Download PDF
CC BY-NC-ND 4.0 · Appl Clin Inform 2022; 13(05): 1207-1213
DOI: 10.1055/s-0042-1758735
Research Article

Information and Data Visualization Needs among Direct Care Nurses in the Intensive Care Unit

Heidi L. Lindroth
1   Department of Nursing, Mayo Clinic, Rochester, Minnesota, United States
2   Center for Aging Research, Regenstrief Institute, School of Medicine, Indiana University, Indianapolis, Indiana, United States
,
Yuliya Pinevich
3   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
4   Department of Anesthesiology and Intensive Care for Cardiac Surgery, Republican Clinical Medical Center, Belarus
,
Amelia K. Barwise
5   Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Sawsan Fathma
3   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Daniel Diedrich
3   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Brian W. Pickering
3   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Vitaly Herasevich
3   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
› Author Affiliations Funding None.
› Further Information
   Permissions and Reprints

Abstract

Objectives Intensive care unit (ICU) direct care nurses spend 22% of their shift completing tasks within the electronic health record (EHR). Miscommunications and inefficiencies occur, particularly during patient hand-off, placing patient safety at risk. Redesigning how direct care nurses visualize and interact with patient information during hand-off is one opportunity to improve EHR use. A web-based survey was deployed to better understand the information and visualization needs at patient hand-off to inform redesign.

Methods A multicenter anonymous web-based survey of direct care ICU nurses was conducted (9–12/2021). Semi-structured interviews with stakeholders informed survey development. The primary outcome was identifying primary EHR data needs at patient hand-off for inclusion in future EHR visualization and interface development. Secondary outcomes included current use of the EHR at patient hand-off, EHR satisfaction, and visualization preferences. Frequencies, means, and medians were calculated for each data item then ranked in descending order to generate proportional quarters using SAS v9.4.

Results In total, 107 direct care ICU nurses completed the survey. The majority (46%, n = 49/107) use the EHR at patient hand-off to verify exchanged verbal information. Sixty-four percent (n = 68/107) indicated that current EHR visualization was insufficient. At the start of an ICU shift, primary EHR data needs included hemodynamics (mean 4.89 ± 0.37, 98%, n = 105), continuous IV medications (4.55 ± 0.73, 93%, n = 99), laboratory results (4.60 ± 0.56, 96%, n = 103), mechanical circulatory support devices (4.62 ± 0.72, 90%, n = 97), code status (4.40 ± 0.85, 59%, n = 108), and ventilation status (4.35 + 0.79, 51%, n = 108). Secondary outcomes included mean EHR satisfaction of 65 (0–100 scale, standard deviation = ± 21) and preferred future EHR user-interfaces to be organized by organ system (53%, n = 57/107) and visualized by tasks/schedule (61%, n = 65/107).

Conclusion We identified information and visualization needs of direct care ICU nurses. The study findings could serve as a baseline toward redesigning an EHR interface.

Keywords

data visualization - intensive care unit - nurse - electronic health record

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the Mayo Clinic Institutional Review Board.


