Evidence map›Paper›PMID 33150095›Full record

ArticleIEEE journal of translational engineering in health and medicine2020

A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing.

Emanuele Frontoni, Luca Romeo, Michele Bernardini, Sara Moccia, Lucia Migliorelli, Marina Paolanti, Alessandro Ferri, Paolo Misericordia, Adriano Mancini, Primo Zingaretti

Open access · goldAbstract read
In one paragraph

Article in IEEE journal of translational engineering in health and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 3 pooled it
3.9field-weighted citation impact, top 5% 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

10 citing papers in PubMed, 3 syntheses or guidelines pooled it, 28 citations in OpenAlex.

  1. Pooled it
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  5. AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025
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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

10 authors at 2 institutions in 1 country.

Emanuele FrontoniDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Luca RomeoDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Michele BernardiniDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Sara MocciaDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Lucia MigliorelliDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Marina PaolantiDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Alessandro FerriDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Paolo MisericordiaFederazione Italiana Medici di Medicina Generale (FIMMG)00144RomeItaly.
Adriano ManciniDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Primo ZingarettiDepartment of Information EngineeringUniversità Politecnica delle Marche60131AnconaItaly.
Marche Polytechnic University · ITSocietà Italiana di Medicina Generale · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective Decision support systems (DSS) have been developed and promoted for their potential to improve quality of health care. However, there is a lack of common clinical strategy and a poor management of clinical resources and erroneous implementation of preventive medicine. Methods To overcome this problem, this work proposed an integrated system that relies on the creation and sharing of a database extracted from GPs' Electronic Health Records (EHRs) within the Netmedica Italian (NMI) cloud infrastructure. Although the proposed system is a pilot application specifically tailored for improving the chronic Type 2 Diabetes (T2D) care it could be easily targeted to effectively manage different chronic-diseases. The proposed DSS is based on EHR structure used by GPs in their daily activities following the most updated guidelines in data protection and sharing. The DSS is equipped with a Machine Learning (ML) method for analyzing the shared EHRs and thus tackling the high variability of EHRs. A novel set of T2D care-quality indicators are used specifically to determine the economic incentives and the T2D features are presented as predictors of the proposed ML approach. Results The EHRs from 41237 T2D patients were analyzed. No additional data collection, with respect to the standard clinical practice, was required. The DSS exhibited competitive performance (up to an overall accuracy of 98%±2% and macro-recall of 96%±1%) for classifying chronic care quality across the different follow-up phases. The chronic care quality model brought to a significant increase (up to 12%) of the T2D patients without complications. For GPs who agreed to use the proposed system, there was an economic incentive. A further bonus was assigned when performance targets are achieved. Conclusions The quality care evaluation in a clinical use-case scenario demonstrated how the empowerment of the GPs through the use of the platform (integrating the proposed DSS), along with the economic incentives, may speed up the improvement of care.

Indexed as

Cloud computingdecision support systemelectronic health recordsgeneral practicetype 2 diabetes

Identifiers

PMID33150095
PMCPMC7605604
OpenAlexW3095863210

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.