An Analysis of Electronic Health Record Work to Manage Asynchronous Clinical Messages among Breast Cancer Care Teams

 

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Abstract

Objective Asynchronous messaging is an integral aspect of communication in clinical settings, but imposes additional work and potentially leads to inefficiency. The goal of this study was to describe the time spent using the electronic health record (EHR) to manage asynchronous communication to support breast cancer care coordination.

Methods We analyzed 3 years of audit logs and secure messaging logs from the EHR for care team members involved in breast cancer care at Vanderbilt University Medical Center. To evaluate trends in EHR use, we combined log data into sequences of events that occurred within 15 minutes of any other event by the same employee about the same patient.

Results Our cohort of 9,761 patients were the subject of 430,857 message threads by 7,194 employees over a 3-year period. Breast cancer care team members performed messaging actions in 37.5% of all EHR sessions, averaging 29.8 (standard deviation [SD] = 23.5) messaging sessions per day. Messaging sessions lasted an average of 1.1 (95% confidence interval: 0.99–1.24) minutes longer than nonmessaging sessions. On days when the cancer providers did not otherwise have clinical responsibilities, they still performed messaging actions in an average of 15 (SD = 11.9) sessions per day.

Conclusion At our institution, clinical messaging occurred in 35% of all EHR sessions. Clinical messaging, sometimes viewed as a supporting task of clinical work, is important to delivering and coordinating care across roles. Measuring the electronic work of asynchronous communication among care team members affords the opportunity to systematically identify opportunities to improve employee workload.

Supplementary Material

The plots that display underlying distributions for all summary statistics presented in the manuscript can be accessed via the following link: https://vanderbilt.box.com/s/swyrsi2cjcaks4u4m0fq86lh7ozjvzl6

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, which was reviewed by Vanderbilt University Institutional Review Board.

Publication History

Received: 07 December 2020

Accepted: 23 July 2021

Article published online:
15 September 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

