Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter November 29, 2018

Electronic health records, communication, and data sharing: challenges and opportunities for improving the diagnostic process

  • Martha Quinn , Jane Forman , Molly Harrod , Suzanne Winter , Karen E. Fowler , Sarah L. Krein , Ashwin Gupta , Sanjay Saint , Hardeep Singh and Vineet Chopra EMAIL logo
From the journal Diagnosis



Diagnosis requires that clinicians communicate and share patient information in an efficient manner. Advances in electronic health records (EHRs) and health information technologies have created both challenges and opportunities for such communication.


We conducted a multi-method, focused ethnographic study of physicians on general medicine inpatient units in two teaching hospitals. Physician teams were observed during and after morning rounds to understand workflow, data sharing and communication during diagnosis. To validate findings, interviews and focus groups were conducted with physicians. Field notes and interview/focus group transcripts were reviewed and themes identified using content analysis.


Existing communication technologies and EHR-based data sharing processes were perceived as barriers to diagnosis. In particular, reliance on paging systems and lack of face-to-face communication among clinicians created obstacles to sustained thinking and discussion of diagnostic decision-making. Further, the EHR created data overload and data fragmentation, making integration for diagnosis difficult. To improve diagnosis, physicians recommended replacing pagers with two-way communication devices, restructuring the EHR to facilitate access to key information and improving training on EHR systems.


As advances in health information technology evolve, challenges in the way clinicians share information during the diagnostic process will rise. To improve diagnosis, changes to both the technology and the way in which we use it may be necessary.

Corresponding author: Vineet Chopra, MD, MSc, Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA; and Department of Internal Medicine, University of Michigan Medical School, 2800 Plymouth Road, Building 16 #432W, Ann Arbor, MI 48109, USA
aMartha Quinn and Jane Forman contributed equally to the development of this paper.
  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This project was supported by grant number P30HS024385 from the Agency for Healthcare Research and Quality (funder id: 10.13039/100000133). The funding source played no role in study design, data acquisition, analysis or decision to report these data. Dr. Chopra is supported by funding from the Agency of Healthcare Research and Quality (1-K08-HS022835-01 and 1 R18 HS025891-01). Dr. Krein is supported by a VA Health Services Research and Development Research Career Scientist Award (RCS 11-222). Dr. Singh is partially supported by Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality or the Department of Veterans Affairs.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.


1. Graber ML, Franklin N, Gordon R. Diagnostic error in internal medicine. Arch Intern Med 2005;165:1493–9.10.1001/archinte.165.13.1493Search in Google Scholar PubMed

2. Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf 2014;23:727–31.10.1136/bmjqs-2013-002627Search in Google Scholar PubMed PubMed Central

3. Graber ML. The incidence of diagnostic error in medicine. BMJ Qual Saf 2013;22:ii21–7.10.1136/bmjqs-2012-001615Search in Google Scholar PubMed PubMed Central

4. Henry J, Pylypchuk Y, Searcy T, Patel V. 2016. Adoption of electronic health record systems among U.S. non-federal acute care hospitals: 2008–2015. ONC data brief, no.35. Washington, DC: Office of the National Coordinator for Health Information Technology.Search in Google Scholar

5. Graber ML, Byrne C, Johnston D. The impact of electronic health records on diagnosis. Diagnosis 2017;4:211–23.10.1515/dx-2017-0012Search in Google Scholar PubMed

6. Singh H, Naik AD, Rao R, Petersen LA. Reducing diagnostic errors through effective communication: harnessing the power of information technology. J Gen Intern Med 2008;23:489–94.10.1007/s11606-007-0393-zSearch in Google Scholar PubMed PubMed Central

7. Sittig DF, Singh H. Electronic health records and national patient-safety goals. New Engl J Med 2012;367:1854–60.10.1056/NEJMsb1205420Search in Google Scholar PubMed PubMed Central

8. Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003;348:2526–34.10.1056/NEJMsa020847Search in Google Scholar PubMed

9. Verghese A, Shah NH, Harrington RA. What this computer needs is a physician: humanism and artificial intelligence. JAMA 2018;319:19–20.10.1001/jama.2017.19198Search in Google Scholar PubMed

10. Perrem LM, Fanshawe TR, Sharif F, Plüddemann A, O’Neill MB. A national physician survey of diagnostic error in paediatrics. Eur J Pediatr 2016;175:1387–92.10.1007/s00431-016-2772-0Search in Google Scholar PubMed

11. Palojoki S, Pajunen T, Saranto K, Lehtonen L. Electronic health record-related safety concerns: a cross-sectional survey of electronic health record users. JMIR Med Inform 2016;4:e13.10.2196/medinform.5238Search in Google Scholar PubMed PubMed Central

