Local Investment in Training Drives Electronic Health Record User Satisfaction
Open accessAbstract
Background and Significance Despite decades of effort and billions of dollars of investment, the electronic health record (EHR) has not lived up to its potential to improve care, reduce costs, or revolutionize the experience for caregivers.[ 1 ] Many people point to poor technical usability as a root cause of these failings.[ 2 ] To find solutions to these challenges, the Arch Collaborative organizations (signed below) are working together to jointly study the feedback of their EHR users. After collecting responses from over 72,000 physicians, nurses, advanced practice professionals, and residents across 156 provider organizations, we are identifying key opportunities to derive greater value from the EHR investments that our organizations have collectively made (See details of survey methods in the [ supplementary Material ], available in the online version). The extensive feedback from tens of thousands of users reveals critical gaps in users' understanding of how to optimize their EHR. Therefore, we as an industry have an opportunity to improve EHR adoption by investing in EHR learning and personalization support for caregivers. If health care organizations offered higher-quality educational opportunities for their care providers—and if providers were expected to develop greater mastery of EHR functionality—many of the current EHR challenges would be ameliorated.[ 3 ] We came to this conclusion after discovering the wide variation in EHR experience that exists within all EHR customer bases (see [ Table 1 ]). This variation cannot be ignored as it is not caused by differences in regulatory burden or programing design. We express concern that user competency often does not receive the strong focus it needs. These findings do not negate the need for EHR developers to continue to improve their user interfaces to be more intuitive, nor do they negate the critical need to reexamine the current regulatory and billing requirements that drive so much of the clinical documentation burden faced by providers today, but we believe that a greater focus on education and training is the overlooked opportunity that could enable EHR technology to drive substantial gains in the quadruple aim.[ 4 ] [ 5 ] Table 1 Variation in experience by EHR Number of organizations with vendor deployed (and >10 surveys collected) Lowest organization net EHR experience score Highest organization net EHR experience score Vendor 1 104 −13 73 Vendor 2 26 −51 43 Vendor 3 13 −58 31 Vendor 4 12 −41 54 Vendor 5 7 −26 42 Vendor 6 5 −15 21 Vendor 7 5 −60 −42 Abbreviation: EHR, electronic health record. Protection of Human and Animal Subjects This study is considered exempt from the Institutional Review Board review as defined by 45 CFR 46.101(b). * The list of Arch Collaborative members appears in [ Supplementary Material ] (available in the online version). Supplementary Material Supplementary Material
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Survey respondents may conflate their EHR usability with the burdens of documentation due to regulatory, clerical, or administrative requirements or local implementation that manifest in the EHR.
“Fourth, survey respondents may conflate their EHR usability with the burdens of documentation due to regulatory, clerical, or administrative requirements or local implementation that manifest in the EHR (eg, individual proficiency).12,36,37”
From The Association Between Perceived Electronic Health Record Usability and Professional Burnout Among US Physicians (2020) · cites this paper as [37]
In Background and Significance (page not provided), the paper distinguishes technical usability from user competency and says regulatory and billing requirements drive clinical documentation burden. It does not establish that survey respondents conflate these issues, or specifically address clerical and administrative requirements as sources of that conflation. (LLM, conf=0.79)
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Cites
