Journal of Healthcare Engineering

Journal of Healthcare Engineering / 2014 / Article

Research Article | Open Access

Volume 5 |Article ID 715674 | https://doi.org/10.1260/2040-2295.5.2.163

Giordano Lanzola, Enea Parimbelli, Giuseppe Micieli, Anna Cavallini, Silvana Quaglini, "Data Quality and Completeness in a Web Stroke Registry as the Basis for Data and Process Mining", Journal of Healthcare Engineering, vol. 5, Article ID 715674, 22 pages, 2014. https://doi.org/10.1260/2040-2295.5.2.163

Data Quality and Completeness in a Web Stroke Registry as the Basis for Data and Process Mining

Received01 Sep 2013
Accepted01 Feb 2014

Abstract

Electronic health records often show missing values and errors jeopardizing their effective exploitation. We illustrate the re-engineering process needed to improve the data quality of a web-based, multicentric stroke registry by proposing a knowledge-based data entry support able to help users to homogeneously interpret data items, and to prevent and detect treacherous errors. The re-engineering also improves stroke units coordination and networking, through ancillary tools for monitoring patient enrollments, calculating stroke care indicators, analyzing compliance with clinical practice guidelines, and entering stroke units profiles. Finally we report on some statistics, such as calculation of indicators for assessing the quality of stroke care, data mining for knowledge discovery, and process mining for comparing different processes of care delivery. The most important results of the re-engineering are an improved user experience with data entry, and a definitely better data quality that guarantees the reliability of data analyses.

