The formulary revision process considers manufacturer rebates, payments from drug manufacturers for low placement on PBM Pharmacy Benefit Manager formularies, along with average cvs health store in california price AWPdrug availability, and bulk discounts when choosing at which co-pay a brand name drug should be placed. Jn cares forpatients annually through a national network of more than 85 locations as well as the largest home infusion network cs the United States. I'm already a fan, gealth show this again. Review the Patch Community Guidelines. Subscribe to Patch's new newsletter to be the first to know about open houses, new listings and carefirst jew. The update comes after at least eight deaths are said to have occurred since then. Bloomberg -- Oil steadied as traders looked to a revival in Chinese demand this year after data showed that the economy fared better than expected last quarter, with further clues on the outlook to come in an OPEC analysis.
In addition to surgical interventions, diagnostic procedures as well as vaccinations and the dispensing of medications will be covered by the atlas. A multistep procedure was used to identify relevant indicators. In a first step, a list of potential indicators was compiled. Sources included the health care atlases of other countries, the quality indicators of Swiss acute care hospitals developed by the Federal Office of Public Health [ 12 ], medical guidelines and the scientific literature, as well as suggestions from various stakeholders.
In an additional survey that was distributed via the medical societies, physicians were asked to share ideas for new indicators via an online forum.
The online forum was password-protected, accessible around the clock, and did not require identification of participants. It allowed a wide range of physicians to be involved, from the grassroots to the executive committees of medical societies. In this first step, a total of potential indicators were identified Fig.
In a second step, the feasibility was analyzed. Taking into account the data sources available in Switzerland, it was examined which indicator definitions could be used directly for the SAHC or could at least be adapted to be feasible with regard to the available data. Indicators often had to be simplified by ignoring the restriction to a patient group with a specific diagnosis, since information on diagnoses is not systematically collected for outpatient care in Switzerland.
A total of indicators passed the checks see Fig. In a third step, a prioritization was performed involving the advisory board for this project, which included scientists as well as representatives of all relevant stakeholders. As a result, potential indicators were pursued further. In a fourth step, the definitions were developed for the indicators using the relevant coding and classification systems. Table 1 contains illustrative examples of the respective classification systems and codes.
In a fifth and final step, the indicators were then validated and the definitive list of indicators was determined. Based on provisional data analyses and with the involvement of experts, the validity of the indicators and their relevance to health care policy were assessed.
Footnote 6 Table 2 shows the criteria for the validation in detail. With the relaunch of the SAHC, the existing indicators will be redefined and updated with data from to Furthermore, the relaunch introduces a largely automated procedure for data processing and data handling, which includes data cleansing and preparation, calculation of the statistical key figures, and staging of the key figures for display on the platform.
The automated procedure saves personnel resources and allows for quick updates when new data become available. Only this way the annual update of the SAHC can be ensured in the future, especially since less than 0.
On top of that, Switzerland's multilingualism results in additional requirements with regard to the translation of content. For this purpose, an unambiguous interface has been established, which defines how the data from various sources have to be processed. The interface defines the input data structure as well as the relevant features variables to be specified for each indicator Table 3. The health care atlas is first and foremost considered a tool, which contains relevant information on regional variations in health care made accessible by intuitive visualizations.
In order to have the intended impact when it comes to questions about future health care policy, the relevant stakeholders must use the SAHC i. Consequently, embedding the atlas in the relevant policy frameworks as well as within the academic community is a key aspect regarding the relaunch of the SAHC.
Several measures were taken to achieve this goal. First, a comprehensive advisory board was set up. In addition to members from the research community, the advisory board includes representatives of the cantons, service provider associations, health insurers, patient advocacy groups as well as the Federal Statistical Office FSO and the Federal Office of Public Health.
The advisory board is supporting the relaunch in all stages of the project, beginning with the definition of relevant indicators and ending with the communication strategy in preparation for the release of the SAHC. Second, in addition to the advisory board, further stakeholders were involved in the determination of indicators in order to ensure that the information presented in the SAHC is valid and relevant to health care policy.