The Triple Aim: Care, Health, And Cost
Berwick, D. M., Nolan, T. W., & Whittington, J. (2008) · Health Affairs · doi:10.1377/hlthaff.27.3.759 · Cited by 5,136 · see citing works
From Triple to Quadruple Aim: Care of the Patient Requires Care of the Provider
Bodenheimer, T., & Sinsky, C. (2014) · The Annals of Family Medicine · doi:10.1370/afm.1713 · Cited by 3,462 · see citing works
Han, Y. Y., Carcillo, J. A., Venkataraman, S. T., Clark, R. S. B., Watson, R. S., Nguyen, T. C., Bayir, H., & Orr, R. A. (2005) · Pediatrics · doi:10.1542/peds.2005-1287 · Cited by 1,149 · see citing works
Mixed Results In The Safety Performance Of Computerized Physician Order Entry
Metzger, J. B., Welebob, E. M., Bates, D. W., Lipsitz, S. R., & Classen, D. C. (2010) · Health Affairs · doi:10.1377/hlthaff.2010.0160 · Cited by 166 · see citing works
Longhurst, C., Parast, L. M., Sandborg, C. I., Widen, E., Sullivan, J., Hahn, J. S., Dawes, C. G., & Sharek, P. J. (2010) · PEDIATRICS · doi:10.1542/peds.2009-3271 · Cited by 130 · see citing works
Self-citationSittig, D. F., Belmont, E., & Singh, H. (2017) · Healthcare · doi:10.1016/j.hjdsi.2017.06.004 · Cited by 103 · see citing works
Transitional Chaos or Enduring Harm? The EHR and the Disruption of Medicine
Rosenbaum, L. R. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1509961 · Cited by 76 · see citing works
Successful Physician Training Program for Large Scale EMR Implementation
Stevens, L. A., Mailes, E., Goad, B. A., Longhurst, C., & Pantaleoni, J. L. (2015) · Applied Clinical Informatics · doi:10.4338/aci-2014-09-cr-0076 · Cited by 47 · see citing works
Self-citationNational trends in safety performance of electronic health record systems in children’s hospitals
Chaparro, J. D., Classen, D. C., Danforth, M., Stockwell, D. C., & Longhurst, C. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw134 · Cited by 38 · see citing works
Self-citationDesigning An Individualized EHR Learning Plan For Providers
Stevens, L. A., DiAngi, Y. T., Schremp, J. D., Martorana, M. J., Miller, R. E., Lee, T. C., & Pageler, N. M. (2017) · Applied Clinical Informatics · doi:10.4338/aci-2017-04-0054 · Cited by 29 · see citing works
A New EHR Training Curriculum and Assessment for Pediatric Residents
Stroup, K., Sanders, B., Bernstein, B. A., Scherzer, L., & Pachter, L. M. (2017) · Applied Clinical Informatics · doi:10.4338/aci-2017-06-ra-0091 · Cited by 24 · see citing works
Human Factors Engineering in HI: So What? Who Cares? and What's in It for You?
Elkin, P. L. (2012) · Healthcare Informatics Research · doi:10.4258/hir.2012.18.4.237 · Cited by 20 · see citing works
Pereira, A. G., Kim, M. S., Seywerd, M., Nesbitt, B., Pitt, M. B., & Collaborative, O. B. O. T. M. E. (2018) · Applied Clinical Informatics · doi:10.1055/s-0038-1635096 · Cited by 20 · see citing works
Self-citationThe Value of Clinical Teachers for EMR Implementations and Conversions
Pantaleoni, J. L., Longhurst, C., & Stevens, L. A. (2015) · Applied Clinical Informatics · doi:10.4338/aci-2014-09-ie-0075 · Cited by 15 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Melnick, E. R., Dyrbye, L. N., Sinsky, C. A., Trockel, M., West, C. P., Nedelec, L., Tutty, M. A., & Shanafelt, T. (2020) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2019.09.024 · Cited by 413 · see citing works
Lee, T. C., Shah, N. U., Haack, A., & Baxter, S. L. (2020) · Informatics · doi:10.3390/informatics7030025 · Cited by 133 · see citing works
Melnick, E. R., West, C. P., Nath, B., Cipriano, P. F., Peterson, C., Satele, D. V., Shanafelt, T., & Dyrbye, L. N. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab059 · Cited by 105 · see citing works
Nguyen, O. T., Jenkins, N. J., Khanna, N., Shah, S., Gartland, A., Turner, K. L., & Merlo, L. J. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa339 · Cited by 97 · see citing works
Eschenroeder, H. C., Manzione, L. C., Adler‐Milstein, J., Bice, C., Cash, R., Duda, C., Joseph, C., Lee, J. S., Maneker, A., Poterack, K. A., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab053 · Cited by 90 · see citing works
