Information-Motivation-Behavioral Skills Model Supplemented With the Moderated-Mediation Path: A Framework for Interpreting Patients’ Online Medical Services Utilization

Abstract

Purpose

This study aimed to explore the internal determinants affecting patients’ utilization of online medical services (OMS) based on the information-motivation-behavioral skills model from a behavioral perspective.

Design

A cross-sectional study.

Setting

This study was conducted in three medical institutions in Jiangsu Province, China.

Subjects

470 internet users were enrolled from patients who came to the outpatient clinics.

Measures

A self-administered questionnaire with feasible reliability and validity was used to investigate the demographic characteristics and OMS utilization-related information, motivation, behavioral skills, intention, and behavior.

Analysis

According to the constructed framework, structural equation modeling was used to test the relationships between those factors and OMS utilization behaviors.

Results

All direct paths are established except the path between information and intention. Information and motivation positively affected OMS utilization behavior through behavioral skills and intention (P < .001). Motivation and behavioral skills could positively influence OMS utilization behavior through intention (P < .01). Motivation was found to be the largest predictor of OMS utilization behavior. Moreover, gender played a moderating role in the interpretation of the behavior.

Conclusions

Interventions should be conducted regarding information, motivation, and behavioral skills to promote patients’ use of OMS. At the same time, the impact of gender on intervention effectiveness should also be considered.

Keywords

patient information-motivation-behavioral skills model health care online medical services utilization

Purpose

Online Medical Services (OMS) are an innovative healthcare mode that provides services and information through the internet and related technologies.¹ Regarding its cross-space and convenience, OMS has become an important means to solve the structural imbalance of medical resources.² When major infectious public health events occur, it is the key to reducing the risk of nosocomial cross-infection and solving the dilemma in patients’ seeking medical services.³ However, the utilization of OMS in some developing countries is much lower compared to developed countries, and promoting these services is still a challenge.⁴ After the outbreak of COVID-19, the Chinese government has issued a series of OMS-related policies, which has led to a surge in the utilization of OMS.⁵ The number of OMS users in China was 300 million by 2022, accounting for only 28.5% of the total internet users.⁶ The COVID-19 epidemic has also driven the development of telemedicine in the United States, with the number of telemedicine visits growing rapidly and reaching a peak within two months.³ However, the number of telemedicine visits declined later, while the number of in-person visits grew rapidly and far exceeded the number of online users. As the COVID-19 control policies played an effective role and in-person offline medical is no longer restricted, it remains to be seen whether online healthcare will continue to be accepted by patients. Therefore, it is necessary to understand the determinants of patients’ OMS utilization, so as to provide the policy basis for promoting the development of online healthcare.

Many studies regarded OMS as an emerging technology to explore people’s acceptance of it, mainly based on technology acceptance models and the unified theory of acceptance and use of technology.⁷ However, OMS utilization also a kind of healthy behavior that is adopted by patients to improve their health status. Studies based on behavioral theories can more systematically identify the factors and mechanisms influencing health-related behaviors.⁸ Meanwhile, the behavioral theory is also an important factor in guiding the interventions to improve e-health utilization.⁹ Therefore, it is recommended to explore the internal factors that influence patients’ use of OMS from a behavioral perspective. The information-motivation-behavioral skills (IMB) model, proposed by Fisher in 1992, holds that an individual’s behavior change is influenced by information, motivation, and behavioral skills.¹⁰ This model has been shown to have good explanatory power for various behaviors, but the applicability of its interpretation in OMS utilization is unclear. Although some qualitative or quantitative studies have focused on the beliefs, attitudes, or skills of the OMS users, these possible influencing factors need to be further explored for the specific behavioral path mechanism of OMS practice.^11-13

The influencing factors and their interaction mechanism proposed by the IMB model can deeply explain the formation mechanism of healthy behavior. Information in the model refers to knowledge that is highly relevant to health behaviors. Information could directly influence condom use behaviors in new sexual partners,¹⁴ and could also influence the acquired immune deficiency syndrome prevention behaviors in college students through behavioral skills.¹⁵ Motivation refers to all perceptions associated with behavior that not only have a direct impact on behavior but can also indirectly affect behavior through behavioral skills.^16,17 Behavioral skills are practical skills based on self-efficacy, and lacking these skills may hinder engagement in healthy behaviors.¹⁶ Behavioral skills can directly influence behavior and work as a mediating variable of information or motivation to influence behavior. Several studies have shown that behavioral skills are essential for behavioral transformation and maintenance.¹⁸ Meanwhile, behavior intention has been proved to be a significant predictor of behavior and is the most direct influencing factor of behavior.¹⁹ Since the utilization of OMS in China is low, and having behavioral intention is the prerequisite for behavior occurrence, this study also considered the direct effect of behavioral intention on OMS utilization.

Therefore, this study used the IMB model as the theoretical framework to explore the internal influencing factors of patients’ OMS utilization behavior. The path relationship hypotheses are shown in Figure 1. When trying to use the IMB model to explain the OMS utilization behavior, it is still unclear whether the components of this model are in tandem with a moderator. Previous studies have shown that gender has an impact on online health service use behavior,¹³ and has a moderating effect on online health adoption behavior.²⁰ Furthermore, the mediating role of behavioral skills on OMS utilization and the possible moderating effect of gender were also explored.

Figure 1.

The conceptual framework of the research hypothesis model.

Methods

Participants

A cross-sectional study was conducted in the outpatient clinics of three medical institutions in Jiangsu Province, China using the cluster random sampling method. All patients who met the inclusion and exclusion criteria from June to August 2020 were enrolled. Before the survey, uniformly trained investigators explained the contents of this study to the patients and judged whether they met the inclusion criteria by brief communication. Inclusion criteria: Internet users who agreed to participate (Internet users are Chinese citizens who use the Internet for at least one hour per week on average); be conscious; ability to understand and write. Minors (<18 years old) were excluded. Face-to-face investigations was conducted after obtaining informed consent. The investigators accompanied the participants to answer the questions participants encountered when filling in the questionnaire and checked the quality of the filling. Total of 491 questionnaires were obtained, 470 (95.7%) of which were valid, and the sample size meets the requirements of this study.²¹

Measures

To investigate the factors influencing the OMS utilization behavior, a self-administered questionnaire with feasible reliability and validity was developed. The first part was sociodemographic characteristics, including age, gender, educational level, employment status, and so on. Then, based on clarifying the operational definition of each construct of the IMB model, items on information, motivation, behavioral skills, intention, and behavior associated with OMS utilization were created concerning previously published literature.^22-24 After group discussion, the initial scale was determined. A pre-survey of 50 patients was conducted using convenience sampling at one of the institutions. According to their feedback, the content of the questionnaire was refined, the form of language expression was adjusted, and a preliminary reliability and validity test of the questionnaire was performed.

The final questionnaire contains 4 subscales with a total of 13 items (Table Appendix 1 A1). Information refers to the knowledge related to OMS. This construct consisted of three items, including items like “Understand the content of online medical services in China.” Motivation refers to the perceptions associated with OMS. This construct consisted of three items, with items like “Online medical services are more convenient than offline medical services.” Behavioral skills construct contains three items, including items like “I can easily access the resources and methods I need to use online medical services.” Intention was measured by four items, with items like “Online medical services will be an integral part of my medical process.” All the items were measured with a 5-point Likert-type scale, with anchors ranging from 1 (absolutely unknown/disagree) to 5 (absolutely know/agree). The Kaiser–Meyer–Olkin was .920, Bartlett’s test of sphericity was significant (P < .001), and the total variance explained by these dimensions was 80.326%. The Cronbach’s alpha of these dimensions ranged from .825 to .917, and the total Cronbach’s alpha was .938.

Before asking about OMS utilization behavior, we first introduced its concept in the questionnaire to help the participants make judgments. The OMS in this study refers to a new medical service model in which patients receive medical services from the treating doctors on the online medical platform through cell phones, computers, and other internet terminal devices. It mainly includes disease consultation, online revisits, remote medical diagnosis and treatment, chronic disease management, rehabilitation guidance, etc. OMS utilization behavior was a dichotomous variable, measured with “Have you ever used (including with the help of others) online medical services before?”. The answer was “yes” or “no”. If the participants were not sure whether their previous behavior belongs to the use of OMS, they could consult the investigators.

