The Most Important Telemedicine Patient Satisfaction Dimension: Patient-Centered Care

Abstract

Background:

Telemedicine provider success requires patient satisfaction. The SERVQUAL model was used to identify the most salient telemedicine patient satisfaction dimensions.

Materials and Methods:

The author surveyed 440 telemedicine patients using Likert items to examine satisfaction levels. Four performance dimensions of telemedicine service were identified and examined. Factor analysis was used to validate the telemedicine performance dimensions measured, and regression analysis was used to test the effects of the service performance dimensions on telemedicine patient satisfaction.

Results:

The SERVQUAL model provided reliable measures of satisfaction dimensions. Four dimensions of satisfaction were identified, and patient-centered care was shown to be the most significant dimension. Patient perceptions of health benefits received from the telemedicine service were also found to impact patient satisfaction. The other two dimensions, monetary and non-monetary costs, did not have a significant effect on patient satisfaction.

Discussion:

Patient satisfaction was effectively measured as a multidimensional construct by using the service-marketing SERVQUAL model. The value that patients place on provider “soft skills” (i.e., bedside manner) during provider–patient interactions was demonstrated. Therefore, health care providers could develop and embrace patient-centered communication, such as having an empathetic and caring attitude, showing responsiveness to the emotional needs of the patient, and providing assurance to the patient to improve telemedicine patient satisfaction.

Conclusions:

The SERVQUAL model is useful to create a comprehensive, multidimensional construct for telemedicine patient satisfaction, which can lead to improved telemedicine patient satisfaction. The multidimensional approach highlights satisfaction dimensions where targeted improvements are most appropriate and, thus, can provide more focused practice guidance to providers.

Introduction

Telemedicine is defined as the provision of remote health care by means of a variety of telecommunication tools (e.g., smartphones, wireless devices, etc.), and it is a rapidly growing subsection of the health care industry.¹ In 2019, the global telemedicine market size was valued at ∼$60 billion, and by 2027 the market size is projected to exceed $550 billion.² Telemedicine advantages—decreased provider/patient disease exposure, increased provider accessibility, and conservation of hospital resources—are well documented^3
–5; however, the rapid expansion is a recent occurrence.⁶

The COVID-19 pandemic is a likely explanation for this phenomenon. Since social distancing reduces viral propagation, the pandemic is providing immense pressure to utilize telemedicine. Further, increased reimbursement brought on by changes to payment schemes of various private payers and Medicaid as well as a general loosening of regulations regarding patient privacy are also likely drivers.⁷

Regardless of the cause, being able to understand the multidimensional nature of patient satisfaction in this area will become increasingly important.^8,9 Studies have shown that patient treatment outcomes can be influenced by their attitude toward telemedicine.^10,11 Therefore, telemedicine patient satisfaction may be a precursor to better patient health outcomes because satisfied patients are more likely to adhere to health care advice.¹² Also, satisfaction with a service has been shown to be a precursor for repeat purchase behavior and repeat purchases lead to increased revenue.^{10,11,13
–15} Therefore, providers can potentially improve patient health outcomes and increase business revenue by improving patient satisfaction with a detailed understanding of where service improvements are needed.

Previous studies examined telemedicine patient satisfaction with survey data^16

–19 across a variety of health care areas, including dermatology,²⁰ emergency care,²¹ oncology,²² and primary care.²³ In most studies, telemedicine patient satisfaction was loosely defined as the perception that general service expectations were met, and it was typically measured with a global measure of satisfaction.²⁴ In a few studies, select dimensions of telemedicine patient satisfaction were considered.

However, a difficulty emerged while trying to compare results from these studies as researchers used various indicators of satisfaction.²⁵ Simply, satisfaction means different things to different people. Therefore, a comprehensive, multidimensional approach to examine satisfaction is an essential precursor for understanding this metric.^24,25 It could also establish a way to compare these dimensions head-to-head to determine which dimensions have the largest impact.

Before the pandemic, telemedicine received limited attention from researchers. As a result, a generally accepted, comprehensive, and multidimensional telemedicine patient satisfaction measurement model has yet to be established. Doing so would allow for the collection of data that is consistent and comparable across studies and within longitudinal studies. Fortunately, the service-marketing literature offers a methodology for measuring service satisfaction as a comprehensive multidimensional index with the SERVQUAL model.^26
–28 This model has been adapted to examine satisfaction for many services and can also be adapted to measure telemedicine satisfaction.

