Сross-analysis of big data in accreditation of health specialists

Marina B Chelyshkova,Vasiliy V Akhunov,Evgeniy M Dorozhkin,Natalia N Naydenova,Tatyana V Semenova,Aleksey A Malygin

doi:10.29333/ejgm/93469

Abstract

Objective:The relevance of this study is due to the mass accreditation of health professionals that is developing in Russia, which requires innovative measurement tools and opens new opportunities for a well-founded cross-analysis of specialists’ professional readiness quality. Purpose of the study: The purpose of this article is to present approved methodical approaches to the transformation of accreditation data into a format suitable for secondary analysis of medical schools graduates quality based on the requirements of Professional Standards.Method:The leading methods of secondary data analysis are: a) codification of indicators in the primary data accumulation array; b) statistical processing of study results (evaluation of the relationships between the arrays of primary data accumulation and instrumental data, the correlation of test scores obtained by accreditation results with the labor functions of Professional Standards); c) the creation of representative samples for data analysis. The implementation of methods is carried out in the mode of working with arrays of big data, which also uses the method of cross-analysis to identify additional factors that affect to specialists’ professional readiness quality.Results:As a results of the research, there were: 1) approaches to the codification of data in the array and their secondary analysis were developed; 2) three samples were constructed with an estimation of representativeness for different strata, including subjects, assignments and corresponding labor functions; 3) the matrix of primary data in the specialty “Pediatrics” was verified using the example of the results of students from 50 medical universities in Russia.Conclusion:Approbation of methods of secondary data analysis conducted on representative samples of the subjects showed the effectiveness of the developed approaches that should be used when analyzing large data sets in the procedures of certification or accreditation. The materials of the article can be useful for specialists in the field of assessing the quality of education or assessing the professional readiness of health professionals, managers, professors and pedagogical staff of medical schools, specialists of centers for independent assessment of qualifications.

Highlights

Relevance of the ProblemThe primary analysis of the accreditation data, aimed at comparing the scores of the subjects with passing scores at its individual stages, allows only to make decisions on accreditation, but it does not provide any detailed information on the quality of the professional readiness of health professionals
To compress information from 3112 tasks involved in the accreditation for testing, a representative sample of tasks for cross-analysis was compiled
It was proved that the sample of tasks for cross-analysis with a probability of 99% significantly correlates with the entire data matrix with a coefficient of almost 0.9

Summary

Introduction

Relevance of the ProblemThe primary analysis of the accreditation data, aimed at comparing the scores of the subjects with passing scores at its individual stages, allows only to make decisions on accreditation, but it does not provide any detailed information on the quality of the professional readiness of health professionals. The data from secondary analysis will allow the Ministry of Health to make a corrective impact on its decisions based on feedback, based on the structuring and consolidation of information about the quality of medical and pharmaceutical education in Russia. Based on this information, strategic directions for the development of medical education can be outlined, ways of implementing the main directions of educational policy in accordance with the needs of society and the state can be determined.

Methods

Results

Conclusion