Background And Aims: Spinal canal stenosis is one of the most common vertebral column diseases, which can lead to disability. Developing a registry system can help in research on the prevention and effective treatment of it. This study designs a minimum data set (MDS) as the first step in creating a registry system for spinal canal stenosis.

Method: The present research is of applied-descriptive type, performed in 2022. First, the applicable data elements about the disease were selected from a vast range of English and Farsi references, including peer reviewed articles, academic books, credible websites, and medical records of hospitalized patients. Through the extracted data, the primary MDS plan was designed as a questionnaire. The validity of the questionnaire was conducted via asking the opinion of experts (neurosurgeons, physiotherapists, epidemiologists, and health information management specialists). Also, its reliability was calculated via Cronbach coefficient, which was 86%. Finally, the MDS of the spinal canal stenosis national registry system (for Iran) was confirmed through a two stage Delphi technique. Data analysis was applied through descriptive statistics via SPSS21 software.

Results: The proposed MDS is offered in two general sets of data: administrative and clinical. For the administrative data set, 40 data elements had been proposed, as five classes. Twenty-six of them were confirmed. In the clinical section, 95 data elements had been proposed in 14 classes; 94 of which were finally confirmed.

Conclusion: Since there is no spinal canal stenosis MDS available, this study can be a turning point in the standardization of the data on this disease. Moreover, these precise, coherent, and standard data elements can be contributed to improving disease management and enhancing the public healthcare quality. Also, the MDS proposed in this study can help researchers and experts, design a spinal canal stenosis registry system in other countries.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10618433PMC
http://dx.doi.org/10.1002/hsr2.1671DOI Listing

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