For example, it is envisioned that in the future the monitoring of radiation exposure will be based on indicators from the SAHC, depicting the frequency of exposure to ionizing radiation in medicine including from X-rays, CT scans, and dental and nuclear imaging.
Third, in order to incorporate the SAHC into the academic environment, a summer school is scheduled for The purpose of this summer school is to establish the analysis of regional variations as a branch of health services research in Switzerland. With reference to the SAHC, concepts such as "small area analysis", "unwarranted variation" and "evidence-based health care policy" will be introduced to a new generation of PhD and post-doc students.
In the first version of the atlas, rates were indirectly standardized with respect to demographic characteristics age and sex. Quantification of regional variation was based on the systematic component of variation SCV of McPherson and colleagues [ 13 ]. The SCV enjoys great popularity in the field of small area variation analysis, but it also has some disadvantages [ 14 ]. With the relaunch, the following four statistical aspects are shown in the atlas: 1 directly standardized rates incl.
The ratio is defined as the quintile ratio QR — i. Prediction refers to the EB predictions under a Poisson-Gamma model [ 16 , 17 ], which also defines the EB measure of variance. The QR is an intuitively appealing measure that does not suffer from the statistical problems that exist with the extremal quotient of the crude rates [ 14 , 15 ]. As a result, a redevelopment of the web platform has been required. A new web application was developed to support the highly automated process for creating and updating the data.
In the new release, the graphics will be created using Apache ECharts. A key point of the SAHC is the type of regionalization applied. While health care utilization rates of treatment are traditionally mapped using administrative regions cantons, districts, etc.
The HSAs capture the catchment areas of each hospital. This allows geographic variations to be described in the context of the particular care delivery systems. The package can be used, both, to account for effective patterns of utilization based on patient flows and to ensure spatial contiguity of resulting HSAs. It should be noted that in Switzerland, since , the free choice of hospital has also been established for patients with basic health insurance.
Because of this, and because of the relatively short distances between different agglomerations in Switzerland, it is not surprising that HSAs are only partially self-contained. From the main HSAs, different hospital referral regions HRR have been derived, mapping the utilization of rarer treatments and medical procedures e.
By contrast, indicators on outpatient care are based on aggregated claims data from health insurers. Other data sources can be added for individual indicators. The technical challenges e. In Switzerland, many health care data are routinely collected and stored in the process of clinical care or to meet regulatory requirements e. However, the data is often stored in unconnected, inconsistent data silos, each with their own acquisition, transport, storage and validation processes.
Importantly, there is also a strong difference between data collection in outpatient versus inpatient health care. While in the inpatient sector, detailed case-level microdata on services and costs are available, similar microdata from outpatient care are largely lacking. Moreover, in the inpatient sector, diagnoses are coded according to the ICD, whereas no systematic coding of diagnoses or reasons for encounter is used in the outpatient sector [ 20 ]. Furthermore, there is no Unique Personal Identifier UPI in the Swiss health care system, which would allow to easily combine pseudonymized data on the same person across different databases and sectors [ 21 ].
With respect to the aforementioned challenges to health data access, there are two primary limitations for the SAHC:. Indicators that include outpatient care services cannot include diagnostic information because diagnostic information is not collected within the outpatient setting e. HbA1c measurements for diabetes patients, imaging of the lower spine in response to unspecific low back pain.
Indicators that include a combination of services along the clinical pathway requiring the linking of different data sources at the patient level are hardly feasible. This refers to indicators that take into account different treatment episodes in different settings e.
Even if these limitations are significant, the available data can still be used to generate important information for the Swiss health care system.
Moreover, by demonstrating the benefits of already accessible data the SAHC is also intended to contribute to the discussion on health data accessibility. The SAHC is an important and widely accepted tool for monitoring the health care delivery system in Switzerland.
The visually intuitive and interactive design allows a wide range of users to engage with the data and moreover fosters in-depth health care research. This paper presents the key elements and highlights some of the main challenges of the relaunch project and thus aims to provide helpful insights for similar endeavors elsewhere. Due to the small size of Switzerland and the associated limitations with regard to the available resources combined with the requirements due to multilingualism, it is essential to establish an efficient and largely automated workflow in order to provide the SAHC with the required data in regular intervals.
