Evidence map›Paper›PMID 41212473›Full record

ArticleHealth care management science2025

A decision support tool for the location, districting and dimensioning of Community Health Houses.

Martina Doneda, Ettore Lanzarone, Carlotta Franchi, Sara Mandelli, Angelo Barbato, Alessandro Nobili, Giuliana Carello

Abstract read
In one paragraph

Article in Health care management science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

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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

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

7 authors.

Martina DonedaDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.ORCID http://orcid.org/0000-0002-1660-3100
Ettore LanzaroneDepartment of Management, Information and Production Engineering, University of Bergamo, Dalmine (BG), Italy. ettore.lanzarone@unibg.it.ORCID https://orcid.org/0000-0001-8816-9086
Carlotta FranchiDepartment of Health Policy, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.ORCID http://orcid.org/0000-0001-6802-8710
Sara MandelliDepartment of Health Policy, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.ORCID http://orcid.org/0000-0003-3553-7564
Angelo BarbatoDepartment of Health Policy, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.ORCID http://orcid.org/0000-0001-7142-700X
Alessandro NobiliDepartment of Health Policy, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.ORCID http://orcid.org/0000-0002-7813-0572
Giuliana CarelloDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.ORCID http://orcid.org/0000-0001-5163-9865

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Community Health Houses (CHHs) are new entities in the Italian National Health Service that have been envisaged to provide proximity care to an increasingly aging population, and bear some similarities to other facilities in countries that have historically focused on public healthcare. This work proposes an integrated decision support system (DSS) for their planning, envisioned during the aftermath of the COVID-19 pandemic, which highlighted the frailty of the existing system. The DSS is based on an integer linear programming (ILP) model that simultaneously makes location, districting and dimensioning decisions for CHH, and accounts for accessibility and equity requirements. Based on Italian law yet designed in a parametrized way that makes it adaptable to several contexts, the DSS is able to design a hub and spoke network, which considers the provision of both mandatory and additional services. The sizes of the former are determined by directly taking into account the population served, while those of the latter are determined according to the specific demand for these services, accounting for diverse needs arising from different territories. The DSS also uses territorial units that refer to recognizable administrative areas. This ensures that the districting is easily recognized and accepted by the population. In addition to the ILP formulation, three decomposition-based matheuristics are proposed, which allow suitable solutions to be found within a reasonable time also for large and heterogeneous instances, while maintaining the flexibility of the ILP formulation. Computational results on synthetic realistic instances validated the DSS, while its application to a real-life case in a Northern Italian province demonstrated the effectiveness of the heuristic approaches and provided a proof of concept for its practical application.

Indexed as

COVID-19Decision Support TechniquesHumansItalyPandemicsSARS-CoV-2Community Health HousesDecision support systemDimensioningIntegrated network planningLocation and districtingOperations research operations management

Identifiers

PMID41212473
PMCPMC12743699

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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.