MonAT: A Visual Web-based Tool to Profile Health Data Quality

Monica Noselli, Dan Mason, Mohammed A. Mohammed, Roy A. Ruddle

2017

Abstract

Electronic Health Records (EHRs) are an important asset for clinical research and decision making, but the utility of EHR data depends on its quality. In health, quality is typically investigated by using statistical methods to profile data. To complement established methods, we developed a web-based visualisation tool called MonAT Web Application (MonAT) for profiling the completeness and correctness of EHR. The tool was evaluated by four researchers using anthropometric data from the Born in Bradford Project (BiB Project), and this highlighted three advantages. The first was to understand how missingness varied across variables, and especially to do this for subsets of records. The second was to investigate whether certain variables for groups of records were sufficiently complete to be used in subsequent analysis. The third was to portray longitudinally the records for a given person, to improve outlier identification.

References

  1. Coorevits, P., Sundgren, M., Klein, G. O., Bahr, A., Claerhout, B., Daniel, C., Dugas, M., Dupont, D., Schmidt, A., Singleton, P., De Moor, G., and Kalra, D. (2013). Electronic Health Records: New Opportunities for Clinical Research. Journal of Internal Medicine, 274:547-560.
  2. Dungey, S., Beloff, N., Puri, S., Boggon, R., Williams, T., and Rosemary, A. (2014). A Pragmatic Approach for Measuring Data Quality in Primary Care Databases. In Biomedical and Health Informatics (BHI), 2014 IEEE-EMBS International Conference on, pages 797- 800.
  3. Farhangfar, A., Kurgan, L., and Dy, J. (2008). Impact of Imputation of Missing Values on Classification Error for Discrete Data. Pattern Recognition, 41(12):3692- 3705.
  4. Gschwandtner, T., Aigner, W., Miksch, S., Gärtner, J., Kriglstein, S., Pohl, M., and Suchy, N. (2014). Timecleanser: A visual analytics approach for data cleansing of time-oriented data. In Proceedings of the 14th international conference on knowledge technologies and data-driven business, page 18. ACM.
  5. Johnson, S. G., Speedie, S., Simon, G., Kumar, V., and Westra, B. L. (2015). A Data Quality Ontology for the Secondary Use of EHR Data. AMIA 2015 Annual Symposium Proceedings, pages 1937-1946.
  6. Kandel, S., Parikh, R., Paepcke, A., Hellerstein, J. M., and Heer, J. (2012). Profiler : Integrated Statistical Analysis and Visualization for Data Quality Assessment. Proceedings of Advanced Visual Interfaces, AVI, pages 547-554.
  7. Kohlhammer, J., Keim, D., Pohl, M., Santucci, G., and Andrienko, G. (2011). Solving Problems With Visual Analytics. In Procedia Computer Science, volume 7, pages 117-120.
  8. Nakagawa, S. and Freckleton, R. P. (2008). Missing Inaction: the Dangers of Ignoring Missing Data. Trends in Ecology and Evolution, 23(11):592-596.
  9. Raynor, P. (2008). Born in Bradford, a Cohort Study of Babies Born in Bradford, and their Parents: Protocol for the Recruitment Phase. BMC public health, 8:327.
  10. Rubin, D. B. (1976). Inference and Missing Data. Biometrika, 63:581-592.
  11. Stausberg, J., Nasseh, D., and Nonnemacher, M. (2015). Measuring Data Quality: A Review of the Literature between 2005 and 2013. Studies in health technology and informatics, 210:712-6.
  12. Weiskopf, N. G., Hripcsak, G., Swaminathan, S., and Weng, C. (2013). Defining and Measuring Completeness of Electronic Health Records for Secondary Use. Journal of Biomedical Informatics, 46:830-836.
  13. Weiskopf, N. G. and Weng, C. (2013). Methods and Dimensions of Electronic Health Record Data Quality Assessment: Enabling Reuse for Clinical Research. Journal of the American Medical Informatics Association : JAMIA, 20:144-51.
  14. West, V. L., Borland, D., and Hammond, W. E. (2014). Innovative Information Visualization of Electronic Health Record Data: a Systematic Review. Journal of the American Medical Informatics Association : JAMIA, pages 1-7.
  15. Wright, J., Small, N., Raynor, P., Tuffnell, D., Bhopal, R., Cameron, N., Fairley, L., A Lawlor, D., Parslow, R., Petherick, E. S., Pickett, K. E., Waiblinger, D., and West, J. (2013). Cohort profile: The Born in Bradford Multi-Ethnic Family Cohort Study. International Journal of Epidemiology, 42:978-991.
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Paper Citation


in Harvard Style

Noselli M., Mason D., Mohammed M. and Ruddle R. (2017). MonAT: A Visual Web-based Tool to Profile Health Data Quality . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 26-34. DOI: 10.5220/0006114200260034


in Bibtex Style

@conference{healthinf17,
author={Monica Noselli and Dan Mason and Mohammed A. Mohammed and Roy A. Ruddle},
title={MonAT: A Visual Web-based Tool to Profile Health Data Quality},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={26-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006114200260034},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - MonAT: A Visual Web-based Tool to Profile Health Data Quality
SN - 978-989-758-213-4
AU - Noselli M.
AU - Mason D.
AU - Mohammed M.
AU - Ruddle R.
PY - 2017
SP - 26
EP - 34
DO - 10.5220/0006114200260034