Telemedicine patient satisfaction has been shown to be impacted by different service performance dimensions. For example, it has been shown to be impacted by provider communication interactions and patient–provider relationships,^29
–31 the effectiveness of the health care received resulting from greater access to a specialist,³² and telemedicine costs in terms of finances and convenience.^22,33 These service factors seem to fall into four telemedicine service performance dimensions. One factor relates to the health benefits obtained (e.g., health care effectiveness, specialist access).

The second relates to the patient-centered care received (e.g., positive communications and relationships with the provider). The third and fourth relate to monetary and non-monetary (e.g., time, effort, etc.) costs of the service. Although each of these four factors has been shown to impact telemedicine patient satisfaction, the collective impact of these service performance dimensions has not been studied in a comprehensive way.

The SERVQUAL model requires the use of multiple items to measure each of the service dimensions that are appropriate for a given service. Included in the Model are service dimensions such as: service outcome reliability; service provider responsiveness, assurance, and empathy; and tangible as well as intangible service costs.²⁸ Service reliability is defined as whether the service accomplished expected outcomes. For telemedicine, service outcome reliability can be referred to as the patient's perceived health benefits received.

Service responsiveness, assurance, and empathy are indicators of how well the service provider treated the service receiver. Regarding telemedicine service, these are indicators of patient-centered care. The tangible and intangible costs of service consist of the various monetary and non-monetary costs of the service.

The SERVQUAL model creates an index for a multidimensional telemedicine satisfaction construct by using Likert scale items to examine rater perceptions of the service dimensions based on the use of multiple measures for each dimension.²⁶ Applying the SERVQUAL model to telemedicine patient satisfaction, it is hypothesized that patient satisfaction is directly related to their perceptions of health benefits (H1) and patient-centered care (H2) and inversely related to monetary (H3) and non-monetary (H4) costs. Further, using this comprehensive, multidimensional model, this study seeks to determine which service satisfaction dimension has the largest impact on telemedicine satisfaction.

Materials and Methods

The study population comprised U.S. patients who received telemedicine services between January and March of 2021. The independent variables were patient perceptions of the four service performance dimensions: health benefits, patient-centered care, monetary costs, and non-monetary costs. The dependent variable was telemedicine patient satisfaction. Centiment, a third-party survey service company, was utilized to solicit telemedicine patients’ service experience data via convenience sampling. Centiment's patient panel included 578 U.S. patients who had received telemedicine services within the past year. The questionnaire used was author generated, posted on the Centiment survey platform, and sent by Centiment to their panel patients.

The survey was conducted in April of 2021. Before collecting data from U.S. patients, the author obtained human subjects research approval from Arkansas Tech University. The collected data are stored with the author and per Institutional Review Board approval regulations is not publicly available. Patient participation was voluntary, and respondents were required to provide participation consent. Respondent anonymity was accomplished by not requiring patients to share personally identifiable information. To prevent duplication of respondents, Centiment used a unique tagging system that assigned a custom variable to each respondent entering the survey.

Assessment of the independent and dependent variables was computed by using 7-point Likert scales, as is common in marketing service satisfaction research.³⁴ The responses were scaled by anchors, where one indicated that the respondent strongly disagreed with the statement and seven indicated strong agreement with the statement. Four items were used to assess patient perceptions of telemedicine service health benefits. Fourteen items were used to assess patient perceptions of patient-centered care. Four items were used to assess patient perceptions of telemedicine monetary costs, and five items were used to measure the telemedicine non-monetary costs.

Means were computed from the responses to the individual items associated with a given independent variable. The dependent variable was measured with two aspects of satisfaction, appreciation for the service and overall satisfaction. The two dependent variable items were combined to compute a mean satisfaction score for each patient observed. Lastly, patients were asked to provide some demographic characteristics. Details are provided in the Appendix A1.

