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The Virtual Doctor Is In: The Effect of Telehealth Visits on Patient Experience

Authors:

Abstract

COVID-19 has accelerated the adoption of telehealth. With this shift comes a need for empirically based research regarding the effect of telehealth on patient experience. The present study employed an online survey (N = 996) examining whether a patient's perceptions of a telehealth visit predicts (a) the likelihood that they will schedule a future telehealth visit, and (b) their recall of clinical information. Participants viewed a video of a real clinician delivering information on a COVID-19 antibody test and responded to demographic, socioemotional, and cognitive items. We found that for every 1-point increase in an individual’s satisfaction with their interaction with the doctor, they were .73 times more likely to revisit the doctor (p < .01). These results provide insight for researchers and medical professionals regarding patient perceptions of virtual encounters and suggest best practices to consider as we further integrate telehealth.
The Virtual Doctor Is In: The Effect of Telehealth Visits on Patient
Experience
Ja-Naé Duane
Bentley University
jduane@bentley.edu
Morgan Stosic
University of Maine
morgan.stosic@maine.edu
Jonathan Ericson
Bentley University
jericson@bentley.edu
Brigitte N. Durieux
Dana-Farber Cancer Institute
brigitten_durieux@dfci.harvard.edu
Justin J. Sanders
McGill University
Justin.Sanders@mcgill.ca
Erryca Robicheaux
Bentley University
robicheaux.e@gmail.com
Danielle Blanch-Hartigan
Bentley University
dhartigan@bentley.edu
Abstract
COVID-19 has accelerated the adoption of
telehealth. With this shift comes a need for empirically
based research regarding the effect of telehealth on
patient experience. The present study employed an
online survey (N = 996) examining whether a patient's
perceptions of a telehealth visit predicts (a) the
likelihood that they will schedule a future telehealth
visit, and (b) their recall of clinical information.
Participants viewed a video of a real clinician
delivering information on a COVID-19 antibody test,
and responded to demographic, socioemotional, and
cognitive items. We found that for every 1-point
increase in an individual’s satisfaction with their
interaction with the doctor, they were .73 times more
likely to revisit the doctor (p < .01). These results
provide insight for researchers and medical
professionals regarding patient perceptions of virtual
encounters and suggest best practices to consider as
we further integrate telehealth.
1. Introduction
During the COVID-19 pandemic, patient and
clinician interactions through virtual telehealth visits
increased dramatically since 2019 [1,2]. For many,
this forced shift introduced a new medical practice
setting [3]. As a result, both clinicians and patients
have had to adjust their physical environments (e.g.,
home-based offices) for virtual visits [4]. The adoption
of telehealth has continued to increase despite the
relaxation of shelter-in-place mandates, and there are
indicators that telehealth is here to stay [3]. Although
recent research explores how clinicians conduct these
visits [5,6], there is little empirical evidence regarding
how these virtual encounters impact patient
experiences.
Information technologies, such as telehealth
environments, provide opportunities for behavior
change support systems [7] that can foster positive
health outcomes [8]. However, measuring such health
behaviors within these digital systems can be a
challenge [7]. Researchers have indicated that patient
engagement and health outcomes can be improved
through IT platforms (e.g., telehealth) [9].
Additionally, recent research suggests that virtual
visits affect clinician behavior and communication
[10,11] compared to in-person visits. Recommended
behaviors such as maintaining a neutral posture and
eye contact can help provide patients with a "webside
manner" [6,12-14]. However, there is still a need for
more research that examines how these virtual
environments affect the patient and their experience.
Therefore, we ask the following research question:
does a patient's perceptions of a telehealth visit predict
(a) the likelihood that they will schedule future
telehealth encounters, and (b) the likelihood of
recalling clinical information? To address this
research question, we conducted an online study that
provided participants with a simulated telehealth
appointment where participants received information
about a COVID-19 antibody test from an actual
physician, and responded to demographic,
socioemotional, and cognitive questions.
This research contributes to the literature in two
ways. First, it provides much-needed empirical
evidence to this budding area of research. Secondly, it
provides insight to both researchers and clinicians
regarding experiential and cognitive impacts of
telehealth appointments.
Proceedings of the 55th Hawaii International Conference on System Sciences | 2022
Page 3845
URI: https://hdl.handle.net/10125/79804
978-0-9981331-5-7
(CC BY-NC-ND 4.0)
The rest of the paper is organized as follows.
Section 1 provides a theoretical background for this
study. Section 2 outlines the research methodology.
