Download PDF
Appl Clin Inform 2023; 14(04): 763-771
DOI: 10.1055/a-2130-2197
Research Article

The Detection of Date Shifting in Real-World Data

Laura Evans
1   TriNetX, LLC., Boston, Massachusetts, United States
,
Jack W. London
1   TriNetX, LLC., Boston, Massachusetts, United States
2   Department of Cancer Biology, Thomas Jefferson University, Philadelphia, Pennsylvania, United States
,
Matvey B. Palchuk
1   TriNetX, LLC., Boston, Massachusetts, United States
› Author Affiliations Funding This work was funded by TriNetX, LLC.
› Further Information
    Permissions and Reprints

Abstract

Objectives Analysis of health care real-world data (RWD) provides an opportunity to observe the actual patient diagnostic, treatment, and outcome events. However, researchers should understand the possible limitations of RWD. In particular, the dates in these data may be shifted from their actual values, which might affect the validity of study conclusions.

Methods A methodology for detecting the presence of shifted dates in RWD was developed by considering various approaches to confirm the expected occurrences of medical events, including unique temporal occurrences as well as recurring seasonal or weekday patterns in diagnoses or procedures. Diagnosis and procedure data was obtained from 71 U.S. health care data provider organizations (HCOs), members of the TriNetX global research network. Synthetic data was generated for various degrees of date shifting corresponding to the diagnoses and procedures studied, yielding the resulting patterns when various degrees of shifting (including no shift) were applied. These patterns were compared with those produced for each HCO to predict the presence and degree of date shifting. These predictions were compared with statements of date shifting by the originating HCOs to determine the predictive accuracy of the methods studied.

Results Twenty-eight of the 71 HCOs analyzed were predicted by methodology and confirmed by their data providers to have shifted data. Likewise, 39 were predicted and confirmed to not have shifted data. With four HCOs, agreement between predicted and stated date shifting status was not obtained. The occurrence of routine medical exams, only happening during weekdays, for these U.S. HCOs was most predictive (0.92 correlation coefficient) of the presence or absence of date shifting.

Conclusion The presence of date shifting for U.S. HCOs may be reliably detected assessing whether the routine exams should always occur on weekdays.

Keywords

data quality - electronic health record - secondary use - real-world data

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in this project.




Publication History

Received: 15 February 2023

Accepted: 14 July 2023

Accepted Manuscript online:
17 July 2023

Article published online:
27 September 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Evans L, London JW, Palchuk MB. Assessing real-world medication data completeness. J Biomed Inform 2021; 119: 103847
    CrossrefPubMedGoogle Scholar
  • 2 Kayaalp M. Patient privacy in the era of big data. Balkan Med J 2018; 35 (01) 8-17
    CrossrefPubMedGoogle Scholar
  • 3 Office for Civil Rights, HHS. Standards for privacy of individually identifiable health information. Final rule. Fed Regist 2002; 67 (157) 53181-53273
    PubMedGoogle Scholar
  • 4 Kayaalp M. Modes of de-identification. AMIA Annu Symp Proc 2018; 2017: 1044-1050
    PubMedGoogle Scholar
  • 5 Liu J, Erdal S, Silvey SA. et al. Toward a fully de-identified biomedical information warehouse. AMIA Annu Symp Proc 2009; 2009: 370-374
    PubMedGoogle Scholar
  • 6 Hripcsak G, Mirhaji P, Low AF, Malin BA. Preserving temporal relations in clinical data while maintaining privacy. J Am Med Inform Assoc 2016; 23 (06) 1040-1045
    CrossrefPubMedGoogle Scholar
  • 7 Anderson N, Abend A, Mandel A. et al. Implementation of a deidentified federated data network for population-based cohort discovery. J Am Med Inform Assoc 2012; 19 (e1) e60-e67
    CrossrefPubMedGoogle Scholar
  • 8 Cimino JJ. The false security of blind dates: chrononymization's lack of impact on data privacy of laboratory data. Appl Clin Inform 2012; 3 (04) 392-403
    Article in Thieme ConnectPubMedGoogle Scholar
  • 9 Sweeney L. Only you, your doctor, and many others may know. Technology Science. 2015092903. Accessed September 28, 2015 at: https://techscience.org/a/2015092903/
    PubMedGoogle Scholar
  • 10 London JW, Fazio-Eynullayeva E, Palchuk MB, Sankey P, McNair C. Effects of the COVID-19 pandemic on cancer-related patient encounters. JCO Clin Cancer Inform 2020; 4 (04) 657-665
    CrossrefPubMedGoogle Scholar
  • 11 London JW, Fazio-Eynullayeva E, Palchuk MB, McNair C. Evolving effect of the COVID-19 pandemic on cancer-related encounters. JCO Clin Cancer Inform 2022; 6 (06) e2100200
    CrossrefPubMedGoogle Scholar
  • 12 Cathey L. Timeline: Tracking Trump alongside scientific developments on hydroxychloroquine.. ABC News. Accessed February 15, 2023 at: https://abcn.ws/3krSgW7
    PubMedGoogle Scholar
  • 13 Topaloglu U, Palchuk MB. Using a federated network of real-world data to optimize clinical trials operations. JCO Clin Cancer Inform 2018; 2: 1-10
    CrossrefPubMedGoogle Scholar