• References

• 1 Chase DA, Ash JS, Cohen DJ, Hall J, Olson GM, Dorr DA. The EHR's roles in collaboration between providers: a qualitative study. AMIA Annu Symp Proc 2014; 2014: 1718-1727
  PubMedSearch in Google Scholar
• 2 Sharma N, O'Hare K, O'Connor KG, Nehal U, Okumura MJ. Care coordination and comprehensive electronic health records are associated with increased transition planning activities. Acad Pediatr 2018; 18 (01) 111-118
  CrossrefPubMedSearch in Google Scholar
• 3 Lansmann S, Klein S. How much collaboration? Balancing the needs for collaborative and uninterrupted work. Res Papers 2018; (December): 1-19
  PubMedSearch in Google Scholar
• 4 Saag HS, Shah K, Jones SA, Testa PA, Horwitz LI. Pajama time: working after work in the electronic health record. J Gen Intern Med 2019; 34 (09) 1695-1696
  CrossrefPubMedSearch in Google Scholar
• 5 Gardner RL, Cooper E, Haskell J. et al. Physician stress and burnout: the impact of health information technology. J Am Med Inform Assoc 2019; 26 (02) 106-114
  CrossrefPubMedSearch in Google Scholar
• 6 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
  CrossrefPubMedSearch in Google Scholar
• 7 Overhage JM, McCallie Jr D. Physician time spent using the electronic health record during outpatient encounters: a descriptive study. Ann Intern Med 2020; 172 (03) 169-174
  CrossrefPubMedSearch in Google Scholar
• 8 Barber LK, Santuzzi AM. Please respond ASAP: workplace telepressure and employee recovery. J Occup Health Psychol 2015; 20 (02) 172-189
  CrossrefPubMedSearch in Google Scholar
• 9 Lieu TA, Altschuler A, Weiner JZ. et al. Primary care physicians' experiences with and strategies for managing electronic messages. JAMA Netw Open 2019; 2 (12) e1918287-e10
  CrossrefPubMedSearch in Google Scholar
• 10 Arndt BG, Beasley JW, Watkinson MD. et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017; 15 (05) 419-426
  CrossrefPubMedSearch in Google Scholar
• 11 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
  CrossrefPubMedSearch in Google Scholar
• 12 Steitz BD, Levy MA. Evaluating the scope of clinical electronic messaging to coordinate care in a breast cancer cohort. Stud Health Technol Inform 2019; 264: 808-812
  PubMedSearch in Google Scholar
• 13 Steitz BD, Unertl KM, Levy MA. Characterizing communication patterns among members of the clinical care team to deliver breast cancer treatment. J Am Med Inform Assoc 2019; 51 (04) 549-8
  PubMedSearch in Google Scholar
• 14 Murphy DR, Reis B, Kadiyala H. et al. Electronic health record-based messages to primary care providers: valuable information or just noise?. Arch Intern Med 2012; 172 (03) 283-285
  CrossrefPubMedSearch in Google Scholar
• 15 Shanafelt TD, Gradishar WJ, Kosty M. et al. Burnout and career satisfaction among US oncologists. J Clin Oncol 2014; 32 (07) 678-686
  CrossrefPubMedSearch in Google Scholar
• 16 Agarwal R, Sands DZ, Schneider JD. Quantifying the economic impact of communication inefficiencies in U.S. hospitals. J Healthc Manag 2010; 55 (04) 265-281
  PubMedSearch in Google Scholar
• 17 Edwards A, Fitzpatrick L-A, Augustine S. et al. Synchronous communication facilitates interruptive workflow for attending physicians and nurses in clinical settings. Int J Med Inform 2009; 78 (09) 629-637
  CrossrefPubMedSearch in Google Scholar
• 18 Revere D, Painter I, Oberle M, Baseman JG. Health-care provider preferences for time-sensitive communications from public health agencies. Public Health Rep 2014; 129 (06, Suppl 4) 67-76
  CrossrefPubMedSearch in Google Scholar
• 19 Kane B, Sands DZ. for the AMIA Internet Working Group, Task Force on Guidelines for the Use of Clinic-Patient Electronic Mail. Guidelines for the clinical use of electronic mail with patients. The AMIA Internet Working Group, Task Force on Guidelines for the Use of Clinic-Patient Electronic Mail. J Am Med Inform Assoc 1998; 5 (01) 104-111
  CrossrefPubMedSearch in Google Scholar
• 20 Whittaker S, Sidner C. Email Overload. In: New York, New York, USA: ACM Press; 1996: 276-283
  Search in Google Scholar
