Download PDF
Appl Clin Inform 2021; 12(04): 788-799
DOI: 10.1055/s-0041-1733909
Review Article

Interaction Time with Electronic Health Records: A Systematic Review

Yuliya Pinevich
1   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Kathryn J. Clark
1   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Andrew M. Harrison
2   Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Brian W. Pickering
1   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Vitaly Herasevich
1   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
› Author Affiliations Funding None.
› Further Information
Also available at
   Permissions and Reprints

Abstract

Background The amount of time that health care clinicians (physicians and nurses) spend interacting with the electronic health record is not well understood.

Objective This study aimed to evaluate the time that health care providers spend interacting with electronic health records (EHR).

Methods Data are retrieved from Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily, (Ovid) Embase, CINAHL, and SCOPUS.

Study Eligibility Criteria Peer-reviewed studies that describe the use of EHR and include measurement of time either in hours, minutes, or in the percentage of a clinician's workday. Papers were written in English and published between 1990 and 2021.

Participants All physicians and nurses involved in inpatient and outpatient settings.

Study Appraisal and Synthesis Methods A narrative synthesis of the results, providing summaries of interaction time with EHR. The studies were rated according to Quality Assessment Tool for Studies with Diverse Designs.

Results Out of 5,133 de-duplicated references identified through database searching, 18 met inclusion criteria. Most were time-motion studies (50%) that followed by logged-based analysis (44%). Most were conducted in the United States (94%) and examined a clinician workflow in the inpatient settings (83%). The average time was nearly 37% of time of their workday by physicians in both inpatient and outpatient settings and 22% of the workday by nurses in inpatient settings. The studies showed methodological heterogeneity.

Conclusion This systematic review evaluates the time that health care providers spend interacting with EHR. Interaction time with EHR varies depending on clinicians' roles and clinical settings, computer systems, and users' experience. The average time spent by physicians on EHR exceeded one-third of their workday. The finding is a possible indicator that the EHR has room for usability, functionality improvement, and workflow optimization.

Keywords

electronic health records and systems - inpatient (inpatient CPOE) - workflow - time and motion - task performance and analysis

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.