References

  1. The Committee on Quality Health Care in America, Crossing the quality chasm: a new health system for the 21st century. National Academy Press, ISBN 0-309-07280-8, 2001. Accessed Dec 25, 2013 http://www.nap.edu/catalog.php?record_id=10027.
  2. J. Schmittdiel, T. Bodenheimer, N. A. Solomon, R. R. Gillies, and S. M. Shortell, “Brief report: The prevalence and use of chronic disease registries in physician organizations. A national survey,” Journal of General Internal Medicine, vol. 20, no. 9, pp. 855–858, 2005. View at: Google Scholar
  3. M. Colias, “Disease registries,” Hospitals and Health Networks, vol. 79, no. 2, pp. 62–64, 66–68, 2005. View at: Google Scholar
  4. D. G. T. Arts, N. F. De Keizer, and G. J. Scheffer, “Defining and improving data quality in medical registries: A literature review, case study, and generic framework,” Journal of the American Medical Informatics Association, vol. 9, no. 6, pp. 600–611, 2009. View at: Google Scholar
  5. S. I. Goldberg, A. Niemierko, and A. Turchin, “Analysis of Data Errors in Clinical Research Databases,” in Proceedings of the AMIA Annual Symposium, pp. 242–246, 2008. View at: Google Scholar
  6. A. K. Jha, C. M. DesRoches, E. G. Campbell et al., “Use of electronic health records in U.S. hospitals,” New England Journal of Medicine, vol. 360, no. 16, pp. 1628–1638. View at: Google Scholar
  7. H. Yan, R. Gardner, and R. Baier, “Beyond the focus group: understanding physicians' barriers to electronic medical records,” Joint Commission Journal on Quality and Patient Safety, vol. 38, no. 4, pp. 184–191, 2012. View at: Google Scholar
  8. D. Sleeth-Keppler, “White Paper: Garbage,” in Garbage Out. Survey-Question Design. A Free Report. VALSTM Strategic Business Insights, Accessed Dec 5, 2013 http://www.strategicbusinessinsights.com/vals/free/2010-02whtpprsurveydes.pdf. View at: Google Scholar
  9. P. Hartzband and J. Groopman, “Off the record— avoiding the pitfalls of going electronic,” New England Journal of Medicine, vol. 358, no. 16, pp. 1656–1658, 2008. View at: Google Scholar
  10. L. W. Peute, K. F. Driest, R. Marcilly, S. Bras Da Costa, M. C. Beuscart-Zephir, and M. W. Jaspers, “A Framework for reporting on human factor/usability studies of health information technologies,” Studies on Health Technologies and Informatics, IOS Press, vol. 194, pp. 54–60, 2013. View at: Google Scholar
  11. A. Di Carlo, “Human and economic burden of stroke,” Age and Ageing, vol. 38, no. 1, pp. 4–5, 2009. View at: Google Scholar
  12. Stroke Unit Trialists' Collaboration, “Organised inpatient (stroke unit) care for stroke,” Cochrane Database of Systematic Reviews, vol. 17, no. 4, Article ID CD000197, 2007. View at: Google Scholar
  13. P. Langhorne, J. D. Lewsey, P. S. Jhund et al., “Estimating the impact of stroke unit care in a whole population: an epidemiological study using routine data,” Journal of Neurology Neurosurgery and Psychiatry, vol. 81, no. 12, pp. 1301–1305, 2010. View at: Google Scholar
  14. A. Bersano, L. Candelise, R. Sterzi, G. Micieli, M. Gattinoni, and A. Morabito, “Stroke Unit care in Italy. Results from PROSIT (Project on Stroke Services in Italy). A nationwide study,” Neurological Sciences, vol. 27, no. 5, pp. 332–339, 2006. View at: Google Scholar
  15. L. Candelise, M. Gattinoni, A. Bersano, G. Micieli, R. Sterzi, and A. Morabito, “Stroke-unit care for acute stroke patients: an observational follow-up study,” The Lancet, vol. 369, no. 9558, pp. 299–305, 2007. View at: Google Scholar
  16. A. Cavallini and G. Micieli, “Lombardia stroke unit network project,” Neurological Sciences, vol. 27, Suppl 3, pp. S268–S272, 2006. View at: Google Scholar
  17. G. F. Gensini, B. Dilaghi, and A. Zaninelli, “Italian SPREAD Guidelines: from past to future,” Neurological Sciences, vol. 27, Suppl 3, pp. S254–S257, 2006. View at: Google Scholar
  18. G. Micieli, A. Cavallini, S. Quaglini, G. Fontana, and M. Duè, “The Lombardia Stroke Unit Registry: 1-year experience of a web-based hospital stroke registry,” Neurological Sciences, vol. 31, no. 5, pp. 555–564, 2010. View at: Google Scholar
  19. M. H. Rahbar, N. R. Gonzales, M. Ardjomand-Hessabi et al., “The University of Texas Houston Stroke Registry (UTHSR): implementation of enhanced data quality assurance procedures improves data quality,” BMC Neurology, vol. 13, no. 61, 2013. View at: Google Scholar