Data Analyses

Continuous variables were described by mean ± standard deviation (SD) or median (interquartile range, IQR); categorical variables were described by frequency and percentage. The score of each construct was represented by the mean value of all items in the construct. Research hypotheses were tested by the structural equation modelling. Factor loadings (>.60 and significant), composite reliability (CR, >.70), and average variance extracted values (AVE, >.50) were used to examine the validity and reliability of scales.²⁵ Model fit was assessed using the χ² and degree of freedom ratio (χ²/df ≤ 3.0), Tucker-Lewis index (TLI, >.90), comparative fit index (CFI, >.90), root mean square error of approximation (RMSEA, <.06) and standardized root mean square residual (SRMR, <.08).^26,27 The indirect effect was examined using the bootstrapping method, with 10000 sampling times set to calculate the significance.²⁸ The model’s explanatory power was represented by R² (.19, small; .33, medium; .67, large).²⁵ All analyses were conducted using SPSS 25.0 and Mplus 8.0. The two-sided test P < .05 was considered statistically significant.

Results

Sample Characteristics

Most of the participants were female (58.9%), below junior college degree (65.3%), registered in rural areas (53.6%), and had basic medical insurance for urban employees (53.2%). The median age was 44.5 (31, 53) years. The majority of the participants got married (87.0%). More than a quarter (29.1%) had chronic diseases. In addition, about 22.6% of participants had used OMS before (Table 1).

Table 1.

Demographic Characteristics of the Participants (n = 470).

Characteristics	Median/Frequency	IQR/Percentage
Age (years)	44.5	(31, 53)
Gender
Male	193	41.1%
Female	277	58.9%
Educational level
Under junior college degree	307	65.3%
Junior college and above	163	34.7%
Marital status
Others^a	61	13.0%
Married	409	87.0%
Employment status
Unemployed	159	33.8%
Employed	311	66.2%
Place of residence
Rural	252	53.6%
Urban	218	46.4%
Medical insurance
Basic medical insurance for urban employees	250	53.2%
Basic medical insurance for urban and rural residents	211	44.9%
Other medical insurance	9	1.90%
Chronic diseases
No	333	70.9%
Yes	137	29.1%
Monthly household income (USD)
≤1000	188	40.0%
1001-2000	140	29.8%
2001-3000	105	22.3%
>30,00	37	7.9%
OMSU
No	364	77.4%
Yes	106	22.6%

Notes: ^aSingle/Divorced/Widowed; OMSU = online medical services utilization.

Scores of Each Construct and the Correlations Between Them

Among all the constructs, the intention had got the highest score (3.20 ± 1.07), while the score of the motivation was the lowest (2.77 ± .84). The scores of the information and behavioral skills were 3.12 ± .93 and 2.98 ± 1.05, respectively. The correlation coefficients between all constructs were statistically significant (Table 2).

Table 2.

Scores of Each Construct and the Correlations Between Them.

Constructs	Mean ± SD	OMSU	IF	MT	BS	IT
OMSU		1
IF	3.12 ± .93	.332^**	1
MT	2.77 ± .84	.320^**	.564^**	1
BS	2.98 ± 1.05	.284^**	.708^**	.596^**	1
IT	3.20 ± 1.07	.353^**	.595^**	.611^**	.644^**	1

Notes: IF = Information; MT = Motivation; BS = Behavioral skills; IT = Intention to use; OMSU = Online medical services utilization; ^*P < .05, ^**P < .01.

Results of the Measurement Model

The factor loadings were all greater than .70 and significant, indicating that this questionnaire had good item reliability. The CR of each construct was greater than .80, indicating that the internal consistency was good. The AVE of all constructs was greater than .60, which means that the convergent validity was good. Besides, the square root of the AVE of each construct (bold numbers on the diagonal) was more than its correlation coefficient with other constructs, which exhibited ideal discriminant validity (Table 3). The fit of the model with only latent variables: χ² = 151.783, df = 59, χ²/df = 2.573, RMSEA = .058, CFI = .980, TLI = .973, SRMR = .031.

Table 3.

Reliability and Validity Results of the Measurement Model.