Section 3 provides our results. Section 4 discusses our
observations based on our findings. Finally, Section 5
discusses the paper's implications for research and
practice and provides recommendations for future
research.
1.1. Telehealth environments
Telehealth environments differ from traditional
clinical settings for several reasons. First, by being in
one's own environment during the experience, there is
a sense of familiarity [15] that both the patient and the
clinician experience. This provides an opportunity for
social connection [16] that may support the patient-
clinician relationship. For example, attending a virtual
visit from one’s home enhances the likelihood of self-
disclosure of thoughts and feelings [17,18], which may
not occur in a clinical setting. Finally, the sharing of
personal environments may shift the power dynamic
[15] between the patient and the clinician. The patient
is no longer in a traditional setting that is out of their
control. Instead, they are in an environment they are
familiar with, and they may feel more empowered due
to this shifted power dynamic [15].
Both patients and clinicians rely on non-verbal
cues [5]. For example, cues such as eye contact or
facial activity provide individuals with contextual
clues about how to interpret the information they are
receiving from the other person [19]. However, within
virtual environments, non-verbal cues may be reduced
[4] or distorted [20,21], which could lead to a loss of
pertinent information [22,23]. Technological factors
such as bandwidth and poor video resolution could
decrease patient satisfaction [4]. However, it is unclear
how these positive and negative aspects of the
telehealth experience as well as other perceptions of
care during the visit could influence recall and
intention to follow-up after a telehealth appointment.
1.2. The present study
In sum, previous research has primarily examined
how clinicians conduct telehealth visits [e.g., 5,6]. As
a result, we know little about whether a patient's
perceptions of a telehealth visit predict the likelihood
that they will schedule a future telehealth visit and the
likelihood that they will recall clinical information
presented during the visit. To examine this research
question, we conducted an online study in which
participants viewed a video of a real clinician
delivering information on a COVID-19 antibody test,
and responded to demographic, socioemotional, and
cognitive items. We used two ordered logistic
regression to test the following hypotheses:
(H1) The more positive a patient’s perceptions of their
telehealth visit, the higher the likelihood that they will
revisit the doctor in a telehealth environment.
(H2) The more positive a patient’s perceptions of their
telehealth visit, the more likely they will recall clinical
information.
2. Methodology
2.1. Participants
Participants were recruited through Amazon
Mechanical Turk (MTurk). We conducted a sensitivity
power analysis using G*Power to identify the smallest
effect size we were powered to detect. For a linear
multiple regression F-test, we used the following input
parameters: α (two-sided) = .05, power = .80, number
of predictors = 5. This resulted in the power to detect
a small effect size (f2 = 0.01) [24]. A total of 1096
participants consented to participate in the study. 100
participants were excluded for not passing attention
check questions or reporting video player issues,
yielding a final sample size of N = 996 (65.6% M,
34.2% F; MAGE = 34.91 years, SDAGE = 11.13 years).
All participants provided their informed consent in
accordance with the requirements of Bentley
University’s Institutional Review Board (IRB) and
were compensated $0.50 for successfully completing
the study. The total time required to complete the study
was 15 minutes or less.
2.2. Procedure
This study utilized the analogue patient
methodology [25,26]. Participants were asked to
imagine themselves as a patient at a telehealth
appointment in which they would receive information
about a COVID-19 antibody test from an actual
physician.
Participants watched a 30-second video of a
clinician presenting information on a COVID-19
antibody test. The video of the physician was recorded
against a green screen so that the background could be
altered while holding the verbal and nonverbal
communication of the physician constant. Participants
were randomly assigned to view the video with one of
six backgrounds, which varied in the number of visible
objects (e.g., plants, family photos, certifications).
Page 3846
After watching the video, Participants completed a
survey regarding their impressions of the physician,
their memory for both the physician and the virtual
interaction, and demographic questions.
2.3. Measures
This study aimed to determine whether a patient's
perceptions of a telehealth visit predicted the
likelihood they would schedule future telehealth
encounters (H1), as well as the likelihood of recalling
clinical information presented during the encounter
(H2). In order to test these hypotheses, several
measures were employed. First, participants were
given five questions regarding their satisfaction with
the doctor (Interaction Satisfaction). They also rated
his overall communication (Doctor's Overall
Communication). Next, there were five questions on
the immersiveness (Immersion) of the interaction with
the doctor. All socioemotional responses were
gathered using 5-point scales based on previous
research [27]. Participants were asked to indicate the
likelihood of revisiting the doctor within a telehealth
environment (Revisit Doctor), on a 5-point scale from
1 (strongly agreeing with intending to revisit this
doctor) to 5 (strongly disagreeing with intending to
revisit this doctor).