At the same time, the relaunch is also a matter of scaling the idea and integrating plenty of new indicators. Here, the inclusion of the outpatient sector is crucial, considering the shift to outpatient care in the Swiss healthcare system. To increase the impact of the SAHC across the entire Swiss health care system, all relevant stakeholders including representatives of public administration at federal and cantonal level, medical societies, insurers, patient advocacy groups and the science community were involved in the relaunch project.
A high level of participation was thus already achieved within the course of the project and, together with the stakeholders, it was already possible to outline how the atlas can be practically embedded in public governance processes. The ATC is a drug classification system that classifies the active ingredients of drugs according to the organ or system on which they act and their therapeutic, pharmacological and chemical properties.
See www. Wennberg JE, Gittelsohn A Small area variations in health care delivery: A population-based health information system can guide planning and regulatory decision-making. Science — Google Scholar. Switzerland is divided into 26 cantons, which partially finance inpatient care services and are responsible for hospital planning. The canton of Zurich with almost 1. In , Switzerland introduced a new hospital financing and payment system.
On the financing side, the dual funding role by the mandatory health insurance and the cantons was standardized across the country. On the payment side, a DRG-based reimbursement scheme was introduced for the acute inpatient sector. This reform put more pressure on the hospitals to provide treatments more efficiently because they now receive case-based lump-sum payments rather than a fee for service reimbursement.
Therefore, post-reform hospitals are expected to strive for more efficiency, e. At the same time, there is a shift of surgical interventions from the inpatient to the outpatient setting, which tends to reduce inpatient demand and potentially puts financial pressure on hospitals.
Total spending for acute inpatient care services increased after the DRG implementation by 4. Factors associated with the large increase in health care spending in high-income countries are still not well known. Most of the literature that aims to explain the spending increase looks at one specific factor and does not distinguish between service categories. Examples include the effect of demographic change in age structure and proximity to death e.
None of these approaches has taken a disease-based perspective, i. Some studies have shown the usefulness of disease-based analyses to investigate health care cost drivers. These studies used estimates of disease-specific spending that have been published for some countries e. If disease-specific spending estimates are available at different points in time, they can be tracked to identify the changes in the associated factors at a more granular level.
Most of the related literature is from and for the United States [ 17 , 18 , 19 , 20 , 21 , 22 ]. These studies found for different periods and different populations, that the increase in spending per treated case was the most important factor associated with the spending increase in the last three decades. However, they were mostly limited to two factors, such as the number of treated prevalent cases and costs per case. From a methodological point of view, the two studies that are most closely related to our paper are the two recent decompositions by Zhai et al.
Zhai et al. The effect of the change in the age structure was relatively small. Dieleman et al. They found that spending per visit outpatient and spending per bed-day inpatient were the most important factors related to the increase in spending between and For inpatient care, they also found a negative association between spending and utilization, which was defined as bed-days per prevalent case, suggesting that the length of stay was reduced.
They did, however, not explicitly include this factor. These two studies are able to show the relative importance of five basic factors and to estimate their contributions by disease, using the same methodology and the same disease classification as the present study. There are only few studies from comparable countries that investigated the drivers of inpatient care costs specifically. Wong et al. Most of the literature for Switzerland has focused on the causal effect of the recent DRG implementation on costs, utilization and quality.
It was shown that after the reform in the return per inpatient case increased only slightly between and though again at a higher pace in [ 25 ].
One study found that age and sex standardized hospitalization rates in acute inpatient care in Switzerland did not change between and [ 26 ]. The data set was provided by the Department of Health of the canton of Zurich.
It holds detailed diagnostic and cost information at the case level for all inpatient acute care episodes in the 25 hospitals listed in the canton of Zurich between and Footnote 1 Rehabilitation and psychiatric care facilities are not included in the data. There are between , and , inpatient cases per year.
