Keywords telemedicine - telehealth - health services - COVID-19 - health inequality - health
system
Background and Significance
Background and Significance
Telemedicine leverages telecommunication technologies to enable the delivery of health
care services remotely to patients.[1 ]
[2 ] With telemedicine, patients could gain access to needed care without in-person visits
to health providers. Telemedicine is believed to have many benefits,[3 ]
[4 ]
[5 ] such as addressing health care access problems,[6 ]
[7 ]
[8 ] providing health services in a more timely and cost-effective manner,[9 ]
[10 ]
[11 ] and improving patient satisfaction.[12 ]
[13 ] These benefits have been demonstrated by various telemedicine applications. For
example, convenient access makes telemedicine an amiable tool for patients who have
challenges in accessing in-person health care, such as patients with movement disorders,[14 ]
[15 ] and pediatric patients in underserved areas.[16 ]
[17 ] Telemedicine systems provide user-friendly functionalities that could empower patients'
self-care capabilities, such as improving treatment adherence for sleep apnea,[18 ] diabetes monitoring,[19 ] and chronic disease management.[20 ]
The recent outbreak of coronavirus significantly increased the relevance of telemedicine.[21 ]
[22 ]
[23 ] The pandemic imposed a significant burden on many hospital systems around the world.
Many hospital systems were overwhelmed by in-person patient visits and the extra care
needed by the infected patients in the setting of a highly contagious pathogen. To
reduce the spread of infection, health policymakers and administrators, such as the
U.S. Centers for Disease Control (CDC)[24 ] and the Health Resources and Services Administration (HRSA),[25 ] encouraged hospitals and patients to use telemedicine to access health services
remotely. Telemedicine became a critical tool for many health systems to ensure timely
patient access to health care needs. Due to the quick escalation of the pandemic,
many providers and patients had to adopt the use of telemedicine in a relatively short
time. While telemedicine has been rolled out at scale quickly across health care systems,
little has been done to understand the socioeconomic implication of this transition.
We hypothesized that socioeconomic differences could create gaps in telemedicine adoption
among different patient groups during this period. Scant literature is available investigating
the impact of socioeconomic factors on the adoption of telemedicine services by patients.
Understanding the possible gaps and impacts of socioeconomic challenges is a crucial
step toward addressing health care inequities which is one of the leading health indicators
for the Healthy 2030 framework.[26 ] The objective of this study was to assess the impact of socioeconomic factors on
the adoption of telemedicine services in a health system during the coronavirus disease
2019 (COVID-19) pandemic in Milwaukee metropolitan area.
We sought to understand how socioeconomic factors impacted the utilization of telemedicine
services, further subdivided into video visit and audio-only (telephone) visit adoption.
While telemedicine has been rolled out at scale quickly across health care systems,
little has been done to understand the socioeconomic implication of this transition.
Methods
Telemedicine Implementation
Prior to the COVID-19 public health emergency, (PHE), telemedicine at the Froedtert
Health System was available for primary care patients under select, commercial insurance
plans. In response to the PHE, the shortages of personal protective equipment, and
insurance coverage by both government and commercial payors, telemedicine was rapidly
deployed across all specialties. During the stay-at-home order, clinic staff reviewed
each reason for visit with the patient and the provider; much of routine follow-up
care was either deferred or changed to virtual. Patients without video capabilities
(e.g., smartphone, internet connection, or familiarity with computer/phone software
interface) were scheduled as telephone visits. At the end of the stay-at-home order,
the mode of care was guided by a scheduling grid of visit types that were eligible
for virtual care and then by the patient preference.
Retrospective Analysis of Patient Populations
A deidentified patient dataset was acquired from the Medical College Wisconsin & the
Froedtert Health System consisting of 1,365,021 patients with ambulatory visits (inclusive
of telemedicine). The medical system is one of two academic medical centers in Wisconsin.
The medical system has more than 45 health centers and clinics with over 1.2 million
patient visits in 2019.
We defined patients who accessed telemedicine services one or more times as the telemedicine
group. Patients who did not accessed telemedicine but had one or more in-person visits
were categorized into the in-person group. We identified 20,189 telemedicine patient
users during the study period between March 1st, 2020, and August 31st, 2020. During
this period, telemedicine services were encouraged to be utilized for nonemergent
visits, though the in-person nonemergent services were still open for access with
the note that routine care was systematically changed to telemedicine or deferred
during the State of Wisconsin's Stay-At-Home order running from March 23, 2020 to
May 13, 2020. The criterion for determining telemedicine users was by the patients'
encounter information that was recorded in the EHR system. The telemedicine system
had not been systematically deployed in the hospital until the COVID-19 pandemic.
