IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v21y2018i4d10.1007_s10729-017-9404-8.html
   My bibliography  Save this article

The impact of e-visits on patient access to primary care

Author

Listed:
  • Xiang Zhong

    (University of Florida)

  • Peter Hoonakker

    (University of Wisconsin)

  • Philip A. Bain

    (Dean Health System)

  • Albert J. Musa

    (Dean Health System)

  • Jingshan Li

    (University of Wisconsin)

Abstract

To improve patient access to primary care, many healthcare organizations have introduced electronic visits (e-visits) to provide patient-physician communication through secure messages. However, it remains unclear how e-visit affects physicians’ operations on a daily basis and whether it would increase physicians’ panel size. In this study, we consider a primary care physician who has a steady patient panel and manages patients’ office and e-visits, as well as other indirect care tasks. We use queueing-based performance outcomes to evaluate the performance of care delivery. The results suggest that improved operational efficiency is achieved only when the service time of e-visits is smaller enough to compensate the effectiveness loss due to online communications. A simple approximation formula of the relationship between e-visit service time and e-visit to office visit referral ratio is provided serving as a guideline for evaluating the performance of e-visit implementation. Furthermore, based on the analysis of the impact of e-visits on physician’s capacity, we conclude that it is not the more e-visits the better, and the condition for maximal panel size is investigated. Finally, the expected outcomes of implementing e-visits at Dean East Clinic are discussed.

