Appl Clin Inform 2021; 12(04): 877-887
DOI: 10.1055/s-0041-1735257
Research Article

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

Bryan D. Steitz
1   Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Kim M. Unertl
1   Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Mia A. Levy
1   Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
2   Division Hematology, Oncology and Cell Therapy, Department of Medicine, Rush University School of Medicine, Chicago, Illinois, United States
› Institutsangaben
Funding B.D.S. was supported by the 4T15LM007450 training grant from the United States National Library of Medicine.

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.




Publikationsverlauf

Eingereicht: 07. Dezember 2020

Angenommen: 23. Juli 2021

Artikel online veröffentlicht:
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
  • 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
  • 3 Lansmann S, Klein S. How much collaboration? Balancing the needs for collaborative and uninterrupted work. Res Papers 2018; (December): 1-19
  • 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
  • 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
  • 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
  • 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
  • 8 Barber LK, Santuzzi AM. Please respond ASAP: workplace telepressure and employee recovery. J Occup Health Psychol 2015; 20 (02) 172-189
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 15 Shanafelt TD, Gradishar WJ, Kosty M. et al. Burnout and career satisfaction among US oncologists. J Clin Oncol 2014; 32 (07) 678-686
  • 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
  • 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
  • 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
  • 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
  • 20 Whittaker S, Sidner C. Email Overload. In: New York, New York, USA: ACM Press; 1996: 276-283
  • 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
  • 22 Argenti PA. Stop letting email control your work day. Harv Bus Rev 2017; (September): 1-6
  • 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
  • 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
  • 25 Fong A, Ratwani RM. Understanding Emergency Medicine Physicians Multitasking Behaviors Around Interruptions. Yadav K, ed. Acad Emerg Med. 2018; 25 (10) 1164-1168
  • 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
  • 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
  • 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
  • 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
  • 30 Glaveski S. Stop letting push notifications ruin your productivity. Harv Bus Rev 2019
  • 31 Wolf ZR. Uncovering the hidden work of nursing. Nurs Health Care 1989; 10 (08) 463-467
  • 32 Vanderbilt University Medical Center Factsheet. Accessed 2018 at: https://prd-medweb-cdn.s3.amazonaws.com/documents/patientandvisitorinfo/files/Factsheet_2018_v29_web.pdf
  • 33 Denny JC, Giuse DA, Jirjis JN. The vanderbilt experience with electronic health records. Semin Colon Rectal Surg 2005; 16 (02) 59-68
  • 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
  • 35 Giuse DA. Supporting communication in an integrated patient record system. AMIA Annu Symp Proc 2003; 1065-1
  • 36 Steitz BD, Levy MA. A social network analysis of cancer provider collaboration. AMIA Annu Symp Proc 2017; 2016: 1987-1996
  • 37 Vora P. Web Application Design Patterns. Elsevier; 2009. DOI: 10.1016/B978-0-12-374265-0.X0001-1
  • 38 Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. 2006 ;Complex Systems:1695. Accessed 2021 at: http://igraph.org
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 49 Zhang J, Walji MF. TURF: toward a unified framework of EHR usability. J Biomed Inform 2011; 44 (06) 1056-1067
  • 50 Kellogg KM, Fairbanks RJ, Ratwani RM. EHR usability: get it right from the start. Biomed Instrum Technol 2017; 51 (03) 197-199
  • 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
  • 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
  • 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
  • 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
  • 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
  • 56 Sulieman L, Gilmore D, French C. et al. Classifying patient portal messages using Convolutional Neural Networks. J Biomed Inform 2017; 74: 59-70
  • 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):
  • 58 Cross R, Rebele R, Grant A. Collaborative overload. Harvard business review. Accessed January 2016 at: https://hbr.org/2016/01/collaborative-overload
  • 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
  • 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
  • 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
  • 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
  • 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