MvcSiteMapProvider
log4net
Client-side
jQuery
Angular 1.6
RequireJS
UnderscoreJs
Font Awesome
Select2
Fancytree
Bootstrap
Highcharts
Summernote
Testing
Moq
Nunit

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 would facilitate fast, accurate, and secure data exchange.

Streamlining Data Operations in Healthcare

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 the stress from finances, purchasing, HR, pharmacy cost, revenue, and technology.

The Problem

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.

Do you also need to streamline data operations in your healthcare business?

Reach out to Langate to develop a solution that would 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 are tolerable towards data variance. This feature will help them 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 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.

The Process

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.

The Solution

We developed the following features

To help the client reach the business objectives, we offered the following solutions:

Streamlining Data Operations in Healthcare

Want to boost your healthcare business with the power of modern technologies?

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 the mistakes are found and reported automatically, which reduces personnel time and resources spent as well.
  • Data transfer has never been as automated and flawless as it is now.
  • We see it as a step towards better data exchange that eliminates burnout caused by organizing, finding, and fixing data in healthcare, a better quality of data entries, and room for more accurate data analysis in the industry.
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