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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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Book cover Cognitive Informatics for Biomedicine

Part of the book series: Health Informatics ((HI))

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 HIT 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.

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

  7. 7.

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

  8. 8.

    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

  9. 9.

    http://www.tobii.com/

  10. 10.

    http://www.smivision.com/

  11. 11.

    http://kinectforwindows.org

  12. 12.

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

  13. 13.

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

  14. 14.

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

  15. 15.

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

  16. 16.

    http://chronoviz.com/

Additional Readings

  • Dumais, S., Jeffries, R., Russell, D. M., Tang, D., & Teevan, J. (2014). Understanding user behavior through log data and analysis. In J. S. Olson & W. Kellogg (Eds.), Ways of knowing in HCI (pp. 349–372). New York: Springer.

    Google Scholar 

  • Laxman, S., & Sastry, P. S. (2006). A survey of temporal data mining. Sadhana-Academy Proceedings in Engineering Sciences, 31(2), 173–198.

    Google Scholar 

  • Weibel, N., Emmenegger, C., Lyons, J., Dixit, R., Hill, L. L., & Hollan, J. D. (2013). Interpreter-mediated physician-patient communication: Opportunities for multimodal healthcare interfaces. In Proceedings of the 7th international conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth’13) (pp. 113–120).

    Google Scholar 

  • Weibel, N., Rick, S., Emmenegger, C., Ashfaq, S., Calvitti, A., & Agha, Z. (2015). LAB-IN-A-BOX: Semi-automatic tracking of activity in the medical office. Personal and Ubiquitous Computing, 19(2), 317–334.

    Google Scholar 

  • Zheng, K., Padman, R., Johnson, M. P., & Diamond, H. S. (2009). An interface-driven analysis of user interactions with an electronic health records system. Journal of the American Medical Informatics Association, 16(2), 228–237.

    Google Scholar 

  • Zheng, K., Haftel, H. M., Hirschl, R. B., O’Reilly, M., & Hanauer, D. A. (2010). Quantifying the impact of health IT implementations on clinical workflow: A new methodological perspective. Journal of the American Medical Informatics Association, 17(4), 454–461.

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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.

Author information

Authors and Affiliations

  1. Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Kai Zheng Ph.D.

  2. School of Information, University of Michigan, Ann Arbor, MI, USA

    Kai Zheng Ph.D. & David A. Hanauer M.D., M.S.

  3. Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, USA

    David A. Hanauer M.D., M.S.

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

    Nadir Weibel Ph.D.

  5. Veteran Affairs Medical Research Foundation, HSRD VA San Diego Healthcare System, San Diego, CA, USA

    Nadir Weibel Ph.D.

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

    Zia Agha M.D.

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

    Zia Agha M.D.

Authors
  1. Kai Zheng Ph.D.
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  2. David A. Hanauer M.D., M.S.
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  3. Nadir Weibel Ph.D.
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  4. Zia Agha M.D.
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Corresponding author

Correspondence to Kai Zheng Ph.D. .

Editor information

Editors and Affiliations

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

    Vimla L. Patel

  2. Columbia University, Weill Cornell College of Medicine, New York, NY, USA

    Vimla L. Patel

  3. Arizona State University, Scottsdale, AZ, USA

    Vimla L. Patel  & David R. Kaufman  & 

  4. University of Illinois at Chicago, Chicago, IL, USA

    Thomas G. Kannampallil

  5. The New York Academy of Medicine, New York, NY, USA

    Thomas G. Kannampallil

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Zheng, K., Hanauer, D.A., Weibel, N., Agha, Z. (2015). Computational Ethnography: Automated and Unobtrusive Means for Collecting Data In Situ for Human–Computer Interaction Evaluation Studies. In: Patel, V.L., Kannampallil, T.G., Kaufman, D.R. (eds) Cognitive Informatics for Biomedicine. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-17272-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-17272-9_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17271-2

  • Online ISBN: 978-3-319-17272-9

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