Constructs	Factor loadings	CR	AVE	Discriminant validity
Constructs	Factor loadings	CR	AVE	IF	MT	BS	IT
IF	.804-.857	.874	.699	.836
MT	.769-.797	.824	.610	.661	.781
BS	.860-.903	.910	.772	.787	.704	.879
IT	.747-.898	.906	.708	.676	.744	.729	.841

Notes: IF = Information; MT = Motivation; BS = Behavioral skills; IT = Intention to use; CR = Composite reliability; AVE = Average variance extracted values. The bolded numbers on the diagonal line are the square root values of AVE.

Structural Model Testing

Direct and mediation effects

The model’s fit based on the research hypothesis was good: χ² = 170.219, df = 71, χ²/df = 2.394, RMSEA = .055, CFI = .932, TLI = .913, SRMR = .030. All direct path coefficients were statistically significant except for the one between information and intention (Table 4). Motivation could positively affect OMS utilization through two indirect paths, one path influenced behavior through intention (β = .252, P < .001), and the other impacted behavior through behavioral skills and intention (β = .060, P < .01). There were also two indirect paths between information and OMS utilization, but only the path that information impacted OMS utilization behavior through behavioral skills and intention was established (β = .105, P < .01). In addition, behavioral skills also had a positive effect on OMS utilization behavior through intention (β = .198, P < .001). The R² for OMS utilization behavior was .348, indicating that the model had moderate explanatory power.

Table 4.

Results of Path Coefficient and Mediation Effect of the Structural Model.

Paths	Estimate	P-Value	Bootstraping95%CI		Bcbootstraping95%CI
Paths	Estimate	P-Value	Lower	Upper	Lower	Upper
Direct effect
IF - BS	.571	<.001	.463	.678	.461	.677
MT - BS	.327	<.001	.204	.440	.202	.438
BS - IT	.311	<.001	.148	.462	.153	.466
IF - IT	.148	.068	−.012	.303	−.015	.299
MT - IT	.427	<.001	.310	.554	.306	.550
IT - OMSU	.590	<.001	.481	.690	.479	.689
Indirect effect
IF - IT - OMSU	.088	.078	−.007	.189	−.008	.187
IF - BS - IT - OMSU	.105	<.01	.048	.171	.052	.177
MT - IT - OMSU	.252	<.001	.171	.346	.171	.345
MT - BS - IT - OMSU	.060	<.01	.027	.096	.031	.103
BS – IT - OMSU	.198	<.001	.089	.280	.093	.286
Total effect
Total effects from BS to OMSU	.198	<.001	.089	.280	.093	.286
Total effects from IF to OMSU	.192	<.001	.112	.282	.114	.283
Total effects from MT to OMSU	.312	<.001	.224	.404	.225	.407

Notes: IF = Information; MT = Motivation; BS = Behavioral skills; IT = Intention to use; OMSU = Online medical services utilization.

Moderating effect

As shown in Table 5, gender had moderating effects on the model paths. Model fit indices all pointed to good model fit (χ² = 168.984, df = 160, χ²/df = 1.056, RMSEA = .015, CFI = .994, TLI = .993, SRMR = .037). The direct path coefficient between information and intention remained not significant in both groups. Among the other direct paths of both groups, only the path between behavioral skills and intention didn’t establish in the female group model. Except for the indirect effect of information on OMS utilization through intention was not statistically significant within either group, the coefficients of the other indirect paths in the male group were statistically significant. However, in the female group, only the indirect effect of motivation on OMS utilization through intention existed (β = .419, P < .001), which was significantly higher than that in the male group (difference β = .242, P < .05).

Table 5.

Model Results With Gender as a Moderator Variable.

Paths	Female		Male		Difference (Female-Male)
Paths	Estimate	95%CI	Estimate	95%CI	Estimate	95%CI
IF - BS	.576^***	(.393, .764)	.698^***	(.518, .887)	−.122	(−.370, .131)
MT - BS	.360^***	(.161, .545)	.369^***	(.183, .543)	−.009	(−.265, .255)
IF - IT	.170	(−.088, .422)	.111	(−.245, .438)	.059	(−.351, .489)
MT - IT	.728^***	(.494, 1.008)	.330^**	(.116, .550)	.398^*	(.087, .756)
BS - IT	.194	(−.052, .430)	.583^***	(.277, .883)	−.390^*	(−.768, −.007)
IT - OMSU	.576^***	(.435, .714)	.539^***	(.356, .713)	.038	(−.191, .253)
IF - IT - OMSU	.098	(−.049, .257)	.060	(−.124, .258)	.038	(−.206, .279)
IF - BS - IT - OMSU	.064	(−.013, .165)	.219^***	(.105, .401)	−.155^*	(−.341, −.005)
MT - IT - OMSU	.419^***	(.266, .615)	.178^*	(.061, .333)	.242^*	(.026, .477)
MT - BS - IT - OMSU	.040	(−.003, .100)	.116^***	(.056, .213)	−.076	(−.177, .005)
BS - IT - OMSU	.112	(−.030, .252)	.314^***	(.155, .501)	−.203	(−.429, .012)