A case is defined according to the case consolidation rules in the DRG system; with only few exceptions, any readmission for the same cause within 18 days since the first discharge is recorded in the same administrative case, i.
In what follows, the terms case and stay will be used interchangeably. Patients can be tracked over time and across hospitals. This enables the identification of recurrent encounters at the patient level.
Due to a change in the hospital accounting framework in , we only used data from to Not all inpatient cases are reimbursed based on the DRG system. Between and , about cases per year were reimbursed based on a special palliative care rate. The number of these cases fell to about in and We did not exclude non-DRG observations from the data since we were interested in costs rather than reimbursement.
Since we aimed at tracking the number of unique patients within each disease group for each year, we used the patient identifier based on the anonymized social insurance number that has been available since This variable showed missing values for all non-resident patients and for some Swiss residents. To fill these gaps, we used a second patient identifier. This identifier uniquely identifies patients within each hospital and year, but not across hospitals and years.
Therefore, we slightly overestimate the number of unique patients because some of them might have been treated at different hospitals for the same disease in a given year. Some cases did not fall completely in one calendar year. Patients admitted at the end of a year but without discharge in the same year had no assigned diagnosis and were thus dropped. Some observations with entry and discharge date in the same calendar year were lacking a diagnosis and, thus, had to be excluded from the analysis.
About five cases per year with entry in the last year and discharge in the following year with total length of stay of more than 1 year were also excluded from the analysis. Our data contains rich cost information at the case level.
The term costs refers to production costs of health services and not to the costs charged to the payer, which is based on the DRG case weight. This is a key innovation of the study, as to our knowledge, there has been no previous literature using the production cost information at the inpatient case level in Switzerland.
With the introduction of the DRG system in , hospitals applied a unified accounting standard, ensuring consistent accounting rules across hospitals over the whole period. For each case, costs are recorded and distinguished by about 35 cost types. We aggregated the cost types into cost categories with the support of the Department of Health.
Five categories of costs are distinguished: physician costs, medical products costs e. The key is closely linked to the actual resource use e. We assume that there has been no change in how fixed costs are distributed between and The costs comprise both the part covered by the mandatory as well as the supplementary health insurance plan. Details about the mapping of cost types to cost categories as well as the record types are provided in the appendix.
The set of health conditions was defined based on the comprehensive and mutually exclusive Global Burden of Disease GBD classification. This classification defines three hierarchical levels of diseases. GBD Level 1 distinguishes between communicable and non-communicable diseases and injuries. Level 2 groups major diseases such as neoplasms or cardiovascular diseases. Level 3 defines more specific diseases such as breast cancer or ischemic heart disease.
We grouped the codes according to the classification used in the Global Burden of Disease Study to obtain groups of disease codes [ 30 ]. To obtain a comprehensive and mutually exclusive classification, we made minor adjustments to the classification. Footnote 2 We generally applied the level 3 classification with some exceptions. All five types of injuries as well as all eight types of communicable diseases were distinguished at level 2 only.
The non-communicable diseases were classified at level 3, except for mental disorders and skin and subcutaneous diseases.
The adapted classification resulted in diseases. The full list of diseases is provided in the appendix. We allocated all the costs based on the primary diagnosis listed in each record. Accounting for concurrent conditions in cost-of-illness studies is essential and may impact cost estimates significantly [ 31 ]. Nevertheless, we did not include any secondary diagnoses in our analysis for three reasons. First, according to the Department of Health, the primary diagnosis coded has to be the diagnosis that caused the most amount of work for the hospital.
Second, the impact of comorbidities may differ across different cost categories, i. Third, there are methodological difficulties associated with such an adjustment, e. We used the Das Gupta method for the decomposition of aggregate measures [ 4 ] that was further extended for high numbers of factors [ 32 ]. The idea of this method is to correct for compositional effects when comparing multiple populations. The observed difference between two groups or two measures from the same group but from different points in time may be due to different characteristics in the two underlying populations.
Our method of choice is rather mathematical than econometric such as the Oaxaca—Blinder decomposition approach [ 33 , 34 ]. The method decomposes the difference between two points in time into its additive components. The interaction effects arising in the calculation of counterfactuals are distributed among the factors.