Self-citationElectronic Health Records: A Critical Appraisal of Strengths and Limitations
Kataria, S., & Ravindran, V. K. (2020) · The Journal of the Royal College of Physicians of Edinburgh · doi:10.4997/jrcpe.2020.309 · Cited by 84 · see citing works
National Trends in the Safety Performance of Electronic Health Record Systems From 2009 to 2018
Classen, D. C., Holmgren, A. J., Co, Z., Newmark, L. P., Seger, D. L., Danforth, M., & Bates, D. W. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.5547 · Cited by 83 · see citing works
Building the evidence-base to reduce electronic health record–related clinician burden
Dymek, C., Kim, B. I., Melton, G. B., Payne, T. H., Singh, H., & Hsiao, C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa238 · Cited by 83 · see citing works
Melnick, E. R., Ong, S. Y., Fong, A., Socrates, V., Ratwani, R. M., Nath, B., Simonov, M., Salgia, A. D. T., Williams, B., Marchalik, D., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab011 · Cited by 67 · see citing works
Electronic Health Record Optimization and Clinician Well-Being: A Potential Roadmap Toward Action
Shah, T., Kitts, A. B., Gold, J. A., Horvath, K. A., Ommaya, A., Opelka, F. G., Sato, L., Schwarze, G., Upton, M., & Sandy, L. (2020) · NAM Perspectives · doi:10.31478/202008a · Cited by 45 · see citing works
Nguyen, O. T., Shah, S., Gartland, A., Parekh, A., Turner, K. L., Feldman, S. S., & Merlo, L. J. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa289 · Cited by 43 · see citing works
Nurses’ Perception and Satisfaction Toward Electronic Medical Record System
Ramoo, V., Kamaruddin, A., Nawawi, W. N. F. W., Che, C. C., & Kavitha, R. R. (2022) · Florence Nightingale Journal of Nursing · doi:10.5152/fnjn.2022.22061 · Cited by 42 · see citing works
A Scoping Review of Health Information Technology in Clinician Burnout
Wu, D. T. Y., Xu, C. T., Kim, A., Bindhu, S., Mah, K. E., & Eckman, M. H. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1731399 · Cited by 39 · see citing works
Team and Electronic Health Record Features and Burnout Among Family Physicians
Rotenstein, L. S., Hendrix, N., Phillips, R. L., & Adler‐Milstein, J. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.42687 · Cited by 31 · see citing works
Self-citationLourie, E. M., Utidjian, L., Ricci, M. F., Webster, L., Young, C., & Grenfell, S. M. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa248 · Cited by 31 · see citing works
Nguyen, O. T., Turner, K. L., Apathy, N. C., Magoč, T., Hanna, K., Merlo, L. J., Harle, C. A., Thompson, L. A., Berner, E. S., & Feldman, S. S. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab272 · Cited by 28 · see citing works
Physician Burnout and Timing of Electronic Health Record Use
Micek, M. A., Arndt, B. G., Tuan, W., Trowbridge, E. R., Dean, S. M., Lochner, J., Sampene, E., & Pandhi, N. (2020) · ACI Open · doi:10.1055/s-0039-3401815 · Cited by 27 · see citing works
Self-citationInpatient EHR User Experience and Hospital EHR Safety Performance
Classen, D. C., Longhurst, C., Davis, T., Adler‐Milstein, J., & Bates, D. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.33152 · Cited by 26 · see citing works
Self-citationRule, A., & Hribar, M. R. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab230 · Cited by 25 · see citing works
Mohan, V., Garrison, C., & Gold, J. A. (2021) · JMIR Medical Informatics · doi:10.2196/29374 · Cited by 23 · see citing works
Evaluation and practical application of prompt-driven ChatGPTs for EMR generation
Ding, H., Xia, W., Zhou, Y., Wei, L., Feng, Y., Wang, Z., Song, X., Li, R., Mao, Q., Chen, B., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01472-x · Cited by 20 · see citing works
Ruppel, H., Bhardwaj, A., Manickam, R., Adler‐Milstein, J., Flagg, M., Ballesca, M. A., & Liu, V. X. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.0512 · Cited by 20 · see citing works
Self-citationThe impact of electronic medical records on clinical documentation: A case study
Torab-Miandoab, A., Samad‐Soltani, T., Jodati, A., Akbarzadeh, F., & Rezaei‐Hachesu, P. (2025) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_320_24 · Cited by 19 · see citing works
