Download PDF
CC BY-NC-ND 4.0 · Appl Clin Inform 2022; 13(04): 774-777
DOI: 10.1055/a-1892-1437
Invited Editorial

Measuring and Maximizing Undivided Attention in the Context of Electronic Health Records

You Chen
1   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
,
Julia Adler-Milstein
2   Department of Medicine, University of California, San Francisco, San Francisco, California, United States
,
Christine A. Sinsky
3   American Medical Association, Chicago, Illinois, United States
› Author Affiliations Funding This research was supported, in part, by the National Library of Medicine of the National Institutes of Health, U.S. Department of Health and Human Services under Award Number R01LM012854.
› Further Information
Also available at
   Permissions and Reprints

Undivided attention is a clinician's superpower.[1] Often called deep work,[2] being in the flow, or being in the zone—when health professionals are able to perform their responsibilities with full focus and presence,[3] the care itself is safer and the care process is more satisfying to patients and clinicians alike.[4] The opposite of this state is split attention, moments when clinicians lose focus and, as a result, risk missing important incoming data—whether a cue from the patient's body language or tone of voice,[5] a relevant element of the past medical history, or an abnormal test result.

The design of the clinical environment can support or undermine clinicians' ability to provide undivided attention. It is readily apparent when, for example, the environment impedes a physician's ability to listen intently to his/her patient's symptoms, context, and concerns or a pharmacist's ability to perform medication reconciliation without interruption. Yet we currently have no standard metrics for this important state of work. Without such measures, there is no basis to assess current levels of undivided attention or the impact of efforts to increase undivided attention with associated benefits in terms of safety, patient and clinician experience, and other important outcomes.

This commentary identifies two key interactions where undivided attention is both critical and rare—the clinician–patient interaction and the clinician–electronic health record (EHR) interaction. We then propose proxy metrics of undivided attention during these interactions—ATTNPT and ATTNEHR ([Table 1]). These metrics, derived from the EHR, can be used for both operational improvements and research, by characterizing the current clinical environment, determining the association between undivided attention and other outcomes, and optimizing the care environment.

Table 1

Metrics for undivided attention to patient ATTNPT and undivided attention to individual EHR tasks ATTNEHR

ATTNPT = Clinician undivided attention to patient during visits/scheduled hours

PSH = Patient scheduled hours (from clarity)

EHRPSH = Total EHR hours from log-in to log-out during those same PSH (from UAL)*

Example: A clinician with 4 hours of patient scheduled time with 1 hour of EHR time during those 4 hours would have ATTNPT = (4–1)/4 = 0.75 = 75%.

*UAL data determines EHR time as “inactive” if there is no mouse or keyboard movement for 5 seconds.

ATTNEHR = Clinician undivided attention to individual EHR tasks, i.e., entering orders, viewing archived patient data, or ordering diagnostic tests.

EHRTASK = EHR hours on tasks (from UAL*)

EHRAB = EHR hours on attentional blinks, including pop-up alerts, electronic inbox messages, mandatory dialog box, or navigation from screen to screen during those same EHRTASK (from UAL + )

Example: A clinician with 4 hours of EHR time on tasks and half hour with attentional blinks would have ATTNEHR = (4–0.5)/4 = 0.875 = 87.5%.

*UAL determines tasks. A task represents a group of individual user actions performed within a certain time frame to accomplish some given clinical function using the EHR. Based on UAL, it is measured as an ordered list of user actions that occur sequentially until two actions are spaced in time by more than a certain cutoff. EHR hours on a task are calculated as the sum of its constitutive action durations.

+ UAL contains information of alerts, inbox messages, dialog box, narrator, navigator, and tabs of the encounter, note, order, and result, which can be leveraged to determine attentional blinks. EHR hours on attention blinks are calculated as the sum of durations of actions enabling attentional blinks to occur.


