Evidence map›Paper›PMID 42040787›Full record

ArticleFrontiers in digital health2026

Preparing real-world data through common data model harmonization of cancer patient records in the COMNet platform at the Modena Oncology Center.

Luca Moscetti, Enrico Calanchi, Elisa Pettorelli, Andrea Spallanzani, Federica Bertolini, Rossella Fogliani, Mirko Orsini, Laura Delsante, Monica Civallero, Roberta Depenni and 6 more

Abstract read
In one paragraph

Article in Frontiers in digital health, 2026. 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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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

16 authors.

Luca MoscettiDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Enrico CalanchiDatariver, Modena, Italy.
Elisa PettorelliDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Andrea SpallanzaniDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Federica BertoliniDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Rossella FoglianiServizio Tecnologie dell'informazione, University Hospital of Modena, Modena, Italy.
Mirko OrsiniDatariver, Modena, Italy.
Laura DelsanteDatariver, Modena, Italy.
Monica CivalleroDivision of Medical Oncology, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy.
Roberta DepenniDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Katia Di EmidioDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Fabio GelsominoDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Annalisa FontanaDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Federico PiacentiniDivision of Medical Oncology, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy.
Roberto SabbatiniDivision of Medical Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy.
Massimo DominiciDivision of Medical Oncology, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The transition from paper medical records to electronic health records (EHRs) has enabled the extraction of substantial real-world data, which can support future real-world evidence generation. This study aimed to convert heterogeneous oncology data from local EHR systems-collectively referred to as COMNet-into a standardized data model. In particular, the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) was adopted to harmonize routinely collected clinical data into a common database, thereby enabling standardized secondary use and large-scale analyses. Methods: Demographic and clinical parameters routinely collected at the Modena Cancer Center were retrospectively extracted from COMNet and harmonized into the OMOP-CDM through an Extract-Transform-Load process supported by the European Health Data and Evidence Network (EHDEN). Results: We identified 85,026 persons with at least one recorded condition occurrence. Discrepancies were observed across OMOP-CDM domains-particularly in visit occurrence and drug exposure-reflecting changes in documentation practices and source systems over time. The temporal trend of data migration to the electronic platform revealed two significant peaks, corresponding to initial data entry and subsequent digitalization of hospital facilities and pharmacy records. Discussion: The harmonization process revealed data inconsistencies, including incompleteness and missing data, reflecting challenges inherent in the transition from paper-based records to electronic systems and the coexistence of different legacy software platform. Conclusions: Harmonizing COMNet data into the OMOP-CDM produced a standardized real-world data resource that can support future observational research and participation in federated network studies. Ongoing initiatives, including the EHDEN project, are supporting the further development of COMNet to improve interoperability and enhance structured data capture.

Indexed as

data harmonizationelectronic health recordsOMOP Common Data Modeloncologyreal-world data

Identifiers

PMID42040787
PMCPMC13102797

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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 OpenQuestion graph.