Alexander Geissler

Background: Between 2008 and 2018, the share of robotic-assisted surgeries (RAS) for radical prostatectomies (RPEs) has increased from 3 to 46% in Germany. Firstly, we investigate if this diffusion of RAS has contributed to RPE treatment centralization. Secondly, we analyze if a hospital’s use of an RAS system influenced patients’ hospital choice. Methods: To analyze RPE treatment centralization, we use (bi-) annual hospital data from 2006 to 2018 for all German hospitals in a panel-data fixed effect model. For investigating RAS systems’ influence on patients’ hospital choice, we use patient level data of 4614 RPE patients treated in 2015. Employing a random utility choice model, we estimate the influence of RAS as well as specialization and quality on patients’ marginal utilities and their according willingness to travel. Results: Despite a slight decrease in RPEs between 2006 and 2018, hospitals that invested in an RAS system could increase their case volumes significantly (+ 82% compared to hospitals that did not invest) contributing to treatment centralization. Moreover, patients are willing to travel longer for hospitals offering RAS (+ 22% than average travel time) and for specialization (+ 13% for certified prostate cancer treatment centers, + 9% for higher procedure volume). The influence of outcome quality and service quality on patients’ hospital choice is insignificant or negligible. Conclusions: In conclusion, centralization is partly driven by (very) high-volume hospitals’ investment in RAS systems and patient preferences. While outcome quality might improve due to centralization and according specialization, evidence for a direct positive influence of RAS on RPE outcomes still is ambiguous. Patients have been voting with their feet, but research yet has to catch up.

Most developed countries spend a large amount of their health budget on hospital capacities and inpatient services. However, those capacities and services are often not comprehensively planned what leads to vague service delivery steering and non-need driven hospital facilities. Switzerland is different as the planning procedure was completely reformed in 2012 and is further refined in 2021/2022. The Canton of Zurich, the frontrunner in Switzerland, has made a comprehensive update of its hospital capacity planning model for acute, psychiatric, and rehabilitative care. The result of this model is the hospital list. This list includes all hospitals which fulfil predefined quality, efficiency, and need requirements. Hospitals on the list receive a mandate to provide inpatient treatments for specific and selected service groups (n=196), clustered in three areas (acute care, psychiatry, rehabilitation). The underlying health care policy process is transparent and is characterized by a high participation of all relevant actors. The building blocks of the planning model are a classification system of service groups, different quality and efficiency requirements attached to these groups, and an analysis of current and future need for health care. Hospitals which are willing to perform services must apply and demonstrate that the requirements are fulfilled. The Canton then decides needs-based which hospital can deliver which services.

Purpose: To calculate unstratified and patient-specific meaningful improvement (MI) and patient acceptable symptom states (PASS) for the WOMAC total score in patients after total hip (THR) or total knee replacement (TKR). Methods: A retrospective observational cohort study. Anchor-based receiver operator characteristics curves were used to estimate MI and PASS thresholds. Results: Recovery paths were specific to individual characteristics of patients. An unstratified 12-months MI threshold of 28.1 (PASS: 13.3) and 17.8 (PASS: 15.8) for patients after THR and TKR, respectively, would un-fairly detect critical recovery paths. Conclusions: Thresholds for treatment success need to be as patient-specific as possible.

Background Diagnosis-related group (DRG)-based hospital payment can potentially be inadequately low (or high) for highly variable, highly specialized, and/or low volume care. DRG-based payment can be combined with other payment mechanisms to avoid unintended consequences of inadequate payment. The aim of this study was to analyze these other payment mechanisms for acute inpatient care across six countries (Germany, Denmark, England, Estonia, France, the United States [Medicare]). Methods Information was collected about elements excluded from DRG-based payment, the rationale for exclusions, and payment mechanisms complementing DRG-based payment. A conceptual framework was developed to systematically describe, visualise and compare payment mechanisms across countries. Results Results show that the complexity of exclusion mechanisms and associated additional payment components differ across countries. England and Germany use many different additional mechanisms, while there are only few exceptions from DRG-based payment in the Medicare program in the United States. Certain areas of care are almost always excluded (eg, certain areas of cancer care or specialized pediatrics). Denmark and England use exclusion mechanisms to steer service provision for highly complex patients to specialized providers. Conclusion Implications for researchers and policy-makers include: (1) certain areas of care might be better excluded from DRG-based payment; (2) exclusions may be used to incentivize the concentration of highly specialized care at specialized institutions (as in Denmark or England); (3) researchers may apply our analytical framework to better understand the specific design features of DRG-based payment systems.

