Teleconsultations Quality During the COVID-19 Pandemic in Poland in the Opinions of Generation Z Adults

Introduction

The World Health Organization (WHO) declared the outbreak of the COVID-19 pandemic on March 11, 2020. The SARS-CoV-2 virus has become a public health threat at the international level.¹ Habits, routines, and ways of taking care of one’s health and the relationships with a physician have changed. Teleconsultation became the main channel of providing medical advice during the pandemic in many countries, including Poland.² To supply remote medical services by utilizing telecommunication technologies for diagnosis and assessment, physicians have used videoconferences or virtual communication software, telephone visits, or “electronic written visits.”^3,4

Polish doctors and patients had to get used to the new way of communication, including remote communication and remote diagnosis, because telemedicine was not common in Poland before the pandemic. However, thanks to telemedicine solutions, many specialists have become more accessible.⁵

The main groups of factors that contribute to the assessment of the quality of teleconsultations include the communication skills of their providers, the technological solutions used, and their efficiency. The first of these is related to the role of behavioral elements, communication skills including proper interviewing and correct explanation of the health problem to the patient, and feedback. This helps to establish a relationship and a better mutual understanding, and builds trust and a sense of security.⁶ The second group of factors includes technological solutions, also known as system solutions, such as access to the system, lack of technical problems, electronic medical documentation,⁷ type of system,⁸ and the responsiveness of a website or application.

In Poland, teleconsultations are legally regulated.⁹ They are defined as advice that is offered remotely via telephone or the internet. It is emphasized in the literature that the resulting doctor–patient relationship may have a significant positive or negative impact on the treatment outcomes.¹⁰ It can be assumed that the patients who can very accurately assess the quality of online activities are young people from the so-called Generation Z.

According to the literature, generation is a dual concept, referring to both family and kinship structures on one hand and age on the other hand. It is a biological–cultural phenomenon. Individuals belonging to the same generation live in a specific place and time and in a sociocultural and historical context. They have common experiences in which they participate or which they witness.

This necessitates creative solutions and adaptive strategies, as well as the use of one’s own potential.¹¹

The description of Generation Z may be as follows: the members of Generation Z, known as the kids of the digital revolution, were born in the years 1995–2012 and do not know life without personal computers, mobile devices, or the social media. They communicate quickly, especially through mobile applications.^12,13 Duffet claims that this generation is independent, individualistic, and ambitious, very active, goal oriented, and closely observing.¹⁴ According to Seemiller and Grace,¹⁵ members of this generation have very different motivations, learning styles, sets of skills, and social concerns than the older generation. There are also theories that the definition of a generation is flexible and belonging to a specific generation determines the represented behaviors and habits.¹⁶

In our work, we follow the scholarly majority that claims that small, yet meaningful, differences across generations exist.¹⁷ Generation Z can now drive social change and innovation in the world of medicine and the emerging types of online relationships.

The aim of this study is to collect and evaluate the opinions of representatives of the Generation Z—university students—on the satisfaction with telemedicine provided by primary care physicians and medical specialists in various fields, as well as with the quality of physician–patient communication during televisits.

Methods

Our research was based on an online survey using Microsoft Forms during the period from April to June 2021 in selected universities in Poland. The link to the questionnaire was sent to the students of universities (mainly the Poznań University of Economics and Business, the Poznań University of Medical Sciences, Adam Mickiewicz University, the School of Banking and Management) and distributed also among student organizations associated with these universities. The study was approved by the Bioethics Committee of the Poznań University of Medical Sciences (No. KB-143/21).

The inclusion criterion of a participant in the survey was age older than 18 (since only people aged 18+ may legally single handedly consult a physician in Poland), as well as the experience of at least one online consultation with a physician during the COVID-19 pandemic. The sample ultimately consisted of 424 students. The research tools were as follows: a validated Doctor–Patient Communication (DPC) questionnaire¹⁸ as proposed by Sustersic et al. (13 questions with answers measured on a 4-point scale), the Telehealth Satisfaction Scale (TeSS)¹⁹ (10 questions with a 4-point scale of answers), and a self-authored questionnaire containing 24 closed-ended and 2 open-ended questions on demographic data and the evaluation of teleconsultations and their provision.

The DPC questionnaire consisted of questions on speaking skills, on providing the necessary information, explaining the advantages and disadvantages of the treatment strategy, the doctor’s way of speaking (was it calm and friendly?), the trust in the doctor, and so on. The DPC scores for an individual respondent could be in the range 13–52. On the DPC assessment scale, three categories were distinguished: high (>50 points), intermediate (36–50 points), and low (≤35 points).

