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Computational Ethnography: Automated and Unobtrusive Means for Collecting Data In Situ for Human–Computer Interaction Evaluation Studies

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Human Computer Interaction in Healthcare

Part of the book series: Cognitive Informatics in Biomedicine and Healthcare ((CIBH))

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Abstract

Computational ethnography is an emerging family of methods for conducting human–computer interaction (HCI) studies in healthcare. Computational ethnography often leverages automated and less obtrusive means for collecting in situ data that reflect end users’ true, unaltered behaviors of interacting with a software system or a device in naturalistic settings. In this chapter, we introduce the concept of computational ethnography and common types of digital trace data available in healthcare environments, as well as commonly used approaches to analyzing computational ethnographical data. At the end of the chapter, we use two use cases to illustrate how this new family of methods has been applied in healthcare to study end users’ interactions with technological interventions in their everyday routines.

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Notes

  1. 1.

    HIPAA, or the Health Insurance Portability and Accountability Act, defines policies, procedures, and guidelines for maintaining the privacy and security of protected health information as well as outlining offenses and sets civil and criminal penalties for violations.

  2. 2.

    § 482.24 Condition of Participation: Medical Record Services. http://www.gpo.gov/fdsys/granule/CFR-2011-title42-vol5/CFR-2011-title42-vol5-sec482-24/content-detail.html

  3. 3.

    The Health Information Technology for Economic and Clinical Health Act, or the HITECH Act, sets meaningful use of EHRs as a critical national goal and allocates incentive funds to accelerate their adoption. The HITECH Act contains specific privacy and security requirements, mainly through software certification, to ensure adequate protection of protected health information stored in EHRs.

  4. 4.

    The Office of the National Coordinator for Health Information Technology (ONC) is the principal federal entity responsible for coordinating nationwide efforts to support the adoption of health IT and the promotion of nationwide health information exchange. It was created in 2004 and is organizationally located within the Office of the Secretary for the U.S. Department of Health and Human Services. http://www.healthit.gov

  5. 5.

    ASTM E2147-01: Standard Specification for Audit and Disclosure Logs for Use in Health Information Systems. http://www.astm.org/Standards/E2147.htm

  6. 6.

    https://cliir.ucsf.edu/portfolio/national-research-network-ehr-audit-log-data

  7. 7.

    http://www.techsmith.com/morae.html

  8. 8.

    https://turf.shis.uth.tmc.edu/turfweb/

  9. 9.

    NISTIR 7804: Technical Evaluation, Testing and Validation of the Usability of Electronic Health Records. http://www.nist.gov/manuscript-publication-search.cfm?pub_id=909701

  10. 10.

    http://www.tobii.com/

  11. 11.

    http://www.smivision.com/

  12. 12.

    http://kinectforwindows.org

  13. 13.

    http://www.microsoft.com/en-us/kinectforwindowsdev/

  14. 14.

    Wifi, cellular, and ZigBee triangulation technologies have also been developed and used for RTLS.

  15. 15.

    http://www.astm.org/Standards/E1384.htm

  16. 16.

    http://www.astm.org/Standards/E2184.htm

  17. 17.

    http://chronoviz.com/

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Further Reading

  • Adler-Milstein J, Adelman JS, Tai-Seale M, Patel VL, Dymek C. EHR audit logs: a new goldmine for health services research? J Biomed Inform. 2020;101:103343.

    Article  PubMed  Google Scholar 

  • Dumais S, Jeffries R, Russell DM, Tang D, Teevan J. Understanding user behavior through log data and analysis. In: Olson JS, Kellogg W, editors. Ways of knowing in HCI. New York: Springer; 2014. p. 349–72.

    Chapter  Google Scholar 

  • Laxman S, Sastry PS. A survey of temporal data mining. Sadhana. 2006;31(2):173–98.

    Article  Google Scholar 

  • Rule A, Melnick ER, Apathy NC. Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures. J Am Med Inform Assoc. 2022;30(1):144–54.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sinsky CA, Rule A, Cohen G, et al. Metrics for assessing physician activity using electronic health record log data. J Am Med Inform Assoc. 2020;27(4):639–43.

