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LAB-IN-A-BOX: semi-automatic tracking of activity in the medical office

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Abstract

Patient-centered healthcare and increased efficiency are major goals of modern medicine, and physician–patient interaction and communication are a cornerstone of clinical encounters. The introduction of the electronic health record (EHR) has been a key component in shaping not only organization, clinical workflow and ultimately physicians’ clinical decision making, but also patient–physician communication in the medical office. In order to inform the design of future EHR interfaces and assess their impact on patient-centered healthcare, designers and researchers must understand the multimodal nature of the complex physician–patient–EHR system interaction. However, characterizing multimodal activity is difficult and expensive, often requiring manual coding of hours of video data. We present our Lab-in-a-Box solution that enables the capture of multimodal activity in real-world settings. We focus here on the medical office where our Lab-in-a-Box system exploits a range of sensors to track computer-based activity, speech interaction, visual attention and body movements, and automatically synchronize and segment this data. The fusion of multiple sensors allows us to derive initial activity segmentation and to visualize it for further interactive analysis. By empowering researchers with cutting-edge data collection tools and accelerating analysis of multimodal activity in the medical office, our Lab-in-a-Box has the potential to uncover important insights and inform the next generation of Health IT systems.

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Notes

  1. http://kinectforwindows.org.

  2. http://www.vicon.com.

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

  4. http://www.dev-audio.com.

  5. http://www.smivision.com/redm.

  6. Although we are not using any additional devices for sensing activity in the medical office, ChronoSense already supports data collection with LeapMotion for tracking finger movements (http://leapmotion.com), and the development version of the new Kinect for Windows v2. We are in the process of integrating also affordable eye trackers such as the EyeTribe (https://theeyetribe.com).

  7. This can be configured, but with standard laptop computers, an interval of 0.1 s (10 Hz) works best.

  8. http://www.vamp-plugins.org.

  9. In our ongoing studies we experienced several failures of the Morae software, as well as of the eye tracking, and continuous changes in the physical distance of the physician from the Kinect sometimes prevented continuous data collection.

  10. http://leapmotion.com.

  11. http://threegear.com.

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Acknowledgments

This research was funded by AHRQ Grant 1 R01 HS021290-01A1, Zia Agha PI. We would like to thank all participants of our ongoing studies (physicians and patients) without which we could not collect the rich data that we used as a baseline for designing the Lab-in-a-Box. Many thanks also to our colleagues participating in the QUICK and PACE research who helped the development of our tools with their advices. Also thanks to Shimona Carvalho for working on an early version of ChronoSense and to Jenny Tsao for her work on expanding it. A special acknowledgment to Adam Fouse, the designer and developer of ChronoViz who made possible the tight integration of the many data stream collected with Lab-in-a-Box with the development of ChronoViz templates, and finally to Jim Hollan, Ed Hutchins and the DCog-HCI lab at UCSD who were instrumental in bootstrapping this line of research.

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Authors and Affiliations

  1. University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA

    Nadir Weibel, Steven Rick, Colleen Emmenegger & Zia Agha

  2. Veteran Affairs Medical Research Foundation, HSRD VA San Diego Healthcare System, San Diego, CA, 92161, USA

    Nadir Weibel, Steven Rick, Shazia Ashfaq & Alan Calvitti

  3. West Health Institute, 10350 N Torrey Pines Rd, La Jolla, CA, 92037, USA

    Zia Agha

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  1. Nadir Weibel
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Correspondence to Nadir Weibel.

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Weibel, N., Rick, S., Emmenegger, C. et al. LAB-IN-A-BOX: semi-automatic tracking of activity in the medical office. Pers Ubiquit Comput 19, 317–334 (2015). https://doi.org/10.1007/s00779-014-0821-0

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