The data elements extracted from the EHR system included age, gender, race, ethnicity,
ZIP code, and insurance status. The ZIP code was used to correlate patient information
with socioeconomics information collected from census data.
Extracting Social Economics Data from Census Community Survey
The study area was in the Milwaukee, including the following counties: Jefferson,
Kenosha, Milwaukee, Ozaukee, Racine, Walworth, Washington, and Waukesha. The study
area is the primary service area of the hospital system, including eight counties.
The study area includes a metropolitan area that has a diverse racial population (2.08
million residents), including 77.9% White, 13.8% Black, 2.4% Asian, and 5.9% other
racial groups. About 9.2% of the population were Hispanic or Latino. Patients who
did not reside in this area were excluded. The 126 ZIP codes of the counties were
collected. The ZIP code was used to link the patient information to the Census American
Community Survey (ACS) Community Survey 5-year estimate data. The census data elements
used in this study included the median age, median income, White ratio, poverty rate,
and college education ratio in the ZIP code areas.
Utilization Outcome and Variables
We used electronic health record data for analysis. Telehealth visits were either
interactive video/audio (video, Group A ) or telephone-based (audio only, Group B ) depending on how patients access the telemedicine. If patients had both a video
and an audio-only visit, they were included in both populations. If patients had an
in-person visit but neither a video nor an audio-only visit, they were included in
the in-person-only category. For each patient, we collected data including age, gender,
race, ethnicity, insurance type, and ZIP code.
The telemedicine adoption rate (TAR) in each ZIP code of the SE Wisconsin region was
assessed to determine the impacts of median income, poverty rate, White ratio, and
college education ratio. The adoption rate was defined as the unique patients that
used the telemedicine system from one of the eight counties during the study period.
The TAR was calculated using the formula:
Eq. 1: Telemedicine adoption rate (TAR)
TAR = Total patients who used telemedicine in the ZIP area ÷ Total population in the
ZIP area
Two types of TAR were calculated, including TAR(A) for the video users and TAR(B)
for the telephone users.
Statistical Analysis
The telemedicine adoption patterns were analyzed across the social determinant factors
for patients at the SE Wisconsin region. The telemedicine patient groups were compared
with hospital patients who had in-person office visits. The telemedicine visit system
was deployed in March 2020, at the beginning of the COVID-19 pandemic; telemedicine
visits between March 1st, 2020 and August 31st, 2020 were analyzed. Patients who used
telemedicine services were further divided by their telemedicine service types, video
visit (Group A ) and telephone visit (Group B ). The time span for the comparison group (in-person visits: Group C ) was from January 1st, 2019, to August 31st, 2020. Group D was the population background of the study region collected from the ACS census data.
In the comparison group, we included patients in 2019 to form a representative in-person
cohort not affected by the challenges of the COVID-19 pandemic. Group A and Group
B were compared with Group C in five aspects, including age, gender, race, insurance
type, and ethnicity. Odds ratios (ORs) were used to compare the effect sizes between
groups.
The impacts of social economic factors on TAR were analyzed on the ZIP code level
within the eight counties of SE Wisconsin. This area is selected because the hospital
system primarily serves these counties. Linear regression was first used to assess
the individual correlation between the social determinants and TAR rates. Multiple
regression analysis was then used to analyze the collective impact of the social determinant
factors on the TAR rates.
The statistical tests and analyses were performed using statistical tool R (version
3.6.1), and p < 0.05 was used as a threshold to determine statistical significance. See [Supplementary Appendix A ] (available in the online version) for details for statistical analysis. To visualize
the geographic distribution of adoption, the adoption rates of the ZIP areas were
represented on a map.
Results
Gaps in Adoption across the Patient Population
A total of 104,139 unique patients accessed telemedicine services between March 1st,
2020, and August 31st, 2020. In the baseline comparison group, 453,848 patients used
in-person office visits from January 1st, 2019, to August 31st, 2020 ([Table 1 ]). Patients who used the telemedicine platform were generally older compared with
the health system patients (median age, Group A: 48.12 vs. Group B: 57.58 vs. Group
C: 46.71, p < 0.001). The telephone user group was significantly older (median difference 11
years) than the in-person visit group (median 57.6 vs. 46.7 years old, p < 0.001), while the video visit group was slightly older than the in-person visit
group.
