Financial risk allocation and provider incentives in hospital-insurer contracts in The Netherlands.

In healthcare systems with a purchaser-provider split, contracts are an important tool to define the conditions for the provision of healthcare services. Financial risk allocation can be used in contracts as a mechanism to influence provider behavior and stimulate providers to provide efficient and high-quality care. In this paper, we provide new insights into financial risk allocation between insurers and hospitals in a changing contracting environment. We used unique nationwide data from 901 hospital-insurer contracts in The Netherlands over the years 2013, 2016, and 2018. Based on descriptive and regression analyses, we find that hospitals were exposed to more financial risk over time, although this increase was somewhat counteracted by an increasing use of risk-mitigating measures between 2016 and 2018. It is likely that this trend was heavily influenced by national cost control agreements. In addition, alternative payment models to incentivize value-based health care were rarely used and thus seemingly of lower priority, despite national policies being explicitly directed at this goal. Finally, our analysis shows that hospital and insurer market power were both negatively associated with financial risk for hospitals. This effect becomes stronger if both hospital and insurer have strong market power, which in this case may indicate a greater need to reduce (financial) uncertainties and to create more cooperative relationships.

Barriers to payment reform: Experiences from nine Dutch population health management sites.

Population health management (PHM) initiatives aim for better population health, quality of care and reduction of expenditure growth by integrating and optimizing services across domains. Reforms shifting payment of providers from traditional fee-for-service towards value-based payment models may support PHM. We aimed to gain insight into payment reform in nine Dutch PHM sites. Specifically, we investigated 1) the type of payment models implemented, and 2) the experienced barriers towards payment reform. Between October 2016 and February 2017, we conducted 36 (semi-)structured interviews with program managers, hospitals, insurers and primary care representatives of the sites. We addressed the structure of payment models and barriers to payment reform in general. After three years of PHM, we found that four shared savings models for pharmaceutical care and five extensions of existing (bundled) payment models adding providers into the model were implemented. Interviewees stated that reluctance to shift financial accountability to providers was partly due to information asymmetry, a lack of trust and conflicting incentives between providers and insurers, and last but not least a lack of a sense of urgency. Small steps to payment reform have been taken in the Dutch PHM sites, which is in line with other international PHM initiatives. While acknowledging the autonomy of PHM sites, governmental stewardship (e.g. long-term vision, supporting knowledge development) can further stimulate value-based payment reforms.

Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases.

BACKGROUND: To indicate inefficiencies in health systems, previous studies examined regional variation in healthcare spending by analyzing the entire population. As a result, population heterogeneity is taken into account to a limited extent only. Furthermore, it clouds a detailed interpretation which could be used to inform regional budget allocation decisions to improve quality of care of one chronic disease over another. Therefore, we aimed to gain insight into the drivers of regional variation in healthcare spending by studying prevalent chronic diseases. METHODS: We used 2012 secondary health survey data linked with claims data, healthcare supply data and demographics at the individual level for 18 Dutch regions. We studied patients with diabetes (n = 10,767) and depression (n = 3,735), in addition to the general population (n = 44,694). For all samples, we estimated the cross-sectional relationship between spending, supply and demand variables and region effects using linear mixed models. RESULTS: Regions with above (below) average spending for the general population mostly showed above (below) average spending for diabetes and depression as well. Less than 1% of the a-priori total variation in spending was attributed to the regions. For all samples, we found that individual-level demand variables explained 62-63% of the total variance. Self-reported health status was the most prominent predictor (28%) of healthcare spending. Supply variables also explained, although a small part, of regional variation in spending in the general population and depression. Demand variables explained nearly 100% of regional variation in spending for depression and 88% for diabetes, leaving 12% of the regional variation left unexplained indicating differences between regions due to inefficiencies. CONCLUSIONS: The extent to which regional variation in healthcare spending can be considered as inefficiency may differ between regions and disease-groups. Therefore, analyzing chronic diseases, in addition to the traditional approach where the general population is studied, provides more insight into the causes of regional variation in healthcare spending, and identifies potential areas for efficiency improvement and budget allocation decisions.

Are low-value care measures up to the task? A systematic review of the literature.

BACKGROUND: Reducing low-value care is a core component of healthcare reforms in many Western countries. A comprehensive and sound set of low-value care measures is needed in order to monitor low-value care use in general and in provider-payer contracts. Our objective was to review the scientific literature on low-value care measurement, aiming to assess the scope and quality of current measures. METHODS: A systematic review was performed for the period 2010-2015. We assessed the scope of low-value care recommendations and measures by categorizing them according to the Classification of Health Care Functions. Additionally, we assessed the quality of the measures by 1) analysing their development process and the level of evidence underlying the measures, and 2) analysing the evidence regarding the validity of a selected subset of the measures. RESULTS: Our search yielded 292 potentially relevant articles. After screening, we selected 23 articles eligible for review. We obtained 115 low-value care measures, of which 87 were concentrated in the cure sector, 25 in prevention and 3 in long-term care. No measures were found in rehabilitative care and health promotion. We found 62 measures from articles that translated low-value care recommendations into measures, while 53 measures were previously developed by institutions as the National Quality Forum. Three measures were assigned the highest level of evidence, as they were underpinned by both guidelines and literature evidence. Our search yielded no information on coding/criterion validity and construct validity for the included measures. Despite this, most measures were already used in practice. CONCLUSION: This systematic review provides insight into the current state of low-value care measures. It shows that more attention is needed for the evidential underpinning and quality of these measures. Clear information about the level of evidence and validity helps to identify measures that truly represent low-value care and are sufficiently qualified to fulfil their aims through quality monitoring and in innovative payer-provider contracts. This will contribute to creating and maintaining the support of providers, payers, policy makers and citizens, who are all aiming to improve value in health care.

Individual and Regional-level Factors Contributing to Variation in Length of Stay After Cerebral Infarction in Six European Countries.

Using patient-level data for cerebral infarction cases in 2007, gathered from Finland, Hungary, Italy, the Netherlands, Scotland and Sweden, we studied the variation in risk-adjusted length of stay (LoS) of acute hospital care and 1-year mortality, both within and between countries. In addition, we analysed the variance of LoS and associations of selected regional-level factors with LoS and 1-year mortality after cerebral infarction. The data show that LoS distributions are surprisingly different across countries and that there is significant deviation in the risk-adjusted regional-level LoS in all of the countries studied. We used negative binomial regression to model the individual-level LoS, and random intercept models and ordinary least squares regression for the regional-level analysis of risk-adjusted LoS, variance of LoS, 1-year risk-adjusted mortality and crude mortality for a period of 31-365 days. The observed variations between regions and countries in both LoS and mortality were not fully explained by either patient-level or regional-level factors. The results indicate that there may exist potential for efficiency gains in acute hospital care of cerebral infarction and that healthcare managers could learn from best practices.

Has variation in length of stay in acute hospitals decreased? Analysing trends in the variation in LOS between and within Dutch hospitals.

BACKGROUND: We aimed to get better insight into the development of the variation in length of stay (LOS) between and within hospitals over time, in order to assess the room for efficiency improvement in hospital care. METHODS: Using Dutch national individual patient-level hospital admission data, we studied LOS for patients in nine groups of diagnoses and procedures between 1995 and 2010. We fitted linear mixed effects models to the log-transformed LOS to disentangle within and between hospital variation and to evaluate trends, adjusted for case-mix. RESULTS: We found substantial differences between diagnoses and procedures in LOS variation and development over time, supporting our disease-specific approach. For none of the diagnoses, relative variance decreased on the log scale, suggesting room for further LOS reduction. Except for two procedures in the same specialty, LOS of individual hospitals did not correlate between diagnoses/procedures, indicating the absence of a hospital wide policy. We found within-hospital variance to be many times greater than between-hospital variance. This resulted in overlapping confidence intervals across most hospitals for individual hospitals' performances in terms of LOS. CONCLUSIONS: The results suggest room for efficiency improvement implying lower costs per patient treated. It further implies a possibility to raise the number of patients treated using the same capacity or to downsize the capacity. Furthermore, policymakers and health care purchasers should take into account statistical uncertainty when benchmarking LOS between hospitals and identifying inefficient hospitals.

Effects of regulated competition on key outcomes of care: cataract surgeries in the Netherlands.

Similar to several other countries, the Netherlands implemented market-oriented health care reforms in recent years. Previous studies raised questions on the effects of these reforms on key outcomes such as quality, costs, and prices. The empirical evidence is up to now mixed. This study looked at the variation in prices, volume, and quality of cataract surgeries since the introduction of price competition in 2006. We found no price convergence over time and constant price differences between hospitals. Quality indicators generally showed positive results in cataract care, though the quality and scope of the indicators was suboptimal at this stage. Furthermore, we found limited between-hospital variation in quality and there was no clear-cut relation between prices and quality. Volume of cataract care strongly increased in the period studied. These findings indicate that health insurers may not have been able to drive prices down, make trade-offs between price and quality, and selectively contract health care without usable quality information. Positive results coming out from the 2006 reform should not be taken for granted. Looking forward, future research on similar topics and with newer data should clarify the extent to which these findings can be generalized.

