Graphical Presentations of Clinical Data in a Learning Electronic Medical Record

BACKGROUND  Complex electronic medical records (EMRs) presenting large amounts of data create risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes models of intensive care unit (ICU) physicians' data access patterns to identify and then highlight the most relevant data for each patient. OBJECTIVES  We used insights from literature and feedback from potential users to inform the design of an EMR display capable of highlighting relevant information. METHODS  We used a review of relevant literature to guide the design of preliminary paper prototypes of the LEMR user interface. We observed five ICU physicians using their current EMR systems in preparation for morning rounds. Participants were interviewed and asked to explain their interactions and challenges with the EMR systems. Findings informed the revision of our prototypes. Finally, we conducted a focus group with five ICU physicians to elicit feedback on our designs and to generate ideas for our final prototypes using participatory design methods. RESULTS  Participating physicians expressed support for the LEMR system. Identified design requirements included the display of data essential for every patient together with diagnosis-specific data and new or significantly changed information. Respondents expressed preferences for fishbones to organize labs, mouseovers to access additional details, and unobtrusive alerts minimizing color-coding. To address the concern about possible physician overreliance on highlighting, participants suggested that non-highlighted data should remain accessible. Study findings led to revised prototypes, which will inform the development of a functional user interface. CONCLUSION  In the feedback we received, physicians supported pursuing the concept of a LEMR system. By introducing novel ways to support physicians' cognitive abilities, such a system has the potential to enhance physician EMR use and lead to better patient outcomes. Future plans include laboratory studies of both the utility of the proposed designs on decision-making, and the possible impact of any automation bias.

[1]  Jacqueline A Merrill,et al.  The Development of Heuristics for Evaluation of Dashboard Visualizations , 2018, Applied Clinical Informatics.

[2]  Brian W. Pickering,et al.  Data Utilization for Medical Decision Making at the Time of Patient Admission to ICU* , 2013, Critical care medicine.

[3]  William W. Stead,et al.  Computational Technology for Effective Health Care , 2009 .

[4]  Vimla L. Patel,et al.  Cognitive Informatics for Biomedicine: Human Computer Interaction in Healthcare , 2015 .

[5]  Adam Wright,et al.  Graphical display of diagnostic test results in electronic health Records: a comparison of 8 systems , 2015, J. Am. Medical Informatics Assoc..

[6]  S. Visweswaran,et al.  Patient-Specific Explanations for Predictions of Clinical Outcomes. , 2019, ACI open.

[7]  V. Herasevich,et al.  Novel Representation of Clinical Information in the ICU , 2010, Thrombosis and Haemostasis.

[8]  Carol Grbich,et al.  Qualitative Data Analysis: An Introduction , 2007 .

[9]  Silvia Miksch,et al.  Connecting time-oriented data and information to a coherent interactive visualization , 2004, CHI.

[10]  Richard J. Holden,et al.  Cognitive performance-altering effects of electronic medical records: an application of the human factors paradigm for patient safety , 2011, Cognition, Technology & Work.

[11]  J. Sweller,et al.  Cognitive load theory in health professional education: design principles and strategies , 2010, Medical education.

[12]  K. Mosier,et al.  Human Decision Makers and Automated Decision Aids: Made for Each Other? , 1996 .

[13]  Tosha B. Wetterneck,et al.  Technology Evaluation: Workarounds to Barcode Medication Administration Systems: Their Occurrences, Causes, and Threats to Patient Safety , 2008, J. Am. Medical Informatics Assoc..

[14]  Richard J. Holden,et al.  Using a sociotechnical framework to understand adaptations in health IT implementation , 2013, Int. J. Medical Informatics.

[15]  Geraldine Fitzpatrick,et al.  Dashboards for improving patient care: Review of the literature , 2015, Int. J. Medical Informatics.

[16]  B. Karsh,et al.  A human factors engineering paradigm for patient safety: designing to support the performance of the healthcare professional , 2006, Quality and Safety in Health Care.

[17]  David Bawden,et al.  Perspectives on information overload , 1999 .

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

[19]  Gilles Clermont,et al.  Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR , 2017, CRI.

[20]  B. Karsh,et al.  Interruptions and Distractions in Healthcare: Review and Reappraisal Method Inclusion and Exclusion Criteria Nih Public Access , 2022 .

[21]  Suzanne Bakken,et al.  Beyond Copy and Paste: Clinician Approaches to Meeting Information Needs During Note Writing , 2014, MIE.

[22]  Michael J. Muller.  PICTIVE—an exploration in participatory design , 1991, CHI.

[23]  Electronic Health Record Usability Interface Design Considerations , 2009 .

[24]  Vitaly Herasevich,et al.  The Effect of an Electronic Checklist on Critical Care Provider Workload, Errors, and Performance , 2016, Journal of intensive care medicine.

[25]  Rasmus Rasmussen.  Electronic whiteboards in emergency medicine: a systematic review , 2012, IHI '12.

[26]  L. Currie,et al.  Clinical cognition and biomedical informatics: Issues of patient safety. , 2006, Studies in health technology and informatics.