• 21 Chui M, Manyika J, Bughin J. et al. The social economy: Unlocking value and productivity through social technologies. McKinsey Glob Inst 2012; (July): 1-184
  PubMedSearch in Google Scholar
• 22 Argenti PA. Stop letting email control your work day. Harv Bus Rev 2017; (September): 1-6
  PubMedSearch in Google Scholar
• 23 Vermeylen L, Braem S, Notebaert W. The affective twitches of task switches: Task switch cues are evaluated as negative. Cognition 2019; 183: 124-130
  CrossrefPubMedSearch in Google Scholar
• 24 Reed CC, Minnick AF, Dietrich MS. Nurses' responses to interruptions during medication tasks: a time and motion study. Int J Nurs Stud 2018; 82: 113-120
  CrossrefPubMedSearch in Google Scholar
• 25 Fong A, Ratwani RM. Understanding Emergency Medicine Physicians Multitasking Behaviors Around Interruptions. Yadav K, ed. Acad Emerg Med. 2018; 25 (10) 1164-1168
  PubMedSearch in Google Scholar
• 26 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
  CrossrefPubMedSearch in Google Scholar
• 27 Gurvich I, O'Leary KJ, Wang L, Van Mieghem JA. Collaboration, Interruptions, and Changeover Times: Workflow Model and Empirical Study of Hospitalist Charting. MSOM; 2019
  Search in Google Scholar
• 28 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
  Thieme ConnectPubMedSearch in Google Scholar
• 29 Khairat S, Burke G, Archambault H, Schwartz T, Larson J, Ratwani RM. Perceived burden of EHRs on physicians at different stages of their career. Appl Clin Inform 2018; 9 (02) 336-347
  Thieme ConnectPubMedSearch in Google Scholar
• 30 Glaveski S. Stop letting push notifications ruin your productivity. Harv Bus Rev 2019
  PubMedSearch in Google Scholar
• 31 Wolf ZR. Uncovering the hidden work of nursing. Nurs Health Care 1989; 10 (08) 463-467
  PubMedSearch in Google Scholar
• 32 Vanderbilt University Medical Center Factsheet. Accessed 2018 at: https://prd-medweb-cdn.s3.amazonaws.com/documents/patientandvisitorinfo/files/Factsheet_2018_v29_web.pdf
  PubMed
• 33 Denny JC, Giuse DA, Jirjis JN. The vanderbilt experience with electronic health records. Semin Colon Rectal Surg 2005; 16 (02) 59-68
  CrossrefPubMedSearch in Google Scholar
• 34 Danciu I, Cowan JD, Basford M. et al. Secondary use of clinical data: the Vanderbilt approach. J Biomed Inform 2014; 52 (0C): 28-35
  CrossrefPubMedSearch in Google Scholar
• 35 Giuse DA. Supporting communication in an integrated patient record system. AMIA Annu Symp Proc 2003; 1065-1
  PubMedSearch in Google Scholar
• 36 Steitz BD, Levy MA. A social network analysis of cancer provider collaboration. AMIA Annu Symp Proc 2017; 2016: 1987-1996
  PubMedSearch in Google Scholar
• 37 Vora P. Web Application Design Patterns. Elsevier; 2009.
  CrossrefSearch in Google Scholar
• 38 Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. 2006 ;Complex Systems:1695. Accessed 2021 at: http://igraph.org
  PubMedSearch in Google Scholar
• 39 Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys 2004; 69 (2 Pt 2): 026113
  CrossrefPubMedSearch in Google Scholar
• 40 Landon BE, Keating NL, Onnela J-P, Zaslavsky AM, Christakis NA, O'Malley AJ. Patient-sharing networks of physicians and health care utilization and spending among medicare beneficiaries. JAMA Intern Med 2018; 178 (01) 66-73
  CrossrefPubMedSearch in Google Scholar
• 41 Landon BE, Onnela J-P, Keating NL. et al. Using administrative data to identify naturally occurring networks of physicians. Med Care 2013; 51 (08) 715-721
  CrossrefPubMedSearch in Google Scholar
• 42 Dunn AG, Westbrook JI. Interpreting social network metrics in healthcare organisations: a review and guide to validating small networks. Soc Sci Med 2011; 72 (07) 1064-1068
  CrossrefPubMedSearch in Google Scholar
• 43 Crotty BH, Tamrat Y, Mostaghimi A, Safran C, Landon BE. Patient-to-physician messaging: volume nearly tripled as more patients joined system, but per capita rate plateaued. Health Aff (Millwood) 2014; 33 (10) 1817-1822
  CrossrefPubMedSearch in Google Scholar