Spending more money, saving more lives? The relationship between avoidable mortality and healthcare spending in 14 countries.

Healthcare expenditures rise as a share of GDP in most countries, raising questions regarding the value of further spending increases. Against this backdrop, we assessed the value of healthcare spending growth in 14 western countries between 1996 and 2006. We estimated macro-level health production functions using avoidable mortality as outcome measure. Avoidable mortality comprises deaths from certain conditions "that should not occur in the presence of timely and effective healthcare". We investigated the relationship between total avoidable mortality and healthcare spending using descriptive analyses and multiple regression models, focussing on within-country variation and growth rates. We aimed to take into account the role of potential confounders and dynamic effects such as time lags. Additionally, we explored a method to estimate macro-level cost-effectiveness. We found an average yearly avoidable mortality decline of 2.6-5.3% across countries. Simultaneously, healthcare spending rose between 1.9 and 5.9% per year. Most countries with above-average spending growth demonstrated above-average reductions in avoidable mortality. The regression models showed a significant association between contemporaneous and lagged healthcare spending and avoidable mortality. The time-trend, representing an exogenous shift of the health production function, reduced the impact of healthcare spending. After controlling for this time-trend and other confounders, i.e. demographic and socioeconomic variables, a statistically significant relationship between healthcare spending and avoidable mortality remained. We tentatively conclude that macro-level healthcare spending increases provided value for money, at least for the disease groups, countries and years included in this study.

Decomposing cross-country differences in quality adjusted life expectancy: the impact of value sets.

BACKGROUND: The validity, reliability and cross-country comparability of summary measures of population health (SMPH) have been persistently debated. In this debate, the measurement and valuation of nonfatal health outcomes have been defined as key issues. Our goal was to quantify and decompose international differences in health expectancy based on health-related quality of life (HRQoL). We focused on the impact of value set choice on cross-country variation. METHODS: We calculated Quality Adjusted Life Expectancy (QALE) at age 20 for 15 countries in which EQ-5D population surveys had been conducted. We applied the Sullivan approach to combine the EQ-5D based HRQoL data with life tables from the Human Mortality Database. Mean HRQoL by country-gender-age was estimated using a parametric model. We used nonparametric bootstrap techniques to compute confidence intervals. QALE was then compared across the six country-specific time trade-off value sets that were available. Finally, three counterfactual estimates were generated in order to assess the contribution of mortality, health states and health-state values to cross-country differences in QALE. RESULTS: QALE at age 20 ranged from 33 years in Armenia to almost 61 years in Japan, using the UK value set. The value sets of the other five countries generated different estimates, up to seven years higher. The relative impact of choosing a different value set differed across country-gender strata between 2% and 20%. In 50% of the country-gender strata the ranking changed by two or more positions across value sets. The decomposition demonstrated a varying impact of health states, health-state values, and mortality on QALE differences across countries. CONCLUSIONS: The choice of the value set in SMPH may seriously affect cross-country comparisons of health expectancy, even across populations of similar levels of wealth and education. In our opinion, it is essential to get more insight into the drivers of differences in health-state values across populations. This will enhance the usefulness of health-expectancy measures.

Impact of disease management programs on healthcare expenditures for patients with diabetes, depression, heart failure or chronic obstructive pulmonary disease: a systematic review of the literature.

OBJECTIVE: Evaluating the impact of disease management programs on healthcare expenditures for patients with diabetes, depression, heart failure or COPD. METHODS: Systematic Pubmed search for studies reporting the impact of disease management programs on healthcare expenditures. Included were studies that contained two or more components of Wagner's chronic care model and were published between January 2007 and December 2009. RESULTS: Thirty-one papers were selected, describing disease management programs for patients with diabetes (n=14), depression (n=4), heart failure (n=8), and COPD (n=5). Twenty-one studies reported incremental healthcare costs per patient per year, of which 13 showed cost-savings. Incremental costs ranged between -$16,996 and $3305 per patient per year. Substantial variation was found between studies in terms of study design, number and combination of components of disease management programs, interventions within components, and characteristics of economic evaluations. CONCLUSION: Although it is widely believed that disease management programs reduce healthcare expenditures, the present study shows that evidence for this claim is still inconclusive. Nevertheless disease management programs are increasingly implemented in healthcare systems worldwide. To support well-considered decision-making in this field, well-designed economic evaluations should be stimulated.