Table 1
Comparison between telemedicine user groups with in-person visits
Group A: Telemedicine (Video)
n = 57915
Group B: Virtual check-in (Telephone)
n = 46,224
Group C: In-person visit
n = 453,848
Group D: SE Wisconsin region (n = 2,083,474)
Effect size
Odds ratio: A vs. C (95% CI)
Effect size
Odds ratio: B vs. C (95% CI)
Age group
Median age
48.12
57.58
46.71
47.1
0.06 (0.05,0.07)
0.48 (0.40,0.49)
Gender
Female
62.00%
58.20%
55.90%
51.1%
1.29 (1.27, 1.31)
1.10 (1.08, 1.12)
Male
38.00%
41.80%
44.10%
48.9%
0.78 (0.76, 0.79)
0.91 (0.9, 0.93)
Race
White
79.00%
76.20%
73.80%
77.9%
1.34 (1.31, 1.37)
1.14 (1.11, 1.16)
Black
12.90%
18.40%
14.70%
13.8%
0.86 (0.84, 0.88)
1.31 (1.28, 1.35)
Asian
2.00%
1.40%
2.50%
2.4%
0.79 (0.75, 0.84)
0.55 (0.51, 0.60)
Other
3.80%
3.70%
4.40%
5.9%
0.85 (0.81, 0.89)
0.82 (0.78, 0.87)
Unknown
2.40%
0.30%
4.70%
0%
0.5 (0.47, 0.53)
0.07 (0.06, 0.08)
Insurance
Private
65.00%
56.60%
56.50%
57.2%
1.43 (1.41, 1.46)
1.00 (0.99, 1.02)
Public
32.30%
42.70%
36.50%
31.4%
0.83 (0.82, 0.85)
1.30 (1.27, 1.32)
Other
1.20%
0.30%
0.60%
3.6%
1.90 (1.75, 2.06)
0.47 (0.39, 0.55)
Self-funded
0.10%
0.10%
0.60%
7.7%
0.08 (0.06, 0.12)
0.16 (0.12, 0.21)
Ethnicity
Latino
3.90%
3.90%
4.80%
9.2%
0.81 (0.77, 0.84)
0.79 (0.75, 0.83)
Non-Latino
93.60%
95.80%
91.00%
90.8%
1.46 (1.41, 1.51)
2.28 (2.17, 2.39)
unknown
2.40%
0.30%
4.20%
0%
0.57 (0.54, 0.60)
0.07 (0.06, 0.09)
The telemedicine users (both video and telephone groups) were significantly more likely
to be female than male (Video OR: 1.29 Telephone: 1.10, p < 0.001). White patients were significantly more likely to use telemedicine services—both
video (OR 1.34) and telephone (OR 1.14) groups compared with non-White groups. Black
patients were less likely to use video visits (OR 0.86) and significantly more likely
to use telephone visits (OR 1.31) compared with non-Blacks. Asian patients' usage
rates were lower in both telemedicine modalities (Video OR 0.79, Telephone OR 0.55).
Similarly, the Latino population TARs were also low (Video OR 0.75, Telephone OR 0.85)
compared with non-Latinos.
The telemedicine patients who utilized video visits were significantly more likely
to have private insurance (OR 1.43; 95% confidence interval or CI 1.41–1.46) than
public insurance (OR 0.83; 95% CI 0.82–0.85), while the telephone users were more
likely to use public insurance (OR 1.30; 95% CI 1.27–1.32) and less likely to be privately
insured (OR 1.00; 95% CI 0.99–1.02).
Linear Regression Analysis
Among the analyzed ZIP code areas ([Table 2 ], [Fig. 1 ]), the linear regression results showed that median income (coefficient 1.65, p < 0.001) and college education (coefficient 1.58, p < 0.001) had a significant positive correlation with video visits. Income and college
education also had a positive correlation with telephone visits; however, the correlation
was not statistically significant. The ZIP code areas' White population ratio had
a positive correlation with video visits and a negative correlation with telephone
visits.
