Using the technology acceptance model to examine acceptance of telemedicine by cancer patients in an ambulatory care setting

Abstract

Background:

Telemedicine has demonstrated benefits for cancer patients including the potential to improve care coordination and patient outcomes. Since June 2020, teleconsultations have been implemented in the National Cancer Centre Singapore.

Objectives:

This study aims to assess cancer patients acceptance of telemedicine as a complement to traditional in-person care and identify factors affecting their acceptance.

Methods:

An online self-administered questionnaire was designed using a modified technology acceptance model (TAM) previously validated to predict acceptance of telemedicine by patients and factors affecting acceptance. Descriptive statistics were used to summarise data on demographic factors and TAM construct scores. Univariate and multivariate logistic regression were used to determine how demographics factors and TAM constructs influenced acceptance.

Results:

Respondents (n = 278; mean age 59 years) were mostly female (67.6%), Chinese (86.3%) and received parenteral chemotherapy (72.6%). Technology access and confidence were generally moderate to high, while past telemedicine use was low (18%). Overall, more than half (59.7%) expressed acceptance. The odds of acceptance were significantly higher if respondents agreed that their healthcare access would improve by using telemedicine (OR 4.17, 95% CI 1.71–10.16) or they would have the necessary resources for using telemedicine (OR 4.54, 95% CI 2.30–8.97).

Conclusion:

Acceptance of telemedicine was high amongst respondents. Facilitating conditions such as having necessary resources and perceived improved access were identified as main predictors of high acceptance. Telemedicine services should work to improve these aspects, leverage on advantages and address disadvantages brought up by patients.

Keywords

Telemedicine telehealth Covid-19 oncology

Introduction

Telemedicine is defined as the “systematic provision of healthcare services over physically separate environments via Information and Communications Technology (ICT)”.¹ In oncology, telemedicine services can help cancer patients cope with long-term impact of the disease.² Examples of tele-oncology interventions include cancer tele-genetics, cancer-related tele-applications, remote chemotherapy supervision, symptom management, survivorship care, palliative care, telepathology, and increased access to cancer clinical trials.³ Teleconsultations were introduced in the National Cancer Centre Singapore (NCCS) in June 2020 and monthly consults have increased from 115 in June 2020 to 231 in May 2021.

Using telemedicine can support patients in self-managing their disease,⁴ save time and money,^5,6 and improve timeliness of care.⁷ Telemedicine can also alleviate caregiver burden, with minimal impact on existing care resources.⁸ More recently, the COVID-19 pandemic has driven telemedicine use to continue delivering care remotely,⁹ protecting patients from unnecessary exposure to the virus.¹⁰ The value of implementing telemedicine also lies in its potential to refine coordination of patient-centred care.¹¹

However, studies also reveal a large proportion of patients still prefer traditional in-person care to remote care whenever possible.^12,13,14 Numerous barriers such as lack of social support, lack of technological infrastructure and patients’ preferences hampering adoption have been ascertained.¹⁵ Notably, patient acceptance of telemedicine has been perceived as a major determinant of its widespread and sustained uptake,^16,17 and assessing it is important during initial development and evaluation of digital health interventions.¹⁸ However, current acceptance among cancer patients is unclear. Acceptability studies so far have heterogeneous study methods and tended to focus on a sole intervention or cancer type, making it difficult to generalise findings to a wider range of services or patient population.¹⁹ Most studies depict telemedicine as a standalone service, and it is unclear if patient acceptance would change if it is unambiguously positioned as an adjunct to traditional care instead.

Rapid technological advancement, coupled with lowered barriers to technology, may lead to a higher acceptance of telemedicine.²⁰ Encouragingly, other studies have previously found that the Internet is commonly used by cancer patients to search for disease-related information and support.^21,22,23 Finally, patients may be more receptive towards telemedicine if reassured that the traditional mode of in-person care would not be replaced completely.

Therefore, our study objectives were to: (1) assess cancer patients’ acceptance of telemedicine as a complement to traditional in-person care in the ambulatory care setting, (2) identify factors which affect this acceptance, and (3) make recommendations based on evidence from study findings.

Methods

Study cohort and design

This was a cross-sectional study conducted at NCCS, a multi-disciplinary cancer centre managing approximately 70% of all public-sector institution cancer patients in Singapore. Ethics review approval with exemption was obtained from the SingHealth Centralised Institutional Review Board (2020/2691).

Participants were recruited via convenience sampling. Patients included were aged 21 years old and above, proficient in English or Mandarin, and had attended at least one in-person consultation at NCCS. Participation was anonymous and voluntary.

Variables and outcome measures

A self-administered patient questionnaire was devised in three overarching parts. The first part collected information on demographics, technology-related factors and past telemedicine experience. Social support items were derived from the Duke Social Support and Stress Scale.²⁴ The second part was adapted from an extended technology acceptance model (TAM)^25,26 to measure telemedicine acceptance and factors affecting acceptance. The final part was on potential advantages and disadvantages of telemedicine²⁷ to further understand patient attitudes and hence propose better-tailored recommendations. Agreement with statements in the latter two parts were rated on a 5-point Likert scale from “strongly disagree” to “strongly agree”. Respondents were also invited to provide an open-ended remark about their opinion on telemedicine.