Cognitive measures examined each participant’s
recall of the clinical information (Clinical
Information) that the doctor presented during the
telehealth experience (e.g., True or False: The
COVID-19 antibody test is negative in about 30% of
people who did have infection.). Additionally,
participants were asked their level of comfort with
telehealth (Telehealth Comfort). To indicate their most
recent telehealth appointment (Last Telehealth
Appointment), and to provide basic demographic
information. Finally, we coded the six different office
backgrounds in terms of the number of Environmental
Factors that were visible (e.g., family photos,
diplomas, books). The office background with the
least number of visible objects was coded a 1 while the
office background with the greatest number of visible
objects was coded a number 6.
2.4. Data analysis
There were two analyses conducted for this study.
First, to assess the likelihood of a participant revisiting
this doctor within a telehealth environment (H1), an
ordered logistic regression with robust standard errors
was used to conduct the analysis. An ordered logistic
regression is a model used for categorical dependent
variables. For example, when survey choices can be
answered as “never”, “monthly”, “weekly” or “daily”,
an ordered logit regression can be used for predicting
outcomes by more than one response category.
We also assessed the predicted likelihood of
recalling the information provided by the doctor (H2).
To do so, an ordered logit model with robust standard
errors was used to conduct the analysis. The dependent
variable, Clinical Information (e.g., “The test can be
used to diagnose active COVID-19 cases. True or
False?”), is a cognitive measure to assess the amount
of clinical information participants recall from the
interaction. Participants received 1 if no clinician
information was recalled, 2 if a quarter of the
information was recalled (Some Clinical Information),
3 if three-quarters of the information was recalled
(Most Clinical Information), and 4 if all the clinical
information was recalled. An alpha level of .10 was set
a priori for all statistical tests.
3. Results
3.1. Pearson correlations
Before conducting the ordered logit analysis on
the likelihood of a participant revisiting the doctor
within a telehealth environment, we computed Pearson
correlations between the main variables. Table 1
shows the resulting correlation matrix. All variables
had significant correlations (all ps < .05) except for the
environment variable, which had negligible
correlation. This lack of significance for the
environment variable may indicate multicollinearity
problems, which led to the exclusion of this variable
from the final logistic regression analyses. All other
variables were kept for the logit models due to
significance despite varying correlation strengths.
Table 1. Pearson correlations
(1)
(2)
(3)
(4)
(5)
(7)
(8)
(1) Revisit Doctor
1.0
(2) Interaction
Satisfaction
0.8*
1.0
(3) Comfort with
Telehealth
-0.2
-0.3*
1.0
(4) Memory of
Interaction
0.1*
0.1*
-0.1
1.0
(5) Immersion
-0.5
-0.6*
0.3*
-0.0*
1.0
(6) Doctor's
Overall
Communication
0.6*
0.8*
-0.3
-0.1*
-0.4*
(7) Last Telehealth
Appointment
0.1*
0.1*
-0.2*
0.2*
-0.1*
1.0
(8) Environmental
Factors
0
0
0
0
-0.0
-0.0
1.0
*** p<.01, ** p<.05, * p<.10
Page 3847
3.2. Likelihood of revisiting the doctor
Odds ratios of revisiting the doctor are reported in
Table 2. The overall ordered logit model was
statistically significant (pseudo-R2 = .468, p < .0001).
This suggests that most variables have at least
marginal effect sizes, and that satisfaction with the
interaction and the doctor's overall communication
had the most prominent effects. Consistent with H1,
the ordered logit model indicates that for every 1-point
increase in Interaction Satisfaction (e.g., “Overall,
how satisfied were you with the quality of care you
received from this doctor?”), the odds of actually
revisiting the doctor for those participants who
strongly agreed that they would revisit the doctor are
.73 times more likely than other participants (p < .01),
holding all other factors constant. In other words, the
odds of revisiting the doctor decrease as satisfaction
with the interaction decreases.
Regarding the Doctor's Overall Communication
(“How would you rate the overall communication by
this doctor?”), for every 1-point increase in the
communication rating, the odds of revisiting the
doctor for those who strongly agree that they would
revisit the doctor are .16 times more likely than other
participants (p < .01). As seen with this variable (Table
2), the correlation becomes negative when participants
who either somewhat agree that they would revisit this
doctor through those who somewhat disagree.
