Scaling a data retrieval and mining application to the enterprise-wide level.

Area of Science:

• Health Informatics
• Data Science in Healthcare
• Clinical Data Management

Background:

• Healthcare institutions struggle with effective utilization of stored clinical and administrative data.
• Limited availability of user-friendly tools hinders accurate and efficient data retrieval.
• Existing tools like DXtractor are effective for specialized datasets but not enterprise-wide solutions.

Purpose of the Study:

• To develop and describe Goldminer, a novel data retrieval and mining application.
• To enable authorized personnel to query large clinical and demographic data stores via a non-programmer interface.
• To address and present solutions for scaling data retrieval tools to an enterprise level.

Main Methods:

• Development of the Goldminer application with a graphical, non-programmer interface.
• Leveraging and extending the capabilities of the previously developed DXtractor tool.
• Addressing challenges associated with scaling data retrieval solutions for enterprise-wide use.

Main Results:

• Successful development of Goldminer, facilitating easier access to institutional data.
• Demonstrated ability to query clinical and demographic data stores through an intuitive interface.
• Identified and implemented solutions for enterprise-level data retrieval scalability.

Conclusions:

• Goldminer enhances institutional capacity for data-driven decision-making.
• The application simplifies complex data retrieval processes for authorized personnel.
• The project offers valuable insights into overcoming challenges in enterprise-wide healthcare data management.