The questionnaire was available in either English (Annex 1) or Mandarin (Annex 2). Both versions were hosted on the commercial e-survey platform Qualtrics (Qualtrics, Provo, UT, USA) and distributed via Short Message Service (SMS) to patients who had attended the Ambulatory Treatment Unit at least once in 2020. Data collection ran from 5 to 18 September 2020.

Technology acceptance model

Technology acceptance model is a validated model which predicts and explains user acceptance of new technologies (Figure 1).²⁶ “Perceived usefulness” is the degree to which the technology is believed to enhance job performance, while “perceived ease of use” is the degree to which use of the technology is seen as effortless.²⁶ These two variables demonstrated positive correlations with usage intention, the equivalent of user acceptance.²⁸ Therefore, measuring perceived usefulness and perceived ease of use by end-users enables one to predict their acceptance of a new technology.

Figure 1.

Diagram of original TAM.

Kamal et al.’s extended TAM model which had additional constructs: trust, social influence, facilitating conditions, technology anxiety, resistance to use, and perceived risk (Table 1) added to the original TAM. By accounting for more factors potentially affecting acceptance in the digital health context, the model attempted to measure patient acceptance more accurately. Each TAM construct was measured by 2 or 3 items (Annex 3 in Data Supplement). The extended TAM model which was chosen for the study was specifically validated to investigate telemedicine acceptance in patients.²⁵ (Figure 2)

Table 1.

Definitions of additional TAM constructs.²⁵

Construct	Definition
Trust	The extent to which a patient believes in the services offered by telemedicine
Social influence	The degree to which a patient thinks the people around him/her would want him/her to use telemedicine
Facilitating conditions	The presence of sufficient institutional and technical support for a patient to adopt telemedicine
Technology anxiety	The unease felt by a patient when using telemedicine for the first time
Resistance to use	Psychological barrier towards changes in the status quo brought about by telemedicine
Perceived risk	The extent to which a patient is uncertain about whether the outcomes of participating in a telemedicine-related activity are desirable

Figure 2.

Diagram of extended TAM.

Statistical analysis

Descriptive statistics were used to summarise demographic factors and TAM construct scores. A single value for each TAM construct was obtained by averaging scores of items measuring the same construct. Respondents were then categorised into different groups based on their mean usage intention score: those scoring above 3 would have minimally agreed with at least one of the statements measuring usage intention, and therefore were considered part of the “high acceptance” group. Conversely, scores equal to or less than 3 were considered “low acceptance”.

To study factors affecting acceptance, univariate and multivariate logistic regressions were performed. The independent variables were TAM constructs and demographic factors, while the dependent variable was acceptance. TAM constructs were studied as continuous predictors while demographic factors were studied as categorical variables. The independent variables used for multivariate regression contained all TAM constructs and demographic factors significant in the univariate analysis. Odds ratios and their 95% confidence intervals were computed. All statistical analyses were conducted at a significance level of 0.05. Results were analysed in IBM SPSS Statistics for Windows, Version 23.0 (IBM Corp., Armonk, NY, USA).

Perceived advantages and disadvantages were reported descriptively as percentage agreement with each statement. Agreement was defined as a rating of “agree” or “strongly agree”. For open-ended responses, thematic analysis was done to identify recurring themes which could supplement the discussion on perceived advantages and disadvantages. Quotes were labelled and presented in the results if they sufficiently reflected the main sentiment of similar responses.

Results

Demographics

Out of 3088 SMS recipients, 278 (9.0%) respondents completed the survey and were included in this study. The mean age of the respondents was 59 years old. Majority of respondents were female (188/278, 67.6%) and Chinese (240/278, 86.3%). Majority of respondents were receiving parenteral chemotherapy (159/219, 72.6%) and oral anti-cancer medications (56/219,25.6%) (Table 2). Majority of respondents had never used telemedicine before (228/278, 82.0%) (Table 3). These patients were largely satisfied with their experience (mean 3.98, SD 1.22).

Table 2.

Demographics of respondents.

