Data Lakes for Pooling T1D Patients CGM Data to Enable EHR Integration and Prevent Data Silos: A Patient Pathway & Clinical Workflow Analysis
Analysis of T1D patient CGM data pathways identifying workflow fragmentation and data silos. Proposes a formal graph-based model for bottleneck detection and a data lake architecture for NHS CGM-EHR integration.
Data Lakes for Pooling T1D Patients CGM Data
Enabling EHR Integration and Preventing Data Silos
Abstract
Modern developments in healthcare data collection and management aim to improve patient outcomes. For instance, consider type 1 diabetes (T1D) patients, who benefit from enhanced continuous glucose monitoring (CGM) devices. These devices can provide time-sensitive alerts and facilitate automated data collection for patient-practitioner retrospectives, greatly improving patients’ ability to manage their disease. However, CGM vendors often package their devices with proprietary software and rely on third-party infrastructure. This leads to interoperability challenges across clinical systems, fragmented workflows, and increased cognitive burden for clinicians, who grapple with a heterogeneous device landscape encumbered by data silos. This work aims to begin addressing these concerns by first analysing T1D patient data pathways through process modelling. By translating a traditional swimlane (business process) diagram to a formal graph-based representation, we propose a technique for identifying data-flow bottlenecks in complex multi-actor networks. Following this, we discuss an approach to centralised ingestion of CGM data based on a data lake-like architecture (inspired by the Scottish DataLoch initiative). We hope our investigation provides a refreshing perspective on some of the many issues arising from the fragmented nature of the healthcare industry.
Links
- Report: PDF