1. Clinical Workflow Visualization: Representation of clinician activity from location tracking
S69: Oral Presentations – Advances in Workflow Analysis
Akshay Vankipuram
Arizona State University, Tempe, AZ
Stephen Traub MD
Mayo Clinic, Phoenix, AZ
Vimla L. Patel PhD
New York Academy of Medicine, New York City, NY

2. Disclosure
My team and I have no relevant relationships with commercial interests to disclose.
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3. Learning Objectives
Understand the use of automated location tracking in monitoring elements of clinical workflow.
Understand the process of representing those elements visually for exploration and potentially deeper insight.
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4. Acknowledgements
This project was supported by grant number R01HS from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors, and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.
Thanks to
Arizona State University
Hiral Soni
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5. Clinical workflow analysis
“A workflow is a description of a sequence of operations or activities performed by various entities or agents in the system”1
Clinical workflow analysis entails tracking:
Tasks/Activities
Coordination
Knowledge/Information transfer
Etc.
Capturing temporal and spatial relationships is an important part of data collection.
Time and motion studies
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6. Automated location tracking
Continuous monitoring is a necessity.
Time and task intensive.
Intrusive
Automated location tracking (ALT) can help refine and streamline data capture.
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7. What can we track? Track entities in a clinical environment
Patients
Clinicians
Assets
Activities associated with any movement are potentially trackable.
Behaviors can be tracked.
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8. Why visualization? Aim is to aid exploration and reasoning.
Visual analytics is “science of analytical reasoning facilitated by interactive visual interfaces.”
Thomas JJ, Cook KA. A visual analytics agenda. IEEE computer graphics and applications Jan;26(1):10-3.
Visual analytics allows for:
deeper analysis/exploration of data.
presenting any results in a way that is meaningful to the target audience (clinicians, administrators, researchers).
One important consideration is multi-dimensional exploration.
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9. Three dimensions of exploration:
Visualizing ALT data
What can we represent?
Movement patterns
Underlying Activities
Multiple patient visits
Proximity (basic interactions)
Three dimensions of exploration:
Time2
Location1, 3
Personnel/Clinical Role4
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10. Case Study: Emergency Department (ED)
Part of a larger scale effort to analyze the impact of EHR systems on ED workflow.
Complex environment with rapid and at times concurrent tasks and flow.
Leveraging existing relatively sophisticated ALT technology
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11. RTLS setup Versus® technology
Radio-Frequency Identification (RFID) + Infrared (IR)
Ceiling mounted receivers and transmitter badges
59 Locations within the ED
EHR systems located at Physician Workspace
Data from 5 physicians only
8 months of captured data (approx rows)
TrackedID
Location
Start
End
Physician 1
Exam 7
8/1/2016 8:47:02am
8/1/2016 8:53:12am
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12. Representing movement patterns
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13. Representing movement patterns
Behavior modeling
Contrast physicians or compare behavior before and after
Interventions
Process modifications
Environmental disruptions
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14. Representing movement patterns
Hierarchy chart/ Organizational chart
Provides a structured view of relationships between entities of interest
Represents a quick summary of behavior.
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15. Hierarchy chart
Physician 2
Physician 3
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16. Representing movement patterns
There are differences in the relationships between locations for the two physicians.
Can we quantify the relationships?
Transition probabilities help quantify the relationship between locations.
Transition probability is the probability of the next location of a physician given their current location.
How can we represent transition probabilities?
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17. Representing transition probabilities
Physician 2
Physician 3
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18. Visualizing probabilities: From Exam rooms
Physician 2
Physician 3
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19. Extracting activities from RTLS data
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20. Multiple patient visits
Observed during shadowing
Interviews suggested that this was linked to frustration associated with time spent on EHR.
Some physicians preferred to treat multiple patients.
Multi-tasking can increase cognitive load.
We can track this
Multiple exam room visits between EHR workspace visits.
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21. Visualizing activities : Physician
Multiple patient visits
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22. Visualizing tasks: Physician
Multiple patients per hour of shift
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23. Visualizing tasks: time
Physician 3 – Multiple patients
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24. Considerations ALT is only one piece of the puzzle
These representations can be enhanced with Nursing data, EHR data.
Workspace (EHR use) is an opaque entity here
EHR log files can help increase detail of representations
Potentially simulate sections of clinical work in the ED
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25. Value of ALT Takeaways Continuous monitoring
Contrast data before and after interventions or process changes
Discover new quality metrics
Contrast data across personnel
E.g. Experts vs novices
Provide feedback to clinicians
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26. References
Vankipuram M, Kahol K, Cohen T, Patel VL. Toward automated workflow analysis and visualization in clinical environments. Journal of biomedical informatics Jun 30;44(3):
Henry J, Pylypchuk Y, Searcy T, Patel V. Adoption of Electronic Health Record Systems among US Non-Federal Acute Care Hospitals: Retrieved from healthit. gov/evaluations/data-briefs/nonfederal-acute-care-hospital-ehr-adoption php
Aigner W, Miksch S, Müller W, Schumann H, Tominski C. Visualizing time-oriented data—a systematic view. Computers & Graphics Jun 30;31(3):401-9.
Miclo R, Fontanili F, Marquès G, Bomert P, Lauras M. RTLS-based Process Mining: Towards an automatic process diagnosis in healthcare. InAutomation Science and Engineering (CASE), 2015 IEEE International Conference on 2015 Aug 24 (pp ). IEEE.
Yen PY, Kelley M, Lopetegui M, Rosado AL, Migliore EM, Chipps EM, Buck J. Understanding and Visualizing Multitasking and Task Switching Activities: A Time Motion Study to Capture Nursing Workflow. InAMIA Annual Symposium Proceedings 2016 (Vol. 2016, p ). American Medical Informatics Association.
Google. Google Charts [Internet]. [cited October 31st, 2017]. Available from:
Bostock M, Ogievetsky V, Heer J. D³ data-driven documents. IEEE transactions on visualization and computer graphics Dec;17(12):
Versus. Technology Overview [Internet]. [cited October 31st, 2017]. Available from:
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27. Email me at: Akshay.Vankipuram@asu.edu
Thank you!
me at: