a Weighted balance is based on diagnostic output produced by the kmatch module.
b Mean is the standard difference in means between weighted respondents and weighted nonrespondents; standard difference is 0 when perfectly balanced. Standard difference in means is rounded to 3 significant digits.
c Ratio represents the ratio of variances of weighted nonrespondents to variance of weighted respondents. Ratio is 1 when perfectly balanced. Ratio of variances is rounded to 3 significant digits.
d MD: medical doctor.
e DO: osteopathic doctor.
f Reports mean years since residency.
Characteristics | Substantial first-month pandemic telemedicine use, OR (95% CI) | Substantial telemedicine use June 2020, OR (95% CI) | |||
<10% of patients | Ref | Ref | |||
>10% of patients | 2.65 (0.37-18.80) | ||||
Low | Ref | Ref | |||
High | 5.21 (0.96-28.35) | ||||
Midwest | Ref | Ref | |||
Northeast | 1.92 (0.45-8.14) | 3.3 (0.90-12.05) | |||
South | 0.53 (0.10-2.84) | 1.19 (0.24-5.95) | |||
West | 1.10 (0.33-3.67) | 2.81 (0.88-9.01) | |||
Female | Ref | Ref | |||
Male | 0.57 (0.22-1.49) | 0.73 (0.27-1.93) | |||
Primary care | Ref | Ref | |||
Critical care | 1.57 (0.51-4.84) | 0.90 (0.28-2.90) | |||
Emergency medicine | |||||
Hospital medicine | |||||
Infectious disease | 0.95 (0.32-2.85) | 2.27 (0.71-7.30) | |||
0-10 years | Ref | Ref | |||
11-20 years | 1.87 (0.50-6.98) | 1.90 (0.54-6.60) | |||
21-30 years | 1.92 (0.60-6.15) | 1.97 (0.55-7.03) | |||
31-40 years | 1.11 (0.22-5.63) | 1.12 (0.20-6.29) | |||
More than 40 years | 0.29 (0.02-3.91) | 1.27 (0.15-10.69) |
a OR: odds ratio.
b Ref: reference.
c Italicized values are significant.
This cross-sectional national survey of frontline clinicians found that higher rates of telemedicine adoption by early June 2020 were associated with prepandemic telemedicine use and recent local COVID-19 case counts. This is the first study that we know of to compare pandemic telemedicine use across outpatient and inpatient frontline specialties. Increases in the use of telemedicine were noted in all frontline specialties but were most marked for infectious disease, critical care, and primary care physicians.
Across physician gender, specialties, census regions, and years in practice, this study found a substantial increase in telemedicine use in the early months of the pandemic. Previous studies have shown similar rapid telemedicine uptake, but these studies have focused primarily on clinic-based specialties [ 5 , 6 , 32 , 33 ]. This study shows telemedicine use rates increased with similar rapidity in the hospital-based specialty of critical care, with less substantial increases seen also in emergency and hospital medicine. Telemedicine use continued to rise through 2021, as pandemic fears persisted and telemedicine infrastructure continued to expand [ 16 ].
The increased use of telemedicine during the pandemic among those who had previously used telemedicine is not surprising, as this likely reflects preexisting local infrastructure for telemedicine. Previous use of telemedicine implies hospital and physician readiness to ramp up the use. Multiple previous telemedicine implementation models have emphasized the necessity of preexisting infrastructure, including coordinated hardware and software platforms, audiovisual integration, 24/7 information technology support, and clinician training in achieving telemedicine success [ 34 - 36 ]. Previous work has shown that successful completion of telemedicine relies, in part, on clinician comfort with technology [ 37 ].
If we consider telemedicine as a preventative strategy in the face of the pandemic threat to patient and clinician safety, our findings are consistent with the Protection Motivation Theory (PMT), a behavioral theory developed to understand human responses to fear [ 38 ]. PMT posits that response to fear-inducing situations is influenced by 2 main factors that are (1) threat appraisal (an individual’s perceived severity of and vulnerability to the threat), and (2) coping appraisal (an individual’s ability to respond to the threat with resources at hand). Applied to this study, telemedicine uptake may be influenced by an individual’s perceived threat from the pandemic, as well as their belief (or lack of belief) that telemedicine will help them respond to that threat, which is likely dependent on both environmental and individual factors. Furthermore, exploration through qualitative analyses may help more clearly explain how PMT factors of threat and coping appraisal impact telemedicine uptake and other adaptations in the face of pandemic threat. Such exploration may have important implications for the adoption of new technologies in responding to future public health emergencies.