12. Asan O, Chiou E, Montague E. Quantitative ethnographic study of physician workflow and interactions with electronic health record systems. Int J Ind Ergon 2015;49:124–30.10.1016/j.ergon.2014.04.004Search in Google Scholar PubMed PubMed Central

13. Asan O, D. Smith P, Montague E. More screen time, less face time – implications for EHR design. J Eval Clin Pract 2014;20:896–901.10.1111/jep.12182Search in Google Scholar PubMed PubMed Central

14. Street Jr RL, Liu L, Farber NJ, Chen Y, Calvitti A, Zuest D, et al. Provider interaction with the electronic health record: the effects on patient-centered communication in medical encounters. Patient Educ Couns 2014;96:315–9.10.1016/j.pec.2014.05.004Search in Google Scholar PubMed PubMed Central

15. Higginbottom G, Pillay JJ, Boadu NY. Guidance on performing focused ethnographies with an emphasis on healthcare research. Qual Rep 2013;18:1–6.10.46743/2160-3715/2013.1550Search in Google Scholar

16. Savage J. Participative observation: standing in the shoes of others? Qual Health Res 2000;10:324–39.10.1177/104973200129118471Search in Google Scholar PubMed

17. Chopra V, Harrod M, Winter S, Forman J, Quinn M, Krein S, et al. Focused ethnography of diagnosis in academic medical centers. J Hosp Med 2018;13:E1–5.10.12788/jhm.2966Search in Google Scholar PubMed PubMed Central

18. Mulhall A. In the field: notes on observation in qualitative research. J Adv Nurs 2003;41:306–13.10.1046/j.1365-2648.2003.02514.xSearch in Google Scholar PubMed

19. Johnson M, O’Hara R, Hirst E, Weyman A, Turner J, Mason S, et al. Multiple triangulation and collaborative research using qualitative methods to explore decision making in pre-hospital emergency care. BMC Med Res Methodol 2017;17:11.10.1186/s12874-017-0290-zSearch in Google Scholar PubMed PubMed Central

20. Borkan J. Immersion/crystallization. Doing Qualitative Research 1999;2:179–94.Search in Google Scholar

21. Patton M. Qualitative research and evaluation methods, 3rd ed. Thousand Oaks, California: Sage, 2002.Search in Google Scholar

22. National Academies of Sciences, Engineering, and Medicine. Improving diagnosis in health care. Washington, DC: National Academies Press, 2016 Jan 29.Search in Google Scholar

23. Upadhyay DK, Sittig DF, Singh H. Ebola US patient zero: lessons on misdiagnosis and effective use of electronic health records. Diagnosis 2014;1:283–7.10.1515/dx-2014-0064Search in Google Scholar PubMed PubMed Central

24. Schiff GD, Bates DW. Can electronic clinical documentation help prevent diagnostic errors? N Engl J Med 2010;362: 1066–9.10.1056/NEJMp0911734Search in Google Scholar PubMed

25. Coiera E. When conversation is better than computation. J Am Med Inform Assoc 2000;7:277–86.10.1136/jamia.2000.0070277Search in Google Scholar PubMed PubMed Central

26. El-Kareh R, Hasan O, Schiff GD. Use of health information technology to reduce diagnostic errors. BMJ Qual Saf 2013;22(Suppl 2):ii40–51.10.1136/bmjqs-2013-001884Search in Google Scholar PubMed PubMed Central

27. Asan O, Carayon P. Human factors of health information technology – challenges and opportunities. Int J Hum-Comput Int 2017;33:255–7.10.1080/10447318.2017.1282755Search in Google Scholar PubMed PubMed Central

28. Patel VL, Kannampallil TG. Human factors and health information technology: current challenges and future directions. Yearb Med Inform 2014;9:58.10.15265/IY-2014-0005Search in Google Scholar PubMed PubMed Central

29. Gupta A, Harrod M, Quinn M, Manojlovich M, Fowler KE, Singh H, et al. Mind the overlap: how system problems contribute to cognitive failure and diagnostic errors. Diagnosis 2018;5:151–6.10.1515/dx-2018-0014Search in Google Scholar PubMed PubMed Central

30. Monahan T, Fisher JA. Benefits of ‘observer effects’: lessons from the field. Qual Res 2010;10:357–76.10.1177/1468794110362874Search in Google Scholar PubMed PubMed Central

Received: 2018-06-26
Accepted: 2018-10-19
Published Online: 2018-11-29
Published in Print: 2019-08-27

©2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 25.9.2023 from
Scroll to top button