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Designing A Medical Errors Control Model For Tehran University Of Medical Science Hospitals
Amir Ashkan Nasiripour, Pouran Raeissi, Farhad Ghaffari, Mohhamadreza Maleki, Mehrnush Jafari,
Volume 8, Issue 1 (5-2014)
Abstract

 Background and Aim: Healthcare processes have caused many dangers to patients, and the increase of medical errors is one of the most important consequences of such processes. The present research is conducted to reduce medical errors through presenting a model to control them.

 Materials and Methods: In this mixed (quantitative-qualitative) research, a conceptual model was assembled. Then using the model and an interview, a questionnaire was made. The interview and the researcher-made questionnaire were used to collect data. The statistical population included the related people and the practitioners involved in medical errors in Tehran University of medical Sciences (TUMS) hospitals. The sample consisted of 252 employees who were non-randomly selected from those hospitals. Once the affecting factors were determined, the data were analyzed through factor analysis technique. The gathered data were analyzed using descriptive and inferential statistics. Finally, the research model was presented.

 Results: The selected individuals pointed out 9 factors controlling the medical errors: culture, factors associated with patients, factors related to providers, factors associated with errors, structural factors, role of disclosure, error registration, individual factors related to reporting, and organizational factors related to reporting. The 9 factors are the subdivisions of three main factors which account for 57/46% of the total variance of data. The most decisive power is related to disclosure 0.737 and the least (0.053) pertains to structure.

 Conclusion: Discloser of medical errors and their registration are factors which are effective and essential in controlling medical errors in TUMS hospitals.

 


A Minimum Data Set Of Radiology Reporting System For Exchanging With Electronic Health Record System In Iran
Maryam Ahmadi, Azadeh Bashiri,
Volume 8, Issue 2 (7-2014)
Abstract

 Background and Aim: In order to better design an electronic health record system in the country, determining standardized data elements for creating an integrated information system is important. In this study, the minimum data set of radiology reporting system is determined.

 Materials and Methods: In this descriptive cross-sectional study, 13 radiologists, 3 anesthesiologists, 3 general practitioners and 3 insurance experts working in the Imaging Center of Imam Khomeini hospital in Tehran were chosen. The research tool was a questionnaire having 11 parts. Content validity and test-retest method were used to measure the validity and reliability of the questionnaire, respectively. Data analysis was performed using the SPSS software.

 Results: The highest means reported were radiologists' written explanations and suggestions (9.6), image interpretation (9.5), the name of contrast material (9.4), the name of imaging procedure (9.3) type and date of previous measures (9.1), and the final diagnosis (9) and the lowest averages belonged to referring physician's address (4.8), relationship between patients and the primary individual insured (4.3), and religion (2.2).

 Conclusion: In an electronic health record system, due to the importance of radiology reports for the diagnosis and future management of a patient's clinical problems, it is necessary to pay attention to the minimum set of data related to these reports such as administrative, insurance, patient identity, and clinical data, and the results of radiological examinations for exchanging with electronic health record system.

 


Automatic Spelling Correction in Persian Sonography Text with Neural Networks
Seyed Mohammad Sadegh Dashti, Amid Khatibi Bardsiri, Mehdi Jafari Shahbazzadeh,
Volume 18, Issue 1 (3-2024)
Abstract
Background and Aim: Medical reports and electronic health records are critically important for diagnosis, treatment, patient protection, and medical research. Correcting spelling errors in medical texts is essential to ensure accurate interpretation of information. This research was conducted to automatically correct spelling mistakes in Persian medical texts using neural networks.
Material and Methods: In this study, which was conducted in 2023, a computational model based on artificial intelligence neural networks and dual embedding techniques was developed using Python in a Windows environment. The dual embedding model was fine-tuned for correcting spelling errors in Persian sonography texts. The proposed model employs various techniques for automatic error detection, including dictionary lookup approach and contextual similarity coefficients. Furthermore, features specific to text processing, such as Edit-Distance, along with similarity coefficients, were utilized to automatically select the most appropriate substitute for a misspelled word. The training and testing data for the current model were sourced from a collection of sonography texts from the Imam Khomeini Hospital’s sonography clinic in Tehran.
Results: The proposed model which is based on artificial neural networks, leverages a novel dualembedding architecture to select the best candidate words for correcting both non-word and real-word errors. According to the evaluation results on Persian sonography text, the proposed model achieved an F-Measure accuracy of 90.5% in detecting real-word errors. Furthermore, it demonstrated an impressive 90% accuracy in automatically correcting these real-word errors. Additionally, the model exhibited a strong performance, achieving 90.8% accuracy in correcting non-word errors.
Conclusion: Based on the evaluation results, the proposed method is robust against various changes in word forms and can effectively manage a wide range of morphological and semantic errors, including replacements, transpositions, insertions, and deletions in medical texts. The integration of EditDistance with textual similarity coefficients extracted from the dual embedding model significantly enhanced the accuracy of spelling corrections in Persian sonography texts, ensuring greater validity of such documents. The authors believe that the proposed model represents a significant advancement in the detection and correction of spelling errors in Persian sonography texts.

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