Demographic	Total (n = 278)		High acceptance^a (n = 166)
	n	%	n	%
Age (years)
Mean (SD)	59.36 (10.77)		58.93 (11.05)
≤54	96	34.5	64	38.6
55-64	87	31.3	44	26.5
≥65	95	34.2	58	34.9
Gender
Female	188	67.6	116	69.9
Male	90	32.4	50	30.1
Ethnicity
Chinese	240	86.3	142	85.5
Malay	21	7.6	15	9.0
Indian	10	3.6	3	1.8
Others	7	2.5	6	3.6
Highest education level
Secondary/Pre-University	141	50.7	78	47.0
Tertiary/Postgraduate	100	36.0	66	39.8
Primary	37	13.3	22	13.3
Employment status
Unemployed	153	55.0	86	51.8
Full time	97	34.9	62	37.3
Part time	28	10.1	18	10.8
Monthly income (SGD)
<$1500	24	8.6	14	8.4
$1500–$2500	13	4.7	8	4.8
$2501–$3500	22	7.9	10	6.0
>$3500	44	15.8	35	21.1
Prefer not to say	22	7.9	13	7.8
Not applicable	153	55.0	86	51.8
Primary mode of transport to NCCS
Public bus/train	111	39.9	57	34.3
Private vehicle	89	32.0	63	38.0
Taxi/private hire	76	27.3	45	27.1
Others	2	0.7	1	0.6
Payment category
Singaporean/PR Subsidised	217	78.1	124	74.7
Singaporean/PR private	54	19.4	38	22.9
Non-resident	5	1.8	4	2.4
Foreign resident	2	0.7	0	0.0
Social support^b
Spouse/partner	155	55.8	96	57.8
Children/grandchildren	87	31.3	50	30.1
Brothers/sisters	34	12.2	20	12.0
Parents/grandparents	16	5.8	11	6.6
Other relatives	11	4.0	8	4.8
Neighbours/co-workers	3	1.1	2	1.2
Others	9	3.2	6	3.6
None	59	21.2	31	18.7
No. of supportive persons
0	59	21.2	30	18.1
1	145	52.2	94	56.6
2	60	21.6	34	20.5
≥3	14	5.0	8	4.8
Treatment type^b
Parenteral chemotherapy	159	72.6	92	69.7
Oral anticancer medication	56	25.6	39	29.5
Others^c	12	5.5	8	6.1
None	32	14.6	22	16.7

^aRespondents who agree or strongly agree with at least one statement measuring usage intention.

^bMore than one option may have been selected.

^cThese include clinical trials, radiotherapy, medication for side effects and non-cancer hormone therapy.

Table 3.

Satisfaction with telemedicine.

Variable	Total (n = 278)		High acceptance^a (n = 166)
	n	%	n	%
Past telemedicine use
Yes	50	18.0	39	23.5
Satisfaction with telemedicine
Very satisfied	22	44.0	20	51.3
Somewhat satisfied	16	32.0	10	25.6
Neither satisfied nor dissatisfied	4	8.0	3	7.7
Somewhat dissatisfied	5	10.0	4	10.3
Very dissatisfied	3	6.0	2	5.1
Mean (SD)^b	3.98 (1.22)		4.08 (1.22)

^aRespondents who agree or strongly agree with at least one statement measuring usage intention.

^bOn a scale of 1 to 5, where higher values indicate greater satisfaction.

All demographic factors were analysed for their influence on acceptance of telemedicine (Table 4). Significant predictors of high acceptance were monthly income >$3,500 (odds ratio [OR] 3.28, 95% CI 1.34–8.02; p = 0.01), private transport (OR 1.97, 95% CI 1.15–3.38; p = 0.01), Internet access (OR 1.90, 95% CI 1.16–3.12; p = 0.01), confidence in technology (OR 2.36, 95% CI 1.44–3.85; p = 0.001) and past telemedicine use (OR 2.28, 95% CI 1.38–5.78; p = 0.005).

Table 4.

Influence of demographics on acceptance of telemedicine.

Variable	Total (n = 278)		High acceptance^a (n = 166)		Unadjusted odds ratio (95% CI)^b	p value
	n	%	n	%
Age (years)
<65	183	65.8	108	65.1	Reference
≥65	95	34.2	58	34.9	1.09 (0.66–1.81)	0.74
Gender
Female	188	67.6	116	69.9	1.29 (0.78–2.15)	0.33
Male	90	32.4	50	30.1	Reference
Ethnicity
Chinese	240	86.3	142	85.5	Reference
Non-Chinese	38	13.7	24	14.5	1.18 (0.58–2.40)	0.64
Highest education level
Up to pre-university	178	64.0	100	60.2	Reference
Tertiary and above	100	36.0	66	39.8	1.51 (0.91–2.52)	0.11
Employment status
Unemployed	153	55.0	86	51.8	Reference
Employed	125	45.0	80	48.2	1.39 (0.85–2.25)	0.19
Monthly income (SGD)
Up to $3500	59	57.3	32	47.8	Reference
>$3500	44	42.7	35	52.2	3.28 (1.34–8.02)*	0.01
Primary mode of transport
Public	190	68.3	103	62.0	Reference
Private	88	31.7	63	38.0	2.02 (1.18–3.47)*	0.01
Payment category
Subsidised	217	78.1	124	74.7	Reference
Non-subsidised	61	21.9	42	25.3	1.66 (0.91–3.04)	0.10
Social support
Yes	219	78.8	136	81.9	1.58 (0.89–2.83)	0.12
No	59	21.2	30	18.1	Reference
Treatment type
Parenteral	159	57.2	92	55.4	Reference
Not on parenteral	60	21.6	40	24.1	1.46 (0.78–2.71)	0.24
Missing data	59	21.2	34	20.5	N.A.
Internet access
Yes	174	62.6	114	68.7	1.90 (1.16–3.12)*	0.01
No	104	37.4	52	31.3	Reference
Technology confidence^c
Confident	154	55.4	106	63.9	2.36 (1.44–3.85)*	0.001
Not confident	124	44.6	60	36.1	Reference
Past telemedicine use
Yes	50	18.0	39	23.5	2.28 (1.38–5.78)*	0.005
No	228	82.0	127	76.5	Reference

^aRespondents who agree or strongly agree with at least one statement measuring usage intention.