Table 2
Linear regression modeling of the impact of social determinant factors for telemedicine
usage across SE Wisconsin ZIP code areas
Coefficients
Median income
White ratio
College education
Median age
Video (Group A)
1.65 (p <0.001)
0.16 (p = 0.624)
1.58 (p <0.001)
1.84 (p = 0.11)
Telephone (Group B)
0.50 (p = 0.27)
−0.33 (p = 0.245)
0.57 (p = 0.11)
0.74 (p = 0.45)
Fig. 1 (A ) Video group, linear regression. (B ) Telephone group, linear regression.
Multiple Regression Analysis
To analyze the correlated impact of the social determinant factors, a multiple regression
analysis was conducted on the social determinant factors for the 126 ZIP code areas
in the SE Wisconsin region. [Table 3 ] shows the results—median age, median income (logarithmic transformed), White resident
ratio, and college education were independent predictors for telemedicine use across
the ZIP code areas. The college education rate had a strong correlation with video
visits (coefficient 1.41, p = 0.01) in the multiple regression. The White ratio in the areas had a small negative
correlation for both video and telephone visits (video coefficient −0.953, p < 0.05; phone coefficient −0.99, p < 0.01).
Table 3
Multiple regression modeling of the impact of social determinant factors for telemedicine
usage across SE Wisconsin areas
Social determinant(log value)
Telemedicine groups
Coefficients
Std error
p -Value (Significance: “**” 0.01; “*” 0.05)
Median income
Video
1.376
0.999
0.1711
Phone
0.601
0.8861
0.49895
White ratio
Video
−0.953
0.4204
0.0253*
Phone
−0.9955
0.3729
0.00867**
College education rate
Video
1.4196
0.5524
0.0114*
Phone
0.7369
0.49
0.13529
Median age
Video
0.096
1.6173
0.9529
Phone
0.8949
1.4345
0.05339
Geographic Distribution of TAR
To understand the preference of video and phone telemedicine access, the ratio of
video and phone TAR was calculated for each ZIP code. The video utilization and phone
utilization were documented in the patient encounters as a part of the electronic
patient records. The video utilization versus phone utilization ratio is calculated
by TAR (Video)/TAR(Phone) ([Fig. 2 ] left). Orange colors indicate higher level of video utilization and blue colors
indicate higher level of phone utilization. The video and phone TAR ratio correlated
with college education ([Fig. 3 ] right) (p < 0.01). In this geographic area, higher college-education population was also positively
associated with a higher median income with private insurance.
Fig. 2 (Left ) The TAR ratio of video utilization versus phone utilization. (Right ) Regional college education rate across ZIP. TAR, telemedicine adoption rate.
Fig. 3 Comparing the odd ratios of telemedicine users. Baseline group: all in-person visits
during January 01, 2019 to August 31, 2020. Video visit group: Patients accessed telemedicine
services using multimedia video chat during March 01, 2020 to August 31, 2020. Telephone
visit group: patients accessed telemedicine services using the telephone during March
01, 2020 to August 31, 2020.
Discussion
At the onset of COVID-19 pandemics, many health care systems quickly deployed telemedicine
technologies to address the need for remote visits of health services. For example,
Washington State was the first state in the United States that encountered with COVID
patients. UW Medicine system shared their experience of the rapid rollout of a telehealth
system to support their clinical response of the pandemic.[27 ] Knighton et al[28 ] reported the implementation of a telehealth system in an integrated community-based
health care setting. The system leveraged the Center for Disease Control and Prevention
Pandemic Interval Framework to create a multimodal technology platform. To monitor
patients diagnosed with COVID remotely, many systems including this one[29 ] and the Mass General Brigham health system used remote patient monitoring.[30 ] in the MGB study; RPM reduced readmission for patients with COVID and provide scalable
services upon discharge. While this study focused on ambulatory care, notably this
system and many others also used inpatient telemedicine services to reduce PPE needs
and support infection control. For example, Ong et al[31 ] studied the use of optional devices to support inpatient telehealth services across
seven hospitals. The investigators discovered that large-scale distribution of consumer-grade
devices was feasible and useful for inpatient telehealth services.
Despite the fast growth of telemedicine applications, socioeconomic factors that affect
the access and adoption of telemedicine have not been well-studied. The recent impact
of COVID-19 spurred a wave of deployment and adoption of telemedicine systems in hospital
systems. It is critical to understand how social determinants of health could affect
access to health care through telemedicine. Rodriguez et al have also identified demographic
differences alongside age, race, language, and broadband access as drivers for using
video visits. Further, they identified that practice and clinician variability accounted
for more variation in choice of modality than patient.[32 ] Ortega et al[33 ] reviewed policy changes and outlined important recommendations that health system
can adopt to improve telemedicine for the underserved patients. Our study specifically
investigated the social economics disparities of telemedicine usages among patients
in the SE Wisconsin areas.