Supplementary Material



Publication History

Received: 04 May 2021

Accepted: 03 July 2021

Article published online:
25 August 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Kruse CS, Kristof C, Jones B, Mitchell E, Martinez A. Barriers to electronic health record adoption: a systematic literature review. J Med Syst 2016; 40 (12) 252
    CrossrefPubMedSearch in Google Scholar
  • 2 Wilson K, Khansa L. Migrating to electronic health record systems: a comparative study between the United States and the United Kingdom. Health Policy 2018; 122 (11) 1232-1239
    CrossrefPubMedSearch in Google Scholar
  • 3 Bush RA, Kuelbs C, Ryu J, Jiang W, Chiang G. Structured data entry in the electronic medical record: perspectives of pediatric specialty physicians and surgeons. J Med Syst 2017; 41 (05) 75
    CrossrefPubMedSearch in Google Scholar
  • 4 Menachemi N, Collum TH. Benefits and drawbacks of electronic health record systems. Risk Manag Healthc Policy 2011; 4: 47-55
    CrossrefPubMedSearch in Google Scholar
  • 5 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 Clinic Proceedings. Accessed 2016 at: https://pubmed.ncbi.nlm.nih.gov/27313121/
    PubMed
  • 6 Ehrenfeld JM, Wanderer JP. Technology as friend or foe? Do electronic health records increase burnout?. Curr Opin Anaesthesiol 2018; 31 (03) 357-360
    CrossrefPubMedSearch in Google Scholar
  • 7 Babbott S, Manwell LB, Brown R. et al. Electronic medical records and physician stress in primary care: results from the MEMO Study. J Am Med Inform Assoc 2014; 21 (e1): e100-e106
    CrossrefPubMedSearch in Google Scholar
  • 8 Kroth PJ, Morioka-Douglas N, Veres S. et al. The electronic elephant in the room: physicians and the electronic health record. JAMIA Open 2018; 1 (01) 49-56
    CrossrefPubMedSearch in Google Scholar
  • 9 Khairat S, Xi L, Liu S, Shrestha S, Austin C. Understanding the Association Between Electronic Health Record Satisfaction and the Well-Being of Nurses: survey study. JMIR Nursing. 2020; 3 (01) e13996
    CrossrefPubMedSearch in Google Scholar
  • 10 Harris DA, Haskell J, Cooper E, Crouse N, Gardner R. Estimating the association between burnout and electronic health record-related stress among advanced practice registered nurses. Appl Nurs Res 2018; 43: 36-41
    CrossrefPubMedSearch in Google Scholar
  • 11 Gawande A. Why doctors hate their computers. Published 2018. Accessed December 7, 2018 at: https://www.newyorker.com/magazine/2018/11/12/why-doctors-hate-their-computers
    PubMed
  • 12 Technology OWoTOotNCfHI. Strategy on Reducing Burden Relating to the Use of Health IT and EHRs. Published 2018. Accessed December, 2018 at: https://www.healthit.gov/topic/usability-and-provider-burden/strategy-reducing-burden-relating-use-health-it-and-ehrs 2018
    PubMedSearch in Google Scholar
  • 13 Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol 2009; 62 (10) 1006-1012
    CrossrefPubMedSearch in Google Scholar
  • 14 Evans RS. Electronic health records: then, now, and in the future. Yearb Med Inform 2016; (Suppl. 01) S48-S61
    PubMedSearch in Google Scholar
  • 15 Sirriyeh R, Lawton R, Gardner P, Armitage G. Reviewing studies with diverse designs: the development and evaluation of a new tool. J Eval Clin Pract 2012; 18 (04) 746-752
    CrossrefPubMedSearch in Google Scholar
  • 16 Lambe KA, Lydon S, Madden C. et al. Hand hygiene compliance in the ICU: a systematic review. Crit Care Med 2019; 47 (09) 1251-1257
    CrossrefPubMedSearch in Google Scholar
  • 17 Victores AJ, Coggins K, Takashima M. Electronic health records and resident workflow: a time-motion study of otolaryngology residents. Laryngoscope 2015; 125 (03) 594-598
    CrossrefPubMedSearch in Google Scholar
  • 18 Block L, Habicht R, Wu AW. et al. In the wake of the 2003 and 2011 duty hours regulations, how do internal medicine interns spend their time?. J Gen Intern Med 2013; 28 (08) 1042-1047
    CrossrefPubMedSearch in Google Scholar
  • 19 Asaro PV, Boxerman SB. Effects of computerized provider order entry and nursing documentation on workflow. Acad Emerg Med 2008; 15 (10) 908-915
    CrossrefPubMedSearch in Google Scholar
  • 20 Wong DH, Gallegos Y, Weinger MB, Clack S, Slagle J, Anderson CT. Changes in intensive care unit nurse task activity after installation of a third-generation intensive care unit information system. Crit Care Med 2003; 31 (10) 2488-2494
    CrossrefPubMedSearch in Google Scholar