^bValues bolded and marked with an asterisk (*) are significant at the .05 level.

^cConfidence is defined as a mean total confidence of ≥3, the equivalent of “quite confident” and above.

As technology confidence was a significant factor, univariate analyses were also conducted on each confidence item to pinpoint the more important technologies affecting acceptance (Table 5). Being confident in using video calls (OR 2.75, 95% CI 1.65–4.58; p < 0.001) was most associated with high acceptance, followed by finding the information on the Internet (OR 2.26. 95% CI 1.25–4.07; p = 0.006), wearable devices (OR 2.19, 95% CI 1.29–3.71; p = 0.003) and social network sites (OR 1.65, 95% CI 1.02–2.68; p = 0.04) (Table 6).

Table 5.

Influence of individual technology confidence items on acceptance of telemedicine.

Technology confidence item^c	Total (n = 278)		High acceptance^a (n = 166)		Unadjusted odds ratio (95% CI)^b	p value
	n	%	n	%
Making phone calls
Confident	257	92.4	157	94.6	2.09 (0.85–5.15)	0.10
Not confident	21	7.6	9	5.4	Reference
Sending and receiving text messages
Confident	251	90.3	150	90.4	1.02 (0.46–2.29)	0.96
Not confident	27	9.7	16	9.6	Reference
Sending and receiving e-mails
Confident	222	79.9	136	81.9	1.37 (0.759–2.47)	0.29
Not confident	56	20.1	30	18.1	Reference
Finding information on the Internet
Confident	221	79.5	141	84.9	2.26 (1.25–4.07)*	0.006
Not confident	57	20.5	25	15.1	Reference
Using social networking sites
Confident	157	56.5	102	61.4	1.65 (1.02–2.68)*	0.04
Not confident	121	43.5	64	38.6	Reference
Using video calls
Confident	182	65.5	124	74.7	2.75 (1.65–4.58)*	<0.001
Not confident	96	34.5	42	25.3	Reference
Using wearable devices
Not confident	180	64.7	96	57.8	Reference
Confident	98	35.3	70	42.2	2.19 (1.29–3.71)*	0.003

^aRespondents who agree or strongly agree with at least one statement measuring usage intention.

^bValues bolded and marked with an asterisk (*) are significant at the .05 level.

^cConfidence is defined as a rating of 1 = quite confident or 2 = extremely confident for the respective item.

Table 6.

Mean TAM scores and their influence on acceptance of telemedicine.

TAM construct	Mean (SD)		Unadjusted odds ratio (95% CI)^b	p value
	overall	high acceptance^a
Facilitators
Perceived usefulness	3.47 (0.78)	3.80 (0.65)	8.46 (4.77–14.97)*	<0.001
Perceived ease of use	3.45 (0.78)	3.75 (0.69)	5.82 (3.56–9.51)*	<0.001
Facilitating conditions	3.45 (0.69)	3.74 (0.59)	9.73 (5.34–17.76)*	<0.001
Social influence	3.10 (0.85)	3.39 (0.80)	3.57 (2.35–5.41)*	<0.001
Trust	2.96 (0.56)	3.14 (0.48)	5.83 (3.25–10.47)*	<0.001
Barriers
Resistance to use	3.63 (0.79)	3.55 (0.78)	0.74 (0.54–1.01)	0.06
Technology anxiety	2.97 (0.91)	2.64 (0.87)	0.30 (0.21–0.43)*	<0.001
Perceived risk	2.64 (0.74)	2.41 (0.73)	0.28 (0.18–0.43)*	<0.001
Usage intention	3.41 (0.79)	3.92 (0.44)	N.A.	N.A.

^aRespondents who agree or strongly agree with at least one statement measuring usage intention.

^bValues bolded and marked with an asterisk (*) are significant at the .05 level.

Descriptive statistics of TAM constructs

Amongst the mean TAM scores and its influence on acceptance of telemedicine, the highest-scoring facilitator of acceptance was perceived usefulness (mean 3.47, SD 0.78), while the highest-scoring barrier was resistance to use (mean 3.63, SD 0.79). Compare to scores for overall cohort, mean scores of the high acceptance group were generally higher for facilitators and lower for barriers. All TAM constructs, except for resistance to use, were found to significantly affect acceptance in the univariate analysis.

Upon fitting all TAM constructs and significant demographic factors into a multivariate logistic regression model, significant factors affecting acceptance were facilitating conditions (OR 6.30, 95% CI 1.16–34.28; p = 0.03), followed by perceived usefulness(OR 4.84, 95% CI 1.25–18.80; p = 0.02) (Table 7). Two critical items identified were: “I would be able to have all the necessary resources for using the telemedicine services” (OR 4.54, 95% CI 2.30–8.97; p < 0.001) and “Using telemedicine would improve my access to healthcare services” (OR 4.17, 95% CI 1.71–10.16; p = 0.002).] (Table 8)

Table 7.