The TeSS questionnaire consisted of questions about the atmosphere of the visit, as well as the technical conditions for teleconsultation, image and sound quality, the ease of reaching the website and logging in, and technical procedures. In our approach and analysis, we used the modified Telehealth Satisfaction Scale (MTeSS), in which the question about image quality was removed, since most respondents (85%) did not use it. This means that the MTeSS that we used consisted of nine questions. The MTeSS values for an individual respondent could thus be in the range of 9–36. For the MTeSS assessment grade, three categories were also distinguished: high (>32 points), intermediate (21–32 points), and low (≤20 points).

For the purposes of achieving the formulated goals, selected statistical methods were used, including the most important descriptive statistics, statistical tests (mainly the chi-square independence test), and correspondence analysis. We used correspondence analysis as a useful data science visualization technique for finding out and displaying the relationship between categories. Thanks to this multivariate technique, it was possible to build a graph with visualization of the outcome of two or more data points that related to variables under study. Analyses were performed by using R statistical software.

Results

GROUP CHARACTERISTICS

The survey included 424 participants. The distributions of the most important features describing the surveyed students are presented in Table 1. Students aged 19–27 were surveyed, of whom 44.1% were 19–21 years old, 47.6% were in the 22–24 age range, and 8.3% were older than 24 years. Women predominated in the research sample and accounted for more than 73% of the respondents. With regard to the type of university, 40% of the participants were medical students, with the remaining 60% of the students from other universities.

The vast majority of our subjects were students in the first year (1–3)—67.1%, and nearly 33% from senior years (4–6). Students living in larger towns or cities (with a population more than 50,000) accounted for 44.1%, those in medium-sized towns accounted for 19.8%, and those in small towns or villages accounted for 36.1%. The students who had had two to three telemedicine consultations during the pandemic before they took part in the study turned out to be the most numerous, accounting for 42% of all respondents.

The second-most frequent category was only one medical teleconsultation, reported by 29% of students, while 14.4% had 4–5 visits, and 14.6% had 6 or more. The vast majority of students (68.6%) obtained telemedicine advice under the Polish National Health Fund (NHF), that is, free of charge. Paid counseling was used by nearly 16% of respondents. Similarly, 16% of all the people surveyed used both forms of counseling. Telemedicine consultation mainly concerned primary health care (75.9%), less often there were other reasons for contact with health care professionals, such as advice from a specialist doctor (38.9%), advice on infection with the virus that causes COVID-19 (22.2%), or advice from another specialist, for example, a dietitian or a psychologist (12%).

GENERAL RESULTS

About 60% of the respondents rated the DPC as intermediate (Table 2), only 7.5% of students assessed it as high (a DPC score more than 50). On the contrary, for nearly 33% of respondents the DPC score was low. With regard to satisfaction with telemedicine measured by the MTeSS, we noticed that the ratings of most of the students were intermediate—61.3%. There were a small percentage of respondents who rated their satisfaction with telemedicine high—13.4%. Similarly, the share of those dissatisfied with telemedicine was relatively low—25.2%.

Distributions of DPC and MTeSS in terms of box-plots and histograms are presented in Figure 1. More precisely, the top part of Figure 1 is a histogram that shows the distribution of DPC (the left panel) and MTeSS (the right panel). The x-axis shows the range of variability of the analyzed quantities. In the case of DPC, it is in the range from 13 to 52. In contrast, in relation to the MTeSS, it is in the range 9–36. In both cases, we observe a slight negative asymmetry because skewness coefficients equal −0.45 and −0.19, respectively. For DPC, the mode was equal to 39.

In the case of MTeSS, the mode was 27. It is expressed by the highest bar on a histogram.

Below the histograms there is a box-plot (the left panel for DPC and the right panel for MTeSS), which is a standardized way of displaying a 5-number summary: the minimum (the lowest point on the box-plot), the maximum (the highest point on the box-plot), the sample median (vertical line in the middle of the box), and the first and third quartiles (vertical lines indicating the beginning and the end of the box).

Each point in Figure 1 represents one respondent and the corresponding DPC (the left panel) and MTeSS (the right panel) values. This means that we observe 424 points corresponding to the values of DPC and MTeSS for individual respondents.

In addition, the red dot shows the mean value. For DPC, all the values of the quartiles were the following: Q₁ = 33, Q₂ = 39, and Q₃ = 45. In the case of MTeSS: Q₁ = 20, Q₂ = 26, and Q₃ = 29. Moreover, the remaining descriptive statistics for DPC were as follows: mean—38.4 points, standard deviation—8.5, and minimum value 14 and maximum value 52; for MTeSS: mean—24.7 points, standard deviation—5.9, and minimum value 9 and maximum value 36.

In both cases, we observe a small differentiation of respondents in terms of the values of DPC and MTeSS. It is expressed by the values of coefficient of variation, a standardized measure of dispersion, and for DPC and MTeSS, equal to 22.1% and 23.9%, respectively.

