Evidence map›Paper›PMID 37345227›Full record

ArticleEndocrinology, diabetes & metabolism2023

A quantitative model to ensure capacity sufficient for timely access to care in a remote patient monitoring program.

Annie Chang, Michael Z Gao, Johannes O Ferstad, Paul Dupenloup, Dessi P Zaharieva, David M Maahs, Priya Prahalad, Ramesh Johari, David Scheinker

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Endocrinology, diabetes & metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.5field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Determinants, implementation strategies, and mechanisms of the 4T Program.Journal of clinical & translational endocrinology · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Annie ChangIcahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID 0000-0003-4692-6338
Michael Z GaoDepartment of Management Science and Engineering, Stanford University, Stanford, California, USA.
Johannes O FerstadDepartment of Management Science and Engineering, Stanford University, Stanford, California, USA.
Paul DupenloupDepartment of Management Science and Engineering, Stanford University, Stanford, California, USA.
Dessi P ZaharievaDepartment of Paediatric, Division of Paediatric Endocrinology, Stanford University, Stanford, California, USA.
David M MaahsDepartment of Paediatric, Division of Paediatric Endocrinology, Stanford University, Stanford, California, USA.
Priya PrahaladDepartment of Paediatric, Division of Paediatric Endocrinology, Stanford University, Stanford, California, USA.ORCID 0000-0002-3894-4344
Ramesh JohariDepartment of Management Science and Engineering, Stanford University, Stanford, California, USA.
David ScheinkerDepartment of Management Science and Engineering, Stanford University, Stanford, California, USA.
Stanford Medicine · USStanford University · USApple (Israel) · IL

Funding

Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Pediatric T1D: 4T StudyR18DK122422 · NIDDK · STANFORD UNIVERSITY · PI MAAHS, DAVID MATTHEW · 2020 to 2024
$3.2M
NIDDK NIH HHS R18 DK122422
6 · The paper itself

Abstract

introductionAlgorithm-enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole-population RPM-based care for T1D.

methodsData were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard.

resultsThe primary population-level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic-level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care.

conclusionWe designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM-based care programs.

Indexed as

Diabetes Mellitus, Type 1ChildHealth Services AccessibilityHumansMonitoring, Physiologiccapacity planningdiabetes technologypaediatric endocrinologyremote patient monitoringtype 1 diabetes

Identifiers

PMID37345227
PMCPMC10495556
OpenAlexW4381597233

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.