CFP last date
22 April 2024
Reseach Article

Improving Data Quality in a Resource Constraint Public Health Organization in Nigeria with Divide and Conquer and Lot Quality Assurance Sampling Approach

by Stephen Boerwhoen Dapiap, Babatunde Adeshina Adelekan, Ahmad Tijjani Aliyu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 165 - Number 12
Year of Publication: 2017
Authors: Stephen Boerwhoen Dapiap, Babatunde Adeshina Adelekan, Ahmad Tijjani Aliyu
10.5120/ijca2017914103

Stephen Boerwhoen Dapiap, Babatunde Adeshina Adelekan, Ahmad Tijjani Aliyu . Improving Data Quality in a Resource Constraint Public Health Organization in Nigeria with Divide and Conquer and Lot Quality Assurance Sampling Approach. International Journal of Computer Applications. 165, 12 ( May 2017), 35-43. DOI=10.5120/ijca2017914103

@article{ 10.5120/ijca2017914103,
author = { Stephen Boerwhoen Dapiap, Babatunde Adeshina Adelekan, Ahmad Tijjani Aliyu },
title = { Improving Data Quality in a Resource Constraint Public Health Organization in Nigeria with Divide and Conquer and Lot Quality Assurance Sampling Approach },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 165 },
number = { 12 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 35-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume165/number12/27628-2017914103/ },
doi = { 10.5120/ijca2017914103 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:12:20.406356+05:30
%A Stephen Boerwhoen Dapiap
%A Babatunde Adeshina Adelekan
%A Ahmad Tijjani Aliyu
%T Improving Data Quality in a Resource Constraint Public Health Organization in Nigeria with Divide and Conquer and Lot Quality Assurance Sampling Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 165
%N 12
%P 35-43
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Inconsistency or unstable implementation policies in electronic records management systems are likely to create discrepancy in data which may distorts facts as quality of information is compromised. This leads to taking misleading decisions and actions which may be life threatening circumstances in health settings, business losses, misplacement of priorities and wrong interventions for development. Identifying the causes of data discrepancies should be a priority in driving efforts to improve quality at different levels of data ecosystem. This paper investigated the implication of frequent changes in electronic health records management system implementation policy on data quality in an organization supporting health facilities providing HIV/AIDS services across twelve states in Nigeria through the application of divide and conquers and lot quality assurance sampling methods. Large data discrepancies were discovered using the combined methods and there was tremendous data quality improvement six-month after the intrinsic and contextual data quality validation. The study concluded that frequent changes in electronic data management systems are likely to breed distortions in data quality that may greatly affect effective delivery of the most needed quality services.

References
  1. Ajami, S. and Bagheri-Tadi, T. 2013. Barriers for Adopting Electronic Health Records (EHRs) by Physicians. Acta Inform Med. 2013; 21(2): 129–134.Published online 2013 Jun. doi:  10.5455/aim.2013.21.129-134
  2. Blelloch, G. 2011. Parallel and Sequential Data Structures and Algorithms. Lecture 15-210 (Fall 2011).
  3. CAREWare. Retrieved from http://www.jprog.com/wiki/All-CAREWare-documentation.ashx).
  4. Consultative Committee for Space Data Systems. 2012. Reference model for an Open Archival Information System (OAIS) (Magenta Book CCSDS 650.0-M-2). Retrieved from http://public.ccsds.org/publications/archive/650x0m2.pdf
  5. Elamparithi, M. and Anuratha, V. 2015. World Journal of Computer Application and Technology 3(3): 41-48. Retrieved from http://www.hrpub.org DOI: 10.13189/wjcat.2015.030301.
  6. Gee, M. and Helfert, M. 2013. Cost and Value Management for Data Quality. Handbook of data Quality pp 75-92.
  7. Gibney, E. and Van Noorden, R. 2013. Scientists losing data at a rapid rate. Nature. doi:10.1038/nature.2013.14416. Retrieved from http://dx.doi.org/10.1038/nature.2013.14416.
  8. Herring, M. 2016. Data Is Your Most Valuable Asset. Retrieved from https://dzone.com/articles/data-is-your-most-valuable-asset.
  9. Horthonworks 2016. Data Is Your Most Valuable Asset. Retrieved from http://hortonworks.com/blog/data-is-your-most-valuable-asset.
  10. Hund, L.2014. New tools for evaluating LQAS survey designs. DOI: 10.1186/1742-7622-11-2.
  11. IBM 2016. Data management: Tapping your most valuable asset. Retrieved from https://www.ibm.com/midmarket/us/en/article_DataManagement2_1209.html.
  12. Lin, B. and Chan H.G. 2000. Managing data quality in the health care industry: Some critical issues. Journal of International Information Management, Vol. 9 , Number 1. CSUSB Scholar Works.
  13. MIT Technology Review Custom 2016. Produced in partnership with Oracle. Retrieved from www.technologyreview.com/media.
  14. Peer, L., Green, A, and Stephenson E. 2014. Committing to Data Quality Review. International Journal of Digital Curation 2014, Vol. 9, Iss. 1, 263–291.
  15. Piette, J.D, Lun, K.C, Moura, L.A., Fraser, H.S.F., Mechael, P.N., Powell, J., & Khoja, S.R. 2012. Impacts of e-health on the outcomes of care in low- and middle-income countries: where do we go from here? Bulletin of the World Health Organization 2012;90:365-372. doi: 10.2471/BLT.11.099069.
  16. Shankaranarayanan, G. and Blake, R. 2017. From Content to Context: The Evolution and Growth of Data Quality Research. Journal of Data and Information Quality (JDIQ). Vol.8 Issue 2. ACM New York. Retrieved from http://dl.acm.org/citation.cfm?doid=3035914.2996198.
  17. Sadiq, S., Khodabandehloo,N., Indulska,Y.M. 2010. 20 Years of Data Quality Research: Themes, Trends and Synergies.
  18. Talc. 2003. Assessing Community Health Programs: Using LQAS for Baseline Surveys and Regular Monitoring.
  19. Teleform. Retrieved from https://en.wikipedia.org/wiki/TeleForm.
  20. Teradata 2013. Breaking the Application Barrier: Why Data is the Most Valuable Asset in the Oil and Gas Industry. Retrieved from http://assets.teradata.com/resourceCenter/downloads/WhitePapers/EB6802.pdf
  21. USAID and NUMAT 2010. LQAS Survey Report: A Household Survey on Malaria, HIV&AIDS and TB Interventions in Nine Districts of Northern Uganda.
  22. Vines, T.H., Albert, A. Y.K., Andrew, R.L., Débarre, F., Bock, D.G., Franklin, M.T., Rennison, D.J. 2014. The availability of research data declines rapidly with article age. Current Biology 24(1), 94-97. doi:10.1016/j.cub.2013.11.014.
  23. Wang, R.Y. and Strong, D.M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems 12(4), 5-33. Retrieved from http://mitiq.mit.edu/Documents/Publications/TDQMpub/14_Beyond_Accuracy.pdf
  24. Warme, T. 2014. Data: your most valuable asset. A business case for data governance. Retrieved from https://www.linkedin.com/pulse/20141119183044-243338813-data-your-most-valuable-asset-a-business-case-for-data-governance.
Index Terms

Computer Science
Information Sciences

Keywords

Electronic Medical Records Public Health Data Quality Policy Changes.