Java Development Case Study - Discovery Engine For Healthcare

Industry

Healthcare

Customer Profile

The client is a global leader that provides software for healthcare coding, patient classification & grouping, performance management, and consulting services. Their solutions are used in over 25 countries and 10,000 hospitals worldwide. Their experts also offer insight into important topics, including coding compliance and methods for comparing performance.

Benefits

  • Improved data integrity and decreased laborious manual abstraction.
  • Quicker & more accurate registry completion.
  • Less manual entry, fewer human errors and more time in clinicians’ days for patients.
  • Automated feedback and guidance to help abstractors improve accuracy, completeness and compliance.
  • Faster and more confident decision-making process.

Technology Used

  • AWS
  • Angular 2
  • Java 8
  • RESTful Web Services
  • Hibernate
  • Elastic Search

Customer Situation

Customer needed:

  • An effective process to abstract, analyze and submit accurate registry data.
  • An Improvement in the data integrity by streamlining registry reporting
  • To reduce the burden of internal and external reporting using auditing with an electronic audit trail.
  • To populate a database of disease, or device data elements that multiple departments within the organization can use for additional research and analysis.

Congruent's Solution

The Discovery Engine developed by Congruent could auto-suggest the best answers to registry questions, reducing the noise of abstraction and improving the way data is collected and reviewed.

Discovery Engine for Registries combines the leading healthcare data dictionary with next-generation clinical NLP technology to automate and accelerate registry abstraction.

Solution Features

  • Accelerated pace of registry reporting, compilation and review
  • Consistent & efficient identification / extraction of pertinent registry data elements
  • Reduced laborious task of manual data abstraction due to efficient automated methods
  • Faster auditing processes by quickly locating data within the documentation.
  • Organized and structured clinical data feeding into other systems for analytical workflow and reporting
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