Appl Clin Inform 2021; 12(01): 164-169
DOI: 10.1055/s-0041-1723023
Case Report

A Perioperative Care Display for Understanding High Acuity Patients

Laurie Lovett Novak
1   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Jonathan Wanderer
2   Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
David A. Owens
3   Vanderbilt University Owen Graduate School of Management, Nashville, Tennessee, United States
,
Daniel Fabbri
1   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Julian Z. Genkins
4   Department of Medicine, University of California San Francisco, San Francisco, California, United States
,
Thomas A. Lasko
1   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
› Author Affiliations
Funding L.L.N., D.O., D.F., J.W., and T.A.L. report grant R01 EB0020666 from the National Institute of Biomedical Imaging and Bioengineering.

Abstract

Background The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types.

Objectives In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms.

Methods We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions.

Results Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries.

Conclusion This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.

Protection of Human and Animal Subjects

This project was approved by the Vanderbilt University Institutional Review Board.




Publication History

Received: 11 June 2020

Accepted: 22 December 2020

Article published online:
03 March 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Powsner SM, Tufte ER. Graphical summary of patient status. Lancet 1994; 344 (8919): 386-389
  • 2 Bauer DT, Guerlain S, Brown PJ. The design and evaluation of a graphical display for laboratory data. J Am Med Inform Assoc 2010; 17 (04) 416-424
  • 3 Torsvik T, Lillebo B, Mikkelsen G. Presentation of clinical laboratory results: an experimental comparison of four visualization techniques. J Am Med Inform Assoc 2013; 20 (02) 325-331
  • 4 Lasko TA, Owens DA, Fabbri D, Wanderer JP, Genkins JZ, Novak LL. User-centered clinical display design issues for inpatient providers. Appl Clin Inform 2020; 11 (05) 700-709
  • 5 Crisan A, McKee G, Munzner T, Gardy JL. Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory. PeerJ 2018; 6: e4218
  • 6 Wanderer JP, Nelson SE, Ehrenfeld JM, Monahan S, Park S. Clinical data visualization: the current state and future needs. J Med Syst 2016; 40 (12) 275
  • 7 West VL, Borland D, Hammond WE. Innovative information visualization of electronic health record data: a systematic review. J Am Med Inform Assoc 2015; 22 (02) 330-339
  • 8 Feblowitz JC, Wright A, Singh H, Samal L, Sittig DF. Summarization of clinical information: a conceptual model. J Biomed Inform 2011; 44 (04) 688-699
  • 9 Waller RG, Wright MC, Segall N. et al. Novel displays of patient information in critical care settings: a systematic review. J Am Med Inform Assoc 2019; 26 (05) 479-489
  • 10 Wright MC, Borbolla D, Waller RG. et al. Critical care information display approaches and design frameworks: a systematic review and meta-analysis. J Biomed Inform X 2019; 3: 100041
  • 11 Rind A, Wang TD, Aigner W. et al. Interactive information visualization to explore and query electronic health records. Found Trends Hum-Comput Interact. 2013; 5 (03) 207-298
  • 12 El-Kareh R, Hasan O, Schiff GD. Use of health information technology to reduce diagnostic errors. BMJ Qual Saf 2013; 22 (Suppl. 02) ii40-ii51
  • 13 Alberdi E, Becher J-C, Gilhooly K. et al. Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care. Int J Hum Comput Stud 2001; 55 (03) 191-216
  • 14 Sanders EBN, Stappers PJ. Co-creation and the new landscapes of design. CoDesign 2008; 4 (01) 5-18
  • 15 Jeffery AD, Novak LL, Kennedy B, Dietrich MS, Mion LC. Participatory design of probability-based decision support tools for in-hospital nurses. J Am Med Inform Assoc 2017; 24 (06) 1102-1110
  • 16 Gregory J. Scandinavian approaches to participatory design. Int J Eng Educ 2003; 19 (01) 62-74
  • 17 Bernard HR, Wutich A, Ryan GW. Analyzing Qualitative Data: Systematic Approaches. New York: SAGE publications; 2016
  • 18 American Society of Anesthesiologists. ASA Physical status classification system. Accessed April 5, 2020 from: https://www.asahq.org/standards-and-guidelines/asa-physical-status-classification-system
  • 19 Hagaman DH, Ehrenfeld JM, Terekhov M. et al. Compliance is contagious: using informatics methods to measure the spread of a documentation standard from a preoperative clinic. J Perianesth Nurs 2018; 33 (04) 436-443
  • 20 Croskerry P. From mindless to mindful practice--cognitive bias and clinical decision making. N Engl J Med 2013; 368 (26) 2445-2448
  • 21 Saposnik G, Redelmeier D, Ruff CC, Tobler PN. Cognitive biases associated with medical decisions: a systematic review. BMC Med Inform Decis Mak 2016; 16 (01) 138
  • 22 Woods DD, Watts JC. How not to have to navigate through too many displays. In: Helander MG, Landauer TK, Prabhu PV. eds. Handbook Of Human-Computer Interaction. Amsterdam, Netherlands: Elsevier; 1997: 617-650
  • 23 Jensen LG, Bossen C. Factors affecting physicians' use of a dedicated overview interface in an electronic health record: the importance of standard information and standard documentation. Int J Med Inform 2016; 87: 44-53
  • 24 Hsu W, Taira RK, El-Saden S, Kangarloo H, Bui AA. Context-based electronic health record: toward patient specific healthcare. IEEE Trans Inf Technol Biomed 2012; 16 (02) 228-234