It is notable that primary care, infectious disease, and critical care physicians reported higher pandemic telemedicine use than hospitalists and emergency medicine physicians. While several previous studies have evaluated telemedicine use across various outpatient specialties [ 16 , 33 , 39 ], in free text responses to our survey, several physicians reported that their hospital did not yet have the infrastructure to conduct telemedicine outside of the clinic setting, which could in part explain the higher rates of use among primary care and infectious disease physicians. However, an additional explanatory factor may lie within PMT, as previous studies have suggested primary care and critical care physicians were at the highest risk of contracting and dying from COVID-19 infection [ 40 , 41 ]. Notably, we found higher telemedicine adoption by physicians in regions where the 2-week moving average of COVID-19 cases was high, a situation that increased real and perceived threat, as well as an early rise in telemedicine adoption in the Northeast, the region that experienced pandemic surges before nationwide spread.
It is also notable that several studies have previously reported that telemedicine helps reduce physician burnout [ 12 , 14 , 15 ]. Therefore, our findings regarding factors influencing telemedicine uptake may have important implications for reducing physician burnout, which has known associations with physician turnover, mental health, and medical error [ 25 , 42 ].
Within the hospital setting, participants’ free text responses noted that telemedicine was often used for remote consultation and family communication. For example, one participant noted that telemedicine allowed for “improved communication with the family diaspora.” Consistent with our findings, another survey study of critical care physicians during the pandemic reported telemedicine was most frequently used in intensive care unit settings for clinician, nurse, and patient communication with patient families [ 43 ]. These communications varied from general updates on patient conditions to more in-depth goals of care discussions.
This study was limited by low survey response rates and the potential for selection bias. Our ability to weight responses based on known characteristics of the total sample minimized nonresponse bias, but there is the possibility of enduring bias in unmeasured characteristics. The number of respondents limited our ability to assess differences based on respondent characteristics. Another potential limitation is that of coverage bias, particularly with respect to the undeliverable surveys due to bounced emails. The characteristics of these individuals were unknown, including whether they were still in practice, and these units were therefore eliminated from the study sample, as is standard practice [ 28 ].
We did not explicitly collect information regarding prepandemic technology readiness but rather used prepandemic telemedicine as a proxy. Therefore, we cannot draw an explicit association, but rather can only infer that prepandemic technology readiness may have impacted pandemic telemedicine use. It is also possible that patient characteristics and preferences drove telemedicine uptake during the pandemic, but these factors were not evaluated in this study. Finally, since this study focused on the use of telemedicine in the early pandemic response for only a subset of specialties, we cannot provide information regarding telemedicine use in other specialties or regarding the longevity of telemedicine use throughout the pandemic, although other published works have addressed later time points [ 16 ].
The most important finding of this study was the capacity for rapid uptake of telemedicine under the right set of conditions—in particular, a preexisting telemedicine infrastructure combined with improved reimbursement and clear evidence of benefit given pandemic risks. Health care is notoriously slow to incorporate innovative, evidence-based technologies. However, our data show that telemedicine uptake in the early pandemic was rapid across genders, specialties, geographic regions, and experience levels. Rogers’ theory of diffusion of innovation posits a 5-step process involving knowledge, persuasion, decision, implementation, and confirmation [ 44 ]. This process puts the adopter (in this case, often the clinician) at the center of implementation. However, the pandemic-era implementation of telemedicine highlights the critical role of other factors, such as public policy, external supports, health system or practice and patient infrastructure, and customer demand, in the widespread adoption of a new technology.
Telemedicine use is at a critical juncture as the public health emergency has expired. Clinicians and patients alike have shared its benefits and have developed increasing comfort levels with telemedicine technology; studies during the pandemic showed that patients who received telemedicine visits had higher average patient satisfaction scores than those seen in-person [ 45 ]. However, recent studies suggest that both patients and physicians tend to prefer in-person care, seeking to move away from telemedicine in the postpandemic era [ 46 ]. Still, telemedicine may be particularly well suited to specific patient care scenarios, such as ongoing medical management of chronic conditions, behavioral health care, and communicating with families about hospitalized patients [ 9 ]. Future research must determine optimal applications of telemedicine within each specialty, and what factors will drive its continued use as pandemic fears recede and threat appraisal dissipates.
Continued use of telemedicine will also depend largely on enduring compensation policies. On November 1, 2022, the Centers for Medicare and Medicaid Services issued the 2023 Physician Fee Schedule Final Rule, which began peeling back some of the temporary telemedicine allowances passed in affiliation with the COVID-19 public health emergency. A summary of pandemic-era telemedicine policy changes with actual and impending end dates is provided in Table 3 [ 47 - 51 ].
Pandemic-era telemedicine policy changes | Actual and anticipated end dates |
Suspension of HIPAA restrictions on allowable telemedicine platforms | May 11, 2023 |
Temporary payment parity rules for telemedicine visits | December 31, 2023 |
Compensation for audio-only telephone evaluation and management services | December 31, 2024 |
Internet-based direct supervision of health care services | December 31, 2024 |
Suspension of geographic and originating site restrictions for nonbehavioral telemedicine services | December 31, 2024 |
Temporary telemedicine billing codes, such as those for hospital-based telemedicine encounters | December 31, 2024 |
a Health Insurance Portability and Accountability Act.
