Health IT Usability Focus Section: Data Use and Navigation Patterns among Medical ICU Clinicians during Electronic Chart Review

Abstract

Background A detailed understanding of electronic health record (EHR) workflow patterns and information use is necessary to inform user-centered design of critical care information systems. While developing a longitudinal medical record visualization tool to facilitate electronic chart review (ECR) for medical intensive care unit (MICU) clinicians, we found inadequate research on clinician–EHR interactions.

Objective We systematically studied EHR information use and workflow among MICU clinicians to determine the optimal selection and display of core data for a revised EHR interface.

Methods We conducted a direct observational study of MICU clinicians performing ECR for unfamiliar patients during their routine daily practice at an academic medical center. Using a customized manual data collection instrument, we unobtrusively recorded the content and sequence of EHR data reviewed by clinicians.

Results We performed 32 ECR observations among 24 clinicians. The median (interquartile range [IQR]) chart review duration was 9.2 (7.3–14.7) minutes, with the largest time spent reviewing clinical notes (44.4%), laboratories (13.3%), imaging studies (11.7%), and searching/scrolling (9.4%). Historical vital sign and intake/output data were never viewed in 31% and 59% of observations, respectively. Clinical notes and diagnostic reports were browsed ≥10 years in time for 60% of ECR sessions. Clinicians viewed a median of 7 clinical notes, 2.5 imaging studies, and 1.5 diagnostic studies, typically referencing a select few subtypes. Clinicians browsed a median (IQR) of 26.5 (22.5–37.25) data screens to complete their ECR, demonstrating high variability in navigation patterns and frequent back-and-forth switching between screens. Nonetheless, 47% of ECRs begin with review of clinical notes, which were also the most common navigation destination.

Conclusion Electronic chart review centers around the viewing of clinical notes among MICU clinicians. Convoluted workflows and prolonged searching activities indicate room for system improvement. Using study findings, specific design recommendations to enhance usability for critical care information systems are provided.

Keywords

information seeking behavior - electronic health records - clinical information - intensive care units - EHR interface - data display

Funding

None.

• References

• 1 Stead WW, Lin HS. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: National Academies Press; 2009
• 2 Hilligoss B, Zheng K. Chart biopsy: an emerging medical practice enabled by electronic health records and its impacts on emergency department-inpatient admission handoffs. J Am Med Inform Assoc 2013; 20 (02) 260-267
• 3 Varpio L, Rashotte J, Day K, King J, Kuziemsky C, Parush A. The EHR and building the patient's story: a qualitative investigation of how EHR use obstructs a vital clinical activity. Int J Med Inform 2015; 84 (12) 1019-1028
• 4 Wright MC, Dunbar S, Macpherson BC. , et al. Toward designing information display to support critical care. a qualitative contextual evaluation and visioning effort. Appl Clin Inform 2016; 7 (04) 912-929
• 5 Nolan ME, Pickering BW, Herasevich V. Initial clinician impressions of a Novel Interactive Medical Record Timeline (MeRLin) to facilitate historical chart review during new patient encounters in the ICU. Am J Respir Crit Care Med 2016; 193: A1101
• 6 Pickering BW, Gajic O, Ahmed A, Herasevich V, Keegan MT. Data utilization for medical decision making at the time of patient admission to ICU. Crit Care Med 2013; 41 (06) 1502-1510
• 7 Manor-Shulman O, Beyene J, Frndova H, Parshuram CS. Quantifying the volume of documented clinical information in critical illness. J Crit Care 2008; 23 (02) 245-250
• 8 Kannampallil TG, Franklin A, Mishra R, Almoosa KF, Cohen T, Patel VL. Understanding the nature of information seeking behavior in critical care: implications for the design of health information technology. Artif Intell Med 2013; 57 (01) 21-29
• 9 Hefter Y, Madahar P, Eisen LA, Gong MN. A time-motion study of ICU workflow and the impact of strain. Crit Care Med 2016; 44 (08) 1482-1489
• 10 Carayon P, Wetterneck TB, Alyousef B. , et al. Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit. Int J Med Inform 2015; 84 (08) 578-594
• 11 Kannampallil TG, Jones LK, Patel VL, Buchman TG, Franklin A. Comparing the information seeking strategies of residents, nurse practitioners, and physician assistants in critical care settings. J Am Med Inform Assoc 2014; 21 (e2): e249-e256
• 12 Nolan ME, Cartin-Ceba R, Moreno-Franco P, Pickering BW, Herasevich V. A multi-site survey study of EMR review habits, information needs, and display preferences among medical ICU clinicians evaluating new patients. Appl Clin Inform 2017
• 13 Zheng K, Guo MH, Hanauer DA. Using the time and motion method to study clinical work processes and workflow: methodological inconsistencies and a call for standardized research. J Am Med Inform Assoc 2011; 18 (05) 704-710
• 14 Ahmed A, Chandra S, Herasevich V, Gajic O, Pickering BW. The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Crit Care Med 2011; 39 (07) 1626-1634
• 15 Apker J, Beach C, O'Leary K, Ptacek J, Cheung D, Wears R. Handoff communication and electronic health records: exploring transitions in care between emergency physicians and internal medicine/hospitalist physicians. Proc Int Symp Hum Factors Ergon Heal Care 2014; 3 (01) 162-169
• 16 Kendall L, Klasnja P, Iwasaki J. , et al. Use of simulated physician handoffs to study cross-cover chart biopsy in the electronic medical record. AMIA Annu Symp Proc 2013; 2013: 766-775
• 17 Reichert D, Kaufman D, Bloxham B, Chase H, Elhadad N. Cognitive analysis of the summarization of longitudinal patient records. AMIA Annu Symp Proc 2010; 2010: 667-671
• 18 Zheng K, Padman R, Johnson MP, Diamond HS. An interface-driven analysis of user interactions with an electronic health records system. J Am Med Inform Assoc 2009; 16 (02) 228-237
• 19 Ellsworth MA, Lang TR, Pickering BW, Herasevich V. Clinical data needs in the neonatal intensive care unit electronic medical record. BMC Med Inform Decis Mak 2014; 14 (01) 92
• 20 Zahabi M, Kaber DB, Swangnetr M. Usability and safety in electronic medical records interface design: a review of recent literature and guideline formulation. Hum Factors 2015; 57 (05) 805-834
• 21 Doberne JW, He Z, Mohan V, Gold JA, Marquard J, Chiang MF. Using high-fidelity simulation and eye tracking to characterize EHR workflow patterns among hospital physicians. AMIA Annu Symp Proc 2015; 2015: 1881-1889
• 22 Hirsch JS, Tanenbaum JS, Lipsky Gorman S. , et al. HARVEST, a longitudinal patient record summarizer. J Am Med Inform Assoc 2015; 22 (02) 263-274
• 23 Tang Z, Weavind L, Mazabob J, Thomas EJ, Chu-Weininger MY, Johnson TR. Workflow in intensive care unit remote monitoring: A time-and-motion study. Crit Care Med 2007; 35 (09) 2057-2063
• 24 Ballermann MA, Shaw NT, Mayes DC, Gibney RT, Westbrook JI. Validation of the Work Observation Method By Activity Timing (WOMBAT) method of conducting time-motion observations in critical care settings: an observational study. BMC Med Inform Decis Mak 2011; 11: 32