Spillover effects of supplementary on basic health insurance: evidence from the Netherlands

Like many other countries, the Netherlands has a health insurance system that combines mandatory basic insurance with voluntary supplementary insurance. Both types of insurance are founded on different principles. Since basic and supplementary insurance are sold by the same health insurers, both markets may interact. This paper examines to what extent basic and supplementary insurance are linked to each other and whether these links generate spillover effects of supplementary on basic insurance. Our analysis is based on an investigation into supplementary health insurance contracts, underwriting procedures and annual surveys among 1,700–2,100 respondents over the period 2006–2009. We find that health insurers increasingly use a variety of strategies to enforce a joint purchase of basic and supplementary health insurance. Despite incentives for health insurers to use supplementary insurance as a tool for risk selection in basic insurance, we find limited evidence of supplementary insurance being used this way. Only a minority of health insurers uses health questionnaires when people apply for supplementary coverage. Nevertheless, we find that an increasing proportion of high-risk individuals believe that insurers would not be willing to offer them another supplementary insurance contract. We discuss several strategies to prevent or to counteract the observed negative spillover effects of supplementary insurance

The Dutch Consumer Quality Index: an example of stakeholder involvement in indicator development

Background: Like in several other Western countries, in the Dutch health care system regulated competition has been introduced. In order to make this work, comparable information is required about the performance of health care providers in terms of effectiveness, safety and patient experiences. Without further coordination, external actors will all try to force health care providers to be transparent. For health care providers this might result in a situation in which they have to deliver data for several sets of indicators, defined by different actors. Therefore, in the Netherlands an effort is made to define national sets of performance indicators and related measuring instruments. In this article, the following questions are addressed, using patient experiences as an example: - When and how are stakeholders involved in the development of indicators and instruments that measure the patients' experiences with health care providers? - Does this involvement lead to indicators and instruments that match stakeholders' information needs? Discussion: The Dutch experiences show that it is possible to implement national indicator sets and to reach consensus about what needs to be measured. Preliminary evaluations show that for health care providers and health insurers the benefits of standardization outweigh the possible loss of tailor-made information. However, it has also become clear that particular attention should be given to the participation of patient/consumer organisations. Summary: Stakeholder involvement is complex and time-consuming. However, it is the only way to balance the information needs of all the parties that ask for and benefit from transparency, without frustrating the health care system.

Quantitative data management in quality improvement collaboratives

<p>Abstract</p> <p>Background</p> <p>Collaborative approaches in quality improvement have been promoted since the introduction of the Breakthrough method. The effectiveness of this method is inconclusive and further independent evaluation of the method has been called for. For any evaluation to succeed, data collection on interventions performed within the collaborative and outcomes of those interventions is crucial. Getting enough data from Quality Improvement Collaboratives (QICs) for evaluation purposes, however, has proved to be difficult. This paper provides a retrospective analysis on the process of data management in a Dutch Quality Improvement Collaborative. From this analysis general failure and success factors are identified.</p> <p>Discussion</p> <p>This paper discusses complications and dilemma's observed in the set-up of data management for QICs. An overview is presented of signals that were picked up by the data management team. These signals were used to improve the strategies for data management during the program and have, as far as possible, been translated into practical solutions that have been successfully implemented.</p> <p>The recommendations coming from this study are:</p> <p>From our experience it is clear that quality improvement programs deviate from experimental research in many ways. It is not only impossible, but also undesirable to control processes and standardize data streams. QIC's need to be clear of data protocols that do not allow for change. It is therefore minimally important that when quantitative results are gathered, these results are accompanied by qualitative results that can be used to correctly interpret them.</p> <p>Monitoring and data acquisition interfere with routine. This makes a database collecting data in a QIC an intervention in itself. It is very important to be aware of this in reporting the results. Using existing databases when possible can overcome some of these problems but is often not possible given the change objective of QICs.</p> <p>Introducing a standardized spreadsheet to the teams is a very practical and helpful tool in collecting standardized data within a QIC. It is vital that the spreadsheets are handed out before baseline measurements start.</p