Publication History

Received: 18 April 2022

Accepted: 30 June 2022

Accepted Manuscript online:
05 July 2022

Article published online:
11 August 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 Kissler MJ, Kissler K, Burden M. Toward a medical “ecology of attention”. N Engl J Med 2021; 384 (04) 299-301
    CrossrefPubMedGoogle Scholar
  • 2 Newport C. Deep Work: Rules for Focused Success in a Distracted World. New York: Grand Central Publishing; 2016
    Google Scholar
  • 3 Zulman DM, Haverfield MC, Shaw JG. et al. Practices to foster physician presence and connection with patients in the clinical encounter. JAMA 2020; 323 (01) 70-81
    CrossrefPubMedGoogle Scholar
  • 4 Harry EPR, Kneeland P, Huang G, Stein J, Sweller J. Cognitive load and its implications for health care. NEJM Catal 2018;4(2)
    PubMedGoogle Scholar
  • 5 Sinsky CA. If not for the pause. SGIM Forum 2013; 36 (05) 10-16
    PubMedGoogle Scholar
  • 6 Harry E, Sinsky C, Dyrbye LN. et al. Physician task load and the risk of burnout among US physicians in a national survey. Jt Comm J Qual Patient Saf 2021; 47 (02) 76-85
    PubMedGoogle Scholar
  • 7 Gregory ME, Russo E, Singh H. Electronic health record alert-related workload as a predictor of burnout in primary care providers. Appl Clin Inform 2017; 8 (03) 686-697
    Google Scholar
  • 8 Moy AJ, Aaron L, Cato KD. et al. Characterizing multitasking and workflow fragmentation in electronic health records among emergency department clinicians: using time-motion data to understand documentation burden. Appl Clin Inform 2021; 12 (05) 1002-1013
    Article in Thieme ConnectPubMedGoogle Scholar
  • 9 Barth A, Schneider D. Manipulating the focus of attention in working memory: evidence for a protection of multiple items against perceptual interference. Psychophysiology 2018; 55 (07) e13062
    CrossrefPubMedGoogle Scholar
  • 10 Tajirian T, Stergiopoulos V, Strudwick G. et al. The influence of electronic health record use on physician burnout: cross-sectional survey. J Med Internet Res 2020; 22 (07) e19274
    CrossrefPubMedGoogle Scholar
  • 11 Pfoh ER, Hong S, Baranek L. et al. Reduced cognitive burden and increased focus: a mixed-methods study exploring how implementing scribes impacted physicians. Med Care 2022; 60 (04) 316-320
    CrossrefPubMedGoogle Scholar
  • 12 Ratanawongsa N, Matta GY, Bohsali FB, Chisolm MS. Reducing misses and near misses related to multitasking on the electronic health record: observational study and qualitative analysis. JMIR Human Factors 2018; 5 (01) e4
    CrossrefPubMedGoogle Scholar
  • 13 Benda NC, Meadors ML, Hettinger AZ, Ratwani RM. Emergency physician task switching increases with the introduction of a commercial electronic health record. Ann Emerg Med 2016; 67 (06) 741-746
    CrossrefPubMedGoogle Scholar
  • 14 Westbrook JI, Raban MZ, Walter SR, Douglas H. Task errors by emergency physicians are associated with interruptions, multitasking, fatigue and working memory capacity: a prospective, direct observation study. BMJ Qual Saf 2018; 27 (08) 655-663
    CrossrefPubMedGoogle Scholar
  • 15 Schneider A, Williams DJ, Kalynych C, Wehler M, Weigl M. Physicians' and nurses' work time allocation and workflow interruptions in emergency departments: a comparative time-motion study across two countries. Emerg Med J 2021; 38 (04) 263-268
    CrossrefPubMedGoogle Scholar
  • 16 Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med 2014; 12 (06) 573-576
    Google Scholar
  • 17 Moy AJ, Schwartz JM, Elias J. et al. Time-motion examination of electronic health record utilization and clinician workflows indicate frequent task switching and documentation burden. AMIA Annu Symp Proc 2021; 2020: 886-895