HITECH to 21st century cures: clinician burden and evolving health IT policy
Gettinger, A., & Zayas‐Cabán, T. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa330 · Cited by 18 · see citing works
EMR usability and patient safety: a national survey of physicians
Schwappach, D., Hautz, W., Krummrey, G., Pfeiffer, Y., & Ratwani, R. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01657-4 · Cited by 17 · see citing works
Musa, S., Dergaa, I., Yasin, R. A. S., & Singh, R. (2023) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s414200 · Cited by 16 · see citing works
Analysing EHR navigation patterns and digital workflows among physicians during ICU pre-rounds
Coleman, C., Gotz, D., Eaker, S., James, E., Bice, T. J., Carson, S. S., & Khairat, S. (2020) · Health Information Management Journal · doi:10.1177/1833358320920589 · Cited by 16 · see citing works
Sieja, A., Whittington, M. D., Patterson, V., Markley, K., Holmstrom, H., Rotholz, S., Gonzalez, C., Carpenter, M. S., & Lin, C. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab073 · Cited by 16 · see citing works
English, E. F., Holmstrom, H., Kwan, B. W., Suresh, K., Rotholz, S., Lin, C., & Sieja, A. (2022) · Applied Clinical Informatics · doi:10.1055/s-0041-1740482 · Cited by 16 · see citing works
Building Learning Health Care Systems in Primary Care
McGuire, M. (2019) · Quality Management in Health Care · doi:10.1097/qmh.0000000000000230 · Cited by 14 · see citing works
Automation failures and patient safety
Ruskin, K. J., Ruskin, A. C., & O’Connor, M. (2020) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000000935 · Cited by 14 · see citing works
Multidisciplinary Sprint Program Achieved Specialty-Specific EHR Optimization in 20 Clinics
Sieja, A., Kim, E., Holmstrom, H., Rotholz, S., Lin, C., Gonzalez, C., Arellano, C., Hutchings, S., Henderson, D., & Markley, K. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1728699 · Cited by 14 · see citing works
Explaining Variability in Electronic Health Record Effort in Primary Care Ambulatory Encounters
Overhage, J. M., Qeadan, F., Choi, E. H. E., Vos, D., & Kroth, P. J. (2024) · Applied Clinical Informatics · doi:10.1055/s-0044-1782228 · Cited by 13 · see citing works
Baxter, S. L., Gali, H. E., Chiang, M. F., Hribar, M. R., Lucila Ohno-machado, A., El-Kareh, R. E., Huang, A. E., Chen, H. E., Camp, A. S., Kikkawa, D. O., et al. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1701255 · Cited by 13 · see citing works
Self-citationHertzum, M. (2021) · Synthesis lectures on human-centered informatics · doi:10.2200/s01081ed1v01y202103hci049 · Cited by 13 · see citing works
Hussain, A., Ruowei, W., Xu, X., Jameel, A., Chunhong, S., & Ahmad, S. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13383-4 · Cited by 12 · see citing works
Franks, A. M., Clements, C., Bannister, T., Mays-Kingston, A., Beaty, A., Korkmaz, A., Parker, J. A., & Petrany, S. M. (2024) · The Annals of Family Medicine · doi:10.1370/afm.3073 · Cited by 12 · see citing works
Clinical Documentation as End-User Programming
Rule, A., Goldstein, I. H., Chiang, M. F., & Hribar, M. R. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376205 · Cited by 12 · see citing works
Hollister-Meadows, L., Richesson, R., Gagne, J. C. D., & Rawlins, N. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa333 · Cited by 11 · see citing works
“I didn't know you could do that”: A Pilot Assessment of EHR Optimization Training
Gold, R. B., Bunce, A., Davis, J. V., Nelson, J. C., Cowburn, S., Oakley, J., Carney, S., Horberg, M. A., Dearing, J. W., Melgar, G., et al. (2021) · ACI Open · doi:10.1055/s-0041-1731005 · Cited by 11 · see citing works
Hospital’s adoption of multiple methods of obtaining outside information and use of that information
Everson, J., & Patel, V. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac079 · Cited by 11 · see citing works
Implementation of Health IT for Cancer Screening in US Primary Care: Scoping Review