In the studied health system, we saw that older patients were more likely to use telemedicine
services, possibly reflecting the concerns they had around COVID-19 risk. At the same
time, research has demonstrated that older patients are less likely to be prepared
for telemedicine care,[34 ] whether by video or by telephone, and thus further attention should be paid to understanding
access and difficulty accessing virtual services. We also saw a significantly higher
proportion of female telemedicine users in both video and telephone groups. The gap
of adoption between male patients and female patients was noticeably large (Video:
38.0 vs. 62.0%; Telephone: 41.8 vs. 58.2%) compared with normal hospital visits (44.1%
male vs. 55.9% female). One possible explanation for the gap is that men are less
concerned about COVID-19.[35 ] A recent Reuter-Ipsos survey showed 54% of women were “very concerned” about the
coronavirus while only 45% of men were concerned.[36 ] Women are also much more likely to take action to modify their daily routines to
reduce chances of infections, such as using disinfectants and practicing social distancing.
In general, women also more commonly take the “care manager” role in a family.[37 ] The impact of COVID-19 on the elderly and children could be more alarming to women.
Therefore, women are more supportive than men in some strict measures,[38 ] such as closing schools, banning public gatherings, and stopping transportation.
This could explain the use of greater use of telemedicine instead of in-person visits
during COVID-19 by women. Some studies have also suggested that men have a higher
mortality rate compared with women if infected by coronavirus.[39 ]
[40 ] However, our study shows that the higher risk did not lead to higher telemedicine
utilization. A recent study performed an analysis of 7,742 family medicine encounters
and compared telehealth users with in-person visits.[41 ] This study also discovered a higher usage rate among women. The gender gap in telemedicine
adoption indicates the need to understand the hesitancy among men to utilize virtual
visits and develop strategies to mitigate potential barriers.
Analysis of data showed that White patients had a higher odd ratio of using telemedicine
for both video visits (OR: 1.34; 95% CI 1.31–1.37) and telephone visits (OR: 1.14;
CI 1.11–1.16) when compared with minorities; while Black patients had a lower OR for
video visits (OR:0.86; CI 0.84–0.88) and higher ratio for telephone visits (OR: 1.31;
CI 1.28–1.35) when compared with non-Black. The racial gap for video visit-based telemedicine
adoption was significant (p < 0.001) for Black patients. Hispanic telemedicine users only consisted of 3.9% of
the patients for both video visits (OR: 0.81; CI 0.77–0.84) and telephone visits (OR:
0.79; CI 0.75–0.83), which is lower than the representations of the health system
(3.9 vs. 4.8%). Similarly, Asian patients also have a lower adoption rate in video
visits (OR: 0.79; CI 0.75–0.84) and phone visits (OR: 0.55; CI 0.51–0.60). These results
indicated that minority patients could have challenges in adopting telemedicine services.
However, the barriers that led to such disparities in telemedicine adoption were not
clearly studied. One potential explanation is the effect of the “digital divide.”
Lorence et al examined how racial and ethnic factors could associate with online health
information search.[42 ] They discovered a wide gap between White and Black and between White and Hispanic
patients for online health information searches. More recently, other studies also
showed that the digital divide in health-related technology usage also occurred in
older adults of Black and Hispanic origin compared with White, including online access
of the health record.[43 ]
[44 ]
[45 ] Most of the video-based telemedicine platforms require the patients to access the
services through computers or smartphones. The digital divide could lead to the lagging
adoption of telemedicine in Black and Hispanic patients. As policymakers consider
future coverage of telemedicine services, ongoing coverage for telephone-based visits
will help reduce the access disparities. Additionally, infrastructure in communities
to support high-speed internet access and connection reliability, including broadband
and/or municipal wi-fi networks and stations, may also improve access for disadvantaged
populations.
Private insurance significantly correlated with video TAR (Video OR: 1.43; Telephone
OR: 1.00); public insurance negatively correlated with the video TAR (OR: 0.83; CI
0.82–0.85) but positively correlated with telephone TAR (OR: 1.30; CI 1.27–1.32).
