Determination of Minimum Data Set for Designing a Diagnosis Decision Support System and Medication Follow-Up for Multiple Sclerosis

Background and Purpose: Diagnosis of multiple sclerosis (MS) is complicated because of the lack of definite factor. Decision support systems are expert systems which help physicians in decision-making process. First step in designing the system is identification of a minimum dataset (MDS). This study aimed to determine minimum dataset required to design diagnosis decision support system. Materials and Methods: This research was a descriptive cross-sectional study. Data were gathered from medical guideline approved by Ministry of Health, Treatment and Medical Training, Multiple Sclerosis diagnosis, international guideline of Royal college of England, and McDonald Diagnostic criteria. Data collection tool was a designed checklist consisting of 100 items provided to 25 neurologists and MS fellowships of medical universities and private clinics in Iran. Results: Out of 100 designed information’s items, 10 items were omitted due to CVR less than 0.49. Employment status items, history of MS in 3rd grade relatives, history of viral diseases, orbital MRI, optical coherence tomography, brain CT-scan, ESR, CRP, visually evoked potentials, delay duration of P100 for each eyes are all examples of information elements that have been omitted. Conclusion: Determining the minimum dataset related to MS is an important step in designing diagnosis decision support system and medication follow-up. Therefore, MDSs can help those responsible for gathering standard information of patients with Multiple Sclerosis (MS), and causes improvement in management of information for this disease.