  • 21 Qian S, Yu P, Hailey DM. The impact of electronic medication administration records in a residential aged care home. Int J Med Inform 2015; 84 (11) 966-973
    CrossrefPubMedSearch in Google Scholar
  • 22 Munyisia EN, Yu P, Hailey D. Does the introduction of an electronic nursing documentation system in a nursing home reduce time on documentation for the nursing staff?. Int J Med Inform 2011; 80 (11) 782-792
    CrossrefPubMedSearch in Google Scholar
  • 23 Korst LM, Eusebio-Angeja AC, Chamorro T, Aydin CE, Gregory KD. Nursing documentation time during implementation of an electronic medical record. J Nurs Adm 2003; 33 (01) 24-30
    CrossrefPubMedSearch in Google Scholar
  • 24 Hakes B, Whittington J. Assessing the impact of an electronic medical record on nurse documentation time. Comput Inform Nurs 2008; 26 (04) 234-241
    CrossrefPubMedSearch in Google Scholar
  • 25 Banner L, Olney CM. Automated clinical documentation: does it allow nurses more time for patient care?. Comput Inform Nurs 2009; 27 (02) 75-81
    CrossrefPubMedSearch in Google Scholar
  • 26 Carayon P, Smith P, Hundt AS, Kuruchittham V, Li Q. Implementation of an electronic health records system in a small clinic: the viewpoint of clinic staff. Behav Inf Technol 2009; 28 (01) 5-20
    CrossrefPubMedSearch in Google Scholar
  • 27 Hripcsak G, Vawdrey DK, Fred MR, Bostwick SB. Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc 2011; 18 (02) 112-117
    CrossrefPubMedSearch in Google Scholar
  • 28 Joukes E, Abu-Hanna A, Cornet R, de Keizer NF. Time spent on dedicated patient care and documentation tasks before and after the introduction of a structured and standardized electronic health record. Appl Clin Inform 2018; 9 (01) 46-53
    Thieme ConnectPubMedSearch in Google Scholar
  • 29 Edsall DW, Deshane P, Giles C, Dick D, Sloan B, Farrow J. Computerized patient anesthesia records: less time and better quality than manually produced anesthesia records. J Clin Anesth 1993; 5 (04) 275-283
    CrossrefPubMedSearch in Google Scholar
  • 30 Wormer BA, Colavita PD, Yokeley WT. et al. Impact of implementing an electronic health record on surgical resident work flow, duty hours, and operative experience. Am Surg 2015; 81 (02) 172-177
    CrossrefPubMedSearch in Google Scholar
  • 31 Wang Y, Tian Y, Tian LL, Qian YM, Li JS. An electronic medical record system with treatment recommendations based on patient similarity. J Med Syst 2015; 39 (05) 55
    CrossrefPubMedSearch in Google Scholar
  • 32 Keshavjee K, Troyan S, Holbrook AM, VanderMolen D. COMPLETE Investigators. Measuring the success of electronic medical record implementation using electronic and survey data. Proc AMIA Symp 2001; 309-313
    PubMedSearch in Google Scholar
  • 33 Kannampallil TG, Denton CA, Shapiro JS, Patel VL. Efficiency of emergency physicians: insights from an observational study using EHR log files. Appl Clin Inform 2018; 9 (01) 99-104
    Thieme ConnectPubMedSearch in Google Scholar
  • 34 Chen L, Guo U, Illipparambil LC. et al. Racing against the clock: internal medicine residents' time spent on electronic health records. J Grad Med Educ 2016; 8 (01) 39-44
    CrossrefPubMedSearch in Google Scholar
  • 35 Street Jr RL, Liu L, Farber NJ. et al. Provider interaction with the electronic health record: the effects on patient-centered communication in medical encounters. Patient Educ Couns 2014; 96 (03) 315-319
    CrossrefPubMedSearch in Google Scholar
  • 36 Street Jr RL, Liu L, Farber NJ. et al. Keystrokes, mouse clicks, and gazing at the computer: how physician interaction with the EHR affects patient participation. J Gen Intern Med 2018; 33 (04) 423-428
    CrossrefPubMedSearch in Google Scholar
  • 37 Shabbir SA, Ahmed LA, Sudhir RR, Scholl J, Li YC, Liou DM. Comparison of documentation time between an electronic and a paper-based record system by optometrists at an eye hospital in south India: a time-motion study. Comput Methods Programs Biomed 2010; 100 (03) 283-288
    CrossrefPubMedSearch in Google Scholar
  • 38 Read-Brown S, Sanders DS, Brown AS. et al. Time-motion analysis of clinical nursing documentation during implementation of an electronic operating room management system for ophthalmic surgery. AMIA Annu Symp Proc 2013; 2013: 1195-1204
    PubMedSearch in Google Scholar
  • 39 Read-Brown S, Hribar MR, Reznick LG. et al. Time requirements for electronic health record use in an academic ophthalmology center. JAMA Ophthalmol 2017; 135 (11) 1250-1257
