Introduction 

Technological and organizational changes taking place in the medical area are reflected in the new terms that appeared - smart medicine and fourth-generation medicine.

Smart medicine combines the following set of characteristics formulated by Deloitte ^[1]:

• Appropriate treatment shall be provided in due time, place, and to a due patient;
• Physicians use technology to diagnose and treat diseases more accurately;
• All healthcare ecosystem members communicate effectively and efficiently, and use information;
• Patient data are collected in a single, easily accessible place;
• Staff do the job corresponding to their competencies (e.g. nurses take care of the patient and do not deal with administrative issues);
• Patients are informed and actively involved in treatment plans;
• Lower costs lead to new models that expand medical coverage to new areas and populations, where many medical services were not provided previously;
• Efficiency increases, meaningless costs decrease.

A new trend is the use of patient genetic information, which can radically change the healthcare model shortly ^[2-4].

Continuous monitoring of individual features of a human body can become the key element of a new model, which determines the features of disease development and course. Fourth-generation medicine is not limited to the use of genetic data only, but it is the human genome decoding that is becoming a turning point in predictive and personalized medicine development, providing physicians access to the most extensive array of personal information. In clinical practice, expected regular use of genetic data is often seen as a transition to a new healthcare delivery model - genetic medicine or genetic healthcare ^[5]. In the best-case scenario, developed countries can move to a genetic healthcare model in the next five to ten years. ^[6-8]

It is also predicted that there in the future medicine will show a transition from disease treatment to their prevention by maintaining a healthy lifestyle and carrying out preventive examinations.

Currently, total public health funding in Russia provides severely limited access for patients to treatment based on advanced medical technologies. In the framework of this study, by using the discrete choice methodology, an attempt to assess the willingness of the Russian population to incur additional costs that ensure increased access to advanced medical technologies was made. As part of the work, possibilities to use four additional sources to fund additional programs for the health care and expensive care sectors were considered:

1. Additional voluntary contributions from the working population, using behavioral incentives to expand coverage.
2. A reverse mortgage for senior citizens - homeowners.
3. Government budget co-funding to motivate engagement, attract low-income citizens, and limit excessive risks to financial service providers.
4. Establishment of a trust fund for public assets, including public companies, land, and real estate, trading, and commercial service income of which will be used to co-fund medical care.

At the same time, an additional health insurance system should get the best out of incentives to enhance engagement of the people insured, based on behavioral effects, which provides:

• automatic inclusion in the system;
• automatic contribution escalation (low contribution rate upon accession and its automatic gradual increase to the maximum level as the nominal wage of the insured person increases);
• income tax and insurance premium transfer to the VMI system (voluntary medical insurance) benefiting insured persons, the amount is calculated based on contributions of the VMIinsured person, which gives an increase in the number of contributions by almost one and a half times compared to taxable wage contributions;
• full or partial minimum payment from the budget benefiting insured persons over the periods of insured persons' reduced income below the minimum wage.

The marginal VMI contribution rate can reach 4%, which, regarding the transferred income tax and insurance premiums to the VMI benefiting insured persons, is equivalent to contributions of about 6%. If the payer requests to extend insurance coverage contributions to his/her parents, the contribution rate can be increased up to 6%, which is equivalent to roughly 9%, taking into account supplemental payments within the accrued income tax and insurance premiums.

Materials and Methods

1. Methodological approaches to assessing willingness to pay in the healthcare sector: discrete choice experiments

Discrete choice experiments are a tool to measure individual preferences regarding economic benefits, indicated as benefit attributes as well. In contrast to the classical theory of identified preferences, development of which dates back to Samuelson’s work ^[9], discrete choice experiments enable individuals to express preferences regarding non-market and hypothetical goods. During the discrete choice experiment, several times, respondents compare several hypothetical alternatives, determined by their attributes, i.e. various characteristics, including the price of a product under consideration. Different product alternatives are generated by changing attribute levels. It is assumed that a rational individual will always prefer an alternative with the highest utility level. Observing the choice made by the respondent, the researcher can identify the utility level gained by the respondent from each attribute. This approach, based on the "new demand theory" ideas suggested by Lancaster ^[10], is also known as conjoint analysis in the terminology of Hensher et al. ^[11].