Multivariate regression on factors associated with telemedicine acceptance.

Independent variable	Adjusted odds ratio (95% CI)^a	p value
Perceived usefulness	4.84 (1.25–18.80)*	0.02
Perceived ease of use	1.83 (0.39–8.60)	0.45
Resistance to use	1.08 (0.34–3.46)	0.90
Trust	0.86 (0.17–4.39)	0.86
Technology anxiety	0.50 (0.17–1.44)	0.20
Facilitating conditions	6.30 (1.16–34.28)*	0.03
Social influence	0.60 (0.14–2.50)	0.48
Perceived risk	0.33 (0.09–1.18)	0.09
Monthly income (>$3500)	1.08 (0.25–4.61)	0.92
Primary mode of transport (private)	2.12 (0.44–10.32)	0.35
Internet access (yes)	0.42 (0.09–1.85)	0.25
Technology confidence (confident)	1.18 (0.26–5.43)	0.83
Past telemedicine use (yes)	2.71 (0.20–37.02)	0.46

^aValues bolded and marked with an asterisk (*) are significant at the .05 level.

Table 8.

Multivariate regression on individual items of significant TAM constructs.

TAM statement	Agree^a				Adjusted odds ratio (95% CI)^c	p value
	Total (n = 278)		High acceptance^b (n = 166)
	n	%	n	%
Perceived usefulness
Using telemedicine would be useful in my daily routine.	156	56.1	129	77.7	2.13 (0.90–5.04)	0.08
Using telemedicine would improve my access to healthcare services.	148	53.2	127	76.5	4.17 (1.71–10.16)*	0.002
Using telemedicine would improve the quality of my health care.	112	40.3	95	57.2	1.20 (0.50–2.89)	0.69
Facilitating conditions
I would be able to have all the necessary resources for using the telemedicine services	160	57.6	130	78.3	4.54 (2.30–8.97)*	<0.001
I would acquire sufficient knowledge for using telemedicine service.	160	57.6	123	74.1	1.52 (0.75–3.06)	0.24
Telemedicine will suit well with my health care routine	99	35.6	86	51.8	1.59 (0.68–3.73)	0.29

^aRespondents who agree or strongly agree with the TAM statement in the first column.

^bRespondents who agree or strongly agree with at least one statement measuring usage intention.

^cValues bolded and marked with an asterisk (*) are significant at the .05 level.

Perceived advantages and disadvantages of telemedicine

Almost all respondents (261/278, 93.9%) completed this optional section of the survey, while a small proportion gave open-ended responses (64/278, 23.0%). Respondents generally appreciated that there were both advantages and disadvantages to telemedicine, as a fair amount of agreement was observed across most statements.

For perceived advantages, respondents agreed extensively that telemedicine could enable them to receive more convenient support (218/261, 83.5%), enjoy cost savings from travel (162/261, 62.1%), and get more timely care to meet their needs (158/261, 60.5%) (Figure 3(a)). These advantages were frequently reiterated in the free responses.

Figure 3.

Percentage agreement with each perceived advantage and disadvantage of telemedicine (n = 261).

Other advantages of telemedicine mentioned were regarding continued and safe care during the COVID-19 pandemic, access to overseas expertise, better flexibility in arranging consultations to accommodate other parties, and more cost-effective healthcare delivery.

The greatest disadvantage was the preference for “being able to see the healthcare worker face-to-face” (181/261, 69.3%) (Figure 3(b)). Respondents expressed strong opinions that telemedicine should remain as a complement to traditional care as it would not suit all situations. A major concern behind this was the apparent infeasibility of physical examinations if done remotely. Other disadvantages re-emphasised from those in Figure 3(b) include security issues such as verification of patient identity (144/261, 55.2%) and lack of adequate technical equipment (126/261, 48.3%). Additional worries raised were related to the patient’s ability to use technology, extent of patient privacy in a teleconsultation, and language barriers. Lastly, numerous respondents stated that they were unaware of telemedicine or had not tried it before, making it challenging to form concrete opinions on the system.

Discussion

Demographics

This study found that more than half (59.7%) of cancer patients expressed “high acceptance” of telemedicine. Respondents who were more inclined to accept telemedicine had a higher monthly income and mainly took private transport to NCCS. Consistent with prior research, this may suggest that higher socioeconomic status positively predicts telemedicine acceptance.²⁹ Such patients may be more confident in having the resources to use telemedicine, which also affected acceptance in our study. Demographic factors which concur with past findings included technology-related, relating to Internet access, technology confidence (specifically video calls, Internet, and wearables), and past telemedicine use.³⁰

Respondents who believed that telemedicine would offer them better healthcare access were significantly more likely to accept it. These findings add to current evidence that cancer patients deeply value healthcare which is convenient and readily accessible.¹⁹ The complexity and seriousness of cancer can often be distressing, and thus receiving timely attention can help patients ease their worries and facilitate the recovery progress.²¹

Respondents who felt they did not have the necessary resources for telemedicine were significantly less likely to accept it. This is possibly connected with the finding that 37.4% of respondents lacked access to the Internet, which is an unexpectedly substantial proportion compared to other reports.³¹ For instance, another recent study found that 26.5% of cancer patients in their sample did not have daily Internet access, while national statistics in 2019 suggested that 11% of the country’s population did not use the Internet.³² Another likelihood was that most respondents were unfamiliar with telemedicine, which could have caused uncertainty about what the “necessary resources” might have referred to specifically.