  20. A. Boonstra and H. Broekhuis, “Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions,” BMC Health Services Research, vol. 10, no. 231, 2010. View at: Google Scholar
  21. S. Dornan, F. E. Murray, G. White et al., “An audit of the accuracy of upper gastrointestinal diagnoses in Scottish Morbidity Records 1 data in Tayside,” Health Bulletin, vol. 53, no. 5, pp. 274–279. View at: Google Scholar
  22. A. A. Warsi, S. White, and P. McCulloch, “Completeness of data entry in three cancer surgery databases,” European Journal of Surgical Oncology, vol. 28, no. 8, pp. 850–856, 2002. View at: Google Scholar
  23. H. V. Schaff, M. L. Brown, and J. R. Lenoch, “Data entry and data accuracy,” Journal of Thoracic Cardiovascular Surgery, vol. 140, no. 5, pp. 960–961, 2010. View at: Google Scholar
  24. U. Shrawankar and V. Thakare, “Parameters optimization for improving ASR performance in adverse real world noisy environmental conditions,” International Journal of Human Computer Interaction, vol. 3, no. 3, pp. 58–70, 2012. View at: Google Scholar
  25. S. Panzarasa, S. Quaglini, M. Pessina, A. Cavallini, and G. Micieli, “GIFT: a tool for generating free text reports from encoded data,” in Conf Proc IFMBE, 11th Mediterranean Conference on Medical and Biomedical Engineering and Computing, vol. 16, pp. 152–156, 2007. View at: Google Scholar
  26. R. K. Los, J. Roukema, A. M. van Ginneken, M. de Wilde, and J. van der Lei, “Are structured data structured identically? Investigating the uniformity of pediatric patient data recorded using OpenSDE,” Methods of Information in Medicine, vol. 44, no. 5, pp. 631–638, 2005. View at: Google Scholar
  27. S. Quaglini, P. Ciccarese, G. Micieli, and A. Cavallini, “Non-compliance with guidelines: motivations and consequences in a case study,” in Conf Proc CGP, 2004 Symposium on Computerized Guidelines and Protocols. Studies in health technology and informatics, vol. 101, pp. 75–87, IOS Press, 2004. View at: Google Scholar
  28. S. Quaglini, “Compliance with clinical practice guidelines,” in Computer-Based Medical Guidelines and Protocols: A Primer and Current Trends, Studies in Health Technology and Informatics, Annette Ten Teije, Silvia Miksch, and Peter Lucas, Eds., vol. 139, pp. 160–179, IOS Press, 2008. View at: Google Scholar
  29. S. J. Wang, D. W. Bates, H. C. Chueh et al., “Automated coded ambulatory problem lists: evaluation of a vocabulary and a data entry tool,” International Journal of Medical Informatics, vol. 72, no. 1-3, pp. 17–28, 2003. View at: Google Scholar
  30. S. Panzarasa, S. Quaglini, L. Sacchi, A. Cavallini, G. Micieli, and M. Stefanelli, “Data mining techniques for analyzing stroke care processes,” Conf Proc MEDINFO. Studies in Health Technology and Informatics, vol. 160, no. 2, pp. 939–943, 2010. View at: Google Scholar
  31. M. Dennis, P. A. Sandercock, J. Reid et al., “Effectiveness of thigh-length graduated compression stockings to reduce the risk of deep vein thrombosis after stroke (CLOTS trial 1): a multicentre, randomised controlled trial,” The Lancet, vol. 373, no. 9679, pp. 1958–1965, 2009. View at: Google Scholar
  32. A. Cavallini, E. Tartara, S. Marcheselli, E. Agostoni, S. Quaglini, and G. Micieli, “Improving thrombolysis for acute ischemic stroke in Lombardia stroke centers,” Neurological Sciences, vol. 34, no. 7, pp. 1227–1233, 2013. View at: Google Scholar
  33. S. Montani, G. Leonardi, S. Quaglini, A. Cavallini, and G. Micieli, “Mining and Retrieving Medical Processes to Assess the Quality of Care,” in Conf Proc ICCBR, pp. 233–240, 2013. View at: Google Scholar
  34. B. Matosević, A. Zangerle, M. Furtner et al., “Implementation of thrombolysis in acute stroke— 10-year results of the Innsbruck stroke registry,” Wiener Klinische Wochenschrift, vol. 121, no. 23-24, pp. 750–760. View at: Google Scholar
  35. X. Liu, Y. Xiong, Z. Zhou et al., “China interventional stroke registry: rationale and study design,” Cerebrovascular Diseases, vol. 35, no. 4, pp. 349–354, 2013. View at: Google Scholar
  36. M. G. George, X. Tong, H. McGruder et al., “Paul Coverdell National Acute Stroke Registry Surveillance - four states, 2005-2007,” MMWR Surveillance Summaries, vol. 58, no. 7, pp. 1–23, 2009. View at: Google Scholar
