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Data Management in the Health Sector: A SWOT Analysis

Author

Listed:
  • Crina-Dana IONESCU

    (Bucharest University of Economic Studies)

  • Mihaela CAZACU

    (Bucharest University of Economic Studies)

  • Emilia ȚIȚAN

    (Bucharest University of Economic Studies)

  • Mihaela MIHA

    (Bucharest University of Economic Studies)

  • Daniela-Ioana MANEA

    (Bucharest University of Economic Studies)

Abstract

The lack of data for many indicators and the existence of significant gaps in the availability and comparability of data is a problem of global interest, felt by authorities and agencies specialized in data collection, analysis and use. Their objective is to provide data and indicators of good quality, which will help to correctly inform the political decision-makers and to solve the inequalities in the field of health. Thus, this paper aims to provide clear and coherent solutions to a number of problems identified in data management in general and in the medical sector in particular. The added value of this paper consists in a case study, which presents an x-ray of the current situation of data quality through the analysis of metadata. The research methodology used in the paper includes a swot analysis on the quality of health data, using metadata from three sources corresponding to distinct levels of aggregation: National (Romania), European and Global.

Suggested Citation

  • Crina-Dana IONESCU & Mihaela CAZACU & Emilia ȚIȚAN & Mihaela MIHA & Daniela-Ioana MANEA, 2021. "Data Management in the Health Sector: A SWOT Analysis," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 22(3), pages 290-300, July.
    • Handle: RePEc:rom:rmcimn:v:22:y:2021:i:3:p:290-300
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    File URL: https://www.rmci.ase.ro/no22vol3/01.pdf
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    References listed on IDEAS

    as
    1. Ola Abualghaib & Nora Groce & Natalie Simeu & Mark T. Carew & Daniel Mont, 2019. "Making Visible the Invisible: Why Disability-Disaggregated Data is Vital to “Leave No-One Behind”," Sustainability, MDPI, vol. 11(11), pages 1-11, May.
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    More about this item

    Keywords

    health; data management; data quality; metadata; real-world data.;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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