Owens-Jasey, C., Chen, J., Xu, R., Angier, H. E., Huebschmann, A. G., Fukunaga, M. I., Chaiyachati, K. H., Rendle, K. A. S., Robien, K., DiMartino, L., et al. (2024) · JMIR Cancer · doi:10.2196/49002 · Cited by 10 · see citing works
Electronic health record data mining for artificial intelligence healthcare
Lin, A. L., Chen, W. C., & Hong, J. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00008-9 · Cited by 10 · see citing works
Nguyen, O. T., Vo, S. D., Lee, T., Cato, K., & Cho, H. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae228 · Cited by 9 · see citing works
Analysis of end-user satisfaction with electronic health records in college/university healthcare
Frisina, P. G., Munene, E., Finnie, J., Oakley, J. E., & Ganesan, G. (2020) · Journal of American College Health · doi:10.1080/07448481.2020.1762610 · Cited by 8 · see citing works
Multicenter Analysis of Electronic Health Record Use among Ophthalmologists
Baxter, S. L., Gali, H. E., Mehta, M. C., Rudkin, S., Bartlett, J. D., Brandt, J. D., Sun, C. Q., Millen, M. M., & Longhurst, C. (2020) · Ophthalmology · doi:10.1016/j.ophtha.2020.06.007 · Cited by 8 · see citing works
Self-citationKhairat, S., Chourasia, P. K., Kwong, E. C., Choi, J. M., & Seashore, C. J. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.02.005 · Cited by 7 · see citing works
Physician engagement strategies in health information system implementations
Hudson, D. (2022) · Healthcare Management Forum · doi:10.1177/08404704221131921 · Cited by 6 · see citing works
Fichadia, P. A., Virmani, M., Shah, P., Mahmood, R., Kanwar, R., Singla, A., & Jain, R. (2024) · Baylor University Medical Center Proceedings · doi:10.1080/08998280.2024.2352993 · Cited by 6 · see citing works
Fong, A., Iscoe, M. S., Sinsky, C. A., Haimovich, A. D., Williams, B., O’Connell, R. T., Goldstein, R. L., & Melnick, E. R. (2022) · JMIR Medical Informatics · doi:10.2196/34954 · Cited by 6 · see citing works
Neonatal informatics: past, present and future
Patel, S. Y., Palma, J. P., Hoffman, J., & Lehmann, C. U. (2024) · Journal of Perinatology · doi:10.1038/s41372-024-01924-4 · Cited by 5 · see citing works
Alshammari, T. A., Alshammary, K. M., & Alshammari, F. M. M. (2022) · Journal of Economics and Behavioral Studies · doi:10.22610/jebs.v14i1(j).3293 · Cited by 5 · see citing works
Pisciotta, M., Morrissey, S. E., Bunce, A., Gottlieb, L. M., Donovan, J., Watkins, S. L., Middendorf, M., Sheppler, C. R., Edelmann, A. C., & Gold, R. B. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf045 · Cited by 4 · see citing works
Electronic Health Record Use among Ophthalmology Residents while on Call
Long, C. P., Tai‐Seale, M., El-Kareh, R. E., Lee, J. E., & Baxter, S. L. (2020) · Journal of Academic Ophthalmology · doi:10.1055/s-0040-1716411 · Cited by 4 · see citing works
Olley, R., & Hozynka, J. (2023) · Asia Pacific Journal of Health Management · doi:10.24083/apjhm.v18i1.1711 · Cited by 2 · see citing works
Running to Stand Still: EHR Efficiency and Workload Across Emergency Medicine Residency
Preiksaitis, C. M., Hughes, J., Iscoe, M. S., Makutonin, M., Rider, A. C., Melnick, E. R., & Rose, C. C. (2026) · AEM Education and Training · doi:10.1002/aet2.70253 · Cited by 2 · see citing works
Improving Physician Electronic Health Record Usability and Perception Through Personalized Coaching
Khairat, S., Kwong, E. C., Chourasia, P. K., Choi, J. M., & Seashore, C. J. (2022) · Studies in health technology and informatics · doi:10.3233/shti220897 · Cited by 2 · see citing works
Thomas, T., & Bradley, P. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009052023.010 · Cited by 2 · see citing works
Malhan, A., Manuj, I., Pelton, L. E., & Pavur, R. (2022) · Records Management Journal · doi:10.1108/rmj-06-2021-0026 · Cited by 2 · see citing works
Lawrence, J. G., Tanny, S. P. T., Heaton, V., & Andrew, L. (2020) · ACI Open · doi:10.1055/s-0040-1716747 · Cited by 2 · see citing works
Enhancing Patients' E-Health Engagement Through Artificial Intelligence
Chauhan, A. S., Gulati, C., Mathur, G., & Sankpal, S. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-5468-1.ch002 · Cited by 1 · see citing works