A major aim of a tele-oncology system should be to improve the convenience of accessing healthcare. Patients should find it easier and faster to choose a suitable consultation timing using telemedicine compared to physical clinic visits: this outcome could be a measure of the effectiveness of future tele-oncology services. One consideration for design is user friendliness, especially as confidence was subpar in using video calls. As illustrated by the mean sample age (59 years old), cancer patients are usually older. A straightforward navigation system and layout, as well as provision of instructions for use, may boost use confidence in older patients.³³’

Within the minority of patients who used telemedicine before, mean satisfaction with the service was high. Past telemedicine use was also a significant driver of telemedicine acceptance on univariate analysis. In their free responses, several respondents mentioned not knowing enough about telemedicine to form a definite opinion on it. Patients might have initially set lower expectations for telemedicine if they were not familiar with it. Therefore, conducting a brief telemedicine trial for patients to explore the system informally is a plausible method of increasing familiarity and hence acceptance.³⁴ Technical assistance could be provided throughout the trial, especially for first-time or less confident users, to help raise confidence in using the service and its perceived usefulness.

Attention should be paid to the relatively limited Internet access of respondents, which will impede the success of telemedicine.³⁵ Close to half of respondents were also worried about the equipment for telemedicine not functioning. Nevertheless, general technology access is anticipated to rise with time due to rapid advancements in expanding connectivity, which may promote reception to telemedicine.⁸ Alternatively, future research could seek to confirm and investigate the reasons underlying this phenomenon: previously identified barriers include cost, lack of interest, or not perceiving Internet access as useful.^36,37 This would enable more targeted decision-making for addressing Internet access rates among patients.

Technology acceptance model constructs

Our findings re-emphasise that telemedicine should only complement and not replace traditional care. Out of all TAM constructs, resistance to use was the highest and did not significantly affect acceptance; this suggests that patients might still choose to receive care via standard practices if it suits them, regardless of acceptance status.³⁶ Respondents expressed a marked preference to see healthcare workers in-person, and felt that telemedicine would only suit seasoned, stable patients not requiring physical examinations or tests. Providers should reassure patients that telemedicine will be primarily used as an adjunct, and the option of physical clinic visits would always remain available.

Based on the multivariate logistic regression model, significant factors affecting acceptance to telemedicine were facilitating conditions and perceived usefulness. Though our sample had mean age of 59 years old, close to 65% of our patients were older than 65 years old. Based on published studies, elderly patients were reported to require facilitating conditions as a main factor to accepting telemedicine.^38,39 For telemedicine to occur, there is a need for sufficient knowledge and competency on operating the technological resource which result in an elderly patient’s barrier to acceptance.³⁸

The potential for technology to facilitate timely access to healthcare should be perceived as a useful advantage of telemedicine. Telemedicine would enable remote connections among patients and healthcare providers. As a result, this increases the accessibility for patients to healthcare providers. The notion of perceived usefulness in the acceptance to telemedicine from a healthcare providers’ perspective has been published in various studies supporting its use in healthcare systems.^40,41

Perceived advantages and disadvantages of telemedicine

Our findings also identify points for patient education, as most respondents seemed unfamiliar with telemedicine. Awareness of telemedicine could be spread via healthcare providers or print material, which were the top few preferred information sources for cancer patients.²² Resources for telemedicine should be clearly specified, such as the platform’s compatibility with various operating systems or devices. Concerns about online security were observed in more than half of respondents and should be addressed: patients could be reassured that recent guidelines reinforce telemedicine IT policies to adhere to professional standards and state laws such as the Personal Data Protection Act.¹

Limitations

Limitations of our study include selection bias was likely, as respondents were recruited via convenience sampling. As data collection was web-based, this excluded patients who do not use a mobile phone or the Internet. Therefore, actual technology access and confidence may be lower than reported. The small response rate also suggests that non-response bias may exist, and the opinions of non-respondents remain unknown. Non-response rate was consistent with other web-based surveys on telemedicine^42,43,44

Conclusion

Our study showed that acceptance of telemedicine was high amongst respondents.

Respondents were largely satisfied with their telemedicine experience. Facilitating conditions such as having necessary resources and perceived improved access were identified as main predictors of high acceptance. Amongst the mean TAM scores and its influence on acceptance of telemedicine, the highest-scoring facilitator of acceptance was perceived usefulness, while the highest-scoring barrier was resistance to use. For perceived advantages, respondents agreed extensively that telemedicine could enable them to receive more convenient support, enjoy cost savings from travel and get timelier care to meet their needs. Telemedicine services should work to improve these aspects, leverage on advantages and address disadvantages brought up by patients.