Notes: ^*P < .05, ^**P < .01, ^***P < .001. IF = Information; MT = Motivation; BS = Behavioral skills; IT = Intention to use; OMSU = Online medical services utilization.

Discussion

Online medical services have brought great convenience to meet people’s health needs, but their utilization rate is low, with only 26% in this study. In order to improve the utilization rate of OMS by Internet users, it is necessary to investigate the OMS utilization behavior and its influencing factors. Based on the IMB model, this was the first study to analyze the mediating role of behavioral skills in the formation process of OMS utilization behavior of patients and the moderating role of gender on the paths.

This study revealed that although information couldn’t influence OMS utilization through intention, it appeared to positively impact OMS utilization through behavioral skills. Previous studies have shown that knowledge could positively influence people’s attitudes and frequency of use of mobile health software.¹² Combined with the low score of information in this study, it might be people’ less knowledge about OMS that couldn’t cause changes in behavioral intention. Because OMS is mainly provided to people through related software, providing people with enough information about the software to raise their expectations of its effectiveness might help to increase their usage.²⁹ Some studies have also shown that exposure to advertising can positively impact people’s mHealth acceptance behavior.³⁰ Therefore, service providers could increase the publicity of OMS to improve people’s understanding of it. Moreover, the mediation relationship between information and OMS utilization through behavioral skills also existed in other behaviors that required special skills support.¹⁸ This result suggested that people could improve their OMS utilization skills after having relevant information and then promote the change of OMS utilization behavior. This also highlights the importance of providing adequate information in promoting people’s OMS utilization.

Meanwhile, motivation was the strongest predictor of OMS utilization behavior. Our findings showed that motivation influenced OMS utilization primarily through intention, suggesting that people can promote behavior change if they have sufficient motivation. As a study reported, the successful use of online healthcare tools for older adults greatly depends on the motivation and support they received.³¹ Policies designed to improve attitudes toward e-health use has been proved effective.² In order to increase people’s attention to OMS, the government should issue related policies to support the development of OMS and advocate for people to realize OMS. Doctors, another user group of OMS, also need to take action. Doctors’ advice and social influence could significantly influence patients’ intention to use OMS.³² Some scholars suggested that primary care physicians should be included in online medical consultations to encourage patients to use online consultations.³³ Then, motivation for using OMS in the patients’ perceptions should also be concerned. It has been found that having autonomous motivation was associated with positive outcomes in online healthcare, as the motivation evolves from one’s values.³⁴ At present, people pay attention to not only the quality of OMS but also personal privacy and security.³⁵ Reducing people’s perception of the risks of OMS utilization can also improve people’s acceptance of OMS.³⁶

This study also found that behavioral skills influence behavior through intention and mediate the path from motivation and information to behavior. OMS relies on Internet-enabled devices and requires support from relevant software, which might be challenging for some people. Elderly patients were reported to have the intention to use OMS, but the lack of online operation skills restricted their ability.³⁷ Despite the findings that age impacts people’s use of mobile health services, it is actually their network technology skills that have an impact.³⁸ Multiple publications have mentioned user-friendliness as a facilitating factor to the use of tools.³⁹ Therefore, it is suggested that service platform developers pay attention to people’s experience and optimize the usage process of OMS media. At the same time, it is recommended to provide online medical technology training for physicians first. Physicians appreciate the convenience of online medical technology and will be happy to teach their patients to use it.⁴⁰

Moreover, the findings replicated that gender played a moderating role in model path relationships. The model path framework for the male group was the same as the total population path framework. However, only the path between motivation and behavior through intention was established in the female group, indicating that motivation was the most critical factor in promoting OMS utilization in the female. Information in the male group appeared to positively influence OMS utilization through behavioral skills and intention, whereas this path was not established in the female group. This might be due to women’s low acceptance of mobile phone software and less knowledge about health care on mobile phones.⁴¹ Although the paths of motivation through intention to utilization behavior were established in both groups, the path coefficient was significantly higher in the female group. Women are more interested in health-related information than men and actively explore it through their surroundings.⁴² So even without appropriate technology or information, women’s interest in health-related information can significantly impact their OMS utilization. Therefore, attention should be paid to gender differences when performing interventions.

