EconStor Collection:

The Future of Swiss Hospital Capacity Planning - New impulses from real-world evidence and stakeholder inputs

Thu, 01 Jan 2026 00:00:00 GMT

Title: The Future of Swiss Hospital Capacity Planning - New impulses from real-world evidence and stakeholder inputs Authors: Vogel, Justus; Fu, Enqi; Lang, Charlotte; Ehlig, David; Geissler, Alexander Abstract: Acute somatic hospital care accounts for a major share of Swiss healthcare expenditures. Reports of hospital deficits have increased in recent years, making them a major concern for health policy. These two factors raise questions about the adequacy of the hospital landscape and inpatient service provision, especially regarding the efficient utilization of human resources and infrastructure. Care planning is an important lever for efficient service provision. From 2012 onwards, all cantons have adopted Zurich's Hospital Capacity Planning Model (HCPM), which allocates service mandates to hospitals based on hospital planning service groups (SPLGs), complemented by national regulation on inter-cantonal planning of highly specialized medicine (IVHSM). Despite these reforms, current planning still insufficiently reflects actual patient flows across cantons and does not integrate inpatient and outpatient need for care. This study provides empirical evidence and impulses to inform the future development of hospital - and more broadly - care planning in Switzerland. The research questions (RQs) are: (1) To what extent does current cantonal hospital planning align with real-world patient flows and service mandate utilization? (2) How should SPLGs be allocated to different regional planning levels, considering complexity and urgency? (3) What are health system stakeholders' priorities for care planning (e.g., inter-cantonal planning, inter-sectoral planning)?

Towards value-based payments for joint replacement: A "Pay for Patient Value" (P4PV) model for Switzerland - From defining patient value to setting-up a financial and redistribution model

Thu, 01 Jan 2026 00:00:00 GMT

Title: Towards value-based payments for joint replacement: A "Pay for Patient Value" (P4PV) model for Switzerland - From defining patient value to setting-up a financial and redistribution model Authors: Salvi, Irene; Ehlig, David; Vogel, Justus; Geissler, Alexander Abstract: In Switzerland, elective hip and knee replacements account for substantial healthcare expenditures and are central to debates on quality, appropriateness and value-based care. Existing DRG-based payment systems for acute somatic care remunerate volume and case mix, but do not reflect quality of care. To address this issue, we developed a "pay for patient value" (P4PV) model for hip and knee replacements that links payment to what matters to patients, i.e. clinical outcomes, patient-reported outcome measures (PROMs) and indication quality. We first define "patient value" and measurable indicators of patient value that can be addressed by hospitals. Secondly, we design a budget-neutral financing mechanism to incentivize high value care and lastly specify a formula for redistributing payments based on the achievement of indicators representing patient value. For each step, we combined a targeted literature review with structured advisory processes involving surgeons, hospital managers, specialists from Groupe Mutuel (GM), and registry experts. Existing Swiss datasets and quality initiatives and international experience informed indicator selection, thresholds for bonus assignment, financial re-allocation and payment design. Structured advisory meetings validated the model propositions. The proposed model defines and measures quality according to three dimensions: (i) clinical outcomes, measured as hospital-level risk-adjusted 2-year revision rates; (ii) PROMs, initially measured via participation and 12-month response rates for EQ-5D-5L, Oxford Hip/Knee Scores and a satisfaction question, and subsequently additionally according to meaningful improvement thresholds; and (iii) indication quality, as documentation of shared decision-making via a checklist or equivalent. The budget-neutral financing mechanism is made up of two parts: a 2% withhold on DRG-based payments for hip/knee replacements pooled with 50% of the health insurer's estimated 1-year post-operative cost savings. This pool is redistributed using a weighted model: each hospital receives a composite quality score (40% revision rate, 20% PROM participation rate, 20% PROM response rate, 20% shared decision-making), scored with three-tier thresholds (0/50/100%). Thus, participating hospitals receive a payment relative to their performance in each dimension and the performance of other hospitals. While their performance is calculated based on all patients, payments are only made according to share of patients insured at participating health insurers. The P4PV model offers a framework that takes joint replacement patient value into account by adjusting payments based on clinical and patient-reported outcomes, as well as shared decision-making, while remaining compatible with existing Swiss registries and payment systems. It directs incentives from volume towards patient value, remaining budget neutral and ensuring scalability. Implementation will require legal clarification of contracts, investment in PROM measurement and shared decision-making infrastructure, coordination with cantons and other insurers. For ease of introduction, a shadow invoicing phase is envisaged to prepare hospitals ahead of full rollout and test the willingness of hospitals and insurers to invest in the P4PV model.

