Document Type

Article

Publication Date

11-2026

Identifier

DOI: 10.1177/19322968251367776; PMCID: PMC12536005

Abstract

BACKGROUND: Patient-generated health data (PGHD) represents an opportunity to customize care, particularly in type 1 diabetes (T1D) care where continuous glucose monitor (CGM) and insulin pump usage continues to rise. Previous solutions to integrating CGM data into the electronic health record (EHR) have been limited in their ability to integrate data from multiple sources, ensure data fidelity, integrate data from multiple data streams, and rapidly adapt to changes in data output from numerous vendors. We developed a novel data infrastructure contained outside of the EHR to provide an alternative approach to PGHD integration, enable diabetes centers to identify and predict risk, and to facilitate research and quality improvement.

METHODS: We identified three key capabilities: ingesting and storing a wide variety of data, refining raw data into actionable insights, and visualizing and reporting to decision makers. To meet these requirements, we built a data intelligence platform we coined the diabetes data dock (D-data dock) in the Microsoft Azure cloud platform.

RESULTS: The D-data dock houses approximately 100 million CGM measurements, one million clinical events and insulin bolus records, and a near complete EHR record covering approximately 3000 patients per year from 2016 to 2023. We provide case studies detailing how the D-data dock allows timely monitoring of CGM data, enables novel study designs, and powers machine-learning-informed supplemental care interventions.

CONCLUSIONS: The D-data dock is a novel approach to harnessing disparate data streams to improve patient care, enable timely interventions, and drive innovation to improve the lives and care of people with T1D.

Journal Title

J Diabetes Sci Technol

Volume

20

Issue

6

First Page

1788

Last Page

1797

MeSH Keywords

Humans; Continuous Glucose Monitoring; Diabetes Mellitus, Type 1; Electronic Health Records; Child; Digital Health; Patient Generated Health Data; Blood Glucose; Insulin

PubMed ID

41109844

Keywords

continuous glucose monitor; data integration; data intelligence platform; machine learning; patient generated health data; population health

Comments

Grants and funding

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Publisher's Link: https://doi.org/10.1177/19322968251367776

Download

Share

COinS