To test the goodness of fit of the various independent variable assessment items, exploratory factor analysis was utilized. Then, the various effects of the independent variables on the dependent variable were tested with regression analysis. The basic regression model used is shown next:

Regression Model: Y_i = f (X_i , β_i ) + e_i

where

Y_i = Dependent variable outcome;

X_i = Independent variable outcomes;

β_i = Slope, or change in the dependent variable associated with a change in a given independent variable;

e_i = error term or random variance in the dependent variable.

Results

The survey achieved a 76% response rate, resulting in responses from 440 telemedicine patients. The average age of the respondents was 48 years old, with gender evenly split (50:50 M:F). Sixty percent had a bachelor's degree or higher. Income was spread fairly evenly across all income categories, with a median annual household income of $50,000 to $74,999. The majority of respondents were Caucasian (83%) with ∼5% Asian, African American, and Hispanic, respectively. Native Americans and Pacific Islanders comprised the remaining 2% of the respondents. The sample demographics appeared to be generally representative of the overall U.S. telemedicine patient population.³⁵

Exploratory factor analysis with a varimax rotation was used to examine the relationships between the measurement items and the service dimensions (i.e., independent variables). Table 1 provides the factor eigenvalues computed from the independent variable measurements and shows a cumulative variance explained proportion near 1.0 with four factors. Thus, the factor analysis demonstrates that the independent variables provided four distinct dimensions, or latent factors.

Table 1.

Eigenvalues for Independent Variable Factors (Dimensions)

FACTOR	EIGENVALUE	PROPORTION EXPLAINED	CUMULATIVE
1	12.263	0.6670	0.6670
2	4.3895	0.2787	0.9057
3	0.9987	0.0543	0.9600
4	0.7011	0.0381	0.9982
5	0.3077	0.0167	1.0148
6	0.2531	0.0138	1.0287
7	0.2121	0.0115	1.0403

Note: Eigenvalues are a special set of scalars associated with a linear set of equations (i.e., a matrix equation) that indicates how much variance in outcomes there are in the data that are associated with a linear direction of the data. ³⁶

Table 2 shows that the independent variable measurement items had significant and distinctive loadings on the respective factors (dimensions) that they were designed to measure. That is, the four items used to assess patient health benefits perceptions loaded highly on one factor.

Table 2.

Exploratory Factor Analysis Loadings

MEASURES	HEALTH BENEFITS	PATIENT-CENTERED CARE (RESPONSIVENESS, ASSURANCE, EMPATHY)	MONETARY COST	NON-MONETARY COST
Specialist access	0.695	—	—	—
Overall access	0.763	—	—	—
Disease exposure	0.501	—	—	—
Health care	0.566	—	—	—
Provider knowledge	—	0.694	—	—
Provider courtesy	—	0.772	—	—
Provider credibility	—	0.832	—	—
Provider Trustworthiness	—	0.765	—	—
Information	—	0.769	—	—
Responsiveness	—	0.813	—	—
Privacy	—	0.783	—	—
Communications	—	0.809	—	—
Positive relationships	—	0.791	—	—
Caring providers	—	0.831	—	—
Attentive providers	—	0.892	—	—
Patient health	—	0.835	—	—
Patient interest	—	0.829	—	—
Provider listened	—	0.852	—	—
Higher costs	—	—	0.698	—
Insurance complexities	—	—	0.815	—
Insurance coverage	—	—	0.730	—
Expensive	—	—	0.600	—
Scheduling	—	—	—	0.603
Wait time	—	—	—	0.633
Service complexities	—	—	—	0.831
Equipment problems	—	—	—	0.845
Intimidating technology	—	—	—	0.642

Note: The varimax rotation is a statistical technique that maximizes the shared variance to examine how data correlate with each dimension (factor) of the construct. The values displayed in Table 2 are correlation coefficients showing the relationship between the new factors and the original variables and which of the original variables most contributed (or loaded) to each of the newly proposed underlying factors. ³⁷

The 14 items designed to measure patient-centered care perceptions loaded highly on a distinct latent factor. Similarly, the four items used to measure monetary costs and five items used to assess non-monetary cost perceptions had significant loadings on their respective factors (see actual items in the Appendix A1). Taken together, these findings show that the measurement items provided reliable measures of four telemedicine satisfaction factors (dimensions), thus demonstrating face validity of the independent variable measures.