RELATIONSHIPS BETWEEN DPC AND MTESS SCORES AND CHARACTERISTICS OF THE GROUP

Table 3 presents p-values in the chi-square test of independence between DPC, MTeSS, and selected variables describing students: age, domicile, frequency of telemedicine consultations, way of providing medical services, and the reason for telemedicine consultation. For the last variable, the authors created a variable with two levels: COVID-19 (advice on infection with the virus that causes COVID-19) and non-COVID-19 (other advice). For the remaining variables taken into account, their levels are presented in Table 1. The aim was to check whether there are statistically significant relationships between DPC (respectively, MTeSS) and the aforementioned characteristics of the group of students. Significant relationships occur when p ≤ 0.05 (x denotes insignificance).

Table 3. p-Values in Chi-Square Test of Independence for Doctor–Patient Communication, Modified Telehealth Satisfaction, and Selected Characteristic of the Group of Students

CHARACTERISTIC OF THE GROUP	DOCTOR–PATIENT COMMUNICATION	MODIFIED TELEHEALTH SATISFACTION SCALE
Age	x	x
Domicile	0.025	0.000
Frequency of telemedicine consultations	x	x
Way of providing medical services	0.014	0.026
Reason for telemedicine consultation	x	x

Statistically significant results between DPC (respectively, MTeSS) at the level of 0.05 were obtained for the following characteristics: domicile and the way of providing medical services. This means that the assessment of communication with the doctor by the patient and satisfaction with telemedicine varies depending on the student’s place of residence and the nature of the visit, that is, the assessment of communication depends on whether the visits were provided by the NHF (free of charge for the patient) or were privately paid. On the contrary, age, the frequency of telemedicine consultations, and the relationship of the visit with COVID-19 infection had no effect on the assessment of DPC or patient satisfaction with the visit.

The relationships detected using the chi-square test of independence were presented in the form of perceptual maps resulting from the application of correspondence analysis. Perceptual maps we used to visualize the relationships between DPC, MTeSS, and the variables domicile and the way of providing medical services (see Fig. 2). Appropriate levels of variables of features taken into account are presented in Tables 1 (domicile and the way of providing medical services) and 2 (DPC and MTeSS). Significant relationships were found for these combinations of variables using the chi-square test of independence.

To interpret correspondence analysis, this step was very important and was meant to evaluate whether there is significant dependency between analyzed variables. To better understand results of correspondence analysis, ellipses around corresponding categories of categorical variables taken into account were added. They group the corresponding categories of the variables under consideration and show their interrelationships.

When precisely analyzing the perceptual maps presented in Figure 2, in the context of DPC, it can be noticed that it is better assessed by students living in large cities (more than 500,000 inhabitants and 101–500,000 inhabitants). On the contrary, students living in smaller towns (up to 100,000 inhabitants) assessed DPC as rather low or intermediate. These associations are shown in the upper left panel of Figure 2 and expressed by appropriate ellipses. Interesting conclusions can be drawn from the analysis of the relationship between the assessment of DPC and the manner in which medical services are provided (the upper right panel of Fig. 2). We observed that those students who decided to pay for the telemedicine consultation assessed DPC better than the other two groups.

Similar analyses we carried out for the degree of satisfaction with telemedicine services measured with the use of MTeSS. We noticed that students living in large cities (more than 500,000 inhabitants) rank their satisfaction with telemedicine services as high. On the contrary, those who live in smaller towns (up to 10,000 and 10,000–50,000 inhabitants) are more skeptical of telemedicine services and their satisfaction is low (bottom left panel of Fig. 2). It is also worth noting that satisfaction with telemedicine services is higher in the case of students who have decided to pay only for such services. Those who used telemedicine services under the Polish NHF (free of charge services) or used both free and paid telemedicine services express lower satisfaction (bottom right panel of Fig. 2).

The associations described above clearly show the ellipses around the corresponding categories of two variables: MTeSS and the way of providing medical services.

Discussion

Generation Z is highly sensitive to the quality of services delivered, especially those delivered electronically. As Chayomchai reports, trust in technology is a key factor in the acceptance of online activities during the pandemic.²⁰ Venter claims that young people from Generation Z place a high value on the quality of communication, and they like quick picture information.²¹

The results of our study show that both satisfaction with telemedicine (measured by the MTeSS questionnaire) and communication with a doctor (measured by the DPC questionnaire) was, on average, rated as intermediate. The intermediate rather than high scores may be due to the abovementioned sensitivity to the quality of technology (e.g., the possibility of a videoconference) and proper communication. In our study, telephone-only consultations accounted for 85% of all analyzed cases. In a study by Kludacz-Alessandri et al. from Poland, the main method of contacting the general practitioner was by phone, with only 5% of respondents using video consultation.²²