Suggested Citation

  • Xiang Zhong & Peter Hoonakker & Philip A. Bain & Albert J. Musa & Jingshan Li, 2018. "The impact of e-visits on patient access to primary care," Health Care Management Science, Springer, vol. 21(4), pages 475-491, December.
  • Handle: RePEc:kap:hcarem:v:21:y:2018:i:4:d:10.1007_s10729-017-9404-8
    DOI: 10.1007/s10729-017-9404-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-017-9404-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10729-017-9404-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. T Eldabi & R J Paul & T Young, 2007. "Simulation modelling in healthcare: reviewing legacies and investigating futures," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 262-270, February.
    2. Linda V. Green & Sergei Savin, 2008. "Reducing Delays for Medical Appointments: A Queueing Approach," Operations Research, INFORMS, vol. 56(6), pages 1526-1538, December.
    3. Lixiang Jiang & Ronald Giachetti, 2008. "A queueing network model to analyze the impact of parallelization of care on patient cycle time," Health Care Management Science, Springer, vol. 11(3), pages 248-261, September.
    4. K. T. Marshall, 1968. "Some Inequalities in Queuing," Operations Research, INFORMS, vol. 16(3), pages 651-668, June.
    5. G. J. Taylor & S. I. McClean & P. H. Millard, 2000. "Stochastic models of geriatric patient bed occupancy behaviour," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(1), pages 39-48.
    6. Xiang Zhong & Hyo Kyung Lee & Jingshan Li, 2017. "From production systems to health care delivery systems: a retrospective look on similarities, difficulties and opportunities," International Journal of Production Research, Taylor & Francis Journals, vol. 55(14), pages 4212-4227, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kanwal Yousaf & Zahid Mehmood & Israr Ahmad Awan & Tanzila Saba & Riad Alharbey & Talal Qadah & Mayda Abdullateef Alrige, 2020. "A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer’s disease (AD)," Health Care Management Science, Springer, vol. 23(2), pages 287-309, June.
    2. Rajak, Manindra & Shaw, Krishnendu, 2021. "An extension of technology acceptance model for mHealth user adoption," Technology in Society, Elsevier, vol. 67(C).
    3. Xianyi Wang & Xiaofang Wang & Hui He, 2021. "Contracts to Coordinate Healthcare Providers in the Telemedicine Referral System," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
    4. Cai, Yun & Song, Haiqing & Wang, Shan, 2024. "Managing appointment-based services with electronic visits," European Journal of Operational Research, Elsevier, vol. 315(3), pages 863-878.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sujee Lee & Philip A. Bain & Albert J. Musa & Jingshan Li, 2021. "A Markov chain model for analysis of physician workflow in primary care clinics," Health Care Management Science, Springer, vol. 24(1), pages 72-91, March.
    2. Xiang Zhong & Jie Song & Jingshan Li & Susan M. Ertl & Lauren Fiedler, 2016. "Design and analysis of gastroenterology (GI) clinic in Digestive Health Center of University of Wisconsin Health," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 90-119, June.
    3. Dequan Yue & Jinhua Cao, 2001. "The NBUL class of life distribution and replacement policy comparisons," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(7), pages 578-591, October.
    4. P.-C. G. Vassiliou, 2020. "Laws of Large Numbers for Non-Homogeneous Markov Systems," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1631-1658, December.
    5. Yuta Kanai & Hideaki Takagi, 2021. "Markov chain analysis for the neonatal inpatient flow in a hospital," Health Care Management Science, Springer, vol. 24(1), pages 92-116, March.
    6. Ahmet Bahadır Şimşek & Seher Merdane & Aydanur Belindir & Ayşenur Akbaş, 2021. "Pharmacy Duty Scheduling Problem: Gumushane Case," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 9(1), pages 85-98, June.
    7. Dogru, Ali K. & Melouk, Sharif H., 2019. "Adaptive appointment scheduling for patient-centered medical homes," Omega, Elsevier, vol. 85(C), pages 166-181.
    8. Shenghai Zhou & Yichuan Ding & Woonghee Tim Huh & Guohua Wan, 2021. "Constant Job‐Allowance Policies for Appointment Scheduling: Performance Bounds and Numerical Analysis," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2211-2231, July.
    9. K Katsaliaki & N Mustafee, 2011. "Applications of simulation within the healthcare context," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(8), pages 1431-1451, August.
    10. Izady, Navid, 2019. "An integrated approach to demand and capacity planning in outpatient clinics," European Journal of Operational Research, Elsevier, vol. 279(2), pages 645-656.
    11. Wen-Ya Wang & Diwakar Gupta, 2011. "Adaptive Appointment Systems with Patient Preferences," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 373-389, July.
    12. Morabito, Reinaldo & de Souza, Mauricio C. & Vazquez, Mariana, 2014. "Approximate decomposition methods for the analysis of multicommodity flow routing in generalized queuing networks," European Journal of Operational Research, Elsevier, vol. 232(3), pages 618-629.
    13. S Vanderby & M W Carter, 2010. "An evaluation of the applicability of system dynamics to patient flow modelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1572-1581, November.
    14. S McClean & P Millard, 2007. "Where to treat the older patient? Can Markov models help us better understand the relationship between hospital and community care?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 255-261, February.
    15. Arijit Patra & Chanchal Kundu, 2019. "On generalized orderings and ageing classes for residual life and inactivity time at random time," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(6), pages 691-704, August.
    16. Katsumi Morikawa & Katsuhiko Takahashi & Daisuke Hirotani, 2018. "Performance evaluation of candidate appointment schedules using clearing functions," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 509-518, March.
    17. Mark Isken & Timothy Ward & Steven Littig, 2011. "An open source software project for obstetrical procedure scheduling and occupancy analysis," Health Care Management Science, Springer, vol. 14(1), pages 56-73, March.
    18. Sumi Kim & Seongmoon Kim, 2015. "Differentiated waiting time management according to patient class in an emergency care center using an open Jackson network integrated with pooling and prioritizing," Annals of Operations Research, Springer, vol. 230(1), pages 35-55, July.
    19. Li Luo & Ying Zhou & Bernard T. Han & Jialing Li, 2019. "An optimization model to determine appointment scheduling window for an outpatient clinic with patient no-shows," Health Care Management Science, Springer, vol. 22(1), pages 68-84, March.
    20. Nan Liu & Serhan Ziya & Vidyadhar G. Kulkarni, 2010. "Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 347-364, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:hcarem:v:21:y:2018:i:4:d:10.1007_s10729-017-9404-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.