Since the dependent variable was one factor, reliability of the two items used to measure it was conducted by computing a commonly used social science reliability measure, Cronbach's Alpha coefficient.³⁸ The Cronbach's Alpha coefficient observed for the two telemedicine satisfaction measures was significant at 0.841. Thus, the dependent variable measures have considerable reliability with each other.

Correlation analysis was performed to examine the relationships among the independent and dependent variables. As shown in Table 3, all of the independent variables correlated significantly with telemedicine patient satisfaction. Further, patient perceptions of telemedicine service health benefits and patient-centered care showed a significant direct correlation with patient satisfaction levels, whereas the monetary and non-monetary costs of the service showed significant inverse relationships with satisfaction.

Table 3.

Correlations of Independent Variables with Satisfaction

CONSTRUCTS	HEALTH BENEFITS	PATIENT-CENTERED CARE	MONETARY COST	NON-MONETARY COST
Telemedicine satisfaction	0.61 (p < 0.0001)	0.76 (p < 0.0001)	−0.014 (p = 0.0029)	−0.30 (p < 0.0001)

Thus, correlation analysis confirms the directionality of independent variables relationships with the dependent variable, as stated in the hypotheses. Further, the results in Table 3 show that all observed dimensions are significantly correlated/inversely correlated with patient satisfaction and that patient-centered care has the highest correlation.

The previous measurement diagnostics supported the measures used for the respective independent and dependent variable and their expected relationships. Therefore, stepwise regression analysis, using a 0.05 level of significance, was performed to test the hypotheses. The resulting regression model showed significant explanatory power with an R ² of 0.59 and a mean square error of 211.64 (F value = 310.17; p < 0.0001).

However, as shown in Table 4, only two of the independent variables had significant contributions toward explaining variance in telemedicine patient satisfaction. Patient satisfaction was mostly explained by the patient's perception of the patient-centered care received (e.g., care, responsiveness, assurance, and empathy) during the telemedicine service.

Table 4.

Stepwise Regression Analysis

MODEL	INDEPENDENT VARIABLE(S)	PARAMETER ESTIMATE	PARTIAL R ²	F-VALUE
1	Patient-centered care	0.883	0.58	232.41 (p < 0.0001)
2	Health benefits	0.188	0.01	9.82 (p = 0.0018)

The other independent variable with a significant contribution to patient satisfaction was patient perceptions of the telemedicine health care benefits. Thus, H1 and H2 were confirmed.

Correlation analysis confirmed that patient perceptions of the telemedicine monetary and non-monetary costs had significant inverse relationships with telemedicine satisfaction. However, per regression analysis, neither of these variables significantly explained variances in telemedicine satisfaction. Thus, although the results were consistent with H3 and H4, confirming evidence was not observed.

Although not hypothesized, the mean telemedicine satisfaction index observed was favorable. Specifically, the average satisfaction index was 5.60 on a scale of 7.0, where 7 was the highest satisfaction. As such, the overall patient satisfaction findings observed were consistent with those found in past studies in that the patients were generally satisfied with their telemedicine services.^39,40

Discussion

As expected, the model revealed four latent factors of patient satisfaction. Specifically, the patient experience survey identified that the four defined factors represent distinctive dimensions of the service. Further, telemedicine patient satisfaction was shown to be directly related to the perceived quality of health care benefits and patient-centered care received. Satisfaction was also observed to be inversely related to the monetary and non-monetary cost of the service. However, only the first two factors were found to be significant indicators of telemedicine patient satisfaction.

Satisfaction was impacted by patient perceptions of how they were treated (patient-centered care) and perceived/anticipated positive health outcomes achieved (health benefits). Thus, hypotheses one and two were supported. Patient-centered care had the greatest impact on telemedicine patient satisfaction in that the variance in satisfaction explained by patient-centered care (R ² = 0.58) was considerably higher than the health benefit perceptions (R ² = 0.01). This finding underscores the value patients place on provider “soft skills” (i.e., bedside manner) during provider–patient interactions.

In other words, although a provider's scientific knowledge and skills associated with health care service are important, people skills are essential for patients to be satisfied with the service. This finding underscores how competitive advantage can be achieved by health care providers who develop and embrace patient-centered communication, such as having an empathetic and caring attitude, showing responsiveness to the emotional needs of the patient, and providing assurance to the patient.