Cost of HIV in Switzerland - A cost‐of‐illness analysis to quantify the economic and social impact of the HIV epidemic in Switzerland and its progress towards ending the epidemic

Thu, 01 Jan 2026 00:00:00 GMT

Title: Cost of HIV in Switzerland - A cost‐of‐illness analysis to quantify the economic and social impact of the HIV epidemic in Switzerland and its progress towards ending the epidemic Authors: Salvi, Irene; Fu, Enqi; Geissler, Alexander Abstract: Switzerland has achieved considerable progress in HIV care, with high treatment coverage and viral suppression among diagnosed people living with HIV. However, new infections continue to occur, and the remaining gap in the HIV care cascade is primarily related to diagnosis. In view of upcoming funding cuts, updated estimates of the lifetime economic burden of new HIV infections are needed to inform prevention policy, resource allocation, and progress towards UNAIDS and Swiss National Programme (NAPS) elimination targets. This cost-of-illness study estimated the lifetime direct medical and broader societal costs associated with one new HIV infection in Switzerland. We developed an incidence-based state-transition Markov model to simulate the lifetime progress through CD4 categories-defined health states of a newly infected person living with HIV in Switzerland. The model followed individuals in annual cycles across CD4 >500 cells/µL, CD4 200-500 cells/µL, CD4 <200 cells/µL, and death. Direct medical costs included antiretroviral therapy, outpatient visits, diagnostics and monitoring, and inpatient care. Indirect and broader societal costs included morbidity-related productivity losses, productivity losses due to premature mortality, depression-related costs, and monetised quality-adjusted life-year (QALY) losses. Costs were estimated using a bottom-up approach based on Swiss prices, tariffs, inpatient data, Swiss HIV Cohort Study inputs, Swiss mortality data, and published evidence. Future costs and outcomes were discounted at 3% annually. Sensitivity analyses examined the impact of discounting, age at infection, productivity assumptions, HIV utility ratios, and willingness-to-pay thresholds. A scenario analysis estimated the potential economic implications of reaching the UNAIDS diagnosis target of 95% in Switzerland. In the base case, the total lifetime cost of one new HIV infection in Switzerland was estimated at CHF 731,130. Direct medical costs accounted for CHF 289,584, while indirect and broader societal costs accounted for CHF 441,546. ART was the dominant direct cost component, amounting to CHF 258,224 and representing approximately 89% of direct medical costs. Other direct cost components were smaller: outpatient visits accounted for CHF 8,034, diagnostics for CHF 16,384, and inpatient care for CHF 6,942. Among indirect costs, productivity losses amounted to CHF 198,348, consisting of CHF 124,878 from morbidity-related paid work losses and CHF 73,469 from productivity losses due to premature mortality. Depression-related costs contributed CHF 3,198, while monetised QALY losses accounted for CHF 240,000. Sensitivity analyses showed that total lifetime costs ranged from CHF 611,130 under a lower willingness-to-pay threshold to CHF 1,192,145 in the undiscounted scenario. In the UNAIDS diagnosis scenario, increasing the diagnosed proportion from 93% to 95% was estimated to reduce the undiagnosed population by approximately 352 individuals. Depending on the transmission assumption applied, this corresponded to 24-70 avoided infections and potential lifetime cost savings of CHF 17.8-51.2 million. The lifetime economic burden of a new HIV infection in Switzerland is substantial and extends beyond direct medical costs. While ART is the main driver of direct medical costs, indirect and broader societal costs, particularly productivity losses and quality-of-life losses, account for a larger share of the total burden. These findings support sustained investment in HIV prevention, targeted testing, early diagnosis, and linkage to care. In the Swiss context, further progress towards UNAIDS and NAPS targets may generate meaningful long-term value by preventing new infections and avoiding lifetime medical and societal costs.

Causal Effects and Optimal Policy Learning for Intensive Care Unit Discharge Decisions to Solve Hospital Process Bottlenecks: Approach, Methods, and First Results

Wed, 01 Jan 2025 00:00:00 GMT

Title: Causal Effects and Optimal Policy Learning for Intensive Care Unit Discharge Decisions to Solve Hospital Process Bottlenecks: Approach, Methods, and First Results Authors: Vogel, Justus; Cordier, Johannes; Filipovic, Miodrag Abstract: Intensive care units (ICUs) operate with fixed capacities and face uncertainty such as demand variability, leading to demand-driven, early discharges to free up beds. These discharges can increase ICU readmission rates, negatively impacting patient outcomes and aggravating ICU bottleneck congestion. This study investigates how ICU discharge timing affects readmission risk, with the goal of developing policies that minimize ICU readmissions, managing demand variability and bed capacity. To define a binary treatment, we randomly assign hypothetical discharge days to patients, comparing these with actual discharge days to form intervention and control groups. We apply two causal machine learning techniques (generalized random forest, modified causal forest). Assuming unconfoundedness, we leverage observed patient data as sufficient covariates. For scenarios where unconfoundedness might fail, we discuss an IV approach with different instruments. We further develop decision policies based on individualized average treatment effects (IATEs) to minimize individual patients’ readmission risk. Our sample comprises 12,950 ICU stays (11,873 unique cases) from the Department of Surgical Intensive Medicine of the Cantonal Hospital of St. Gallen admitted between January 01, 2016, and December 31, 2023. We find that for 72% of our sample discharge at point in time 𝑡 as compared to 𝑡+1 increases patients’ readmission risk. Vice versa, 28% of cases profit from an earlier discharge in terms of readmission risk. The range of IATEs is quite large: For 91.4% of ICU stays, an earlier ICU discharge changes a patient’s readmission risk between -0.05 and 0.05 percentage points (-55% and 55% relative change as compared to the average readmission rate of 9.04%). To develop decision policies, we will exploit this treatment heterogeneity and rank patients according to their IATEs and compare IATEs of optimal and actual discharges across all decision points in our observation period. Finally, we outline how we will assess the potential reduction in readmissions and saved bed capacities under optimal policies in a simulation, offering actionable insights for ICU management. We aim to provide a novel approach and blueprint for similar operations research and management science applications in data-rich environments.