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Appl Clin Inform 2021; 12(03): 597-620
DOI: 10.1055/s-0041-1731399
Review Article

A Scoping Review of Health Information Technology in Clinician Burnout

Danny T. Y. Wu
1   Department of Biomedical Informatics, University of Cincinnati College of Medicine, Ohio, United States
2   Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
3   Division of Cardiology, The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
,
Catherine Xu
1   Department of Biomedical Informatics, University of Cincinnati College of Medicine, Ohio, United States
4   Medical Science Baccalaureate Program, University of Cincinnati College of Medicine, Ohio, United States
,
Abraham Kim
1   Department of Biomedical Informatics, University of Cincinnati College of Medicine, Ohio, United States
4   Medical Science Baccalaureate Program, University of Cincinnati College of Medicine, Ohio, United States
,
Shwetha Bindhu
1   Department of Biomedical Informatics, University of Cincinnati College of Medicine, Ohio, United States
4   Medical Science Baccalaureate Program, University of Cincinnati College of Medicine, Ohio, United States
,
Kenneth E. Mah
3   Division of Cardiology, The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
2   Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States
,
Mark H. Eckman
5   Division of General Internal Medicine, University of Cincinnati College of Medicine, Ohio, United States
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Abstract

Background Clinician burnout is a prevalent issue in healthcare, with detrimental implications in healthcare quality and medical costs due to errors. The inefficient use of health information technologies (HIT) is attributed to having a role in burnout.

Objective This paper seeks to review the literature with the following two goals: (1) characterize and extract HIT trends in burnout studies over time, and (2) examine the evidence and synthesize themes of HIT's roles in burnout studies.

Methods A scoping literature review was performed by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with two rounds of searches in PubMed, IEEE Xplore, ACM, and Google Scholar. The retrieved papers and their references were screened for eligibility by using developed inclusion and exclusion criteria. Data were extracted from included papers and summarized either statistically or qualitatively to demonstrate patterns.

Results After narrowing down the initial 945 papers, 36 papers were included. All papers were published between 2013 and 2020; nearly half of them focused on primary care (n = 16; 44.4%). The most commonly studied variable was electronic health record (EHR) practices (e.g., number of clicks). The most common study population was physicians. HIT played multiple roles in burnout studies: it can contribute to burnout; it can be used to measure burnout; or it can intervene and mitigate burnout levels.

Conclusion This scoping review presents trends in HIT-centered burnout studies and synthesizes three roles for HIT in contributing to, measuring, and mitigating burnout. Four recommendations were generated accordingly for future burnout studies: (1) validate and standardize HIT burnout measures; (2) focus on EHR-based solutions to mitigate clinician burnout; (3) expand burnout studies to other specialties and types of healthcare providers, and (4) utilize mobile and tracking technology to study time efficiency.

Keywords

review literature as topic - information technology - electronic health records - burnout - professional

Protection of Human and Animal Subjects

No human or animal subjects were involved in this project.


Authors' Contributions

D.T.Y.W. led this scoping review and coordinated the effort in the research team. D.T.Y.W. designed the study and mentored C.X. to execute the scoping review and write the first draft of the manuscript. A.K. and S.B. helped the paper screening, information extraction and narrative synthesis, and manuscript writing. M.H.E. and K.E.M. reviewed the final draft and contributed significantly to the discussion. All authors reviewed and approved this manuscript before submission.