Supplemental Material

Supplemental Material - Using the technology acceptance model to examine acceptance of telemedicine by cancer patients in an ambulatory care setting

Supplemental Material for Using the technology acceptance model to examine acceptance of telemedicine by cancer patients in an ambulatory care setting by Zhi Yao Chan, Chen Fang Lim, Jo Lene Leow, Feng Yong Chium, Su Wen Lim, Charlotte Hui Min Tong, Jessie Jie Xi Zhou, Moses Min Yuan Tsi, Ryan Ying Cong Tan, and Chew Sui Tjien Lita in Proceedings of Singapore Healthcare

Footnotes

Author contributions

Chan Zhi Yao – data analysis, drafting and submission of manuscript

Lim Chen Fang – planning of study design, data collection, data analysis, drafting of manuscript

Leow Jo Lene - planning of study design, data analysis, drafting of manuscript

Chium Feng Yong – planning of study design, data collection, data analysis, drafting of manuscript

Lim Su Wen - planning of study design, data collection, data analysis, drafting of manuscript

Tong Hui Min Charlotte – provision of data to be part of data collection, drafting of manuscript

Zhou Jie Xi Jessie – provision of data to be part of data collection, drafting of manuscript

Tsi Min Yuan Moses - provision of data to be part of data collection, drafting of manuscript

Tan Ying Cong Ryan - data analysis, drafting of manuscript

Chew Sui Tjien Lita - planning of study design, data collection, data analysis, drafting of manuscript, final review of manuscript

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical approval

Ethics review approval with exemption was obtained from the SingHealth Centralised Institutional Review Board (2020/2691)

Availability of data

The datasets generated and/or analysed during the current study are available upon request with valid reason from corresponding author.

ORCID iD

Jo Lene Leow

Supplemental Material

Supplemental material for this article is available online.

References

MOH . National telemedicine guidelines. https://www.moh.gov.sg/resources-statistics/guidelines/national-telemedicine-guidelines (accessed 19 October 2021).

Harris

Cheevers

Armes

. The emerging role of digital health in monitoring and supporting people living with cancer and the consequences of its treatments. Curr Opin Support Palliat Care 2018; 12: 268–275.

Sirintrapun

Lopez

. Telemedicine in cancer care. Am Soc Clin Oncol Educ Book 2018; 38: 540–545.

de Jong

Ros

Schrijvers

. The effects on health behavior and health outcomes of Internet-based asynchronous communication between health providers and patients with a chronic condition: a systematic review. J Med Internet Res 2014; 16: e19.

Russo

McCool

Davies

. VA telemedicine: an analysis of cost and time savings. Telemed J E Health 2016; 22: 209–215.

Doolittle

Spaulding

Williams

. The decreasing cost of telemedicine and telehealth. Telemed J E Health 2011; 17: 671–675.

Di Cerbo

Morales-Medina

Palmieri

, et al. Narrative review of telemedicine consultation in medical practice. Patient Prefer Adherence 2015; 9: 65–75.

Murad

Ali

Nawaz

, et al. Teleoncology: improving patient outcome through coordinated care. Telemed J E Health 2014; 20: 381–384.

Contreras

Metzger

Beane

, et al. Telemedicine: patient-provider clinical engagement during the COVID-19 pandemic and beyond. J Gastrointest Surg 2020; 24: 1692–1697.

10.

Daggubati

Eichberg

Ivan

, et al. Telemedicine for outpatient neurosurgical oncology care: lessons learned for the future during the COVID-19 pandemic. World Neurosurg 2020; 139: e859–e863.

11.

Clauser

Wagner

Aiello Bowles

, et al. Improving modern cancer care through information technology. Am J Prev Med 2011; 40: S198–S207.

12.

Rodler

Apfelbeck

Schulz

, et al.

Telehealth in uro-oncology beyond the pandemic: toll or lifesaver?

Eur Urol Focus 2020; 6: 1097–1103.

13.

Solomons

Lamb

Lucas

, et al. Examination of the patient-focused impact of cancer telegenetics among a rural population: comparison with traditional in-person services. Telemed J E Health 2018; 24: 130–138.

14.

Kleiboer

Gowing

Holm Hansen

, et al.

Monitoring symptoms at home: what methods would cancer patients be comfortable using?

Qual Life Res 2010; 19: 965–968.

15.

Otto

Harst

. Investigating barriers for the implementation of telemedicine initiatives: a systematic review of reviews. In: 25th Americas conference on information systems, AMCIS 2019, Cancún, Mexico, 15–17 August 2019.

16.

Hede

. Teleoncology gaining acceptance with physicians, patients. J Natl Cancer Inst 2010; 102: 1531–1533.

17.

Broens

THF

Huis in’t Veld

RMHA

Vollenbroek-Hutten

MMR

, et al. Determinants of successful telemedicine implementations: a literature study. J Telemed Telecare 2007; 13: 303–309.

18.

Kowatsch

Otto

Harperink

, et al. A design and evaluation framework for digital health interventions. Inform Technol 2019; 61: 253–263.