Taking a Team Approach: Keep Up with the EHR with a Training and Optimization Program
McEntee, R., Hitt, J. R., & Sieja, A. (2024) · Applied Clinical Informatics · doi:10.1055/a-2437-0185 · Cited by 1 · see citing works
Electronic Health Record Burnout in Surgery and Strategies to Improve Burnout: A Narrative Review
Karwoski, A. S., Hricz, N. W., Schlidt, K., & Rasko, Y. M. (2025) · Cureus · doi:10.7759/cureus.98469 · Cited by 1 · see citing works
Thanabalachandran, L., Zaza, K. J., Hartzell, R., Miller, K., Digby, G. C., Moffat, T., Mushonga, M., Wright, K., Powis, M., Drover, J., et al. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00322 · Cited by 1 · see citing works
The Role of a Nurse Practitioner in a “Big Bang” Electronic Medical Record Implementation
Larson, L. (2021) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000729 · Cited by 1 · see citing works
Olwendo, A. O., Otieno, G., & Rucha, K. (2021) · East African Journal of Science Technology and Innovation · doi:10.37425/eajsti.v2i2.224 · Cited by 1 · see citing works
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Barshay, M., & Richardson, C. R. (2024) · The Annals of Family Medicine · doi:10.1370/afm.3091 · Cited by 1 · see citing works
Physician Well-Being Challenges: An Historical Perspective
Babal, J. C. (2022) · Springer eBooks · doi:10.1007/978-3-031-10843-3_1
Abbassi, H., Rahimi, B., Jebraeily, M., Alinejad, V., & Nourani, A. (2023) · Research Square · doi:10.21203/rs.3.rs-3080907/v1
Electronic Health Record Burnout in Surgery (Preprint)Preprint
Hricz, N. W., Schlidt, K., & Rasko, Y. M. (2024) · doi:10.2196/preprints.65112
Baskaran, V., & Mathis, K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-95652-2_34
Alruwaili, N. S. S., Alotaibi, F. S. M., Alharbi, S. A. E., Alanazi, A. R. A., Alruwaili, B. Q. G., Aalrashidi, B. H., Alanazi, B. R. A., Alshammari, T. K. M., Alshammari, F. S. M., Al-Saloum, A. A. M., et al. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.4669
Quantifying and Visualizing Emergency Physician Workflow: Observational Time-Motion Study
Henreid, A. J., Ioannides, K. L. H., Pevnick, J. M., Cohen, T., Torbati, S. S., Nuckols, T. K., & Berdahl, C. T. (2026) · JMIR Medical Informatics · doi:10.2196/85983
Lee, Y., Harris, L., Rossos, P. G., Chartier, L. B., & Ben‐Yakov, M. (2026) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70648
Bayquen, D. D. A., Narasimhan, P., & Tack, C. (2026) · medRxiv · doi:10.64898/2026.10.29.26366871
- Authors
- Longhurst, C., Davis, T., Maneker, A., Eschenroeder, H., Dunscombe, R., Reynolds, G., Clay, B., Moran, T., Graham, D., Dean, S., Adler-Milstein, J., & Collaborative, O. B. O. T. A.
- Affiliations
- University of California San Diego Health, La Jolla, California, United States
- KLAS Enterprises LLC, Orem, Utah, United States
- Akron, Ohio, United States
- OrthoVirginia (US)
- National Health Service (GB)
- Omaha, Nebraska, United States
- UC San Diego Health System (US)
- Northwestern Medicine (US)
- Springfield, Illinois, United States
- University of Wisconsin–Madison (US)
- University of California San Francisco Center for Clinical Informatics and Improvement Research, San Francisco, California, United States
- Venue
- Applied Clinical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2019-03
- Volume
- 10
- Issue
- 02
- Pages
- 331-335
- ISSN
- 1869-0327 (ISSN-L)
- PMC
- PMC6520075
- OpenAlex
- W2946676436
- Cited by
- 76
- References
- 14
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 13 · 2023: 6 · 2022: 9 · 2021: 13 · 2020: 22 · 2019: 2
- FWCI
- 12.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Applied Clinical Informatics (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- health records, EHR training, EHR usability, EHR adoption, user competence, clinical documentation burden, Quadruple Aim, health care provider education, EHR interoperability
- MeSH terms
- Humans, Personal Satisfaction, Physicians, Software, Electronic Health Records
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