    PubMedGoogle Scholar
  • 18 Semanik MG, Kleinschmidt PC, Wright A. et al. Impact of a problem-oriented view on clinical data retrieval. J Am Med Inform Assoc 2021; 28 (05) 899-906
    CrossrefPubMedGoogle Scholar
  • 19 Sinsky CA, Rule A, Cohen G. et al. Metrics for assessing physician activity using electronic health record log data. J Am Med Inform Assoc 2020; 27 (04) 639-643
    CrossrefPubMedGoogle Scholar
  • 20 Rule A, Chiang MF, Hribar MR. Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods. J Am Med Inform Assoc 2020; 27 (03) 480-490
    CrossrefPubMedGoogle Scholar
  • 21 Hilliard RW, Haskell J, Gardner RL. Are specific elements of electronic health record use associated with clinician burnout more than others?. J Am Med Inform Assoc 2020; 27 (09) 1401-1410
    CrossrefPubMedGoogle Scholar
  • 22 Holmgren AJ, Downing NL, Bates DW. et al. Assessment of electronic health record use between US and non-US health systems. JAMA Intern Med 2021; 181 (02) 251-259
    CrossrefPubMedGoogle Scholar
  • 23 Moore C, Valenti A, Robinson E, Perkins R. Using log data to measure provider EHR activity at a cancer center during rapid telemedicine deployment. Appl Clin Inform 2021; 12 (03) 629-636
    Article in Thieme ConnectPubMedGoogle Scholar
  • 24 Chen Y, Patel MB, McNaughton CD, Malin BA. Interaction patterns of trauma providers are associated with length of stay. J Am Med Inform Assoc 2018; 25 (07) 790-799
    CrossrefPubMedGoogle Scholar
  • 25 Adler-Milstein J, Adelman JS, Tai-Seale M, Patel VL, Dymek C. EHR audit logs: a new goldmine for health services research?. J Biomed Inform 2020; 101: 103343
    CrossrefPubMedGoogle Scholar
  • 26 Adler-Milstein J, Zhao W, Willard-Grace R, Knox M, Grumbach K. Electronic health records and burnout: time spent on the electronic health record after hours and message volume associated with exhaustion but not with cynicism among primary care clinicians. J Am Med Inform Assoc 2020; 27 (04) 531-538
    CrossrefPubMedGoogle Scholar
  • 27 Hripcsak G, Vawdrey DK, Fred MR, Bostwick SB. Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc 2011; 18 (02) 112-117
    CrossrefPubMedGoogle Scholar
  • 28 Melnick ER, Ong SY, Fong A. et al. Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis. J Am Med Inform Assoc 2021; 28 (07) 1383-1392
    CrossrefPubMedGoogle Scholar
  • 29 Rotenstein LS, Holmgren AJ, Downing NL, Bates DW. Differences in total and after-hours electronic health record time across ambulatory specialties. JAMA Intern Med 2021; 181 (06) 863-865
    CrossrefPubMedGoogle Scholar
  • 30 Tai-Seale M, Dillon EC, Yang Y. et al. Physicians' well-being linked to in-basket messages generated by algorithms in electronic health records. Health Aff (Millwood) 2019; 38 (07) 1073-1078
    CrossrefPubMedGoogle Scholar
  • 31 Tai-Seale M, Olson CW, Li J. et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood) 2017; 36 (04) 655-662
    CrossrefPubMedGoogle Scholar
  • 32 Rotenstein LS, Fong AS, Jeffery MM. et al. Gender differences in time spent on documentation and the electronic health record in a large ambulatory network. JAMA Netw Open 2022; 5 (03) e223935-e223935
    CrossrefPubMedGoogle Scholar
  • 33 Lieu TA, Warton EM, East JA. et al. Evaluation of attention switching and duration of electronic inbox work among primary care physicians. JAMA Netw Open 2021; 4 (01) e2031856-e2031856
    CrossrefPubMedGoogle Scholar
  • 34 Chen B, Alrifai W, Gao C. et al. Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning. J Am Med Inform Assoc 2021; 28 (06) 1168-1177
    CrossrefPubMedGoogle Scholar