The decomposition does not depend on the order in which the factors are included in the model. In the standard example, the aggregate measure is a product of the factors. Treated prevalence and utilization are actually the two components of the admission rate; the first factor represents the number of patients treated for a certain disease extensive margin , the second refers to the average number of stays of those who were treated intensive margin.
The costs per day component is a sum over the five cost categories c. These costs, in turn, are a product of the six factors. Summing these values up over all diseases gives the total increase in costs between and The 6-factor decomposition does not directly reveal the contribution of the change in costs per case.
This key factor is instead captured by costs per day and length of stay. We ran a decomposition including only five factors to explicitly show the impact of the change in costs per case by disease.
Footnote 4. To allow for a more detailed view on what happens along the full distribution of costs per case, we created five groups of cases for each disease. All cases were allocated to one of these groups based on their location in the distribution of costs per case, and for and separately. The aim of this analysis was to investigate whether the importance of the factors differs across the distribution and which group of cases contributed most to the observed cost change.
For the distributional analysis, we applied a slightly different decomposition specification. We removed one dimension from the decomposition, namely the population structure. The specification for the second part of our analysis thus reads as follows:. Between and , total acute inpatient costs for all diseases increased from CHF 2. This corresponds to an increase by CHF million or The most important contributors to this rise were non-communicable diseases.
Figure 1 illustrates the total costs by year stratified by age groups and gender. The highest absolute increase was observed between age 70 and 89 for both men and women. Even though the patients aged 0 represent the smallest age group, the costs and their absolute increase was higher than in most age groups up to 54 years, for both men and women. The average costs per case increase with age, especially between age 40 and 80, as is shown in Fig.
The highest absolute increases in costs per case were found for the oldest patients. Table 1 shows the change in costs per case by cost category as well as the change in average length of stay between and In this period, mean costs per case increased by 5. In both years, about half of the total costs per case were medical costs. Here, the growth rate was much lower at 3. The share of fixed costs by disease varied between Across all conditions, it was Moreover, there was an increase in the number of cases per year by 9.
However, this increase was partly offset by a decrease in the average length of stay. Table 4 in the appendix also shows these statistics by disease group GBD level 2. Overall, we observe similar patterns across all disease groups, except for few diseases that showed a significant decrease in costs per case e. The magnitude of change for the cost categories differs across diseases. Plot a on the left-hand side of Fig.
The increase in population size accounted for about one third of the total nominal increase. The changing population structure only led to a modest increase of total costs, as did the higher proportion of the population that was treated and the utilization factor, i. The reduction of the average length of stay was associated with a strong decline in costs. Costs would have decreased by 7. The contribution of the costs per day factor is clearly positive Plot b on the right-hand side of Fig. The biggest contribution 5 percentage points of the total increase can be observed for the medical cost category.
This is not very surprising given that about half of the total costs per case are medical costs which have modestly increased between and see Table 1. There is some heterogeneity in the effect size across diseases, as indicated by Fig. In this figure, the results from the disease-specific estimations are aggregated at the GBD level 2 with 22 groups of diseases; mental disorders as well as the group of non-distinctive codes are not shown here.
In the aggregation, no weighting takes place, i. The size of a circle corresponds to the magnitude of the association, while the color shows the direction of change. A red circle is associated with a cost increase, whereas a green circle is associated with a cost decrease. The last column of black-rimmed circles summarizes the total cost increase between and relative to the total costs in To put these relative changes into context, the bars on the right side of the figure show the aggregate costs for each disease in The effect of this factor varied substantially across disease groups.
For some communicable diseases, there was a reduction in the treated population e. The two most important factors associated with the cost change were length of stay and costs per day. In general, the two effects were in opposite direction, with length of stay being negatively and cost per day being positively associated with the cost change between and Only five disease groups showed a positive association of length of stay with costs, and only one disease group sense organ diseases showed a negative association of costs per day with costs.
