Enhancing Computational Compliance Checking in Healthcare Facilities Through the IDS Standard

The design of healthcare facilities must comply with applicable regulatory requirements, which significantly influence functional, spatial, and performance aspects of healthcare buildings. However, compliance checking is still predominantly conducted through manual procedures, leading to time-consuming workflows and a high risk of errors. In this context, Computational Compliance Checking (CCC) offers the potential to enhance the efficiency and reliability of regulatory verification processes. The adoption of Building Information Modelling (BIM), supported by the open standard Industry Foundation Classes (IFC), enables the formalisation of Information Delivery Specifications (IDS) as machine-interpretable rules for model validation. Additionally, the buildingSMART Data Dictionary (bSDD) contributes to the semantic structuring and classification of information requirements. This study explores how the IDS standard can enhance CCC processes by supporting the automated verification of regulatory requirements. Complementary solutions based on Python are proposed to address cases where IDS alone proves insufficient. A methodology for the semi-automated compliance checking of healthcare regulatory requirements is developed and applied to a case study based on regional regulations in the Veneto Region (Italy). Tested on the inpatient ward of a hospital BIM model, the procedure processed all 35 accreditation requirements, 91.4% through IDS and 8.6% through complementary Python routines, revealing several design non-conformities.

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Publication Details

Journal
Buildings
Published
2026-08-26
DOI
https://doi.org/10.3390/buildings16173404
Primary Topic
BIM and Construction Integration
Type
article
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article

Enhancing Computational Compliance Checking in Healthcare Facilities Through the IDS Standard

Elena Martin Porta, Carlo Zanchetta, Giulia De Caro, Michele Berlato et al.
Buildings
BIM and Construction Integration
article

Enhancing Computational Compliance Checking in Healthcare Facilities Through the IDS Standard

Elena Martin Porta, Carlo Zanchetta, Giulia De Caro, Michele Berlato, Giorgia Marcellino
article en

Abstract

The design of healthcare facilities must comply with applicable regulatory requirements, which significantly influence functional, spatial, and performance aspects of healthcare buildings. However, compliance checking is still predominantly conducted through manual procedures, leading to time-consuming workflows and a high risk of errors. In this context, Computational Compliance Checking (CCC) offers the potential to enhance the efficiency and reliability of regulatory verification processes. The adoption of Building Information Modelling (BIM), supported by the open standard Industry Foundation Classes (IFC), enables the formalisation of Information Delivery Specifications (IDS) as machine-interpretable rules for model validation. Additionally, the buildingSMART Data Dictionary (bSDD) contributes to the semantic structuring and classification of information requirements. This study explores how the IDS standard can enhance CCC processes by supporting the automated verification of regulatory requirements. Complementary solutions based on Python are proposed to address cases where IDS alone proves insufficient. A methodology for the semi-automated compliance checking of healthcare regulatory requirements is developed and applied to a case study based on regional regulations in the Veneto Region (Italy). Tested on the inpatient ward of a hospital BIM model, the procedure processed all 35 accreditation requirements, 91.4% through IDS and 8.6% through complementary Python routines, revealing several design non-conformities.

BuildingsVol. 16(17)
University of Padua (IT)
Industry, innovation and infrastructure
Openalex Percentile: Top 14%
BIM and Construction Integration
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Enhancing Computational Compliance Checking in Healthcare Facilities Through the IDS Standard — Elena Martin Porta, Carlo Zanchetta, et al. · Buildings (2026) | TGRS Research Map | TGRS