Publikationsverlauf

Eingereicht: 28. Januar 2021

Angenommen: 24. Mai 2021

Artikel online veröffentlicht:
07. Juli 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med 2018; 283 (06) 516-529
    CrossrefPubMedGoogle Scholar
  • 2 Rotenstein LS, Torre M, Ramos MA. et al. Prevalence of burnout among physicians: a systematic review. JAMA 2018; 320 (11) 1131-1150
    CrossrefPubMedGoogle Scholar
  • 3 Peckham C. Medscape national physician burnout & depression report 2018. Accessed June 26, 2020 at: https://www.medscape.com/slideshow/2018-lifestyle-burnout-depression-6009235
    Google Scholar
  • 4 Yates SW. Physician stress and burnout. Am J Med 2020; 133 (02) 160-164
    CrossrefPubMedGoogle Scholar
  • 5 Nicholls M. Cardiologists and the burnout scenario. Eur Heart J 2019; 40 (01) 5-6
    CrossrefPubMedGoogle Scholar
  • 6 Shanafelt TD, West CP, Sinsky C. et al. Changes in burnout and satisfaction with work-life integration in physicians and the general US working population between 2011 and 2017. Mayo Clin Proc 2019; 94 (09) 1681-1694
    CrossrefPubMedGoogle Scholar
  • 7 Kapu AN, Borg Card E, Jackson H. et al. Assessing and addressing practitioner burnout: Results from an advanced practice registered nurse health and well-being study. J Am Assoc Nurse Pract 2019; 33 (01) 38-48
    CrossrefPubMedGoogle Scholar
  • 8 Patel RS, Bachu R, Adikey A, Malik M, Shah M. Factors related to physician burnout and its consequences: a review. Behav Sci (Basel) 2018; 8 (11) E98
    CrossrefPubMedGoogle Scholar
  • 9 Stehman CR, Testo Z, Gershaw RS, Kellogg AR. Burnout, drop out, suicide: physician loss in emergency medicine, part I. West J Emerg Med 2019; 20 (03) 485-494
    CrossrefPubMedGoogle Scholar
  • 10 Tawfik DS, Profit J, Morgenthaler TI. et al. Physician burnout, well-being, and work unit safety grades in relationship to reported medical errors. Mayo Clin Proc 2018; 93 (11) 1571-1580
    CrossrefPubMedGoogle Scholar
  • 11 Regehr C, Glancy D, Pitts A, LeBlanc VR. Interventions to reduce the consequences of stress in physicians: a review and meta-analysis. J Nerv Ment Dis 2014; 202 (05) 353-359
    CrossrefPubMedGoogle Scholar
  • 12 Mandeville B, Cooper E, Haskell J, Viner-Brown S, Gardner RL. Use of health information technology by rhode island physicians and advanced practice providers, 2019. R I Med J (2013) 2020; 103 (01) 21-24
    Google Scholar
  • 13 Melnick ER, Harry E, Sinsky CA. et al. Perceived electronic health record usability as a predictor of task load and burnout among US physicians: a mediation analysis. J Med Internet Res 2020; 22 (12) e23382
    Google Scholar
  • 14 Coiera E, Ash J, Berg M. The unintended consequences of health information technology revisited. Yearb Med Inform 2016; (01) 163-169
    PubMedGoogle Scholar
  • 15 Colicchio TK, Cimino JJ, Del Fiol G. Unintended consequences of nationwide electronic health record adoption: challenges and opportunities in the post-meaningful use era. J Med Internet Res 2019; 21 (06) e13313
    CrossrefPubMedGoogle Scholar
  • 16 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
    CrossrefPubMedGoogle Scholar
  • 17 Ozeran L, Schreiber R. Reduce burnout by eliminating billing documentation rules to let clinicians be clinicians: a clarion call to informaticists. Appl Clin Inform 2021; 12 (01) 73-75
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 18 Miyasaki JM, Rheaume C, Gulya L. et al. Qualitative study of burnout, career satisfaction, and well-being among US neurologists in 2016. Neurology 2017; 89 (16) 1730-1738
    CrossrefPubMedGoogle Scholar
  • 19 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
    CrossrefPubMedGoogle Scholar
  • 20 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
    CrossrefPubMedGoogle Scholar
  • 21 Melnick ER, Dyrbye LN, Sinsky CA. et al. The association between perceived electronic health record usability and professional burnout among US physicians. Mayo Clin Proc 2020; 95 (03) 476-487
    CrossrefPubMedGoogle Scholar
  • 22 DeChant PF, Acs A, Rhee KB. et al. Effect of organization-directed workplace interventions on physician burnout: a systematic review. Mayo Clin Proc Innov Qual Outcomes 2019; 3 (04) 384-408
    CrossrefPubMedGoogle Scholar
