Skip to main content
SpringerLink
Log in

Uncovering Data Quality Issues in Big Healthcare Data: Implications for Accurate Analytics

  • Conference paper
  • First Online:
Artificial Intelligence, Data Science and Applications (ICAISE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 838))

  • 245 Accesses

Abstract

Data quality assessment is a critical aspect of analyzing big healthcare data to ensure accurate analytics. This paper emphasizes the crucial role of data quality assessment in the analysis of big healthcare data to ensure accurate analytics. It explores the importance of data quality assessment and its implications for reliable healthcare analytics. The paper discusses various data quality issues that can impact healthcare datasets and emphasizes the need to address them effectively. Methods for data quality assessment are presented, enabling the identification and resolution of data quality issues. Additionally, the paper highlights the use of standardized data quality indicators to evaluate the reliability of healthcare data. We propose an Integrated model for Assessing Data Quality in Healthcare. In conclusion, this research underscores the significance of data quality assessment for trustworthy analytics and informed decision-making in the healthcare domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ardagna, D., Cappiello, C., Samá, W., Vitali, M.: Context-aware data quality assessment for big data. Futur. Gener. Comput. Syst. 89, 548–562 (2018). https://doi.org/10.1016/j.future.2018.07.014

    Article  Google Scholar 

  2. Mashoufi, M., Ayatollahi, H., Khorasani-Zavareh, D., Talebi Azad Boni, T.: Data quality in health care: main concepts and assessment methodologies. Methods Inf. Med. 62(1–02), 5–18 (2023). https://doi.org/10.1055/s-0043-1761500

  3. Nasir, W.M.H.M., Abdullah, R.B., Jusoh, Y.Y.B., Abdullah, S.B.: Big data analytics quality model in enhancing healthcare organizational performance: a content validity study. In: 2023 International Conference on Information Management (ICIM), pp. 25–30 (Mar. 2023). https://doi.org/10.1109/ICIM58774.2023.00011

  4. Taleb, I., Serhani, M.A., Dssouli, R.: Big data quality: a survey. In: 2018 IEEE International Congress on Big Data (BigData Congress), pp. 166–173. San Francisco, CA, USA: IEEE (2018). https://doi.org/10.1109/BigDataCongress.2018.00029

  5. Cai, L., Zhu, Y.: The challenges of data quality and data quality assessment in the big data era 14 (May 2015). https://doi.org/10.5334/dsj-2015-002

  6. Lee, K., Weiskopf, N., Pathak, J.: A framework for data quality assessment in clinical research datasets. AMIA Annu. Symp. Proc. 2017, 1080–1089 (Apr. 2018). Accessed: Jun. 25, 2023. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977591/

  7. Schmidt, C., et al.: Combining visual cleansing and exploration for clinical data. In: 2019 IEEE Workshop on Visual Analytics in Healthcare (VAHC), pp. 25–32 (Oct. 2019). https://doi.org/10.1109/VAHC47919.2019.8945034

  8. Piao, X.: Comparative analysis of the mental health status IoT assisted monitoring of the elderly under the background of big data. In: 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 463–466 (Dec. 2021). https://doi.org/10.1109/ICECA52323.2021.9676107

  9. Guggilam, S., Chandola, V., Patra, A.K.: Large deviations anomaly detection (LAD) for collection of multivariate time series data: applications to COVID-19 data. J. Comput. Sci. 72, 102101 (2023). https://doi.org/10.1016/j.jocs.2023.102101

    Article  Google Scholar 

  10. Lincy, S.S.B.T., Kumar, N.S.: An enhanced pre-processing model for big data processing: a quality framework. In: 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), pp. 1–7 (Mar. 2017). https://doi.org/10.1109/IGEHT.2017.8094109

Download references

Author information

Authors and Affiliations

  1. Engineering Sciences Laboratory, Ibn Tofail University, National School of Applied Sciences, Kenitra, Morocco

    Nisrine Berros, Youness Filaly & Younes El Bouzekri E. L. Idrissi

  2. Image Laboratory, School of Technology, Moulay Ismail University of Meknes, Meknes, Morocco

    Fatna El Mendili

Authors
  1. Nisrine Berros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Youness Filaly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fatna El Mendili
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Younes El Bouzekri E. L. Idrissi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nisrine Berros .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Moulay Ismail University, Errachidia, Morocco

    Yousef Farhaoui

  2. Centre of AI and Robotics, Napier University, Edinburgh, UK

    Amir Hussain

  3. Prince Sultan University, Riyadh, Saudi Arabia

    Tanzila Saba

  4. University Canada West, Vancouver, BC, Canada

    Hamed Taherdoost

  5. Institute of Science, Banaras Hindu University, Varanasi, India

    Anshul Verma

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Berros, N., Filaly, Y., Mendili, F.E., Idrissi, Y.E.B.E.L. (2024). Uncovering Data Quality Issues in Big Healthcare Data: Implications for Accurate Analytics. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-031-48573-2_72

Download citation

Publish with us

Policies and ethics

Search

Navigation