A Comparative Study of Minimum Data Set of Speech Therapy: A Systematic Literature Review

Introduction: A minimum data set improves the potential of data standardization and overcoming the problem of low-quality speech therapy data by providing coherent, complete, and uniform data elements. Therefore, this study was conducted to compare speech therapy minimum data set among different countries. Material and Methods: A systematic review was conducted without time limits in PubMed, Scopus, Web of Science, Embase, SID, Magiran, Elmnet databases, and in the Google search engine to retrieve articles, speech therapy forms, and speech therapy registry sites. Keywords related to speech therapy minimum data set including minimum data set, registry, and speech therapy, were used. First, studies were reviewed based on titles and abstracts. Then, the selected studies from the previous stage were examined independently by two researchers. A similar standard checklist was used to extract and compare the findings. Results: A total of 1710 related records were extracted for review, and finally, six main articles and 11 forms were included in this review. The six original articles included two related to speech therapy minimum data set in the United States, two related to Iran, and one related to Australia and Germany. A comparative review of the most important data elements obtained from the articles and input forms in this review, including identity and admission information, referral information, history, assessment of verbal skills, assessment of non-verbal skills, assessment of organs of production, assessment of cognitive skills, assessment of other aspects of speech, and linguistic and cultural considerations, were information elements related to diagnoses, recommendations, and treatment plans. Conclusion: It could be concluded that an agreed classification system is needed to facilitate communication between speech therapists. This potentially enables further testing of diagnostic and therapeutic hypotheses with more coherent and simultaneous data collection. The challenge ahead is to create a comprehensive and universally agreed-upon classification system that meets the needs of professionals and researchers.