Patients with private insurance generally are younger and have a better income than
patients with public insurance. This can be confirmed by the median age of the telemedicine
users (Video: 48.1 vs. Telephone: 57.5). The telephone telemedicine users were much
older than the video users. It is possible that younger patients adopt technology
better than older patients. Unlike younger patients, who are more familiar with using
computers, smartphones, and video chat, older patients could potentially need more
assistance in video telemedicine visits. However, providing such technical assistance
remotely in a patient-friendly manner is a challenge during the COVID-19 pandemic.
This could create a significant barrier for older patients to access advanced telehealth
services.
We also attempted to analyze the impacts of social determinant factors in telemedicine
adoption in the SE Wisconsin region. In the linear regression analysis ([Table 2 ], [Fig. 1 A–F ]), we identified that the median income and college education rate had a positive
correlation with the TAR in the ZIP areas. The impact of social determinant factors
was more significant for the video visits group. For every 1% increase in median income,
the video TAR rate increased 1.65% (p < 0.001). Similarly, there was a 1.58% increase in video visits TAR for every 1%
increase in the college education rate (p < 0.001). Median income and college education also could have a positive impact on
telephone TAR; however, the statistical test was not significant. Multiple regression
analysis was used to determine the predictors' collective effects on telemedicine
use. In the multiple regression analyses, the college education rate still had a significant
positive correlation with video visits. White ratio became negatively correlated with
telemedicine adoption in the multiple regression analyses in video and phone visit,
although the coefficients were not very high. This result confirms that college education
rate within the ZIP code areas is a key predictor for increased adoption of telemedicine
in the SE Wisconsin area. There are many possible explanations for lower TARs among
disadvantaged populations such as lack of insurance coverage, limited access to high-speed
internet or smartphone, lower health technology literacy, poorer health communication
skills, and less control of work/home life. This study could not uncover the underlying
driving factors; rather, our analyses showed the inequality in telemedicine adaptation,
which is closely linked to social determinant factors.
There are a few potential limitations to this study. Like other social determinant
studies, we did not collect individual patient's income information, and the median
income in a ZIP code was used as an estimate of patient income. Second, the patient
cohort in this study represents a regional hospital system. Milwaukee is one of the
cities with the highest racial segregation score in the United States (U.S.) according
to the racial segregation in U.S. study[46 ]; therefore, the patient characteristics may not be representative of other health
care systems and areas. Third, this study focused on a few key social determinant
factors. There are several potential factors that could be considered for future studies,
including environmental and cultural factors. This study was intended to uncover potential
gaps in the adoption of telemedicine and serve as a template to encourage more research
in this area. Fourth, the pandemic could impact different health service types (e.g.,
cancer treatment, chronic condition management, laboratory services, pharmacy services)
in very different ways. Lastly, we did not include clinical conditions or problems
in this dataset; it is possible, for example, that older patients used more telemedicine
services simply because they had a higher level of illness and needed more services.
Studying how telemedicine could be deployed to address the needs of different health
services will be our future study.
Conclusion
We observed significant disparities among patients in telemedicine adoption during
the COVID-19 pandemic associated with gender, race, income, education, and insurance
type. More studies are needed to investigate causal relationships between the underlying
factors and telemedicine utilization. This study adds to literature that shows that
telemedicine may not expand access to care services for underserved populations without
special attention. With the growing deployment of telemedicine services, specific
strategies are needed to uncover and address barriers to telemedicine adoption in
underserved patient populations.
Clinical Relevance Statement
Clinical Relevance Statement
Telemedicine has been widely deployed in health systems during the COVID-19 pandemic.
This study quantified the impact of socioeconomic determinants factors to telehealth
adoption. This study reveals gaps and challenges in adopting telemedicine in disadvantage
groups, which is crucial to develop strategies to improve telehealth adoption.
Multiple Choice Questions
Multiple Choice Questions
Why patients choose telemedicine for health care?
Remote access to health services.
Test internet quality and connection.
Reduce workload of doctors.
Connect with families.
Correct Answer: The correct answer is option a. Patients use telemedicine services to access health
service remotely.
Which is a barrier for telemedicine adoption?
Correct Answer: The correct answer is option d. Increased service waiting time will be a barrier
for telemedicine adoption because patients expect to access health services through
telemedicine in a more efficient way than traditional in-person visits.