However, this study also has some limitations. Firstly, this is a local cross-sectional study, and the survey scope can be further expanded to explore the generalizability of the study results. Second, self-reported questionnaires may lead to some response bias. Third, because the participants in this study were all from hospital outpatient clinics, they may be more inclined to offline visits, potentially resulting in lower than actual levels of OMS use for the survey. Fourth, OMS contains a wide range of functions, such as disease consultations, online revisits, online drug purchases, etc. Further research can be conducted on whether there are differences in the influencing factors and path relationships among different service functions.

Conclusion

The utilization rate of OMS in this study was low. The IMB model is applicable in interpreting patients’ OMS utilization behavior. All direct paths in the model are established except the path of information to behavior intention. The motivation was found to be the strongest predictor of OMS utilization behavior. Behavioral skills mediated the paths of information and motivation to facilitate the formation of OMS utilization behavior, and gender had moderating effects on the paths. This study provides suitable theoretical bases for explaining OMS utilization behavior, and further interventions can be carried out based on it.

So What?

What is already known on this topic?

Although online medical services (OMS) provide a great convenience in meeting people’s health needs, the utilization rate of OMS is low.

What does this article add?

This study explored the potential influencing factors of OMS utilization behavior and their path relationship based on the information-motivation-behavioral skills model from a behavioral perspective. The results showed that motivation and behavioral skills could positively impact OMS utilization behavior through intention to use. Information and motivation could also indirectly affect OMS utilization behavior through behavioral skills. Gender appeared to play a moderating role in the interpretation of OMS utilization behaviors.

What are the implications for health promotion practice or research?

Understanding the potential influencing factors of OMS utilization may guide the development of effective strategies that could be used to increase OMS uptake.

Footnotes

Acknowledgments

The authors are very grateful to the staff of each institution for their help and support in this investigation.

Author Contributions

Hua You and Qi-feng Wu are the principal investigator of this study, responsible for study design, supervising the whole research process and final submission. Zhi-guang Li, Jin-jin Ge and Chi Zhang contributed in data collection, data analysis, and preparing the manuscript. Xue-qing Peng participated in data collection and assisted in preparing the manuscript.

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the National Natural Science Foundation of China (NSFC) under Grant number 72074122; Philosophy and Social Science Research of Jiangsu Higher Education Institutions of China under Grant number 2020SJA0306 and Cultivation Project of Decision-making Consultation, Institute of Healthy Jiangsu Development, Nanjing Medical University

Ethical Approval

All subjects obtained oral informed consent. This study has been approved by the Ethics Review Committee of Nanjing Medical University ((2020)592).

ORCID iD

Hua You

Appendix

Appendix 1 Table A1.

Items of Each Construct in the IMB Model.

Constructs	Items
Information (IF)	IF1 Understand the content of online medical services in China
	IF2 What are the major online medical services platforms in China, such as online medical services of hospitals, Chunyu Doctor, Haodf, DXY, etc
	IF3 When using online medical services, patients can communicate with doctors by voice, text, pictures or video
Motivation (MT)	MT1 Online medical services are more convenient than offline medical services
	MT2 People who are important to me (such as family members and friends) encourage me to use online medical services
	MT3 Hospitals or medical institutions in my community encourage the use of online medical services
Behavioral skills (BS)	BS1 I have the ability to tell whether an online medical platform is reliable or not
	BS2 I can easily access the resources and methods I need to use online medical services
	BS3 If I encounter difficulties in using online medical services, I have the confidence and ability to solve them
Intention to use (IT)	IT1 When necessary, I will recommend online medical services to others
	IT2 I will use online medical services as well as others around me
	IT3 Online medical services will be an integral part of my medical process
	IT4 I think there is a high probability that I will use online medical services in the future

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