Alexander Geissler
Title
Prof. Dr.
Last Name
Geissler
First name
Alexander
Email
alexander.geissler@unisg.ch
ORCID
Phone
+41 71 224 3200
124 results
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Item type:Publication, Occupational health interventions' impact on absenteeism and economic returns: A systematic review and meta-analysis.(2025-12-17); ;Mueller, Sonja I; Objective: Health-related productivity losses impose a significant burden on health systems and economies. Occupational health interventions (OHI) are increasingly promoted as preventive strategies to reduce work-related illness and enhance productivity. However, their effectiveness often remains unclear, creating a lack of guidance to those deciding on their implementation. The aim of this review was to evaluate the effectiveness of OHI in reducing sickness absenteeism and generating economic returns, focusing on mental health, physical health, and workplace atmosphere interventions (eg, work climate enhancement, leadership training). Methods: A systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline was conducted throughout December 2024. Risk of bias was assessed with Joanna Briggs Institute checklists. A random-effects meta-analysis synthesized OHI effects on sick days and return on investment (ROI). Results: Of 2624 identified studies, 68 across eight industries met eligibility criteria. From these, 23 were included in the meta-analysis: 11 reporting on sick days, and 12 on ROI. OHI were associated with a non-significant reduction in absenteeism [-0.18 days; 95% confidence interval (CI) -2.80-2.43; P=0.890] and a tendency of positive ROI (1.92; 95% CI -0.34-4.17; P=0.096), albeit with statistical uncertainty. Conclusion: We only found effect of OHI on ROI, however, absence effects on sick days do not necessarily imply a lack of effectiveness. We hypothesize that ROI benefits reflect improvements in presenteeism, although not directly measured. Overall, this review guides OHI selection and implementation, urges standardized evaluation, and prioritizes research on presenteeism measurement, non-OECD settings, and qualitative success factors.Type:journal articleJournal:Scandinavian journal of work, environment & healthScopus© Citations 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, PROM-based monitoring and alerts reduce post-surgery healthcare utilization of patients undergoing joint replacement: A secondary analysis of the PROMoting Quality RCT(Springer Science and Business Media LLC, 2025-08-07); ;Schöner, Lukas; ;Wittich, LauraReinhard BusseObjective Healthcare systems increasingly face shortages of medical professionals, and simultaneously experience a rise in demand for healthcare services. In this study, we investigated whether a digital PROM-based monitoring and alert system for hip and knee replacement patients post-surgery can support in decreasing healthcare expenditures and utilization. Methods We used data from the randomized controlled trial PROMoting Quality, focusing on 546 hip and 492 knee replacement patients from nine German hospitals between October 2019 and December 2020 with available claims data. Patients were equally randomized into two groups: one receiving a PROM-based intervention at 1, 3, and 6 months post-surgery, the other receiving standard care. We compared 1-year post-surgery healthcare utilization using mixed-effects regression models. We further extrapolated the intervention effects to the German healthcare system. Findings Results showed post-surgery health expenditure reductions of 7.9% (-318.08, p = 0.015) for hip and 7.3% (-386.72, p = 0.053) for knee replacements. Significant decreases were observed in outpatient care contacts (-1.51, p = 0.005), physiotherapy sessions (-1.65, p = 0.037), and number of prescriptions (-2.14, p = 0.042) for hip replacements. For knee replacement patients, significant determinants of the cost differences were fewer prescriptions (-3.40, p = 0.013) and medical aids (-0.81, p = 0.041). Conclusion Our findings suggest that digital health interventions can reduce utilization and save scarce healthcare resources. It can be hypothesized that the “being taken care of” effect reduced the need for reassurance of the recovery progress, leading to fewer GP visits and decreased utilization of other healthcare services.Type:journal articleJournal:The European Journal of Health EconomicsScopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Utilization rates of hip arthroplasty in OECD countries revisedObjective: In 2014, we reported rising hip arthroplasty utilization across Organization for Economic Cooperation and Development (OECD) countries, particularly among younger patients. Since then, healthcare systems have evolved, demographic shifts are continued, and procedures were impacted by the COVID-19 pandemic. This study aims to reassess these trends through 2022. Methods: We analyzed hip arthroplasty data from the OECD Health Statistics, U.S. Nationwide Inpatient Sample, and World Bank for 27 countries (2005–2022). We compared relative (compound annual growth rates (CAGR)) and absolute (i.e., mean annual differences (MAD)) procedure numbers, stratified by age (≤64 vs. ≥65), and examined associations with GDP, health expenditure, and life expectancy. Sensitivity analyses were performed to account for uncertainty in U.S. data. Results: Between 2011 and 2022, hip arthroplasty utilization grew more slowly than 2005–2011 (CAGR: 1.00% vs. 2.03%; MAD: 1.78 vs. 2.03). Growth from 2011 to 2022 was concentrated among patients aged ≤64 (CAGR 3.08%; MAD: 3.11), while utilization declined among those ≥65 (CAGR: −2.27%; MAD: −12.46). Country-level variation narrowed, with the highest-to-lowest ratio falling from 6.65 (2005) to 2.96 (2022). Despite a sharp drop in 2020 due to COVID-19, most countries recovered by 2022. Sensitivity analyses confirmed the robustness of observed OECD trends. BRIC nations showed faster growth, but data limitations hindered comparisons. Conclusion: Hip arthroplasty utilization continues to increase among younger patients but has declined in older ones. Continued demographic pressure and recovery from COVID-19 disruptions are expected to drive further increases in procedure volume.Type:journal articleJournal:Osteoarthritis and CartilageScopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Retrospective evaluation of interval breast cancer screening mammograms by radiologists and AI(Springer Science and Business Media LLC, 2025-08-04); ;Morant, Rudolf; ;Gräwingholt, AxelObjectives To determine whether an AI system can identify breast cancer risk in interval breast cancer (IBC) screening mammograms. Materials and methods IBC screening mammograms from a Swiss screening program were retrospectively analyzed by radiologists/an AI system. Radiologists determined whether the IBC mammogram showed human visible signs of breast cancer (potentially missed IBCs) or not (IBCs without retrospective abnormalities). The AI system provided a case score and a prognostic risk category per mammogram. Results 119 IBC cases (mean age 57.3 (5.4)) were available with complete retrospective evaluations by radiologists/the AI system. 82 (68.9%) were classified as IBCs without retrospective abnormalities and 37 (31.1%) as potentially missed IBCs. 46.2% of all IBCs received a case score ≥ 25, 25.2% ≥ 50, and 13.4% ≥ 75. Of the 25.2% of the IBCs ≥ 50 (vs. 13.4% of a no breast cancer population), 45.2% had not been discussed during a consensus conference, reflecting 11.4% of all IBC cases. The potentially missed IBCs received significantly higher case scores and risk classifications than IBCs without retrospective abnormalities (case score mean: 54.1 vs. 23.1; high risk: 48.7% vs. 14.7%; p < 0.05). 13.4% of the IBCs without retrospective abnormalities received a case score ≥ 50, of which 62.5% had not been discussed during a consensus conference. Conclusion An AI system can identify IBC screening mammograms with a higher risk for breast cancer, particularly in potentially missed IBCs but also in some IBCs without retrospective abnormalities where radiologists did not see anything, indicating its ability to improve mammography screening quality. Key Points Question AI presents a promising opportunity to enhance breast cancer screening in general, but evidence is missing regarding its ability to reduce interval breast cancers. Findings The AI system detected a high risk of breast cancer in most interval breast cancer screening mammograms where radiologists retrospectively detected abnormalities. Clinical relevance Utilization of an AI system in mammography screening programs can identify breast cancer risk in many interval breast cancer screening mammograms and thus potentially reduce the number of interval breast cancers.Type:journal articleJournal:European Radiology - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Risk factors for interval breast cancer: insights from a decade of a mammography screening program(Springer Science and Business Media LLC, 2025-02-12); ;Rudolf Morant; ;Alena EichenbergerPurpose Breast cancer remains a major global health issue, with mammography screening programs (MSPs) being critical for early detection to improve survival. Interval breast cancers (IBC) are an important quality criterion and have been linked with increased mortality. We aimed to identify risk factors for IBC diagnoses, based on MSP data. Methods In this retrospective cohort study, we merged data from the Swiss MSP “donna” with data from cancer registries from 2010 to 2019 to categorize cases as IBC or screen-detected breast cancer (SBC). We compared the incidence, tumor characteristics, and survival proportions of women with IBC versus SBC. We used a multivariable Poisson regression with robust errors to identify risk factors for IBC diagnoses. Results We identified 1134 breast cancer cases, specifically 251 IBC and 883 SBC. The 7-year survival proportions significantly deviated with 92.9% for women with IBC and 96.4% for women with SBC (p < 0.05). Women with IBC are diagnosed with significantly higher tumor stages (p < 0.05) and have a worse tumor biology in multiple dimensions e.g. larger tumor size or more often triple negative (p < 0.05). Higher breast density (BI-RADS d risk ratio (RR): 3.293), certain age groups (55–59 years RR: 1.345), and a family breast cancer history (RR: 1.299) were identified as significant (p < 0.05) risk factors for IBC diagnoses. Conclusions Women with IBC had lower overall survival proportions than women with SBC, possibly due to higher stages at diagnosis. Increased breast density and a positive family history of breast cancer could encourage MSPs to personalize their screening process (e.g. additional diagnostics).Type:journal articleJournal:Breast Cancer Research and TreatmentScopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A nationwide digital