Benchmarking and reducing length of stay in Dutch hospitals.

BACKGROUND: To assess the development of and variation in lengths of stay in Dutch hospitals and to determine the potential reduction in hospital days if all Dutch hospitals would have an average length of stay equal to that of benchmark hospitals. METHODS: The potential reduction was calculated using data obtained from 69 hospitals that participated in the National Medical Registration (LMR). For each hospital, the average length of stay was adjusted for differences in type of admission (clinical or day-care admission) and case mix (age, diagnosis and procedure). We calculated the number of hospital days that theoretically could be saved by (i) counting unnecessary clinical admissions as day cases whenever possible, and (ii) treating all remaining clinical patients with a length of stay equal to the benchmark (15th percentile length of stay hospital). RESULTS: The average (mean) length of stay in Dutch hospitals decreased from 14 days in 1980 to 7 days in 2006. In 2006 more than 80% of all hospitals reached an average length of stay shorter than the 15th percentile hospital in the year 2000. In 2006 the mean length of stay ranged from 5.1 to 8.7 days. If the average length of stay of the 15th percentile hospital in 2006 is identified as the standard that other hospitals can achieve, a 14% reduction of hospital days can be attained. This percentage varied substantially across medical specialties. Extrapolating the potential reduction of hospital days of the 69 hospitals to all 98 Dutch hospitals yielded a total savings of 1.8 million hospital days (2006). The average length of stay in Dutch hospitals if all hospitals were able to treat their patients as the 15th percentile hospital would be 6 days and the number of day cases would increase by 13%. CONCLUSION: Hospitals in the Netherlands vary substantially in case mix adjusted length of stay. Benchmarking--using the method presented--shows the potential for efficiency improvement which can be realized by decreasing inputs (e.g. available beds for inpatient care). Future research should focus on the effect of length of stay reduction programs on outputs such as quality of care.

Cost of illness: an international comparison. Australia, Canada, France, Germany and The Netherlands.

OBJECTIVES: To assess international comparability of general cost of illness (COI) studies and to examine the extent to which COI estimates differ and why. METHODS: Five general COI studies were examined. COI estimates were classified by health provider using the system of health accounts (SHA). Provider groups fully included in all studies and matching SHA estimates were selected to create a common data set. In order to explain cost differences descriptive analyses were carried out on a number of determinants. RESULTS: In general similar COI patterns emerged for these countries, despite their health care system differences. In addition to these similarities, certain significant disease-specific differences were found. Comparisons of nursing and residential care expenditure by disease showed major variation. Epidemiological explanations of differences were hardly found, whereas demographic differences were influential. Significant treatment variation appeared from hospital data. CONCLUSIONS: A systematic analysis of COI data from different countries may assist in comparing health expenditure internationally. All cost data dimensions shed greater light on the effects of health care system differences within various aspects of health care. Still, the study's objectives can only be reached by a further improvement of the SHA, by international use of the SHA in COI studies and by a standardized methodology.

Measuring and explaining mortality in Dutch hospitals; the hospital standardized mortality rate between 2003 and 2005.

BACKGROUND: Indicators of hospital quality, such as hospital standardized mortality ratios (HSMR), have been used increasingly to assess and improve hospital quality. Our aim has been to describe and explain variation in new HSMRs for the Netherlands. METHODS: HSMRs were estimated using data from the complete population of discharged patients during 2003 to 2005. We used binary logistic regression to indirectly standardize for differences in case-mix. Out of a total of 101 hospitals 89 hospitals remained in our explanatory analysis. In this analysis we explored the association between HSMRs and determinants that can and cannot be influenced by hospitals. For this analysis we used a two-level hierarchical linear regression model to explain variation in yearly HSMRs. RESULTS: The average HSMR decreased yearly with more than eight percent. The highest HSMR was about twice as high as the lowest HSMR in all years. More than 2/3 of the variation stemmed from between-hospital variation. Year (-), local number of general practitioners (-) and hospital type were significantly associated with the HSMR in all tested models. CONCLUSION: HSMR scores vary substantially between hospitals, while rankings appear stable over time. We find no evidence that the HSMR cannot be used as an indicator to monitor and compare hospital quality. Because the standardization method is indirect, the comparisons are most relevant from a societal perspective but less so from an individual perspective. We find evidence of comparatively higher HSMRs in academic hospitals. This may result from (good quality) high-risk procedures, low quality of care or inadequate case-mix correction.