Public reporting on the quality of care is intended to guide patients to the provider with the highest quality and to stimulate a fair competition on quality. We apply a difference-in-differences design to test whether hospital quality has improved more in markets that are more competitive after the first public release of performance data in Germany in 2008. Panel data from 947 hospitals from 2006 to 2010 are used. Due to the high complexity of the treatment of stroke patients, we approximate general hospital quality by the 30-day risk-adjusted mortality rate for stroke treatment. Market structure is measured (comparatively) by the Herfindahl–Hirschman index (HHI) and by the number of hospitals in the relevant market. Predicted market shares based on exogenous variables only are used to compute the HHI to allow a causal interpretation of the reform effect. A homogenous positive effect of competition on quality of care is found. This effect is mainly driven by the response of non-profit hospitals that have a narrow range of services and private for-profit hospitals with a medium range of services. The results highlight the relevance of outcome transparency to enhance hospital quality competition.

A positive relationship between treatment volume and outcome quality has been demonstrated in the literature and is thus evident for a variety of procedures. Consequently, policy makers have tried to translate this so-called volume–outcome rela-tionship into minimum volume regulation (MVR) to increase the quality of care—yet with limited success. Until today, the effect of strict MVR application remains unclear as outcome quality gains cannot be estimated adequately and restrictions to application such as patient travel time and utilization of remaining hospital capacity are not considered sufficiently. Accord-ingly, when defining MVR, its effectiveness cannot be assessed. Thus, we developed a mixed integer programming model to define minimum volume thresholds balancing utility in terms of outcome quality gain and feasibility in terms of restricted patient travel time and utilization of hospital capacity. We applied our model to the German hospital sector and to four surgical procedures. Results showed that effective MVR needs a minimum volume threshold of 125 treatments for cholecystectomy, of 45 and 25 treatments for colon and rectum resection, respectively, of 32 treatments for radical prostatectomy and of 60 treatments for total knee arthroplasty. Depending on procedure type and incidence as well as the procedure’s complication rate, outcome quality gain ranged between 287 (radical prostatectomy) and 977 (colon resection) avoidable complications (11.7% and 11.9% of all complications). Ultimately, policy makers can use our model to leverage MVR’s intended benefit: concentrating treatment delivery to improve the quality of care.

Quality competition among hospitals, induced by patients freely choosing their hospital in a price regulated market, can only be realized if quality differences between hospitals are transparent, understandable, and thus influence patients’ hospital choice. We use data from ~145,000 German patients and ~ 900 hospitals for colorectal resections and knee replacements to investigate whether patients value quality and specialization when choosing their hospital. Using a random utility choice model, we estimate patients’ marginal utilities, willingness to travel and change in hospital demand for quality improvements. Patients respond to service quality and specialization and thus, quality competition seems to be present. Colorectal resection patients are willing to travel longer for more specialized hospitals (+9% for procedure volume, +9% for certification). Knee replacement patients travel longer for hospitals with better service quality (+6%) and higher procedure volume (+12%). However, clinical quality indicators, often difficult to access and interpret, barely play a role in patients’ hospital choice. Furthermore, we find that competition on quality for colorectal resection is rather local, whereas for knee replacement we observe regional competition patterns.

Aim of study: The aim of the study was to investigate whether there are regional differences in the treatment results of elective mammary resections for breast cancer in Switzerland and, if so, whether a possible cause could be found in the hospital planning by the cantons. Methods; Using the Inpatient Quality Indicators (CH-IQI), the quality of outcomes was analysed at the level of the Swiss cantons and compared with the cantonal requirements for carrying out this treatment. Results: Cantonal differences became apparent both in the quality of results based on the quality indicator of breast preservation and in the level of detail of the requirements for structural and process quality. Conclusion: The quality of treatment in Switzerland can hardly be compared in a transparent manner; interpreting the available quality information is demanding and hardly possible for patients. In order to reduce the quality differences shown, hospital planning should be intercantonal, as is the case in highly specialised medicine.

A key concern for a well-functioning health care system is whether competition between providers and its regulation ensures the optimal level of quality of care. Currently, quality of care is often provided inconsistently or insufficiently. There are no uniform or comprehensive approaches for defining and measuring quality, neither from a clinical nor from a patient perspective. Moreover, in economic theory, health care is classified as a credence good and, thus, is characterized by strong information asymmetries between patients, health care providers, and payers. Using classic health economic approaches, this article examines the factors influencing quality of care on both the demand and the supply side and explores current health policy measures to support the optimal level of quality of care in competition between service providers.

Quality transparency is a crucial basis for reducing information asymmetries in the health care system. But what information on inpatient, acute-somatic care is available in Switzerland, how can it be used and what are the consequences? To answer these questions, a review of the data available is undertaken, systematically processed and strengths and weaknesses identified. On this basis, as well as international comparisons, health policy recommendations for an improvement of the current conditions follow. The study shows that quality transparency in Switzerland is limited, despite the national quality contract agreed in 2011. This makes it difficult to strengthen quality competition, to make informed decisions about patients’ choice of hospital and to contract selectively. Further development of the data and measurement instruments available in hospitals in Switzerland is therefore indicated and requires prioritised implementation.