maturity assessment of hospitals – Results from the German DigitalRadar(2024); ;Johannes Hollenbach ;Malte Haring ;Volker Eric AmelungSylvia ThunObjectives In 2019, the German government established the Hospital Future Fund, allocating 4.3 billion Euros, to support investments in the digital infrastructure of hospitals. The DigitalRadar consortium was commissioned by the German Ministry of Health in 2020 to develop a holistic digital maturity model and evaluate the current state of digitalization and the impact of the funding program. To date, the nationwide digitalization of German hospitals has remained a relatively understudied phenomenon. This study aims to address this gap in knowledge by examining the influence of various factors identified by the DigitalRadar maturity model on the digital maturity of hospitals in Germany. In doing so, it seeks to elucidate the implications these findings have for the development of a digital, patient-centred, safe, and high-quality hospital landscape in the country. Methods The model was developed through a scoping review of digital maturity models, requirements set forth in the Hospital Future Act, analysis of components from existing models and feedback from a sounding board. Ultimately, the model includes 234 questions (items) categorized into 7 dimensions of digitalization. It was piloted in 12 hospitals and revised accordingly. 1,624 hospitals (91% of all German hospitals) participated in this self-assessment, as participation was mandatory to receive funding. Results The average DigitalRadar score on a 100-point scale is 33. Maturity is comparatively high in the structures and systems dimension, but low in the clinical processes, exchange of information, telemedicine and patient participation dimensions, suggesting that data exchange is hampered by a lack of interoperability. Drivers of digital maturity are teaching status, size, connectivity, and level of emergency services. Conclusions The transparency gained allows hospitals and regulators to identify areas for improvement and develop digital strategies. Additionally, it enables researchers to analyse, for example, the correlation between digitalization and the quality of care, as well as the mechanisms of action of large-scale funding programs for hospital digitization.Type:journal articleJournal:Health Policy and TechnologyVolume:13Issue:4Scopus© Citations 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Medication adherence halves COPD patients' hospitalization risk - Evidence from Swiss health insurance data(2024); ;Johannes Cordier; Medication adherence is vital for patients suffering from Chronic Obstructive Pulmonary Disease (COPD) to mitigate long-term consequences. The impact of poor medication adherence on inferior outcomes like exacerbations leading to hospital admissions is yet to be studied using real-world data. Using Swiss claims data from 2015-2020, we group patients into five categories according to their medication possession ratio. By employing a logistic regression, we quantify each category’s average treatment effect of the medication possession ratio on hospitalized exacerbations. 13,557 COPD patients are included in the analysis. Patients with high medication adherence (daily medication reserve of 80% to 100%) are 51% less likely to incur exacerbation following a hospital stay than patients with the lowest medication adherence (daily medication reserve of 0% to 20%). The study shows that medication adherence varies strongly among Swiss COPD patients. Furthermore, high medication adherence immensely decreases the risk of hospitalized exacerbations.Type:journal articleScopus© Citations 19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Collection of Components to Design Clinical Dashboards Incorporating Patient-Reported Outcome Measures: Qualitative Study(2024); ; ; ;Carla Walker<jats:sec> <jats:title>Background</jats:title> <jats:p>A clinical dashboard is a data-driven clinical decision support tool visualizing multiple key performance indicators in a single report while minimizing time and effort for data gathering. Studies have shown that including patient-reported outcome measures (PROMs) in clinical dashboards supports the clinician’s understanding of how treatments impact patients’ health status, helps identify changes in health-related quality of life at an early stage, and strengthens patient-physician communication.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This study aims to determine design components for clinical dashboards incorporating PROMs to inform software producers and users (ie, physicians).</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>We conducted interviews with software producers and users to test preselected design components. Furthermore, the interviews allowed us to derive additional components that are not outlined in existing literature. Finally, we used inductive and deductive coding to derive a guide on which design components need to be considered when building a clinical dashboard incorporating PROMs.