Surprisingly, those who somewhat disagree that they
would revisit this doctor have 12.4 times more odds of
revisiting the doctor for every 1-point increase in their
rating of the doctor’s overall communication quality.
These results are consistent with H1.
Regarding the relationship between Telehealth
Comfort (“How comfortable are you using
telehealth?”) and considering Revisiting the Doctor
(“I would visit this doctor again.”), the logit model
also indicates that those who reported feeling more
comfortable with telehealth are .07 times less likely to
revisit the doctor than those participants who indicated
they would strongly agree to Revisiting the Doctor (p
= .10). Although there was a positive and significant
correlation between those who are comfortable with
telehealth and their consideration to revisit, this was a
marginal effect (p > .10). Overall, these results are
consistent with H1.
Additionally, Interaction Satisfaction, Doctor's
Overall Communication, and Telehealth Comfort
proved significant (all p < .10). Both satisfaction with
the interaction and the doctor's communication were
significant (all p < .01), and the comfort with
telehealth variable was also marginally significant (p
< .10). Thus, consistent with H1, these results indicate
that how the patient’s perceptions of the doctor impact
the likelihood that they will revisit that doctor in a
telehealth environment. Furthermore, the doctor’s
ability to communicate effectively within this type of
environment and the individual’s comfort level with
telehealth will marginally impact their odds of
revisiting the doctor; although this finding is
consistent with H1, the results are marginal and
warrant further research.
Table 2. Odds ratios of revisiting a doctor
in a telehealth environment
1
Strongly
Agree
2
Somewhat
Agree
3
Neither
Agree Nor
Disagree
4
Somewhat
Disagree
5
Strongly
Disagree
Interaction
Satisfaction
.725***
-.381***
-.271***
-.052***
-.019***
Memory of
Interaction
.001
-.001
-.001
-.000
-.000
Immersion
-.021
.011
.008
.001
.000
Doctor's Overall
Communication
.155***
-.082***
-.058***
-.011***
.124***
Telehealth
Comfort
-.070*
.037*
.026*
.005*
.001*
Last Telehealth
Appointment
-.005
.002
.002
.000
.000
*** p<.01, ** p<.05, * p<.10
3.3. Likelihood of recalling clinical
information
Odds ratios for recalling clinical information are
reported in Table 3. The Clinical Information variable
was created through the sum of information the
participants retained. The overall model (Table 3) was
statistically significant (pseudo-R2 = .440, p < .0001).
Interaction Satisfaction, Immersion, and Telehealth
Comfort were highly significant (p < .001). The
indicates that as participants feel more immersed and
comfortable within the telehealth setting, that it affects
their level of satisfaction with the experience.
Page 3848
Table 3. Odds ratios of recalling clinical
information in telehealth environments
No Clinical
Information
Some
Clinical
Information
Most
Clinical
Information
All Clinical
Information
Interaction
Satisfaction
-.034***
-.044***
.024***
.054***
Immersion
-.019***
-.025***
.013***
.030***
Doctor's Overall
Communication
-.000
-.000
.000
.000
Telehealth
Comfort
.030**
.039**
-.021**
-.047**
Last Telehealth
Appointment
-.002
-.002
.001
.000
*** p<.01, ** p<.05, * p<.10
When examining Interaction Satisfaction, for
every 1-point increase in the level of satisfaction, the
odds of recalling clinical information was most
prominent for those who recalled all the information at
.05 times more than individuals in the other categories
(p < .01), holding all factors constant. Not
surprisingly, individuals who did not recall any
clinical information during this study are predicted to
be 3 times less likely to recall clinical information in a
telehealth setting in the future. Thus, consistent with
H2, the more the individual is satisfied with the
encounter with the doctor, the more likely they are to
recall clinical information during that encounter.
Regarding the immersiveness of the experience,
we observed a negative effect for participants who
were only able to recall a quarter of the clinical
information provided (-0.025x odds, p < .01) and for
those who were unable to recall any information (-
0.019x odds, p < .01). However, for every 1-point
increase in Immersion, the odds of participants who
recalled all clinical information is .03 more likely than
those individuals in other categories (p <.01). Thus,
consistent with H2, these results suggest that as
participants feel more immersed within the telehealth
environment, their likelihood of retaining clinical
information also increases.
The impact that Telehealth Comfort has on the
probability of recalling clinical information indicates
that those who are comfortable with telehealth but
could not recall more than 1/4th (25%) of the clinical
information have a .03 (p < .01) greater likelihood of
recalling such information compared to individuals
who are not comfortable with telehealth (p < .05).