    Article  PubMed  PubMed Central  Google Scholar 

  • Weibel N, Emmenegger C, Lyons J, Dixit R, Hill LL, Hollan JD. Interpreter-mediated physician-patient communication: opportunities for multimodal healthcare interfaces. Paper presented at: Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth ’13). 2013. p. 113–20. https://eudl.eu/pdf/10.4108/icst.pervasivehealth.2013.252026

  • Weibel N, Rick S, Emmenegger C, Ashfaq S, Calvitti A, Agha Z. LAB-IN-A-BOX: semi-automatic tracking of activity in the medical office. Pers Ubiquit Comput. 2014;19(2):317–34.

    Article  Google Scholar 

  • Zheng K, Haftel HM, Hirschl RB, O’Reilly M, Hanauer DA. Quantifying the impact of health IT implementations on clinical workflow: a new methodological perspective. J Am Med Inform Assoc. 2010;17(4):454–61.

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  • Zheng K, Padman R, Johnson MP, Diamond HS. An interface-driven analysis of user interactions with an electronic health records system. J Am Med Inform Assoc. 2009;16(2):228–37.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgement

We are grateful to Steven Rick who contributed the photos used in this chapter to illustrate computational ethnographical data recording devices deployed in exam rooms.

Discussion Questions

  1. 1.

    The sample audit trail log shown in Table 6.1 exhibits a clinician’s use session with an EHR system. In the “PATIENT_ID” column, it can be observed that the clinician worked primarily on patient “4070370” throughout the session but she or he, rather abruptly, viewed a document belonging to patient “7485199” at 07:23:19 UTC.

    1. (a)

      What might be the possible explanation(s) of this EHR use behavior? Provide one scenario of “inappropriate” use and one scenario of “beneficial” use.

    2. (b)

      How might the EHR system be redesigned to prevent “inappropriate” use, or to facilitate “beneficial” use?

    3. (c)

      If the audit trail log were not available, propose an alternative method of recording data that can capture this behavior.

  2. 2.

    Provide an example wherein your everyday activities leave behind some “digital traces” that can be analyzed using computational ethnographical methods.

    1. (a)

      Identify the data type that best characterizes these digital traces;

    2. (b)

      Propose an analytical method discussed in this chapter to analyze the data;

    3. (c)

      Also discuss what potential insights may be drawn from the analysis. These could be insights for better understanding the user behavior or for informing better design of certain technological systems.

Author information

Authors and Affiliations

  1. Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, CA, USA

    Kai Zheng

  2. Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA

    David A. Hanauer

  3. Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, USA

    Nadir Weibel

  4. West Health Institute, La Jolla, CA, USA

    Zia Agha

  5. Department of Medicine, School of Medicine, University of California, San Diego, La Jolla, CA, USA

    Zia Agha

Authors
  1. Kai Zheng
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  2. David A. Hanauer
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  3. Nadir Weibel
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  4. Zia Agha
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Corresponding author

Correspondence to Kai Zheng .

Editor information

Editors and Affiliations

  1. School of Health Information Science, University of Victoria, Victoria, BC, Canada

    Andre W. Kushniruk

  2. Health Informatics, SUNY Downstate Health Sciences University, New York, NY, USA

    David R. Kaufman

  3. Department of Anesthesiology, Washington University School of Medicine, St Louis, MO, USA

    Thomas G. Kannampallil

  4. Center for Cognitive Studies in Medicine and Public Health, New York Academy of Medicine, New York, NY, USA

    Vimla L. Patel

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Zheng, K., Hanauer, D.A., Weibel, N., Agha, Z. (2024). Computational Ethnography: Automated and Unobtrusive Means for Collecting Data In Situ for Human–Computer Interaction Evaluation Studies. In: Kushniruk, A.W., Kaufman, D.R., Kannampallil, T.G., Patel, V.L. (eds) Human Computer Interaction in Healthcare. Cognitive Informatics in Biomedicine and Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-69947-4_6

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