• 44 Unertl KM, Novak LL, Van Houten C. et al. Organizational diagnostics: a systematic approach to identifying technology and workflow issues in clinical settings. JAMIA Open 2020; 3 (02) 269-280
  CrossrefPubMedSearch in Google Scholar
• 45 Chen Y, Xie W, Gunter CA. et al. Inferring clinical workflow efficiency via electronic medical record utilization. AMIA Annu Symp Proc 2015; 2015: 416-425
  PubMedSearch in Google Scholar
• 46 Chen Y, Lorenzi NM, Sandberg WS, Wolgast K, Malin BA. Identifying collaborative care teams through electronic medical record utilization patterns. JAMA 2016; 14 (03) ocw124-ocw10
  PubMedSearch in Google Scholar
• 47 Hron JD, Lourie E. Have you got the time? Challenges using vendor electronic health record metrics of provider efficiency. J Am Med Inform Assoc 2020; 27 (04) 644-646
  CrossrefPubMedSearch in Google Scholar
• 48 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
  CrossrefPubMedSearch in Google Scholar
• 49 Zhang J, Walji MF. TURF: toward a unified framework of EHR usability. J Biomed Inform 2011; 44 (06) 1056-1067
  CrossrefPubMedSearch in Google Scholar
• 50 Kellogg KM, Fairbanks RJ, Ratwani RM. EHR usability: get it right from the start. Biomed Instrum Technol 2017; 51 (03) 197-199
  CrossrefPubMedSearch in Google Scholar
• 51 Westbrook JI, Georgiou A, Lam M. Does computerised provider order entry reduce test turnaround times? A before-and-after study at four hospitals. Stud Health Technol Inform 2009; 150: 527-531
  PubMedSearch in Google Scholar
• 52 Aarts J, Ash J, Berg M. Extending the understanding of computerized physician order entry: implications for professional collaboration, workflow and quality of care. Int J Med Inform 2007; 76 (Suppl. 01) S4-S13
  CrossrefPubMedSearch in Google Scholar
• 53 Sampson R, Barbour R, Wilson P. Email communication at the medical primary-secondary care interface: a qualitative exploration. Br J Gen Pract 2016; 66 (648) e467-e473
  CrossrefPubMedSearch in Google Scholar
• 54 Tazegul G, Bozoglan H, Ogut TS, Balcı MK. A clinician's artificial organ? Instant messaging applications in medical care. Int J Artif Organs 2017; 40 (09) 477-480
  CrossrefPubMedSearch in Google Scholar
• 55 Cronin RM, Fabbri D, Denny JC, Jackson GP. Automated classification of consumer health information needs in patient portal messages. AMIA Annu Symp Proc 2015; 2015: 1861-1870
  PubMedSearch in Google Scholar
• 56 Sulieman L, Gilmore D, French C. et al. Classifying patient portal messages using Convolutional Neural Networks. J Biomed Inform 2017; 74: 59-70
  CrossrefPubMedSearch in Google Scholar
• 57 Steitz BD, Levy MA. Temporal and atemporal provider network analysis in a breast cancer cohort from an academic medical center (USA). Informatics (MDPI) 2018;5(03):
  PubMed
• 58 Cross R, Rebele R, Grant A. Collaborative overload. Harvard business review. Accessed January 2016 at: https://hbr.org/2016/01/collaborative-overload
  PubMed
• 59 Shanafelt TD, Boone S, Tan L. et al. Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Arch Intern Med 2012; 172 (18) 1377-1385
  CrossrefPubMedSearch in Google Scholar
• 60 Shanafelt TD, Hasan O, Dyrbye LN. et al. Changes in burnout and satisfaction with work-life balance in physicians and the general US working population between 2011 and 2014. Mayo Clin Proc 2015; 90 (12) 1600-1613
  CrossrefPubMedSearch in Google Scholar
• 61 Vaisman A, Wu RC. Analysis of smartphone interruptions on academic general internal medicine wards. Frequent interruptions may cause a ‘crisis mode’ work climate. Appl Clin Inform 2017; 8 (01) 1-11
  Thieme ConnectPubMedSearch in Google Scholar
• 62 Shanafelt TD, Dyrbye LN, Sinsky C. et al. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clin Proc 2016; 91 (07) 836-848
  CrossrefPubMedSearch in Google Scholar
• 63 21st Century Cures Act: Interoperability, Information Blocking, and the ONC Health IT Certification Program. Office of the National Coordinator for Health Information Technology (ONC), Department of Health and Human Services. Accessed 2020 at: https://www.healthit.gov/sites/default/files/cures/2020-03/ONC_Cures_Act_Final_Rule_03092020.pdf
  PubMed