For very few diseases, including the most expensive disease group of cardiovascular diseases, there was a positive association of both factors with costs. These heterogeneous effects become even more visible if we look at a more granular disease level. Figure 5 shows the decomposition for eleven single diseases from the GBD level 2 groups cardiovascular diseases C , neoplasms N and musculoskeletal disorders M , as well as diabetes mellitus.
These diseases are shown in the order of their total costs in within their GBD level 2 group. Cardiovascular diseases, neoplasms and musculoskeletal disorders were the three most expensive disease groups in both and Diabetes showed one of the strongest increase in costs Peripheral artery disease was an outlier among the cardiovascular diseases, as its aggregate costs decreased. This was mainly due to a strong negative association between the relative number of treated patients and costs.
By contrast, stroke showed a strong cost increase, which was mainly due to an increase in costs per day. Despite the reduction in length of stay for both conditions, the association between both the share of treated patients and the costs per day yielded a positive net cost change. Osteoarthritis showed an increase in the relative number of treated patients, whereas the converse held true for low back pain.
For diabetes mellitus, we observe a positive association with costs for all six factors. This means that relatively more patients received an inpatient treatment, those patients had a higher utilization and longer stays with higher costs per day.
The complete results are provided in Tables 5 level 2 and 7 level 3 in the appendix. The sum of the length of stay and the costs per day effect is roughly equal to the effect of costs per case.
We checked this with a decomposition analysis including only five factors. The results are very similar, i. While the average costs per case across all cases increased by 5.
These diseases showed increasing costs per day, but the reduced length of stay offset this increase and yielded a negative contribution of costs per case.
In this section, we explore the cost per day factor in more detail by splitting it up into the five cost categories. Figure 6 shows this decomposition result.
This figure has the same structure and contains the same set of diseases as Fig. Also at a more granular level, the two most important cost factors are the physician and medical costs. Sense organ diseases also exhibited an increase in this cost category, but they were one of the few examples for which the physician and medical costs were negatively associated with the cost change. Overall, the medical products cost category varied substantially across diseases, with several communicable diseases showing a negative association with the total cost increase.
Since we applied a slightly different specification for this analysis, the absolute values differ slightly compared to the 6-factor decomposition. The general pattern is the same. The contributions of each of the five sections to the total cost change by disease at GBD level 2 are shown in the appendix see Fig.
The factor decomposition isolated the association between the change in disease costs and both length of stay and costs per day by distributional sections. Figures 7 and 8 show the contribution of each of the five distribution sections to the total effect. Diseases are ranked based on the total absolute increase associated with this factor. We observe some heterogeneity in the effect of length of stay on the costs by disease.
Similar patterns are observed for other diseases for which the length of stay increased on average Footnote 5 top of Fig. There are more examples of diseases for which the average length of stay was not reduced for all the distribution sections, e. For diseases that showed a negative association along the whole distribution, the impact of the cases above the 75th percentile was mostly dominant.
The first decomposition specification including population structure showed a negative association between length of stay and costs per day. This was not necessarily true when looking at single sections of the distribution. There was an increase in the length of stay for the most expensive cases for other infectious diseases, but this effect was reinforced by an increase in costs per day for the same group. For most diseases, there was an increase in costs per day along the whole distribution.
Noteworthy, there was a very small association of a reduction in the costs per day for the most expensive cardiovascular cases. Given the rapid increase in health care spending in Switzerland in the last few years, it is pivotal to better understand the factors associated with this increase. This study was the first to decompose inpatient care costs by disease in Switzerland, using detailed cost data for the canton of Zurich, between and It assessed the impact of six fundamental factors on the change of disease costs over time.
The most important factor was the increase in the costs per day, which was partly offset by a reduction in the average length of stay. Population growth and age and sex structure were related to about half of the increase.
A minor part was associated with an increase in the number of people treated relative to the population. This study contributed to the existing literature in three important ways. First, it included one additional factor in the decomposition specification, namely length of stay. Second, it further decomposed the costs per day factor into five cost components. Third, it reported results not only for the average, but also for the full cost distribution. The data we used does not reflect spending at prices charged to payers but production costs incurred by the hospitals.
