The reliability of patient information in electronic systems

Nawaf Mithqal Saleh Alshammari; ‏Mohammad Ghatyan Sulaiman Alouthah; ‏Ahmed Saleh Madws Alrshidi; ‏Mateb Falah Nahar Alshammari; ‏Bander Mohammad Haia Alrasheidi; ‏Mansour Fahad Nasser Alshammari; ‏Abdullah Sulaiman Abdullah Alsudais; ‏Hamoud Faraj Freej Alsaadi; ‏Saad Nghimish Khasram Alshammari

doi:10.21744/ijhms.v1n1.2272

Authors

Nawaf Mithqal Saleh Alshammari ‏KSA, National Guard Health Affairs
‏Mohammad Ghatyan Sulaiman Alouthah ‏‏KSA, National Guard Health Affairs
‏Ahmed Saleh Madws Alrshidi ‏KSA, National Guard Health Affairs
‏Mateb Falah Nahar Alshammari ‏‏KSA, National Guard Health Affairs
‏Bander Mohammad Haia Alrasheidi ‏‏KSA, National Guard Health Affairs
‏Mansour Fahad Nasser Alshammari ‏‏KSA, National Guard Health Affairs
‏Abdullah Sulaiman Abdullah Alsudais ‏‏KSA, National Guard Health Affairs
‏Hamoud Faraj Freej Alsaadi ‏KSA, National Guard Health Affairs
‏Saad Nghimish Khasram Alshammari ‏‏KSA, National Guard Health Affairs

Keywords:

coding systems, data quality, data science, electronic health records, machine learning, review

Abstract

Electronic health records (EHRs) are digitized data used for clinical research and data science. Data quality in EHRs is crucial for accurate results, including accuracy, completeness, consistency, credibility, timeliness, accessibility, adequacy, comprehensibility, and interpretability. However, the quality of digital data in EHRs is a major concern, with issues such as incompleteness, duplication, poor organization, fragmentation, and insufficient use of coded data. To improve data accuracy, researchers can use statistical measures and validate data using methods like central tendency, dispersion, and goodness-of-fit tests. Missing data in EHRs can be classified into three forms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Challenges in EHR data include documentation without explicit units of measurement, discrepancies in data collection across organizations, and unstructured text data. To ensure high-quality data, researchers should familiarize themselves with the EHR platform, secondary data sources, and data collection methodologies. Regulations at national and international levels are established to ensure the optimal handling, safeguarding, and usage of personal data, including healthcare information. This study provides an overview of the key elements of high-quality electronic health record (EHR) systems, as well as common practices in their deployment.

Downloads

Download data is not yet available.

References

Azur, M. J., Stuart, E. A., Frangakis, C., & Leaf, P. J. (2011). Multiple imputation by chained equations: what is it and how does it work?. International journal of methods in psychiatric research, 20(1), 40-49.

Bartholomeeusen, S., Kim, C. Y., Mertens, R., Faes, C., & Buntinx, F. (2005). The denominator in general practice, a new approach from the Intego database. Family Practice, 22(4), 442-447.

Bayley, K. B., Belnap, T., Savitz, L., Masica, A. L., Shah, N., & Fleming, N. S. (2013). Challenges in using electronic health record data for CER: experience of 4 learning organizations and solutions applied. Medical care, 51, S80-S86.

Bezin, J., Duong, M., Lassalle, R., Droz, C., Pariente, A., Blin, P., & Moore, N. (2017). The national healthcare system claims databases in France, SNIIRAM and EGB: powerful tools for pharmacoepidemiology. Pharmacoepidemiology and drug safety, 26(8), 954-962.

Bray, F., & Parkin, D. M. (2009). Evaluation of data quality in the cancer registry: principles and methods. Part I: comparability, validity and timeliness. European journal of cancer, 45(5), 747-755. https://doi.org/10.1016/j.ejca.2008.11.032

Chan, K. S., Fowles, J. B., & Weiner, J. P. (2010). Electronic health records and the reliability and validity of quality measures: a review of the literature. Medical Care Research and Review, 67(5), 503-527.