The potential implications of these looming expirations are far-reaching given the widespread telemedicine uptake across specialties demonstrated in our study and others. The inability to bill for critical care telemedicine, which is largely leveraged to involve remote family members in decision-making and care coordination [ 43 ], could impact the ability to provide patient- and family-centered care. Elimination of direct online supervision will dramatically reduce the exposure of trainees to the practice of telemedicine. Loss of compensation for telephone visits will reduce access to care for low-income and rural patients who have less access to broadband internet. Meanwhile, the reinstatement of geographic and originating site restrictions, and the expiration of payment parity rules, could drastically limit telemedicine encounters even in the outpatient setting, resulting in a large-scale reduction in telemedicine use across specialties compared with pandemic levels. While many states have implemented policies requiring payment parity from private payors, other states have not yet implemented such requirements, and these requirements do not apply to Medicaid [ 52 ]. A final consideration in the roll-back of policies supporting telemedicine is the potential effect on readiness for the next pandemic. Maintaining support and infrastructure for telemedicine may enable rapid and life-saving transitions to remote care.
The COVID-19 pandemic brought about rapid incorporation of telemedicine across health care. This survey of frontline clinicians found higher rates of telemedicine adoption in response to the pandemic for physicians working in counties with higher COVID-19 case rates and for physicians with higher prepandemic telemedicine use, particularly in primary care, infectious disease, and critical care specialties. These findings have important implications for the ongoing adoption and maintenance of telemedicine to help reduce burnout, as well as key lessons for responding to public health emergencies. Future research must evaluate the use of telemedicine compared with in-person care on health outcomes and address the impact of policy changes on continued telemedicine use. To sustain the use of telemedicine across settings, the potential benefits of telemedicine in providing patient- and family-centered care and the importance of trainee experience in telemedicine must be communicated to policymakers and the public.
The authors would like to thank the physicians who responded to this survey. This survey was conducted without external funding.
The data sets generated or analyzed during this study are available from the corresponding author on reasonable request.
MYH participated in the data analysis and visualization, produced the original draft of the manuscript, and reviewed and revised the manuscript. GX analyzed the data, participated in data validation and visualization, and reviewed and revised the manuscript. RLK participated in data analysis and visualization and reviewed and revised the manuscript. MM designed the original survey, participated in data collection, and reviewed and revised the manuscript. JM conceptualized and supervised the study, curated the data, participated in data analysis and validation, and critically reviewed and revised the manuscript.
None declared.
CHERRIES (Checklist for Reporting Results of Internet E-Surveys).
American Medical Association |
Checklist for Reporting Results of Internet E-Surveys |
osteopathic doctor |
medical doctor |
odds ratio |
Protection Motivation Theory |
Edited by A Mavragani; submitted 12.07.23; peer-reviewed by M Somai, B Clay, T Aslanidis; comments to author 23.11.23; revised version received 18.01.24; accepted 16.05.24; published 17.07.24.
©Michelle Y Hamline, Guibo Xing, Richard L Kravitz, Marykate Miller, Joy Melnikow. Originally published in JMIR Formative Research (https://formative.jmir.org), 17.07.2024.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
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The background of any study has to set the context for the study. It has to talk about why this study is needed, what gaps the study will seek to fill, and what solutions or gains the study will tentatively offer. To write the background, you often need to do a thorough literature review.
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The background of a study in a research paper helps to establish the research problem or gap in knowledge that the study aims to address, sets the stage for the research question and objectives, and highlights the significance of the research. The background of a study also includes a review of relevant literature, which helps researchers ...
Action research is a research method that aims to simultaneously investigate and solve an issue. In other words, as its name suggests, action research conducts research and takes action at the same time. It was first coined as a term in 1944 by MIT professor Kurt Lewin.A highly interactive method, action research is often used in the social ...
The background of the study is a section in a research paper that provides context, circumstances, and history leading to the research problem or topic being explored. It presents existing knowledge on the topic and outlines the reasons that spurred the current research, helping readers understand the research's foundation and its significance ...
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The background of a study is the first section of the paper and establishes the context underlying the research. It contains the rationale, the key problem statement, and a brief overview of research questions that are addressed in the rest of the paper. The background forms the crux of the study because it introduces an unaware audience to the ...
Action research is a qualitative method that focuses on solving problems in social systems, such as schools and other organizations. The emphasis is on solving the presenting problem by generating knowledge and taking action within the social system in which the problem is located. The goal is to generate shared knowledge of how to address the ...
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Action research is a research method that aims to simultaneously investigate and solve an issue. In other words, as its name suggests, action research conducts research and takes action at the same time. It was first coined as a term in 1944 by MIT professor Kurt Lewin. A highly interactive method, action research is often used in the social ...
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This is a research method called action research. To help you further understand what action research is, here are multiple action research examples you can check out. 1. Research Action Plan Template. Details. File Format. MS Word. Google Docs. Apple Pages.
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