  37. M. P. Lindsay, M. K. Kapral, D. Gladstone et al., “The Canadian Stroke Quality of Care Study: establishing indicators for optimal acute stroke care,” Canadian Medical Association Journal, vol. 172, no. 3, pp. 363–365, 2005. View at: Google Scholar
  38. M. K. Kapral, R. Hall, M. Stamplecoski et al., Registry of the Canadian Stroke Network: Report on the 2008/09 Ontario Stroke Audit. Toronto: Institute for Clinical Evaluative Sciences, 2011.
  39. Y. Wang, L. Cui, X. Ji et al., “The China national stroke registry for patients with acute cerebrovascular events: design, rationale, and baseline patient characteristics,” International Journal of Stroke, vol. 6, no. 4, pp. 355–361, 2011. View at: Google Scholar
  40. K. H. Jung, S. H. Lee, B. J. Kim et al., “Secular trends in ischemic stroke characteristics in a rapidly developed country: results from the Korean Stroke Registry Study (secular trends in Korean stroke),” CirculationCardiovascular Quality and Outcomes, vol. 5, no. 3, pp. 327–334, 2012. View at: Google Scholar
  41. S. Bhaumik, “India launches stroke registry to combat, “epidemic”,” British Medical Journal, vol. 346, Article ID f223, 2013. View at: Google Scholar
  42. A. Cannon, J. B. Kennedy, T. Paterson, and M. Watson, “Ontology-Driven Automated Generation Of Data Entry Interfaces,” in Conf Proc 21st British National Conference on Databases, vol. 3112 of Lecture Notes in Computer Science, pp. 150–164, Springer-Verlag, 2004. View at: Google Scholar
  43. F. Wang, S. Mäs, W. Reinhardt, and A. Kandawasvika, “Ontology based quality assurance for mobile data acquisition,” in Conf Proc 19th international conference on Informatics for Environmental Protection: Networking Environmental Information, pp. 334–341, Brno, Czech Republic, 2005. View at: Google Scholar
  44. N. H. Shah, C. Jonquet, A. P. Chiang, A. J. Butte, R. Chen, and M. A. Musen, “Ontology-driven indexing of public datasets for translational bioinformatics,” BMC Bioinformatics, vol. 10, Suppl 2, p. S1, 2009. View at: Google Scholar
  45. A. Rector, “Knowledge driven software and, “fractal tailoring”: Ontologies in development environments for clinical systems,” in Conf Proc FOIS, Formal Ontology in Information Systems, pp. 17–28, IOS Press, 2010. View at: Google Scholar
  46. S. Schulz and L. Jansen, “Formal ontologies in biomedical knowledge representation,” Yearbook of Medical Informatics, vol. 8, no. 1, pp. 132–146, 2013. View at: Google Scholar
  47. S. N. Murphy, G. Weber, M. Mendis et al., “Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2),” Journal of the American Medical Informatics Association, vol. 17, pp. 124–130, 2010. View at: Google Scholar
  48. S. Falasconi, G. Lanzola, and M. Stefanelli, “Ontology and terminology servers in agent-based Health-care Information Systems,” Methods of Information in Medicine, vol. 36, no. 1, pp. 30–43, 1997. View at: Google Scholar
  49. H. Liyanage, S. T. Liaw, C. Kuziemsky et al., “The Evidence-base for Using Ontologies and Semantic Integration Methodologies to Support Integrated Chronic Disease Management in Primary and Ambulatory Care: Realist Review,” Yearbook of Medical Informatics, vol. 8, no. 1, pp. 147–154, 2013. View at: Google Scholar
  50. T. Zavaliy and I. Nikolski, “Ontology-based information system for collecting electronic medical records data,” in Conf Proc TCSET, IEEE Conference on Telecommunications and Computer Science, p. 125, Lviv-Slavske, Ukraine, 2010. View at: Google Scholar
  51. V. A. Tran, N. Johnson, S. Redline, and G. Q. Zhanga, “OnWARD: Ontology-driven web-based framework for multi-center clinical studies,” Journal of Biomedical Informatics, vol. 44, Suppl 1, pp. S48–S53, 2011. View at: Google Scholar
  52. N. Wahlgren, N. Ahmed, A. Davalos et al., “Thrombolysis with alteplase for acute ischaemic stroke in the Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST): an observational study,” The Lancet, vol. 369, no. 9558, pp. 275–282, 2007. View at: Google Scholar

Copyright © 2014 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Order printed copiesOrder
Views635
Downloads827
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.