  • 23 Guo U, Chen L, Mehta PH. Electronic health record innovations: helping physicians - one less click at a time. Health Inf Manag 2017; 46 (03) 140-144
    PubMedGoogle Scholar
  • 24 Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 2010; 8 (05) 336-341
    CrossrefPubMedGoogle Scholar
  • 25 Tricco AC, Lillie E, Zarin W. et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018; 169 (07) 467-473
    CrossrefPubMedGoogle Scholar
  • 26 Khairat S, Coleman C, Ottmar P, Jayachander DI, Bice T, Carson SS. Association of electronic health record use with physician fatigue and efficiency. JAMA Netw Open 2020; 3 (06) e207385
    CrossrefPubMedGoogle Scholar
  • 27 Cooke A, Smith D, Booth A. Beyond PICO: the SPIDER tool for qualitative evidence synthesis. Qual Health Res 2012; 22 (10) 1435-1443
    CrossrefPubMedGoogle Scholar
  • 28 Furukawa MF, Raghu TS, Spaulding TJ, Vinze A. Adoption of health information technology for medication safety in U.S. Hospitals, 2006. Health Aff (Millwood) 2008; 27 (03) 865-875
    CrossrefPubMedGoogle Scholar
  • 29 Süt N. Study designs in medicine. Balkan Med J 2014; 31 (04) 273-277
    CrossrefPubMedGoogle Scholar
  • 30 AHRQ. Health information technology integration. Published online February 2013. Accessed April 1, 2021 at: https://www.ahrq.gov/ncepcr/tools/health-it/index.html
    Google Scholar
  • 31 Popay J, Roberts H, Sowden A. et al. Guidance on the conduct of narrative synthesis in systematic reviews: a product from the ESRC methods programme. Lancaster University. Accessed 2006 at: https://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/fhm/dhr/chir/NSsynthesisguidanceVersion1-April2006.pdf
    Google Scholar
  • 32 Chen G, Faris P, Hemmelgarn B, Walker RL, Quan H. Measuring agreement of administrative data with chart data using prevalence unadjusted and adjusted kappa. BMC Med Res Methodol 2009; 9: 5
    CrossrefPubMedGoogle Scholar
  • 33 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
    CrossrefPubMedGoogle Scholar
  • 34 Robertson SL, Robinson MD, Reid A. Electronic health record effects on work-life balance and burnout within the I3 population collaborative. J Grad Med Educ 2017; 9 (04) 479-484
    CrossrefPubMedGoogle Scholar
  • 35 Tran B, Lenhart A, Ross R, Dorr DA. Burnout and EHR use among academic primary care physicians with varied clinical workloads. AMIA Jt Summits Transl Sci Proc 2019; 2019: 136-144
    PubMedGoogle Scholar
  • 36 Heponiemi T, Hyppönen H, Vehko T. et al. Finnish physicians' stress related to information systems keeps increasing: a longitudinal three-wave survey study. BMC Med Inform Decis Mak 2017; 17 (01) 147
    CrossrefPubMedGoogle Scholar
  • 37 Hauer A, Waukau HJ, Welch P. Physician burnout in Wisconsin: an alarming trend affecting physician wellness. WMJ 2018; 117 (05) 194-200
    PubMedGoogle Scholar
  • 38 Micek MA, Arndt B, Tuan W-J. et al. Physician burnout and timing of electronic health record use. ACI Open 2020; 04 (01) e1-e8
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 39 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
    CrossrefPubMedGoogle Scholar
  • 40 Tawfik DS, Phibbs CS, Sexton JB. et al. Factors associated with provider burnout in the NICU. Pediatrics 2017; 139 (05) e20164134
    CrossrefPubMedGoogle Scholar
  • 41 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
    CrossrefPubMedGoogle Scholar
  • 42 Linzer M, Poplau S, Babbott S. et al. Worklife and wellness in academic general internal medicine: results from a national survey. J Gen Intern Med 2016; 31 (09) 1004-1010
    CrossrefPubMedGoogle Scholar
  • 43 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
    CrossrefPubMedGoogle Scholar
  • 44 Domaney NM, Torous J, Greenberg WE. Exploring the association between electronic health record use and burnout among psychiatry residents and faculty: a pilot survey study. Acad Psychiatry 2018; 42 (05) 648-652
    CrossrefPubMedGoogle Scholar
  • 45 Mendelsohn D, Despot I, Gooderham PA, Singhal A, Redekop GJ, Toyota BD. Impact of work hours and sleep on well-being and burnout for physicians-in-training: the resident activity tracker evaluation study. Med Educ 2019; 53 (03) 306-315
    CrossrefPubMedGoogle Scholar