The most famous alternative to discrete choice experiments is the contingent valuation method. A certain situation or product is described in detail and respondents are asked to indicate their highest level of willingness to pay for this fixed product. In this case, only the item price attribute, not its characteristics, varies. On the contrary, in discrete choice experiments, all attributes change simultaneously, which enables the evaluation of the significance of the entire set of attributes. Although discrete choice experiments describe the product under consideration in less detail than a standard conditional evaluation study, Louvière et al. ^[12] indicate that this hep analyses the overall range of goods by changing the levels of significant attributes. Probable choice out of multiple goods attributes can be directly considered, which enables statistically evaluate the values​ of willingness to pay for each of the attributes individually. Moreover, the respondents are less likely to behave strategically in comparison to a contingent valuation focused solely to consider the price of a product that produces a negative impact on the indirect consumer utility. Finally, in contingent evaluation, respondents directly answer questions about their willingness to pay, which often leads to a biased estimation; on the contrary, according to Ryan's study, biasing is less frequent in discrete choice experiments ^[13].

In the context of the current study, a particular advantage of discrete choice experiments is that the experiment includes both the attributes describing the services provided within the proposed alternative and their characteristics and the price attribute as a monthly service co-funding by the patient. Thus, the decision to choose from two or more alternatives is made in a scenario closely approximate to the market, where a consumer considers both the characteristics of the economic good and his budget constraints simultaneously. During the discrete choice experiment, respondents are faced to make a simultaneous choice on the co-funding amount and the services received, which enables the researcher to assess the relative significance of each of the attributes and the individual willingness to pay for changed attribute levels, as well as for the product in whole.

When developing reforms to the public health insurance system, quantitative results on the willingness to co-fund services enable answering the question of whether supplementing to insurance coverage is justified and under what conditions they should be included. In particular, in case a risk compulsory medical insurance model is introduced, it is required to assess a change in consumer welfare as a result of insurance coverage growth and an increase in insurance premiums.

2. Empiric base
   1. A discrete choice experiment design to assess the willingness of citizens to pay for medical and longitudinal care services

The choice of attributes and their levels to be applied in the discrete choice experiment was based on the literature study, as well as focus group discussions consisting of citizens with different peculiarities (age, sex, health status, and experience in co-funding treatment). Seven attributes and their levels were selected, as shown in Table 1.

It is clear enough that all attribute type combinations and combinations of their levels account fro the total number of possible scenarios that cannot be implemented during experiment 2∙2∙2∙2∙2∙2∙7⋅6=26∙7⋅6=2688 . To reduce their number, restrictions on possible attribute value combinations are introduced to most accurately reflect a set of potentially implemented scenarios, which, however, does not significantly reduce the number of alternatives. Additionally, design optimization methods are used, which, along with the restrictions imposed, enabled limiting the number of alternatives under the current study, except for the status quo, down to 14. The remaining 14 alternative scenarios are divided into two groups of seven alternatives, each group including one alternative in each set twice to test the consistency of the respondent's decisions. As a result, two sets of alternatives are formed, each of which having eight binary choice scenarios for each respondent. Examples of cards describing discrete choice scenarios are presented in the following section.

1. 2. Conducting a representative sociological study

Data were collected on-line using Yandex.Form platform. The choice of a platform is justified by several factors. Firstly, the ability to publish images with the required resolution to subsequently read the text. Secondly, the ability to create questions in the format required (single or multiple-choice, scale questions), the ability to insert text blocks, the convenient format for study result extraction.

The designer enables distributing the survey form in the format of a link to the questionnaire, which facilitates the invitation of participants to participate in the survey. People who took part in the past waves of this study were invited to participate in it once again, as well as those who took part in other studies and gave consent to take part in the research later.

Invitations to complete the questionnaire were sent by e-mail or via social networks. The letter involved a short explanatory note on the study objectives and organizations that initiated it, as well as a questionnaire link to fill out. Invitations were sent to both participants of past waves of the study devoted to this issue and other study participants. In total, approximately 6,000 letters were sent. For the target audience of Moscow and St. Petersburg, questionnaires were completed easier.

The target audience is represented by two groups of study participants aged between 18 and 55: residents of Moscow, residents of St. Petersburg, and residents of other cities with a population of over 500 thousand people.

The sample size was 700 people represented by sex and age, regarding the number of residents in cities. For Moscow and St. Petersburg, the sample size accounted for 350 people, while the second half of the sample was representative of other large cities.