19.

Liptrott

Bee

Lovell

. Acceptability of telephone support as perceived by patients with cancer: a systematic review. Eur J Cancer Care (Engl); 27: e12643. DOI: 10.1111/ecc.12643

20.

Sin

DYE

Guo

Yong

DWW

, et al. Assessment of willingness to Tele-monitoring interventions in patients with type 2 diabetes and/or hypertension in the public primary healthcare setting. BMC Med Inform Decis Mak 2020; 20: 11.

21.

Chua

Tan

Gandhi

. Information sources and online information seeking behaviours of cancer patients in Singapore. Ecancermedicalscience 2018; 12: 880.

22.

Castleton

Fong

Wang-Gillam

, et al. A survey of Internet utilization among patients with cancer. Support Care Cancer 2011; 19: 1183–1190.

23.

Lleras de Frutos

Casellas‐Grau

Sumalla

, et al. A systematic and comprehensive review of internet use in cancer patients: psychological factors. Psycho Oncol 2020; 29: 6–16.

24.

Parkerson

Michener

, et al. Associations among family support, family stress, and personal functional health status. J Clin Epidemiol 1989; 42: 217–229.

25.

Kamal

Shafiq

Kakria

. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol Soc 2020; 60: 101212. https://ideas.repec.org/a/eee/teinso/v60y2020ics0160791x19300909.html (2020, accessed 19 October 2021).

26.

Davis

. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 1989; 13(3): 319–340. DOI: 10.2307/249008

27.

Edwards

Thomas

Gregory

, et al. Are people with chronic diseases interested in using telehealth? A cross-sectional postal survey. J Med Internet Res 2014; 16: e123.

28.

Holden

Karsh

. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010; 43: 159–172.

29.

Graetz

Huang

Brand

, et al. Bridging the digital divide: mobile access to personal health records among patients with diabetes. Am J Manag Care 2018; 24: 43–48.

30.

Reed

Huang

Graetz

, et al. Patient characteristics associated with choosing a telemedicine visit vs office visit with the same primary care clinicians. JAMA Netw Open 2020; 3: e205873.

31.

Potdar

Thomas

DiMeglio

, et al. Access to internet, smartphone usage, and acceptability of mobile health technology among cancer patients. Support Care Cancer 2020; 28: 5455–5461.

32.

Infocomm Media Development Authority . Infocomm usage-households and individuals, 2020. https://www.imda.gov.sg/infocomm-media-landscape/research-and-statistics/infocomm-usage-households-and-individuals. Accessed 2 Oct, 2020.

33.

Bolle

Romijn

Smets

EMA

, et al. Older cancer patients’ user experiences with web-based health information tools: a think-aloud study. J Med Internet Res 2016; 18: e208.

34.

Cranen

Veld

RHI

Ijzerman

, et al. Change of patients’ perceptions of telemedicine after brief use. Telemed J E Health 2011; 17: 530–535.

35.

Drake

Zhang

Chaiyachati

, et al. The limitations of poor broadband internet access for telemedicine use in rural america: an observational study. Ann Intern Med 2019; 171: 382–384.

36.

Bartlett

Selby

Newsham

, et al. Developing a useful, user-friendly website for cancer patient follow-up: users’ perspectives on ease of access and usefulness. Eur J Cancer Care (Engl) 2012; 21: 747–757.

37.

Bowen

Meischke

Bush

, et al. Predictors of women’s internet access and internet health seeking. Health Care Women Int 2003; 24: 940–951.

38.

Cimperman

Makovec Brenčič

Trkman

. Analyzing older users’ home telehealth services acceptance behavior-applying an Extended UTAUT model. Int J Med Inform 2016; 90: 22–31.

39.

Jaana

Tamim

Sherrard

, et al. Telemonitoring for seniors with chronic heart failure: patient self-care, empowerment, and adoption factors. Health Inform J. Epub ahead of print 4 January 2017. DOI: 10.24251/HICSS.2017.449

40.

Harst

Lantzsch

Scheibe

. Theories predicting end-user acceptance of telemedicine use: systematic review. J Med Int Res 2019; 21: e13117.

41.

Nguyen

Fujioka

Wentlandt

, et al. Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care. BMC Palliat Care 2020; 19: 138.

42.

Darcourt

Aparicio

Dorsey

, et al. Analysis of the implementation of telehealth visits for care of patients with cancer in houston during the COVID-19 Pandemic. JCO Oncol Pract. Epub ahead of print 7 October 2020. DOI: 10.1200/OP.20.00572

43.

Jannati

Nakhaee

Yazdi-Feyzabadi

, et al. A cross-sectional online survey on patients’ satisfaction using store-and-forward voice and text messaging teleconsultation service during the COVID-19 pandemic. Int J Med Inform 2021; 151: 104474.

44.

Sathiyaraj

Lopez

Surapaneni

. Patient satisfaction with telemedicine for prechemotherapy evaluation during the COVID-19 pandemic. Future Oncol 2021; 17: 1593–2160

Supplementary Material

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