Consumer evaluation of complaint handling in the Dutch health insurance market

<p>Abstract</p> <p>Background</p> <p>How companies deal with complaints is a particularly challenging aspect in managing the quality of their service. In this study we test the direct and relative effects of service quality dimensions on consumer complaint satisfaction evaluations and trust in a company in the Dutch health insurance market.</p> <p>Methods</p> <p>A cross-sectional survey design was used. Survey data of 150 members of a Dutch insurance panel who lodged a complaint at their healthcare insurer within the past 12 months were surveyed. The data were collected using a questionnaire containing validated multi-item measures. These measures assess the service quality dimensions consisting of functional quality and technical quality and consumer complaint satisfaction evaluations consisting of complaint satisfaction and overall satisfaction with the company after complaint handling. Respondents' trust in a company after complaint handling was also measured. Using factor analysis, reliability and validity of the measures were assessed. Regression analysis was used to examine the relationships between these variables.</p> <p>Results</p> <p>Overall, results confirm the hypothesized direct and relative effects between the service quality dimensions and consumer complaint satisfaction evaluations and trust in the company. No support was found for the effect of technical quality on overall satisfaction with the company. This outcome might be driven by the context of our study; namely, consumers get in touch with a company to resolve a specific problem and therefore might focus more on complaint satisfaction and less on overall satisfaction with the company.</p> <p>Conclusions</p> <p>Overall, the model we present is valid in the context of the Dutch health insurance market. Management is able to increase consumers' complaint satisfaction, overall satisfaction with the company, and trust in the company by improving elements of functional and technical quality. Furthermore, we show that functional and technical quality do not influence consumer satisfaction evaluations and trust in the company to the same extent. Therefore, it is important for managers to be aware of the type of consumer satisfaction they are measuring when evaluating the handling of complaints within their company.</p

Market structure and hospital–insurer bargaining in the Netherlands

In 2005, competition was introduced in part of the hospital market in the Netherlands. Using a unique dataset of transactions and list prices between hospitals and insurers in the years 2005 and 2006, we estimate the influence of buyer and seller concentration on the negotiated prices. First, we use a traditional structure–conduct–performance model (SCP-model) along the lines of Melnick et al. (J Health Econ 11(3): 217–233, 1992) to estimate the effects of buyer and seller concentration on price–cost margins. Second, we model the interaction between hospitals and insurers in the context of a generalized bargaining model similar to Brooks et al. (J Health Econ 16: 417–434, 1997). In the SCP-model, we find that the market shares of hospitals (insurers) have a significantly positive (negative) impact on the hospital price–cost margin. In the bargaining model, we find a significant negative effect of insurer concentration, but no significant effect of hospital concentration. In both models, we find a significant impact of idiosyncratic effects on the market outcomes. This is consistent with the fact that the Dutch hospital sector is not yet in a long-run equilibrium

Understanding organisational development, sustainability, and diffusion of innovations within hospitals participating in a multilevel quality collaborative

<p>Abstract</p> <p>Background</p> <p>Between 2004 and 2008, 24 Dutch hospitals participated in a two-year multilevel quality collaborative (MQC) comprised of (a) a leadership programme for hospital executives, (b) six quality-improvement collaboratives (QICs) for healthcare professionals and other staff, and (c) an internal programme organisation to help senior management monitor and coordinate team progress. The MQC aimed to stimulate the development of quality-management systems and the spread of methods to improve patient safety and logistics. The objective of this study is to describe how the first group of eight MQC hospitals sustained and disseminated improvements made and the quality methods used.</p> <p>Methods</p> <p>The approach followed by the hospitals was described using interview and questionnaire data gathered from eight programme coordinators.</p> <p>Results</p> <p>MQC hospitals followed a systematic strategy of diffusion and sustainability. Hospital quality-management systems are further developed according to a model linking plan-do-study-act cycles at the unit and hospital level. The model involves quality norms based on realised successes, performance agreements with unit heads, organisational support, monitoring, and quarterly accountability reports.</p> <p>Conclusions</p> <p>It is concluded from this study that the MQC contributed to organisational development and dissemination within participating hospitals. Organisational learning effects were demonstrated. System changes affect the context factors in the theory of organisational readiness: organisational culture, policies and procedures, past experience, organisational resources, and organisational structure. Programme coordinator responses indicate that these factors are utilised to manage spread and sustainability. Further research is needed to assess long-term effects.</p

Do People Taking Flu Vaccines Need Them the Most?

Background: A well targeted flu vaccine strategy can ensure that vaccines go to those who are at the highest risk of getting infected if unvaccinated. However, prior research has not explicitly examined the association between the risk of flu infection and vaccination rates. Purpose: This study examines the relationship between the risk of flu infection and the probability of getting vaccinated. Methods: Nationally representative data from the US and multivariate regression models were used to estimate what individual characteristics are associated with (1) the risk of flu infection when unvaccinated and (2) flu vaccination rates. These results were used to estimate the correlation between the probability of infection and the probability of getting vaccinated. Separate analyses were performed for the general population and the high priority population that is at increased risk of flu related complications. Results: We find that the high priority population was more likely to get vaccinated compared to the general population. However, within both the high priority and general populations the risk of flu infection when unvaccinated was negatively correlated with vaccination rates (r = 20.067, p,0.01). This negative association between the risk of infection when unvaccinated and the probability of vaccination was stronger for the high priority population (r = 20.361, p,0.01). Conclusions: There is a poor match between those who get flu vaccines and those who have a high risk of flu infectio