Respondents were represented by sets of scenarios for discrete choice decision making. In addition to the discrete choice between the "status quo" scenario and the proposed set of alternatives, respondents were asked to answer several general sociological questions during the survey. To ensure the validity of the results, which assumes that the decisions made are based on homogeneous information and are taken in a coordinated way, respondents were asked to describe the attributes and their possible values. Data were collected based on the results of the survey and experiment, while the survey was conducted following a questionnaire that enables gaining information on a significant number of socio-economic, demographic, and behavioral peculiarities of respondents.

In part 1 of the questionnaire, the respondent had to answer 24 questions about their demographic, socioeconomic and medical peculiarities: region of residence, city population, age, sex, self-reported health status, current chronic diseases in the respondent, current chronic diseases in respondent’s relatives, employment type and form, marital status, educational background, trust in public health institutions, trust in private health facilities, VMI availability, number of soughts to health facilities over the past 12 months using a compulsory health insurance, whether the respondent paid for additional medical services and what the amount was, the number of visits to private health facilities over the past 12 months and the amount of services paid for, the amount of total respondent’s expenditures on medicine over the past 12 months, getting to know potentials for advanced technologies to restore or improve health, experience in using advanced technologies to restore or improve health over the past 5 years, the need to provide financial assistance to parents of pre-retirement or retirement age in order to pay for treatment or purchase medicines, Total cost to help parents treat or buy drugs over the past 12 months.

In part 2 of the questionnaire, the respondent compared a standard VMI with an alternative one. At the same time, all respondents in each target group were randomly divided into two subgroups, each of which had one of the two prepared alternatives. This was followed by 8 cards with several VMI options offered to compare. In each pair, the respondent had to choose a more preferable option base on his opinion. In each card, a standard VMI was offered as the first pair, which provides outpatient and inpatient services in private and institutionalized clinics, payment for medicines at outpatient servicing, and access to advanced technologies to restore and improve lost body functions in the event of their loss or significant failure, as well as a free-of-charge public investment fund in the amount of 10% of monthly income to pay for treating the respondent, his parents, and children. A standard VMI cost is 3% of monthly income.

Table 2 shows an example of a card of the first No. A1-A8 alternative scenario set. Similarly, cards of the second No. Б1-Б8 alternative scenario set was drawn up, each of which was distributed to respondents randomly.

Finally, after the discrete choice experiment had been conducted (part 2), the respondent was asked a series of questions from part 3 regarding their income level and risk aversion: income self-assessment, a hypothetical lottery to identify the risk aversion degree, willingness to pay for survival, willingness to pay for access to advanced technologies, the willingness of the respondent to pay a certain fraction of income monthly for gaining access to advanced medical technologies that enable to recover or improve health in the event of its substantial deterioration, willingness to pay for personal manager services, willingness to pay for individual access to expensive treatment, willingness to pay for parents' access to expensive treatment and longitudinal care.

Results and Discussion

1. Data analysis results of a discrete choice experiment using a probit regression method

Evaluation of the results obtained from the respondents selected was carried out by the probit regression method. The econometric model is based on the random utility model:

Vij=αi+β1AMBULj+β2STATj+β3MEDj+β4AUGMENTj

+β5CHRONj+β6PARENTSj+β7INVESTj+βpPRICEj+εij

The assessment of willingness to pay for one of the attributes is calculated as the ratio of the attribute coefficient (its marginal utility) to the price coefficient (marginal income utility). For example, the willingness to pay for an increase of the state investment fund to pay for respondent’s, respondent’s parents, and children treatment per 1% of the monthly income are:

MWTPINVEST=-β7βp

Model evaluation findings obtained from the respondents of Moscow and St. Petersburg are presented in Table 3.

*** - statistical significance rate 1%

According to Table 3, the willingness to pay for the majority of additional services accounts for a significant share of respondents' income on average. The negative value is achieved only for outpatient services and in terms of willingness to use co-finding from the state property fund (where the result was statistically not significant).

The results of the model assessment for respondents from other cities with a population of more than 500 thousand people are presented in Table 4.

According to Table 4, the willingness of residents of other large cities to pay for the majority of additional services also accounts for a significant share of respondents' income on average. However, unlike residents of Moscow and St. Petersburg showing a statistically significant demand for access to advanced technologies, residents of other large cities show a specific demand for an additional service to pay for medicines. At the same time, outside Moscow and St. Petersburg, the willingness to pay for gaining access to advanced technologies is negative and statistically significant.

*** - statistical significance rate 1%, ** - statistical significance rate 5%