Although the correlation analysis did provide evidence that was consistent with all four hypotheses, it was interesting that patient perceptions of monetary and non-monetary costs were not significant indicators of patient satisfaction. This result may have occurred because patients may lack sufficient understandings of the cost breakdowns and value relationships of telemedicine. Also, in the United States, the costs are generally felt long after the health care interaction is completed as billing statements typically take considerable time to go through the insurance and provider billing processes. It is also possible that the questionnaire items used may need to be refined and made more specific.

Overall, the results demonstrated that patient satisfaction can be measured effectively by adapting the SERVQUAL Model for telemedicine service. The findings show that telemedicine is a multidimensional construct with identifiable and measurable dimensions. Using a comprehensive multidimensional approach to measure patients’ satisfaction with telemedicine provides valuable insights into various aspects of patient experiences and perceptions, leading to competitive advantages from telemedicine service improvements, which, in turn, can increase a health care provider's market share and revenue.

This study provides a way to examine subtle aspects of patient satisfaction dimensions, as opposed to using only a global assessment of satisfaction. The SERVQUAL model may be the vehicle that leads to a standardized multidimensional patient satisfaction measure. Additional corroborating research is needed, as is refinement of the measurement items. However, the methods presented allow providers to acquire rich and detailed understandings of patient satisfaction. By examining various service dimensions, providers can identify targeted improvement areas of telemedicine service and then develop an appropriate improvement plan to enhance their services.

A generally accepted telemedicine satisfaction measurement instrument does not currently exist, and it is needed to get a more effective understanding of telemedicine satisfaction. Medical services need measures that demonstrate reliability and validity to make meaningful study comparisons. Although the instrument presented here has demonstrated preliminary reliability and validity, further testing is needed to determine whether the outcomes observed in this study are replicable and generally applicable.

Also, research is needed to determine (1) whether the measurement items used here are sufficient for the measurement of telemedicine satisfaction, (2) whether the questionnaire items need wording refinement, and/or (3) whether additional items are needed to get better measures for a given dimension.

To understand patient perceptions more fully, studies are needed to examine whether telemedicine satisfaction is moderated by various demographic factors such as age, income level, ethnicity, culture, etc. Also, research is needed to compare patient satisfaction across various health care service areas (e.g., primary care, emergency care, various specialties, etc.). Further, it would be beneficial to conduct studies that examine the impact of provider initiatives based on telemedicine patient satisfaction data. For example, using the measurement procedures presented in this study, studies could identify where service improvements could be made and then implement improvement plans.

Follow-up studies could then be performed to determine whether the improvement initiatives implemented result in improvements in satisfaction. Finally, studies are needed to examine whether the providers’ telemedicine service improvement plans translate into increased patient utilization and/or higher revenue for telemedicine providers.

Conclusions

Telemedicine patient satisfaction is an important health care construct, because it leads patients to have greater confidence with health care services and results in greater patient adherence. Also, satisfied patients may be more likely to engage in future telemedicine services, resulting in greater revenue for providers. Therefore, effective assessment of telemedicine patient satisfaction dimensions could be instrumental for health care service improvements and patient health outcomes.

Findings demonstrated that patient satisfaction with telemedicine can be effectively examined by using a multidimensional construct that consists of four dimensions. Thus, the current study contributes to better understandings of factors that impact telemedicine patient satisfaction. As such, this research offers the SERVQUAL model as a method to measure patient satisfaction for telemedicine services. Results from using the SERVQUAL model highlighted where targeted improvements are most appropriate and, thus, provide focused practice guidance to providers.

Findings from this study indicate that patient-centered care has the largest impact on patient satisfaction. As such, it is in this area that telemedicine providers could focus their attention when looking to improve this metric. Although the instrument presented in this study demonstrated preliminary reliability and validity, more testing is needed to determine whether the outcomes observed in this study are replicable and generally applicable.

Footnotes

Acknowledgments

The author would like to thank Dr. Matt Brown (Professor, Arkansas Tech University) for data analysis suggestions. Also, the author thanks the editor and anonymous reviewers for their supportive comments and suggestions.

Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

Appendix

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