[27]  Michael Spenke,et al.  Visualization and interactive analysis of blood parameters with InfoZoom , 2001, Artif. Intell. Medicine.

[28]  Monique W. M. Jaspers,et al.  Pre-Post Evaluation of Physicians' Satisfaction with a Redesigned Electronic Medical Record System , 2008, MIE.

[29]  Lena Mamykina,et al.  Clinical documentation: composition or synthesis? , 2012, J. Am. Medical Informatics Assoc..

[30]  Kari A. Stephens,et al.  Applying a Participatory Design Approach to Define Objectives and Properties of a “Data Profiling” Tool for Electronic Health Data , 2016, CRI.

[31]  John T. Stasko,et al.  How Can Visual Analytics Assist Investigative Analysis? Design Implications from an Evaluation , 2011, IEEE Transactions on Visualization and Computer Graphics.

[32]  Amy Franklin,et al.  Comparing the information seeking strategies of residents, nurse practitioners, and physician assistants in critical care settings. , 2014, Journal of the American Medical Informatics Association : JAMIA.

[33]  V. Herasevich,et al.  The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance* , 2011, Critical care medicine.

[34]  Gilles Clermont,et al.  Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System , 2018, AMIA.

[35]  G. Walton,et al.  Information overload within the health care system: a literature review. , 2004, Health information and libraries journal.

[36]  B. Gross.  The managing of organizations : the administrative struggle , 1965 .

[37]  Jean Bolte,et al.  Toward Designing Information Display to Support Critical Care , 2016, Applied Clinical Informatics.

[38]  유창조.  Naturalistic Inquiry , 2022, The SAGE Encyclopedia of Research Design.

[39]  Heidrun Schumann,et al.  Visualization of Time-Oriented Data , 2011, Human-Computer Interaction Series.

[40]  Edward Rolf Tufte,et al.  The visual display of quantitative information , 1985 .

[41]  Qi Li,et al.  Using qualitative studies to improve the usability of an EMR , 2005, J. Biomed. Informatics.

[42]  Yuval Shahar,et al.  Intelligent visualization and exploration of time-oriented data of multiple patients , 2010, Artif. Intell. Medicine.

[43]  Wanda Pratt,et al.  Knowledge Crystallization and Clinical Priorities: Evaluating How Physicians Collect and Synthesize Patient-Related Data , 2014, AMIA.

[44]  ShneidermanBen,et al.  Interactive Information Visualization to Explore and Query Electronic Health Records , 2013 .

[45]  James J. Cimino,et al.  Evaluation of a system to identify relevant patient information and its impact on clinical information retrieval , 1999, AMIA.

[46]  Eric G. Poon,et al.  Research Paper: The Extent and Importance of Unintended Consequences Related to Computerized Provider Order Entry , 2007, J. Am. Medical Informatics Assoc..

[47]  et al.,et al.  How is the electronic health record being used? Use of EHR data to assess physician-level variability in technology use , 2014, J. Am. Medical Informatics Assoc..

[48]  Anthony Faiola,et al.  Advancing Critical Care in the ICU: A Human-Centered Biomedical Data Visualization Systems , 2011, HCI.

[49]  Vimla L. Patel,et al.  Understanding the nature of information seeking behavior in critical care: Implications for the design of health information technology , 2013, Artif. Intell. Medicine.

[50]  Gilles Clermont,et al.  Development and Preliminary Evaluation of a Prototype of a Learning Electronic Medical Record System , 2015, AMIA.

[51]  M. McHugh.  Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[52]  Yuval Shahar,et al.  Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions , 2006, Artif. Intell. Medicine.

[53]  C Popow,et al.  Support for Fast Comprehension of ICU Data: Visualization using Metaphor Graphics , 2001, Methods of Information in Medicine.

[54]  Jyothsna Giri,et al.  The implementation of clinician designed, human-centered electronic medical record viewer in the intensive care unit: A pilot step-wedge cluster randomized trial , 2015, Int. J. Medical Informatics.

[55]  Richard J Holden,et al.  Provider Use of a Novel EHR display in the Pediatric Intensive Care Unit , 2016, Applied Clinical Informatics.

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

[57]  Ben Shneiderman,et al.  LifeLines: visualizing personal histories , 1996, CHI.

[58]  David S. Pieczkiewicz,et al.  Design and Evaluation of a Web-Based Interactive Visualization System for Lung Transplant Home Monitoring Data , 2007, AMIA.

[59]  Ben Shneiderman,et al.  Visual information seeking in multiple electronic health records: design recommendations and a process model , 2010, IHI.

[60]  Ashenafi Zebene Woldaregay,et al.  Design and Prestudy Assessment of a Dashboard for Presenting Self-Collected Health Data of Patients With Diabetes to Clinicians: Iterative Approach and Qualitative Case Study , 2019, JMIR diabetes.

[61]  Andrew D. Miller,et al.  PD-atricians: Leveraging Physicians and Participatory Design to Develop Novel Clinical Information Tools , 2016, AMIA.