Evidence map›Paper›PMID 39973997›Full record

ArticlemedRxiv : the preprint server for health sciences2025

A Quantitative Framework for Evaluating the Performance of Algorithm-Directed Whole-Population Remote Patient Monitoring for Type 1 Diabetes Care.

Jamie Kurtzig, Johannes O Ferstad, Paul Dupenloup, Dessi P Zaharieva, David M Maahs, Priya Prahalad, Ramesh Johari, Franziska K Bishop, Ananta Addala, David Scheinker

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In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinics continue to adopt remote patient monitoring for type 1 diabetes (T1D) and care models shaped by algorithmic CGM data analysis. No clinic-facing quantitative framework currently exists to track the impact of such algorithm-directed care on patient outcomes and clinical workload. The Teamwork, Targets, Technology, and Tight Control (4T) Study provides precision, whole-population care enabled by algorithms that use continuous glucose monitoring (CGM) data to direct clinician attention to patients with deteriorating glucose management. We used data from the 4T Pilot (n=133) and 4T Study 1 (n=135), in which algorithms use CGM data to identify youth with T1D meeting criteria for clinical review and potential clinician contact. Through iterative data analysis and interviews with diabetes educators and clinicians, we identified metrics for reviewing and revising clinical workloads, glucose management, and timeliness of care. For each metric, we developed an interactive dashboard to provide clinical and administrative leaders with an overview of the program. The metrics to track clinical workload were the total number of youths: (1) in the program, (2) in each study, and (3) cared for by each clinician. The metrics to track glucose management were the number of youths meeting each criterion for review: (4) in total, (5) for each clinician, and (6) for each study. The metric to track timeliness of care was (7) the number of days since meeting criteria for clinical review. When presented at weekly program leadership meetings, the metrics facilitated data-driven decision making about clinical and operational components of the program. We propose a novel quantitative framework for diabetes care teams to supervise and enhance algorithm-directed whole-population T1D care. As the role of algorithms grows in directing clinical effort and prioritizing patients for care, this framework may help clinics track clinical workload, patient outcomes, and the timeliness of care.

Identifiers

PMID39973997
PMCPMC11838643

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.