Finally, those who are comfortable with telehealth and
could recall at least half of the clinical information
provided have a 2-4% decrease in odds of recalling
clinical information versus those who identified as not
comfortable with telehealth (p < .05). This result is
both counterintuitive and may indicate that those who
are less comfortable with telehealth may be more
inclined to listen to the doctor closely than those
individuals who are comfortable in telehealth
environments.
In sum, results were generally consistent with H2
(the more positive a patient’s perceptions of their
telehealth visit, the more likely they will recall clinical
information). There is a correlation between how
satisfied an individual is with a telehealth encounter
and the information that is retained from the visit.
Furthermore, the more immersed the individual can be
within the experience, the more likely they are to retain
the information.
4. Discussion
The present study examined the relationship
between telehealth experiences and patients’
perceptions of the virtual clinical encounter. Though
recent research suggests that clinicians should
consider enhancing virtual environments for overall
patient satisfaction, the results of this study indicate
that the patient’s comfort with telehealth, perception
of the doctor’s communication, and satisfaction with
the virtual encounter may predict intentions to revisit
a doctor in a telehealth environment. We also found
that there very little evidence for spillover effects from
participants’ telehealth experiences prior to this study
in that the effect sizes were marginal and insignificant.
This may be due to telehealth environments being
relatively new for many individuals. Additionally,
since this doctor was a new doctor for the study
participants, they may have treated this telehealth
experience differently than they would if it was with a
doctor they had seen before.
Regarding cognitive responses, although
interaction satisfaction, the feeling of immersion
within the environment, and comfort with telehealth
were significant and may theoretically contribute to
the retention of clinical information [28,29], their
effects sizes were marginal and do not fully explain
whether a patient accurately recalled clinical
information in a telehealth encounter. However, our
findings do indicate that if an individual is slightly
uncomfortable with telehealth visits, then they are
more inclined to listen to the clinical information
presented and recall it.
Other work has demonstrated adequate
information recall among cancer patients in clinical
telehealth visits. For example, patients reported
Page 3849
whether they recall various information categories
being discussed, rather than recalled specific
information from that discussion. [30]. In in-person
clinical settings, other work has found information
recall to be unsatisfactory (cancer patients recalled
about 50% of information correctly) [31]. To assist
patients in recalling crucial clinical information,
doctors can summarize their recommendations in the
“open note” section of a patient’s chart or in a letter to
patients and other members of the care team, as is
common in other health systems (e.g., the NHS in the
United Kingdom). Another recommendation would be
to use “teach back” approaches with patients.
Research has demonstrated positive effects using such
approaches with patients [32-34]. Additionally,
clinicians may want to consider providing clinical
information in varying forms (e.g., written, visual
illustrations) to ensure that patients have the pertinent
information that they need from the clinician.
4.1. Limitations and future directions
This study has several limitations. Although
adequately powered, online samples may not
generalize to real patient populations. The COVID-19
antibody test context may have played a role in recall
and overall experience reported. The online format
and uniform encounter provided consistency across
participants but does not capture all the factors related
to intention to revisit a doctor, or predictors of recall.
We also cannot determine the causal direction of these
relationships. For example, recalling more clinical
information may cause analogue patients to also report
more positive experiences.
Telehealth platforms are designed and employed
in a variety of ways, thus limiting the present study’s
internal and external validity. Additionally, this study
did not examine how web connectivity or digital
interfaces may affect the interaction between the
doctor and the patient or the doctor's communication
of the clinical information. Future research should
leverage this variability that technology as it plays an
essential role in the patient's overall telehealth
experience. For example, researchers could research
the effects of telehealth platforms, as well as the use
experience of such platforms, on the patient’s
experience. Furthermore, future research can explore
whether a prior relationship with a doctor impacts the
patient's ability to retain clinician information within a
virtual environment, such as telehealth.
5. Conclusion
This paper contributes to both telehealth research
and practice by providing empirical evidence relevant
to this growing field. Our results suggests that a
patient’s experience of a telehealth visit may impact
their recall for clinical information and intentions to
seek follow-up care over telehealth. Our results also
suggest several avenues for future research, including
comparing the impact of different telehealth platforms
on patient experience and information recall. For
practitioners, our results suggest that telehealth
encounters may impact a patient’s perceptions and
impressions of care, as well as cognitive and
adherence-related outcomes. In particular, our results
suggest that improving the telehealth patient
experience may lead to more consistent follow-up
care. Telehealth is here to stay, and researchers must
support practitioners as they explore novel ways of
improving the overall patient experience and clinical
outcomes in these virtual environments.
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