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    <title>EconStor Community:</title>
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        <rdf:li rdf:resource="https://hdl.handle.net/10419/336985" />
        <rdf:li rdf:resource="https://hdl.handle.net/10419/338102" />
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    <dc:date>2026-05-04T11:48:22Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/336985">
    <title>The Future of Swiss Hospital Capacity Planning - New impulses from real-world evidence and stakeholder inputs</title>
    <link>https://hdl.handle.net/10419/336985</link>
    <description>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)?</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/338102">
    <title>Open Insurance in der deutschsprachigen Assekuranz: Chancen, Risiken und Entwicklungspotenziale</title>
    <link>https://hdl.handle.net/10419/338102</link>
    <description>Title: Open Insurance in der deutschsprachigen Assekuranz: Chancen, Risiken und Entwicklungspotenziale
Authors: Florin, Gian; Eling, Martin</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/308439.2">
    <title>Causal Effects and Optimal Policy Learning for Intensive Care Unit Discharge Decisions to Solve Hospital Process Bottlenecks: Approach, Methods, and First Results</title>
    <link>https://hdl.handle.net/10419/308439.2</link>
    <description>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.</description>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
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  <item rdf:about="https://hdl.handle.net/10419/319880">
    <title>Assekuranz 2030: Quo Vadis?</title>
    <link>https://hdl.handle.net/10419/319880</link>
    <description>Title: Assekuranz 2030: Quo Vadis?
Authors: Eling, Martin
Abstract: Diese Studie untersucht die Entwicklung der Assekuranz in den letzten 30 Jahren und gibt einen Ausblick auf zukünftige Trends in der Branche. Während sich einige der in früheren Studien prognostizierten Entwicklungen, wie die Internationalisierung und Konsolidierung, bestätigt haben, verliefen andere Trends langsamer oder anders als erwartet. In den kommenden Jahren bleibt die Digitalisierung eine zentrale Herausforderung, doch auch der demografische Wandel, sich verändernde Kundenbedürfnisse und neue regulatorische Anforderungen im Kontext KI und ESG werden die Branche massgeblich prägen. Die Studie identifiziert sieben zentrale Handlungsfelder, darunter die Zukunft der Vorsorge, neue Geschäftsmodelle und den Einfluss von Technologien wie KI und Big Data. Ziel der Studie ist es, Versicherern Orientierung zu bieten, um aufkommende Trends proaktiv zu gestalten und ihre Wettbewerbsfähigkeit langfristig zu sichern.</description>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
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