BMC Health Services Research (Jul 2019)

Evaluating the demographic and clinical minimum data sets of Iranian National Electronic Health Record

  • Reza Abbasi,
  • Reza Khajouei,
  • Moghadameh Mirzaee

DOI
https://doi.org/10.1186/s12913-019-4284-x
Journal volume & issue
Vol. 19, no. 1
pp. 1 – 10

Abstract

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Abstract Background Designing a standard data set is necessary to overcome the dispersion of data among different health information systems. The objective of this study was to evaluate the current demographic and clinical minimum data sets (MDSs) of Iranian National Electronic Health Record (known as SEPAS) and to identify most necessary data elements. Methods Data were collected using a list of current demographic and clinical data of SEPAS and a self-administered questionnaire. All faculty members of six health related fields and the hospital authorities, and IT and HIM administrators of 6 hospitals in Kerman University of Medical Sciences were invited to participate in this study. The content validity of the questionnaire was confirmed by six medical informatics and HIM experts and the reliability was determined by Cronbach’s alpha (α =0.95). SPSS v18 was used to generate descriptive statistics. Results Survey results indicated that 15 data elements should become mandatory elements of MDS for communicating data to SEPAS. These elements include patient’s name, surname, father’s name, nationality, cell number, job, residential address, residence place, passport number (for non-Iranian patients), diagnosis date, death time, death place and the unit of the hospital where the patient died. Moreover, participants suggested 33 additional demographic and clinical data elements to be communicated mandatorily to SEPAS. Conclusion The results of this study showed that the minimum data sets of Iranian national electronic health record needs to be revised. Using the proposed MDSs by this study can improve the quality and efficiency of information and reduce redundancy by adding necessary data and preventing communication of unnecessary data. The method employed in this study can be used for investigating, refining and completing the MDSs of other health information systems.

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