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**Background:** Timely treatment of acute allergic reactions (AARs) is important to minimize reaction severity. Corticosteroid tablets dissolved in water are commonly used in mainstay treatment. A new oral film that dissolves on the tongue provides a faster and less cumbersome alternative to tablets for corticosteroid administration during AARs. This study evaluated patients’ preferences for attributes related to administration mode of corticosteroids in AARs. **Methods:** A web-based survey was sent to a sample from the adult Swedish population (≥18 years) with experience of corticosteroid treatment for AAR. We assessed the willingness to pay (WTP) for attributes related to corticosteroid treatment by applying a discrete choice experiment (DCE) approach. DCE attributes were administration mode, time to symptom relief, and price. The WTP for each attribute was derived using the attribute’s coefficient in a logistic regression analysis. We specified a forced choice (FC) and an unforced choice (UC) model. In the FC model, the respondents chose between 2 hypothetical treatments and in the UC model, between any of 2 hypothetical treatments and their current treatment. **Results:** The final study population included 348 subjects, of which 80% were women. All the evaluated DCE attributes were significant predictors for the treatment choice (p

**Background:** Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY® Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties. However, the economic consequences of forecasting the onset of sepsis are unknown. **Objectives:** The potential cost and cost-effectiveness impact of a machine learning algorithm forecasting the onset of sepsis was estimated in an ICU setting. **Methods:** A health economic model has been developed to capture short-term and long-term consequences of sepsis. The model is based on findings from a randomized, prospective clinical evaluation of NAVOY® Sepsis and from literature sources. Modeling the relationship between time from sepsis onset to treatment and prevalence of septic shock and in-hospital mortality were of particular interest. The model base case assumes that the time to treatment coincides with the time to detection and that the algorithm predicts sepsis 3 hours prior to onset. Total costs include the costs of the prediction algorithm, days spent at the ICU and hospital ward, and long-term consequences. Costs are estimated for an average patient admitted to the ICU and for the healthcare system. The reference method is sepsis diagnosis in accordance with clinical practice. **Results:** In Sweden, the total cost per patient amounts to €16 436 and €16 512 for the algorithm and current practice arms, respectively, implying a potential cost saving per patient of €76. The largest cost saving is for the ICU stay, which is reduced by 0.16 days per patient (5860 ICU days for the healthcare sector) resulting in a cost saving of €1009 per ICU patient. Stochastic scenario analysis showed that NAVOY® Sepsis was a dominant treatment option in most scenarios and well below an established threshold of €20 000 per quality-adjusted life-year. A 3-hour faster detection implies a reduction in in-hospital mortality, resulting in 356 lives saved per year. **Conclusions:** A sepsis prediction algorithm such as NAVOY® Sepsis reduces the cost per ICU patient and will potentially have a substantial cost-saving and life-saving impact for ICU departments and the healthcare system.

**Background:** Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY® Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties. However, the economic consequences of forecasting the onset of sepsis are unknown. **Objectives:** The potential cost and cost-effectiveness impact of a machine learning algorithm forecasting the onset of sepsis was estimated in an ICU setting. **Methods:** A health economic model has been developed to capture short-term and long-term consequences of sepsis. The model is based on findings from a randomized, prospective clinical evaluation of NAVOY® Sepsis and from literature sources. Modeling the relationship between time from sepsis onset to treatment and prevalence of septic shock and in-hospital mortality were of particular interest. The model base case assumes that the time to treatment coincides with the time to detection and that the algorithm predicts sepsis 3 hours prior to onset. Total costs include the costs of the prediction algorithm, days spent at the ICU and hospital ward, and long-term consequences. Costs are estimated for an average patient admitted to the ICU and for the healthcare system. The reference method is sepsis diagnosis in accordance with clinical practice. **Results:** In Sweden, the total cost per patient amounts to €16 436 and €16 512 for the algorithm and current practice arms, respectively, implying a potential cost saving per patient of €76. The largest cost saving is for the ICU stay, which is reduced by 0.16 days per patient (5860 ICU days for the healthcare sector) resulting in a cost saving of €1009 per ICU patient. Stochastic scenario analysis showed that NAVOY® Sepsis was a dominant treatment option in most scenarios and well below an established threshold of €20 000 per quality-adjusted life-year. A 3-hour faster detection implies a reduction in in-hospital mortality, resulting in 356 lives saved per year. **Conclusions:** A sepsis prediction algorithm such as NAVOY® Sepsis reduces the cost per ICU patient and will potentially have a substantial cost-saving and life-saving impact for ICU departments and the healthcare system.

**Background:** In order to facilitate sound economic evaluations of novel treatments, health-economic models of polycythemia vera (PV) must combine effects on surrogate endpoints in trials with disease progression (DP) and mortality in long-term cohort data. **Objective:** We validate an economic model for PV that uses Janus Kinase 2 (JAK2) burden as a surrogate endpoint to predict DP (thrombosis, myelofibrosis, and acute leukemia) and overall survival (OS) based on progression-specific mortality. **Methods:** Long-term observational studies that include information about baseline JAK2 burden were identified via PubMed searches and used to validate the model. Kaplan-Meier (KM) OS curves were extracted using a digitizing software. External validity of the model was analyzed by visually comparing OS curves of the model with the KM curves of the included studies, as well as calculating differences in mean OS estimated as area under the curve (AUC). **Results:** The model’s predictions of cumulative DP were somewhat lower than the published studies. Over 20 years’ time, our base case model predicted a mean OS for a PV patient (15.0–16.5 years), which was in line with the published studies (15.8–17.5 years). Modeled mean OS was almost two years longer (1.6–1.9 years) for patients with JAK2