Towards a Learning Health System for Symptom Management in Hospice Care

Area of Science:

• Health Informatics
• Palliative Care Research
• Data Science in Healthcare

Background:

• Real-world symptom monitoring data is crucial for enhancing hospice care quality.
• A Learning Health System framework facilitates data reuse for quality improvement.
• Developing well-defined datasets is a prerequisite for effective data reuse in healthcare.

Purpose of the Study:

• To identify and discuss challenges in designing comprehensive datasets for hospice care.
• To explore variations in electronic health record systems impacting dataset design.
• To analyze the influence of divergent care processes on data standardization.

Main Methods:

• Qualitative analysis of two distinct electronic health record systems.
• Examination of care process variations within hospice settings.
• Literature review on dataset design principles for healthcare data reuse.

Main Results:

• Significant variations exist between electronic health record systems, complicating data aggregation.
• Divergent clinical workflows and care processes introduce heterogeneity in symptom monitoring data.
• Lack of standardized data elements hinders the creation of a comprehensive, reusable dataset.

Conclusions:

• Addressing electronic health record system and care process variations is critical for developing robust hospice datasets.
• Standardization efforts are needed to enable effective reuse of symptom monitoring data.
• Overcoming these challenges is essential for advancing Learning Health Systems in hospice care.