These results may be due to the fact that before the COVID-19 pandemic, telemedicine consultations were rare, since Poland has still not developed an IT infrastructure to conduct professional video consultations. Results slightly different from ours were obtained by Ramaswamy et al.²³ Their study indicates high satisfaction with telehealth. However, the population selected was 18 years of age to older than 80, urban, and the study was conducted among respondents who reported having had video visits. Fieux et al. reported that patients’ overall satisfaction with telemedicine consultation was excellent, with 87% satisfied or very satisfied.²⁴ Similarly, high satisfaction with telemedicine was reported by respondents surveyed by Barnett et al., whose subjects received telemedicine consultations only through commercial insurance.²⁵

A high level of satisfaction with telemedicine was also noted by Vosburg and Robinson²⁶ and the authors of a Polish study conducted by the Polish NHF and the Ministry of Health,²⁷ who examined the satisfaction with telemedicine services in primary care. Positive opinions were noted also by Kludacz-Alessandri et al. This study found that patients were rather satisfied with the remote access to primary health care.²⁸ The vast majority of patients agreed or strongly agreed with all the positive aspects of the care accessibility dimension. The mean age in their study sample was 55 years. Holtz reports that the overall satisfaction with telemedicine during the pandemic was satisfactory.²⁹

In specialist medicine, such as treatment of phenylketonuria, most (63.4%) participants were satisfied with remote contact with their clinic.^30,31 The study by Isautier et al., similar to the present one since it was conducted on respondents who made their visits using the telephone, found that the majority of respondents stated that their telehealth experience was “just as good as” or “better than” their traditional in-person medical visit experience.³² Also similar was the study by Park et al., which focused on telephone consultations too, and in which satisfaction measured by the telehealth usability questionnaire (TUQ) was found to reach 86%.³³ All studies cited are based on samples that range in age from 18 to 90+. None of them, however, directly applies to Generation Z only.

A key factor that is cited in the literature as having a negative impact on indirect consultation scores is the quality of physician–patient communication. According to Gopichandran and Sakthivel, more than 60% of patients had difficulties in communicating with the doctors.³⁴ The effectiveness of this communication can make or mar the doctor–patient relationship.³⁵ Isautier et al. claim on the basis of their research that an indirect way of communication may be not as effective as face-to-face visits,³² while Park et al. believe that doctors are concerned about the level of communication.³³

Our research has shown that there are factors that can influence both the quality of telemedicine evaluation score (MTeSS) and the individual components of the communication process (DPC). These are place of residence and use of private or social security services.

In the case of place of residence, those respondents who live in larger cities rated telemedicine and physician communication better. This may also be due to what Wang reports: in general, medical care is better rated in urban areas.³⁶ Scott’s observations in this context are also interesting, as they concern pharmaceutical care, but have similar implications.³⁷ Disparities in the use of telehealth at the onset as reported by Pierce may also have an impact.³⁸ A visible and negative phenomenon in Poland, identified as early as 2018, was a rather slowly progressing process of informatization of medical entities, for example, large differences in the functioning of regional e-health platforms.³⁹

The results of our study indicate that age, reason for visit, and their frequency do not influence the evaluation of telemedicine and communication. In the case of the first factor, a different observation is made by Kludacz-Alessandri et al., who noted that age plays a role in the case of opinions about telemedicine services.²⁸ However, it should be emphasized that this study was based on a sample containing people aged 18–70+, whereas our study concerned only young people. The abovementioned authors also found a correlation between age and the evaluation of communication during teleconsultations.

Our study shows that there is a correlation between satisfaction with teleconsultations and the quality of physician–patient communication, as well as a correlation between patient satisfaction and payment for services. Telemedicine services provided for a fee are rated higher than those on the National Health insurance. Barnett et al. found high satisfaction scores for telemedicine among those who paid for services that were commercially insured.²⁵ In contrast, Fieux et al. found no difference in telemedicine satisfaction scores when it came to paid or unpaid services.²⁴

LIMITATIONS OF OUR STUDY

The study was conducted only on a group of students and the results cannot be generalized to the whole population of Generation Z.

Conclusions

The experience of Generation Z with online medical visits introduced in Poland indicates the need to improve the quality of DPC and the functioning of telemedicine platforms with the possibility of using images in teleconsultations.

Moreover, it is also necessary to level out the disproportions in the quality of telemedicine services and DPC between urban and rural areas and between commercial and noncommercial services.

Such action can improve the service delivery process and, consequently, be beneficial for the competitiveness of telemedicine services, which will be based on substantive, medical rather than organizational aspects. Increasing the offer of medical services with quality telemedicine activities (not replacing the in-person visits but complementing them) can be in the long run a relief to the system and a benefit to the economy.

Disclosure Statement

No competing financial interests exist.

Funding Information

The authors received no financial support for the research or publication of this article.

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