Telemedicine Use across Medical Specialties and Diagnoses

Vivian Hsiao; Thevaa Chandereng; Jeffrey A. Huebner; David T. Kunstman; Grace E. Flood; Amye J. Tevaarwerk; David F. Schneider

doi:10.1055/s-0043-1762595

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2023; 14(01): 172-184
DOI: 10.1055/s-0043-1762595

Research Article

Telemedicine Use across Medical Specialties and Diagnoses

Vivian Hsiao

¹School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States

²Department of Surgery, University of Wisconsin, Madison, Wisconsin, United States

,

Thevaa Chandereng

³UW Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, United States

⁴Department of Biostatistics, Columbia University, New York, New York, United States

,

Jeffrey A. Huebner

¹School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States

⁵Department of Family Medicine, University of Wisconsin, Madison, Wisconsin, United States

,

David T. Kunstman

¹School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States

⁵Department of Family Medicine, University of Wisconsin, Madison, Wisconsin, United States

,

Grace E. Flood

¹School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States

,

Amye J. Tevaarwerk

⁶Department of Oncology, Mayo Clinic, Rochester, Minnesota, United States

,

David F. Schneider

¹School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States

²Department of Surgery, University of Wisconsin, Madison, Wisconsin, United States

› Author Affiliations

Abstract

Background The COVID-19 (coronavirus disease 2019) pandemic rapidly expanded telemedicine scale and scope. As telemedicine becomes routine, understanding how specialty and diagnosis combine with demographics to impact telemedicine use will aid in addressing its current limitations.

Objectives To analyze the relationship between medical specialty, diagnosis, and telemedicine use, and their interplay with patient demographics in determining telemedicine usage patterns.

Methods We extracted encounter and patient data of all adults who scheduled outpatient visits from June 1, 2020 to June 30, 2021 from the electronic health record of an integrated academic health system encompassing a broad range of subspecialties. Extracted variables included medical specialty, primary visit diagnosis, visit modality (video, audio, or in-person), and patient age, sex, self-reported race/ethnicity and 2013 rural–urban continuum code. Six specialties (General Surgery, Family Medicine, Gastroenterology, Oncology, General Internal Medicine, and Psychiatry) ranging from the lowest to the highest quartile of telemedicine use (video and audio) were chosen for analysis. Relative proportions of video, audio, and in-person modalities were compared. We examined diagnoses associated with the most and least frequent telemedicine use within each specialty. Finally, we analyzed associations between patient characteristics and telemedicine modality (video vs. audio/in-person, and video/audio vs. in-person) using a mixed-effects logistic regression model.

Results A total of 2,494,296 encounters occurred during the study period, representing 420,876 unique patients (mean age: 44 years, standard deviation: 24 years, 54% female). Medical diagnoses requiring physical examination or minor procedures were more likely to be conducted in-person. Rural patients were more likely than urban patients to use video telemedicine in General Surgery and Gastroenterology and less likely to use video for all other specialties. Within most specialties, male patients and patients of nonwhite race were overall less likely to use video modality and video/audio telemedicine. In Psychiatry, members of several demographic groups used video telemedicine more commonly than expected, while in other specialties, members of these groups tended to use less telemedicine overall.

Conclusion Medical diagnoses requiring physical examination or minor procedures are more likely to be conducted in-person. Patient characteristics (age, sex, rural vs. urban, race/ethnicity) affect video and video/audio telemedicine use differently depending on medical specialty. These factors contribute to a unique clinical scenario which impacts perceived usefulness and accessibility of telemedicine to providers and patients, and are likely to impact rates of telemedicine adoption.

Keywords

telemedicine - health - disparities

Full Text

References

References
1 Demeke HB, Merali S, Marks S. et al. Trends in use of telehealth among health centers during the COVID-19 pandemic - United States, June 26-November 6, 2020. MMWR Morb Mortal Wkly Rep 2021; 70 (07) 240-244
2 Hsiao V, Chandereng T, Lankton RL. et al. Disparities in telemedicine access: a cross-sectional study of a newly established infrastructure during the COVID-19 pandemic. Appl Clin Inform 2021; 12 (03) 445-458
3 Barnett ML, Huskamp HA. Telemedicine for mental health in the United States: making progress, still a long way to go. Psychiatr Serv 2020; 71 (02) 197-198
4 Asiri A, AlBishi S, AlMadani W, ElMetwally A, Househ M. The use of telemedicine in surgical care: a systematic review. Acta Inform Med 2018; 26 (03) 201-206
5 Makhni MC, Riew GJ, Sumathipala MG. Telemedicine in orthopaedic surgery: challenges and opportunities. J Bone Joint Surg Am 2020; 102 (13) 1109-1115
6 McCool RR, Davies L. Where does telemedicine fit into otolaryngology? An assessment of telemedicine eligibility among otolaryngology diagnoses. Otolaryngol Head Neck Surg 2018; 158 (04) 641-644
7 Harst L, Lantzsch H, Scheibe M. Theories predicting end-user acceptance of telemedicine use: systematic review. J Med Internet Res 2019; 21 (05) e13117
8 Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Manag Inf Syst Q 1989; 13 (03) 319-340
9 Venkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. Manag Inf Syst Q 2003; 27 (03) 425-478
10 Cantor J, Sood N, Bravata DM, Pera M, Whaley C. The impact of the COVID-19 pandemic and policy response on health care utilization: evidence from county-level medical claims and cellphone data. J Health Econ 2022; 82: 102581
11 deMayo R, Huang Y, Lin ED. et al. Associations of telehealth care delivery with pediatric health care provider well-being. Appl Clin Inform 2022; 13 (01) 230-241
12 Alipour J, Hayavi-Haghighi MH. Opportunities and challenges of telehealth in disease management during COVID-19 pandemic: a scoping review. Appl Clin Inform 2021; 12 (04) 864-876
13 United States Department of Agriculture. Rural-urban continuum codes. 2020. Accessed January 4, 2023 at: https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/
14 Powell KR, Alexander GL. Consequences of rapid telehealth expansion in nursing homes: promise and pitfalls. Appl Clin Inform 2021; 12 (04) 933-943
15 Luo J, Tong L, Crotty BH. et al. Telemedicine adoption during the COVID-19 pandemic: gaps and inequalities. Appl Clin Inform 2021; 12 (04) 836-844
16 Jelinek R, Pandita D, Linzer M, Engoang JBBN, Rodin H. An evidence-based roadmap for the provision of more equitable telemedicine. Appl Clin Inform 2022; 13 (03) 612-620
17 Terlizzi EP, Zablotsky B. Mental health treatment among adults: United States, 2019. NCHS Data Brief 2020; (380) 1-8
18 Cherry D, Albert M, McCaig L. Mental health-related physician office visits by adults aged 18 and over: United States, 2012–2014. 2018; NCHS Data Brief (311) 1-8
19 Sizer MA, Bhatta D, Acharya B, Paudel KP. Determinants of telehealth service use among mental health patients: a case of rural Louisiana. Int J Environ Res Public Health 2022; 19 (11) 6930
20 Zhu JM, Myers R, McConnell KJ, Levander X, Lin SC. Trends in outpatient mental health services use before and during the COVID-19 pandemic. Health Aff (Millwood) 2022; 41 (04) 573-580