    CrossrefPubMedSearch in Google Scholar
  • 40 Zoghbi V, Caskey RC, Dumon KR. et al. “How to” videos improve residents performance of essential perioperative electronic medical records and clinical tasks. J Surg Educ 2018; 75 (02) 489-496
    CrossrefPubMedSearch in Google Scholar
  • 41 Neri PM, Redden L, Poole S. et al. Emergency medicine resident physicians' perceptions of electronic documentation and workflow: a mixed methods study. Appl Clin Inform 2015; 6 (01) 27-41
    Thieme ConnectPubMedSearch in Google Scholar
  • 42 Hanauer DA, Zheng K, Commiskey EL, Duck MG, Choi SW, Blayney DW. Computerized prescriber order entry implementation in a physician assistant-managed hematology and oncology inpatient service: effects on workflow and task switching. J Oncol Pract 2013; 9 (04) e103-e114
    CrossrefPubMedSearch in Google Scholar
  • 43 Carayon P, Wetterneck TB, Alyousef B. et al. Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit. Int J Med Inform 2015; 84 (08) 578-594
    CrossrefPubMedSearch in Google Scholar
  • 44 Cornell P, Riordan M, Herrin-Griffith D. Transforming nursing workflow, part 2: the impact of technology on nurse activities. J Nurs Adm 2010; 40 (10) 432-439
    CrossrefPubMedSearch in Google Scholar
  • 45 Heaton HA, Wang R, Farrell KJ. et al. Time motion analysis: impact of scribes on provider time management. J Emerg Med 2018; 55 (01) 135-140
    CrossrefPubMedSearch in Google Scholar
  • 46 Hill Jr RG, Sears LM, Melanson SW. 4000 clicks: a productivity analysis of electronic medical records in a community hospital ED. Am J Emerg Med 2013; 31 (11) 1591-1594
    CrossrefPubMedSearch in Google Scholar
  • 47 Mamykina L, Vawdrey DK, Hripcsak G. How do residents spend their shift time? A time and motion study with a particular focus on the use of computers. Acad Med 2016; 91 (06) 827-832
    CrossrefPubMedSearch in Google Scholar
  • 48 Sinsky C, Colligan L, Li L. et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann Intern Med 2016; 165 (11) 753-760
    CrossrefPubMedSearch in Google Scholar
  • 49 Tipping MD, Forth VE, O'Leary KJ. et al. Where did the day go? A time-motion study of hospitalists. J Hosp Med 2010; 5 (06) 323-328
    CrossrefPubMedSearch in Google Scholar
  • 50 Schachner MB, Recondo FJ, Sommer JA. et al. Preimplementation study of a nursing e-chart: how nurses use their time. Stud Health Technol Inform 2015; 216: 255-258
    PubMedSearch in Google Scholar
  • 51 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
  • 52 Cox ML, Farjat AE, Risoli TJ. et al. Documenting or operating: where is time spent in general surgery residency?. J Surg Educ 2018; 75 (06) e97-e106
    CrossrefPubMedSearch in Google Scholar
  • 53 Cox ML, Risoli Jr T, Peskoe SB, Turner DA, Migaly J. Quantified electronic health record (EHR) use by academic surgeons. Surgery 2021; 169 (06) 1386-1392
    CrossrefPubMedSearch in Google Scholar
  • 54 Giliberto JP, Ator G, Carroll TL, Chan T, Vahabzadeh-Hagh A. National trends in daily ambulatory electronic health record use by otolaryngologists. Laryngoscope 2021; 131 (05) 975-981
    CrossrefPubMedSearch in Google Scholar
  • 55 Krawiec C, Haidet P. The impact of patient census on PICU attending electronic health record utilization. Critical Care Medicine Conference: 48th Critical Care Congress of the Society of Critical Care Medicine, SCCM; 2019;47(1 Supplement 1). Accessed 2019 at: https://pubmed.ncbi.nlm.nih.gov/32215894/
    PubMed
  • 56 Maloney SR, Peterson S, Kao AM, Sherrill WC, Green JM, Sachdev G. Surgery resident time consumed by the electronic health record. J Surg Educ 2020; 77 (05) 1056-1062
    CrossrefPubMedSearch in Google Scholar
  • 57 Verma G, Ivanov A, Benn F. et al. Analyses of electronic health records utilization in a large community hospital. PLoS One 2020; 15 (07) e0233004 [Electronic Resource]
    CrossrefPubMedSearch in Google Scholar
  • 58 Wang JK, Ouyang D, Hom J, Chi J, Chen JH. Characterizing electronic health record usage patterns of inpatient medicine residents using event log data. PLoS One 2019; 14 (02) e0205379
    CrossrefPubMedSearch in Google Scholar
  • 59 Kossman SP, Scheidenhelm SL. Nurses' perceptions of the impact of electronic health records on work and patient outcomes. Comput Inform Nurs 2008; 26 (02) 69-77
    CrossrefPubMedSearch in Google Scholar
  • 60 Chaiyachati KH, Shea JA, Asch DA. et al. Assessment of inpatient time allocation among first-year internal medicine residents using time-motion observations. JAMA Intern Med 2019; 179 (06) 760-767
