Richard Heijink

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 Wagners 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 -

Benchmarking and reducing length of stay in Dutch hospitals

16,996 and

Health care performance comparison using a disease-based approach: The EuroHOPE project

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.

Measuring and explaining mortality in Dutch hospitals; The Hospital Standardized Mortality Rate between 2003 and 2005

BackgroundTo 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.MethodsThe 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).ResultsThe 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%.ConclusionHospitals 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

This article describes the methodological challenges associated with disease-based international comparison of health system performance and how they have been addressed in the EuroHOPE (European Health Care Outcomes, Performance and Efficiency) project. The project uses linkable patient-level data available from national sources of Finland, Hungary, Italy, The Netherlands, Norway, Scotland and Sweden. The data allow measuring the outcome and the use of resources in uniformly-defined patient groups using standardized risk adjustment procedures in the participating countries. The project concentrates on five important disease groups: acute myocardial infarction (AMI), ischemic stroke, hip fracture, breast cancer and very low birth weight and preterm infants (VLBWI). The essentials of data gathering, the definition of the episode of care, the developed indicators concerning baseline statistics, treatment process, cost and outcomes are described. The preliminary results indicate that the disease-based approach is attractive for international performance analyses, because it produces various measures not only at country level but also at regional and hospital level across countries. The possibility of linking hospital discharge register to other databases and the availability of comprehensive register data will determine whether the approach can be expanded to other diseases and countries.

Comparing ischaemic stroke in six European countries. The EuroHOPE register study.

BackgroundIndicators 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.MethodsHSMRs 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.ResultsThe 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.ConclusionHSMR 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.

Decomposing cross-country differences in Quality Adjusted Life Expectancy: The impact of value sets

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 studys 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.

Defining Population Health Management: A Scoping Review of the Literature.

The incidence of hospitalizations, treatment and case fatality of ischaemic stroke were assessed utilizing a comprehensive multinational database to attempt to compare the healthcare systems in six European countries, aiming also to identify the limitations and make suggestions for future improvements in the between‐country comparisons.

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

BackgroundThe 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.MethodsWe 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.ResultsQALE 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.ConclusionsThe 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.

Mortality and Length of Stay of Very Low Birth Weight and Very Preterm Infants: A EuroHOPE Study

Population health management (PHM) has increasingly been mentioned as a concept to realize improvements in population health and quality of care while reducing cost growth (the so-called Triple Aim). The concept of PHM has been used in various settings and has been defined in different ways. This study compared the definitions of PHM used in the literature in order to improve the understanding and interpretation of the concept of PHM. A scoping literature search was performed for papers published between January 2000 and January 2015 that defined PHM. PHM definitions were summarized, focusing on: (1) overall aim, (2) PHM activities, and (3) contextual factors. Eighteen articles were retrieved. The overall aim was defined in terms of health (N = 14), costs (N = 8), and/or quality of care (N = 10). Definitions varied regarding the description of PHM activities, though all definitions contained elements in common with disease management and health promotion. Data management, Triple Aim assessment, risk stratification, evaluation, and feedback cycles were less likely to be mentioned. Contextual factors were scarcely brought forward in the definitions. Moderate variations were found across definitions in the way PHM was conceptualized. Frequently, essential elements of PHM were not specified. Differences in conceptualizations of PHM should be taken into account when comparing PHM initiatives that are working toward improvements in population health, (experienced) quality of care, and reduction of costs.