Supplementary Material



Publication History

Received: 13 July 2022

Accepted: 30 September 2022

Article published online:
28 December 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Pinevich Y, Clark KJ, Harrison AM, Pickering BW, Herasevich V. Interaction time with electronic health records: a systematic review. Appl Clin Inform 2021; 12 (04) 788-799
    Article in Thieme ConnectPubMedGoogle Scholar
  • 2 Wisner K, Lyndon A, Chesla CA. The electronic health record's impact on nurses' cognitive work: an integrative review. Int J Nurs Stud 2019; 94: 74-84
    CrossrefPubMedGoogle Scholar
  • 3 Koch SH, Weir C, Haar M. et al. Intensive care unit nurses' information needs and recommendations for integrated displays to improve nurses' situation awareness. J Am Med Inform Assoc 2012; 19 (04) 583-590
    CrossrefPubMedGoogle Scholar
  • 4 Tolentino DA, Gephart SM. State of the science of dimensions of nurses' user experience when using an electronic health record. Comput Inform Nurs 2020; 39 (02) 69-77
    CrossrefPubMedGoogle Scholar
  • 5 Fraczkowski D, Matson J, Lopez KD. Nurse workarounds in the electronic health record: an integrative review. J Am Med Inform Assoc 2020; 27 (07) 1149-1165
    CrossrefPubMedGoogle Scholar
  • 6 Kroth PJ, Morioka-Douglas N, Veres S. et al. Association of electronic health record design and use factors with clinician stress and burnout. JAMA Netw Open 2019; 2 (08) e199609-e199609
    CrossrefPubMedGoogle Scholar
  • 7 Baumann LA, Baker J, Elshaug AG. The impact of electronic health record systems on clinical documentation times: a systematic review. Health Policy 2018; 122 (08) 827-836
    CrossrefPubMedGoogle Scholar
  • 8 National Research Council (US) Committee on Engaging the Computer Science Research Community in Health Care Informatics. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. In: Stead WW, Lin HS, eds. Washington, DC: The National Academies Press; 2009
    Google Scholar
  • 9 Raeisi A, Rarani MA, Soltani F. Challenges of patient handover process in healthcare services: a systematic review. J Educ Health Promot 2019; 8: 173
    CrossrefPubMedGoogle Scholar
  • 10 Müller M, Jürgens J, Redaèlli M, Klingberg K, Hautz WE, Stock S. Impact of the communication and patient hand-off tool SBAR on patient safety: a systematic review. BMJ Open 2018; 8 (08) e022202
    CrossrefPubMedGoogle Scholar
  • 11 Galatzan BJ, Carrington JM, Gephart S. Testing the use of natural language processing software and content analysis to analyze nursing hand-off text data. Comput Inform Nurs 2021; 39 (08) 411-417
    CrossrefPubMedGoogle Scholar
  • 12 Keenan G, Yakel E, Dunn Lopez K, Tschannen D, Ford YB. Challenges to nurses' efforts of retrieving, documenting, and communicating patient care information. J Am Med Inform Assoc 2013; 20 (02) 245-251
    CrossrefPubMedGoogle Scholar
  • 13 Whittenburg L. Workflow viewpoints: analysis of nursing workflow documentation in the electronic health record. J Healthc Inf Manag 2010; 24 (03) 71-75
    PubMedGoogle Scholar
  • 14 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
    CrossrefPubMedGoogle Scholar
  • 15 Herasevich V, Ellsworth MA, Hebl JR, Brown MJ, Pickering BW. Information needs for the OR and PACU electronic medical record. Appl Clin Inform 2014; 5 (03) 630-641
    Article in Thieme ConnectPubMedGoogle Scholar
  • 16 Aakre CA, Chaudhry R, Pickering BW, Herasevich V. Information needs assessment for a medicine ward-focused rounding dashboard. J Med Syst 2016; 40 (08) 183
    CrossrefPubMedGoogle Scholar
  • 17 Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42 (02) 377-381
    Google Scholar
  • 18 Endsley MR. Designing for Situation Awareness: An Approach to User-Centered Design. 2nd ed.. Baton Rouge: Taylor & Francis Group; 2011
    Google Scholar
  • 19 Endsley MR. Toward a theory of situation awareness in dynamic systems. Hum Factors 1995; 37 (01) 32-64
    CrossrefPubMedGoogle Scholar
  • 20 Rothschild JM, Landrigan CP, Cronin JW. et al. The critical care safety study: the incidence and nature of adverse events and serious medical errors in intensive care. Crit Care Med 2005; 33 (08) 1694-1700
    CrossrefPubMedGoogle Scholar
  • 21 Donchin Y, Gopher D, Olin M. et al. A look into the nature and causes of human errors in the intensive care unit. 1995. Qual Saf Health Care 2003; 12 (02) 143-147 , discussion 147–148
    CrossrefPubMedGoogle Scholar
  • 22 Xu X, Rocha RA, Bigelow SM, Wallace CJ, Hanna T, Roemer LK. Understanding nurses' information needs and searching behaviour in acute care settings. AMIA Annu Symp Proc 2005; 2005: 839-843
    PubMedGoogle Scholar
  • 23 Lundgrén-Laine H, Kalafati M, Kontio E, Kauko T, Salanterä S. Crucial information needs of ICU charge nurses in Finland and Greece. Nurs Crit Care 2013; 18 (03) 142-153
    CrossrefPubMedGoogle Scholar
  • 24 Marshall AP, West SH, Aitken LM. Preferred information sources for clinical decision making: critical care nurses' perceptions of information accessibility and usefulness. Worldviews Evid Based Nurs 2011; 8 (04) 224-235
    CrossrefPubMedGoogle Scholar
  • 25 Nguyen OT, Shah S, Gartland AJ. et al. Factors associated with nurse well-being in relation to electronic health record use: a systematic review. J Am Med Inform Assoc 2021; 28 (06) 1288-1297
    CrossrefPubMedGoogle Scholar
  • 26 Anders S, Albert R, Miller A. et al. Evaluation of an integrated graphical display to promote acute change detection in ICU patients. Int J Med Inform 2012; 81 (12) 842-851
    CrossrefPubMedGoogle Scholar
  • 27 Hettinger AZ, Roth EM, Bisantz AM. Cognitive engineering and health informatics: applications and intersections. J Biomed Inform 2017; 67: 21-33
    CrossrefPubMedGoogle Scholar
  • 28 Nolan ME, Siwani R, Helmi H, Pickering BW, Moreno-Franco P, Herasevich V. Health IT usability focus section: data use and navigation patterns among medical ICU clinicians during electronic chart review. Appl Clin Inform 2017; 8 (04) 1117-1126
    Article in Thieme ConnectPubMedGoogle Scholar
  • 29 Tiase VL, Wawrzynski SE, Sward KA. et al. Provider preferences for patient-generated health data displays in pediatric asthma: a participatory design approach. Appl Clin Inform 2021; 12 (03) 664-674
    Article in Thieme ConnectPubMedGoogle Scholar
  • 30 Tolentino DA, Subbian V, Gephart SM. Applying computational ethnography to examine nurses' workflow within electronic health records. Nurs Res 2021; 70 (02) 132-141
    CrossrefPubMedGoogle Scholar