ArticleHealth science reports2026
A Multidimensional Data Set for Obsessive-Compulsive Disorder to Strengthen Research and Care: A Cross-Sectional Delphi Study.
Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- A Multidimensional Data Set for Obsessive-Compulsive Disorder to Strengthen Research and Care: A Cross-Sectional Delphi Study.Health science reports · 2026Article
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Authors and funding
4 authors.
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Abstract
Background and Aims: Obsessive-compulsive disorder (OCD) is a complex psychiatric condition marked by persistent obsessions and compulsions, leading to significant disability and reduced quality of life. Despite effective treatments, delays in diagnosis and fragmented data collection hinder optimal care. Current assessment tools focus primarily on symptoms and severity, lacking comprehensive data on demographics, environmental factors, and service utilization. This study aimed to develop a multidimensional minimum data set (MDS) for OCD to standardize data collection, improve clinical decision-making, and support epidemiological research. Methods: A modified Delphi technique was used, engaging 20 experts (psychiatrists, psychologists, epidemiologists, and health information management specialists) across two consensus rounds. An initial 94 data elements were evaluated, and the final MDS was categorized into seven domains. Results: In the first Delphi round, experts immediately approved 39 items and rejected 4, while 51 required further evaluation. The second round yielded consensus on 27 additional items. The final MDS comprised 66 essential elements, with 28 items ultimately excluded from the final dataset. Conclusion: The proposed MDS bridges gaps in existing OCD tools (e.g., Y-BOCS, DOCS) by incorporating multidimensional data. Its implementation could enhance personalized care, inform public health policies, and address disparities in low-resource settings. Future validation studies are needed to assess its impact on patient outcomes and global applicability.
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