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>A total of 25 design components were identified, of which 16 were already surfaced during the literature search. Furthermore, 9 additional components were derived inductively during our interviews. The design components are clustered in a generic dashboard, PROM-related, adjacent information, and requirements for adoption components. Both software producers and users agreed on the primary purpose of a clinical dashboard incorporating PROMs to enhance patient communication in outpatient settings. Dashboard benefits include enhanced data visualization and improved workflow efficiency, while interoperability and data collection were named as adoption challenges. Consistency in dashboard design components is preferred across different episodes of care, with adaptations only for disease-specific PROMs.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Clinical dashboards have the potential to facilitate informed treatment decisions if certain design components are followed. This study establishes a comprehensive framework of design components to guide the development of effective clinical dashboards incorporating PROMs in health care practice.</jats:p> </jats:sec>Type:journal articleJournal:Journal of Medical Internet ResearchVolume:26DOI:10.2196/55267Scopus© Citations 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How to interpret patient-reported outcomes? - Stratified adjusted minimal important changes for the EQ-5D-3L in hip and knee replacement patients(Springer Science and Business Media LLC, 2024-11-25); ; ; ; Abstract Background As one of the main goals of hip and knee replacements is to improve patients’ health-related quality of life, a meaningful evaluation can be achieved by calculating minimal important changes (MICs) for improvements in patient-reported outcome measures (PROMs). This study aims at providing MICs adjusted for patient characteristics for EQ-5D-3L index score improvements after hip and knee replacements. It adds to existing literature by relying on a large national sample and precise clustering algorithms, and by employing a state-of-the-art methodology for the calculation of improved adjusted MICs. Methodology A retrospective observational study was conducted using the publicly available National Health Service (NHS) PROMs dataset for primary hip and knee replacements. We used information on 252,331 hip replacements and 279,668 knee replacements from all NHS-funded providers in England between 2013 and 2020. Clusters of patients were created based on pre-operative EQ-VAS, depression status, and sex. Unstratified and stratified estimates for meaningful EQ-5D-3L improvements were obtained through anchor-based predictive MICs corrected for the proportion of improved patients and the reliability of transition ratings Results Stratifying patients showed that MICs varied across subgroups based on pre-operative EQ-VAS, depression status, and sex. MICs were larger for patients with worse pre-operative EQ-VAS scores, while patients with better pre-operative scores required smaller MICs to achieve a meaningful change. We show how after stratification the percentage of patients achieving their stratified MIC was better in line with the actual share of improved patients. Larger MICs were found for patients with depression and for female patients. MICs calculated for knee replacements were consistently lower than those for hip replacements. Conclusions Our findings show the importance of adjusting MICs for patients’ characteristics and should be considered for quality-related choices and policy initiatives.Type:journal articleJournal:Journal of Patient-Reported OutcomesVolume:8Issue:1Scopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessing the Relationship between Hospital Process Digitalization and Hospital Quality - Evidence from Germany.(2024-09-13); ;Haering, Alexander; Hospital digitalization aims to increase efficiency, reduce costs, and/ or improve quality of care. To assess a digitalization-quality relationship, we investigate the association between process digitalization and process and outcome quality. We use data from the German DigitalRadar (DR) project from 2021 and combine these data with two process (preoperative waiting time for osteosynthesis and hip replacement surgery after femur fracture, n = 516 and 574) and two outcome quality indicators (mortality ratio of patients hospitalized for outpatient-acquired pneumonia, n = 1,074; ratio of new decubitus cases, n = 1,519). For each indicator, we run a univariate and a multivariate regression. We measure process digitalization holistically by specifying three models with different explanatory variables: (1) the total DR-score (0 (not digitalized) to 100 (fully digitalized)), (2) the sum of DR-score sub-dimensions' scores logically associated with an indicator, and (3) sub-dimensions' separate scores. For the process quality indicators, all but one of the associations are insignificant. A greater DR-score is weakly associated with a lower mortality ratio of pneumonia patients (p < 0.10 in the multivariate regression). In contrast, higher process digitalization is significantly associated with a higher ratio of decubitus cases (p < 0.01 for models (1) and (2), p < 0.05 for two sub-dimensions in model (3)). Regarding decubitus, our finding might be due to better diagnosis, documentation, and reporting of decubitus cases due to digitalization rather than worse quality. Insignificant and inconclusive results might be due to the indicators' inability to reflect quality variation and digitalization effects between hospitals. For future research, we recommend investigating within hospital effects with longitudinal data.Type:journal articleJournal:Journal of medical systemsVolume:48Issue:1Scopus© Citations 1