Choy, K. L., Lee, W. B., & Lo, V. (2003). Design of a case based intelligent supplier relationship management system—the integration of supplier rating system and product coding system. Expert systems with applications, 25(1), 87-100. https://doi.org/10.1016/S0957-4174(03)00009-5

Feder, S. L. (2018). Data quality in electronic health records research: quality domains and assessment methods. Western journal of nursing research, 40(5), 753-766.

Fundation, T.M. (2006), Background issues on data quality. In: The connecting for health common framework

Hamm, J., Kohler, C. G., Gur, R. C., & Verma, R. (2011). Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. Journal of neuroscience methods, 200(2), 237-256. https://doi.org/10.1016/j.jneumeth.2011.06.023

Häyrinen, K., Saranto, K., & Nykänen, P. (2008). Definition, structure, content, use and impacts of electronic health records: a review of the research literature. International journal of medical informatics, 77(5), 291-304. https://doi.org/10.1016/j.ijmedinf.2007.09.001

Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. https://doi.org/10.1016/j.ijpe.2014.04.018

Jørgensen, A. W., Lundstrøm, L. H., Wetterslev, J., Astrup, A., & Gøtzsche, P. C. (2014). Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial. PLoS One, 9(11), e111964.

Kahn, M. G., Raebel, M. A., Glanz, J. M., Riedlinger, K., & Steiner, J. F. (2012). A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Medical care, 50, S21-S29.

Kang, H. (2013). The prevention and handling of the missing data. Korean journal of anesthesiology, 64(5), 402–406.

Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 23(1), 89-109. https://doi.org/10.1016/S0933-3657(01)00077-X

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.005

Larsen, I. K., Småstuen, M., Johannesen, T. B., Langmark, F., Parkin, D. M., Bray, F., & Møller, B. (2009). Data quality at the Cancer Registry of Norway: an overview of comparability, completeness, validity and timeliness. European journal of cancer, 45(7), 1218-1231. https://doi.org/10.1016/j.ejca.2008.10.037

Lee, D., de Keizer, N., Lau, F., & Cornet, R. (2014). Literature review of SNOMED CT use. Journal of the American Medical Informatics Association, 21(e1), e11-e19.

Ludvigsson, J. F., Almqvist, C., Bonamy, A. K. E., Ljung, R., Michaëlsson, K., Neovius, M., ... & Ye, W. (2016). Registers of the Swedish total population and their use in medical research. European journal of epidemiology, 31, 125-136.

Ozair, F. F., Jamshed, N., Sharma, A., & Aggarwal, P. (2015). Ethical issues in electronic health records: A general overview. Perspectives in clinical research, 6(2), 73-76.

Protti, D., Johansen, I., & Perez-Torres, F. (2009). Comparing the application of Health Information Technology in primary care in Denmark and Andalucía, Spain. International journal of medical informatics, 78(4), 270-283. https://doi.org/10.1016/j.ijmedinf.2008.08.002

Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592.

Schweikardt, C., Verheij, R. A., Donker, G. A., & Coppieters, Y. (2016). The historical development of the Dutch Sentinel General Practice Network from a paper-based into a digital primary care monitoring system. Journal of Public Health, 24, 545-562.

Smith, W. G. (2008). Does gender influence online survey participation? A record-linkage analysis of university faculty online survey response behavior. Online submission.

Sterne, J. A., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., ... & Carpenter, J. R. (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. Bmj, 338.

Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). Intrusion detection by machine learning: A review. expert systems with applications, 36(10), 11994-12000. https://doi.org/10.1016/j.eswa.2009.05.029

Tse, J., & You, W. (2011). How accurate is the electronic health record?–a pilot study evaluating information accuracy in a primary care setting. In Health Informatics: The Transformative Power of Innovation (pp. 158-164). IOS Press.

Verheij, R., & van der Zee, J. (2018). Collecting information in general practice:‘just by pressing a single button’?. In Morbidity, Performance and Quality in Primary Care (pp. 265-272). CRC Press.

Wand, Y., & Wang, R. Y. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the ACM, 39(11), 86-95.

Weiskopf, N. G., & 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, 20(1), 144-151.