  • 46 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
    CrossrefPubMedGoogle Scholar
  • 47 Vainiomäki S, Aalto A-M, Lääveri T. et al. Better usability and technical stability could lead to better work-related well-being among physicians. Appl Clin Inform 2017; 8 (04) 1057-1067
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 48 Marckini DN, Samuel BP, Parker JL, Cook SC. Electronic health record associated stress: a survey study of adult congenital heart disease specialists. Congenit Heart Dis 2019; 14 (03) 356-361
    CrossrefPubMedGoogle Scholar
  • 49 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
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 50 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
    CrossrefPubMedGoogle Scholar
  • 51 Kroth PJ, Morioka-Douglas N, Veres S. et al. Association of electronic health record design and use factors with clinician stress and burnout. JAMA Netw Open 2019; 2 (08) e199609
    Google Scholar
  • 52 Heponiemi T, Hyppönen H, Kujala S. et al. Predictors of physicians' stress related to information systems: a nine-year follow-up survey study. BMC Health Serv Res 2018; 18 (01) 284
    CrossrefPubMedGoogle Scholar
  • 53 Adler-Milstein J, Huckman RS. The impact of electronic health record use on physician productivity. Am J Manag Care 2013; 19 (10 Spec No): SP345-SP352
    PubMedGoogle Scholar
  • 54 Anderson J, Leubner J, Brown SR. EHR Overtime: An Analysis of Time Spent After Hours by Family Physicians. Fam Med 2020; Feb 52 (02) 135-137
    CrossrefPubMedGoogle Scholar
  • 55 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
    CrossrefPubMedGoogle Scholar
  • 56 Khairat S, Coleman C, Ottmar P, Bice T, Koppel R, Carson SS. Physicians' gender and their use of electronic health records: findings from a mixed-methods usability study. J Am Med Inform Assoc 2019; 26 (12) 1505-1514
    CrossrefPubMedGoogle Scholar
  • 57 Zhou Y, Ancker JS, Upadhye M. et al. The impact of interoperability of electronic health records on ambulatory physician practices: a discrete-event simulation study. Inform Prim Care 2013; 21 (01) 21-29
    CrossrefPubMedGoogle Scholar
  • 58 Murphy DR, Satterly T, Giardina TD, Sittig DF, Singh H. Practicing clinicians' recommendations to reduce burden from the electronic health record inbox: a mixed-methods study. J Gen Intern Med 2019; 34 (09) 1825-1832
    CrossrefPubMedGoogle Scholar
  • 59 DiAngi YT, Longhurst CA, Payne TH. Taming the EHR (electronic health record) - there is hope. J Fam Med 2016; 3 (06) 1072
    PubMedGoogle Scholar
  • 60 Gardner JS, Plaven BE, Yellowlees P, Shore JH. Remote telepsychiatry workforce: a solution to psychiatry's workforce issues. Curr Psychiatry Rep 2020; 22 (02) 8
    CrossrefPubMedGoogle Scholar
  • 61 Nakagawa K, Yellowlees PM. University of California Technology Wellness Index: a physician-centered framework to assess technologies' impact on physician well-being. Psychiatr Clin North Am 2019; 42 (04) 669-681
    CrossrefPubMedGoogle Scholar
  • 62 Zazove P, McKee M, Schleicher L. et al. To act or not to act: responses to electronic health record prompts by family medicine clinicians. J Am Med Inform Assoc 2017; 24 (02) 275-280
    CrossrefPubMedGoogle Scholar
  • 63 Roy A, Druker S, Hoge EA, Brewer JA. Physician anxiety and burnout: symptom correlates and a prospective pilot study of app-delivered mindfulness training. JMIR Mhealth Uhealth 2020; 8 (04) e15608
    CrossrefPubMedGoogle Scholar
  • 64 Robinson KE, Kersey JA. Novel electronic health record (EHR) education intervention in large healthcare organization improves quality, efficiency, time, and impact on burnout. Medicine (Baltimore) 2018; 97 (38) e12319
    CrossrefPubMedGoogle Scholar
  • 65 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
    CrossrefPubMedGoogle Scholar
  • 66 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
    CrossrefPubMedGoogle Scholar
  • 67 Longhurst CA, Davis T, Maneker A. et al; Arch Collaborative. Local investment in training drives electronic health record user satisfaction. Appl Clin Inform 2019; 10 (02) 331-335
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 68 Stevens LA, DiAngi YT, Schremp JD. et al. Designing an individualized EHR learning plan for providers. Appl Clin Inform 2017; 8 (03) 924-935
