Towards a FHIR-Based Framework for Analyzing Nursing-Related Data in Smaller Sized Care Facilities

Area of Science:

• Health Informatics
• Gerontology
• Data Science

Background:

• Data-driven decision-making (DDDM) offers significant benefits for nursing home operations.
• Smaller facilities face resource limitations (technical and personnel) hindering DDDM implementation.
• Existing data solutions are often too complex or costly for smaller nursing homes.

Purpose of the Study:

• To introduce a conceptual framework for data transformation and analysis tailored to smaller nursing homes.
• To address the resource constraints faced by smaller facilities in leveraging data.
• To facilitate data-driven decision-making to improve care quality and operational efficiency.

Main Methods:

• Development of a conceptual framework for data processing and analysis.
• Deployment of a prototype data pipeline within a German nursing home setting.
• Evaluation of the framework's feasibility and effectiveness in a real-world environment.

Main Results:

• Demonstrated the feasibility of implementing a data analysis pipeline in a smaller nursing home.
• Showcased the effectiveness of the conceptual framework in enabling data transformation and analysis.
• Provided evidence that data-driven approaches can be adapted for resource-limited settings.

Conclusions:

• The proposed framework enables smaller nursing homes to utilize data for improved decision-making.
• The prototype pipeline successfully addressed technical and personnel resource challenges.
• This approach has the potential to enhance the quality of care and life for residents and caregivers in smaller facilities.