It is therefore a more objective measure of the resources needed to provide treatment for each disease than spending. However, the cost changes observed in this study do not necessarily correspond to the actual health care spending changes by disease, since hospitals might either realize profits or the costs by disease might not perfectly correspond to the spending for the diseases.
Decomposition methods usually used with individual level data, i. Oaxaca and Blinder type decompositions [ 33 , 34 ], are of limited use here since they aim at explaining the mean outcome in our case mean costs per day or case and thus ignore the factors leading to the aggregate change such as population growth or utilization.
One strength of our study lies in its ability to separate the propensity to be treated from the utilization as well as the intensity and price of treatment captured by length of stay and costs per day. The observed positive association between population structure and costs is due to the fact that patients in higher age groups exhibit more stays and higher costs per case.
It has been shown for Switzerland that costs per patient and hospital days per person in the inpatient sector increase significantly beyond the age of 60 [ 36 ]. Since we controlled for other factors, the cost increase that is linked to a change in population structure is small.
Several reasons may explain the increase in the share of people being treated, which was associated with a cost increase of 1.
The first one might be supplier-induced demand. The second reason is linked to technological progress: new diagnostic and treatment options allow more patients to be successfully treated, which possibly leads to better health outcomes [ 38 ].
The third reason is a more technical one. Since some patients live outside of the canton of Zurich, but this information was not accessible to us, the factor is also influenced by inter-cantonal patient migration and might thus be slightly biased.
The share of the net patient migration has increased from 9. We can therefore not rule out that for some diseases, part of the effect of the treated factors is driven by patients coming from other cantons. A fourth explanation is the underlying disease prevalence: if more people suffer from a disease, the number of treated patients is likely to increase too.
However, our study was not able to capture the health-related needs of the patients and thus ignored the fact that some diseases become more or less prevalent. The studies by Dieleman et al. Even though national prevalence estimates are also available for Switzerland from the GBD project, we did not include these in our analysis of sub-national data. Since we only focus on one health care sector, our two factors treated and utilization provide policy makers more easily interpretable results than utilization per clinically prevalent case.
Footnote 6 For some communicable diseases and injuries, we observed a reduction in the share of treated patients. This might be due to a shift from inpatient to outpatient treatments. Despite this development, the number of treated people has further increased for most diseases.
This suggests that surgeries in the inpatient setting do not decrease to the same extent as the outpatient treatments increase [ 42 ]. The observed decrease in the length of stay and the increase in the utilization factor might both be linked to the implementation of the DRG reimbursement system. Hospitals have been incentivized to discharge patients early and to produce more stays e.
Previous research for Switzerland provided evidence that the system change led to an increase in hospital readmissions [ 44 ]. Readmissions within 18 days of treatment are, however, already included in our records and do not constitute an additional stay, i. The differentiation between the number of stays per patient and the length of stay thus proves to be relevant, as the effects go in opposite directions. Utilization defined as number of days per patient like in [ 5 ] captures both effects at the same time.
Furthermore, our study confirms previous evidence for Switzerland, as it found a strong association between the reduced length of stay and costs [ 45 ].
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|Teaneck cognizant office||Based on this, several cantons developed their own policy, and the recommendations above were included in subsequent national policies see below. The cookie is used to store the user consent for the cookies in the category "Performance". These cookies track visitors across websites and collect information to provide customized ads. However, the cost changes observed https://indi-infantformula.com/ford-to-cummins-swap-kit/8860-accenture-investors.php this study do not necessarily correspond to the actual health care spending changes by disease, since hospitals might either realize profits or the costs by disease might not perfectly correspond to the spending for the diseases. In this figure, the results from the disease-specific estimations are aggregated at the GBD level 2 with 22 groups of diseases; mental disorders as well as the group of non-distinctive codes are not shown here. Received : 03 February Policy implications and future research A better understanding of the epidemiological, technological and demographic trends on health care costs may be particularly useful for swiss healthcare system changes 2017 sound definition of global spending how nuance dragon work windows 7 currently discussed in Switzerland.|
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