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 69 Denton CA, Soni HC, Kannampallil TG. et al. Emergency physicians' perceived influence of ehr use on clinical workflow and performance metrics. Appl Clin Inform 2018; 9 (03) 725-733
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 70 Pierce RP, Eskridge BR, Rehard L, Ross B, Day MA, Belden JL. The effect of electronic health record usability redesign on annual screening rates in an ambulatory setting. Appl Clin Inform 2020; 11 (04) 580-588
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 71 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med 2010; 363 (06) 501-504
    CrossrefPubMedGoogle Scholar
  • 72 Gesner E, Gazarian P, Dykes P. The burden and burnout in documenting patient care: an integrative literature review. Stud Health Technol Inform 2019; 264: 1194-1198
    PubMedGoogle Scholar
  • 73 Huang C, Koppel R, McGreevey III JD, Craven CK, Schreiber R. Transitions from one electronic health record to another: challenges, pitfalls, and recommendations. Appl Clin Inform 2020; 11 (05) 742-754
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 74 van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW. The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digit Med 2021; 4 (01) 57
    CrossrefPubMedGoogle Scholar
  • 75 Vidotti V, Ribeiro RP, Galdino MJQ, Martins JT. Burnout syndrome and shift work among the nursing staff. Rev Lat Am Enfermagem 2018; 26: e3022
    CrossrefPubMedGoogle Scholar
  • 76 Molero Jurado MDM, Pérez-Fuentes MDC, Gázquez Linares JJG, Simón Márquez MDM, Martos Martínez Á. Burnout risk and protection factors in certified nursing aides. Int J Environ Res Public Health 2018; 15 (06) E1116
    CrossrefPubMedGoogle Scholar
  • 77 Kavalieratos D, Siconolfi DE, Steinhauser KE. et al. “It is like heart failure. it is chronic … and it will kill you”: a qualitative analysis of burnout among hospice and palliative care clinicians. J Pain Symptom Manage 2017; 53 (05) 901-910.e1
    CrossrefPubMedGoogle Scholar
  • 78 Ben-Zur H, Michael K. Burnout, social support, and coping at work among social workers, psychologists, and nurses: the role of challenge/control appraisals. Soc Work Health Care 2007; 45 (04) 63-82
    CrossrefPubMedGoogle Scholar
  • 79 Watson AG, McCoy JV, Mathew J, Gundersen DA, Eisenstein RM. Impact of physician workload on burnout in the emergency department. Psychol Health Med 2019; 24 (04) 414-428
    CrossrefPubMedGoogle Scholar
  • 80 Ilić IM, Arandjelović MŽ, Jovanović JM, Nešić MM. Relationships of work-related psychosocial risks, stress, individual factors and burnout - Questionnaire survey among emergency physicians and nurses. Med Pr 2017; 68 (02) 167-178
    PubMedGoogle Scholar
  • 81 Moukarzel A, Michelet P, Durand A-C. et al. Burnout Syndrome among Emergency Department Staff: Prevalence and Associated Factors. BioMed Res Int 2019; 2019: 6462472
    CrossrefPubMedGoogle Scholar
  • 82 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
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 83 Elnahrawy E, Mart RP. Studying the utility of tracking systems in improving healthcare workflow. In: 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). IEEE. Accessed 2010 at: https://www.computer.org/csdl/proceedings-article/percomw/2010/05470651/12OmNrkBwwX
    Google Scholar
  • 84 Chang Y-T, Syed-Abdul S, Tsai C-Y, Li Y-C. A novel method for inferring RFID tag reader recordings into clinical events. Int J Med Inform 2011; 80 (12) 872-880
    CrossrefPubMedGoogle Scholar
  • 85 Hung GR, Doan Q. Assessment of the reliability of active radiofrequency identification technology for patient tracking in the pediatric emergency department. Pediatr Emerg Care 2013; 29 (02) 162-164
    CrossrefPubMedGoogle Scholar
  • 86 Berg B, Longley G, Dunitz J. Improving clinic operational efficiency and utilization with RTLS. J Med Syst 2019; 43 (03) 56
    CrossrefPubMedGoogle Scholar
  • 87 Lo EV, Wei Y-H, Hwang B-F. Association between occupational burnout and heart rate variability: A pilot study in a high-tech company in Taiwan. Medicine (Baltimore) 2020; 99 (02) e18630
    CrossrefPubMedGoogle Scholar