    CrossrefPubMedSearch in Google Scholar
  • 61 Walker RM, Burmeister E, Jeffrey C. et al. The impact of an integrated electronic health record on nurse time at the bedside: a pre-post continuous time and motion study. Collegian 2020; 27 (01) 63-74
    CrossrefPubMedSearch in Google Scholar
  • 62 Watson MD, Elhage SA, Scully C. et al. Electronic health record usage among nurse practitioners, physician assistants, and junior residents. J Am Assoc Nurse Pract 2020; 33 (03) 200-204
    CrossrefPubMedSearch in Google Scholar
  • 63 Haidar YM, Moshtaghi O, Mahboubi H. et al. Association between electronic medical record implementation and otolaryngologist productivity in the ambulatory setting. JAMA Otolaryngol Head Neck Surg 2017; 143 (01) 20-24
    CrossrefPubMedSearch in Google Scholar
  • 64 Wenger N, Méan M, Castioni J, Marques-Vidal P, Waeber G, Garnier A. Allocation of internal medicine resident time in a Swiss hospital: a time and motion study of day and evening shifts. Ann Intern Med 2017; 166 (08) 579-586
    CrossrefPubMedSearch in Google Scholar
  • 65 Eschenroeder Jr HC, Manzione LC, Adler-Milstein J. et al. Associations of physician burnout with organizational electronic health record support and after-hours charting. J Am Med Inform Assoc 2021; 28 (05) 960-966
    CrossrefPubMedSearch in Google Scholar
  • 66 Amusan AA, Tongen S, Speedie SM, Mellin A. A time-motion study to evaluate the impact of EMR and CPOE implementation on physician efficiency. J Healthc Inf Manag 2008; 22 (04) 31-37
    PubMedSearch in Google Scholar
  • 67 Kochendorfer KM, Morris LE, Kruse RL, Ge BG, Mehr DR. Attending and resident physician perceptions of an EMR-generated rounding report for adult inpatient services. Fam Med 2010; 42 (05) 343-349
    PubMedSearch in Google Scholar
  • 68 Patel VL, Denton CA, Soni HC, Kannampallil TG, Traub SJ, Shapiro JS. Physician workflow in two distinctive emergency departments: an observational study. Appl Clin Inform 2021; 12 (01) 141-152
    Thieme ConnectPubMedSearch in Google Scholar
  • 69 Tai-Seale M, Olson CW, Li J. et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood) 2017; 36 (04) 655-662
    CrossrefPubMedSearch in Google Scholar
  • 70 Kamil RJ, Giddings N, Hoffer M. et al. Electronic health record use among American Neurotology Society members. Otol Neurotol 2018; 39 (09) e876-e882
    CrossrefPubMedSearch in Google Scholar
  • 71 Kumah-Crystal YA, Pirtle CJ, Whyte HM, Goode ES, Anders SH, Lehmann CU. Electronic health record interactions through voice: a review. Appl Clin Inform 2018; 9 (03) 541-552
    Thieme ConnectPubMedSearch in Google Scholar
  • 72 McCormick BJ, Deal A, Borawski KM. et al. Implementation of medical scribes in an academic urology practice: an analysis of productivity, revenue, and satisfaction. World J Urol 2018; 36 (10) 1691-1697
    CrossrefPubMedSearch in Google Scholar
  • 73 Tran BD, Chen Y, Liu S, Zheng K. How does medical scribes' work inform development of speech-based clinical documentation technologies? A systematic review. J Am Med Inform Assoc 2020; 27 (05) 808-817
    CrossrefPubMedSearch in Google Scholar
  • 74 Belden JL, Koopman RJ, Patil SJ, Lowrance NJ, Petroski GF, Smith JB. Dynamic electronic health record note prototype: seeing more by showing less. J Am Board Fam Med 2017; 30 (06) 691-700
    CrossrefPubMedSearch in Google Scholar
  • 75 Dowding D, Merrill JA, Barrón Y, Onorato N, Jonas K, Russell D. Usability evaluation of a dashboard for home care nurses. Comput Inform Nurs 2019; 37 (01) 11-19
    CrossrefPubMedSearch in Google Scholar
  • 76 Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J Am Med Inform Assoc 2005; 12 (05) 505-516
    CrossrefPubMedSearch in Google Scholar
  • 77 Baumann LA, Baker J, Elshaug AG. The impact of electronic health record systems on clinical documentation times: a systematic review. Health Policy 2018; 122 (08) 827-836
    CrossrefPubMedSearch in Google Scholar
  • 78 Moore EC, Tolley CL, Bates DW, Slight SP. A systematic review of the impact of health information technology on nurses' time. J Am Med Inform Assoc 2020; 27 (05) 798-807
    CrossrefPubMedSearch in Google Scholar
  • 79 Lopetegui M, Yen P-Y, Lai A, Jeffries J, Embi P, Payne P. Time motion studies in healthcare: what are we talking about?. J Biomed Inform 2014; 49: 292-299
    CrossrefPubMedSearch in Google Scholar