A Perioperative Care Display for Understanding High Acuity Patients

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.

[1]  Vivian West,et al.  Innovative information visualization of electronic health record data: a systematic review , 2014, J. Am. Medical Informatics Assoc..

[2]  Soojin Park,et al.  Clinical Data Visualization: The Current State and Future Needs , 2016, Journal of Medical Systems.

[3]  Claus Bossen,et al.  Factors affecting physicians' use of a dedicated overview interface in an electronic health record: The importance of standard information and standard documentation , 2016, Int. J. Medical Informatics.

[4]  Torbjørn Torsvik,et al.  Presentation of clinical laboratory results: an experimental comparison of four visualization techniques , 2012, J. Am. Medical Informatics Assoc..

[5]  Pieter Jan Stappers,et al.  Co-creation and the new landscapes of design , 2008 .

[6]  Mary S. Dietrich,et al.  Participatory design of probability-based decision support tools for in-hospital nurses , 2017, AMIA.

[7]  Adam Wright,et al.  Summarization of clinical information: A conceptual model , 2011, J. Biomed. Informatics.

[8]  Pat Croskerry,et al.  From mindless to mindful practice--cognitive bias and clinical decision making. , 2013, The New England journal of medicine.

[9]  Jesse M. Ehrenfeld,et al.  Compliance Is Contagious: Using Informatics Methods to Measure the Spread of a Documentation Standard From a Preoperative Clinic , 2017, Journal of perianesthesia nursing : official journal of the American Society of PeriAnesthesia Nurses.

[10]  David T. Bauer,et al.  The design and evaluation of a graphical display for laboratory data , 2010, J. Am. Medical Informatics Assoc..

[11]  Guilherme Del Fiol,et al.  Novel displays of patient information in critical care settings: a systematic review , 2019, J. Am. Medical Informatics Assoc..

[12]  Daniel Fabbri,et al.  User-Centered Clinical Display Design Issues for Inpatient Providers , 2020, Applied Clinical Informatics.

[13]  Guilherme Del Fiol,et al.  Critical care information display approaches and design frameworks: A systematic review and meta-analysis , 2019, J. Biomed. Informatics X.

[14]  Ricky K. Taira,et al.  Context-Based Electronic Health Record: Toward Patient Specific Healthcare , 2012, IEEE Transactions on Information Technology in Biomedicine.

[15]  Philippe N. Tobler,et al.  Cognitive biases associated with medical decisions: a systematic review , 2016, BMC Medical Informatics and Decision Making.

[16]  Robert Logie,et al.  Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care , 2002, Int. J. Hum. Comput. Stud..

[17]  Robert El-Kareh,et al.  Use of health information technology to reduce diagnostic errors , 2013, BMJ quality & safety.

[18]  E. Tufte,et al.  Graphical summary of patient status , 1994, The Lancet.

[19]  Tamara Munzner,et al.  Evidence-based design and evaluation of a whole genome sequencing clinical report for the reference microbiology laboratory , 2017, bioRxiv.

[20]  J. Gregory Scandinavian Approaches to Participatory Design , 2003 .

[21]  Ben Shneiderman,et al.  Interactive Information Visualization to Explore and Query Electronic Health Records , 2013, Found. Trends Hum. Comput. Interact..