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.

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. (

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