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Highlights
• Artificial intelligence systems for mechanically ventilated patients are increasing.
• The clinical and financial impact of these models are often unexamined.
• We developed a generic health-economic model for artificial intelligence systems.
• This model assesses the cost-effectiveness for many different scenarios.
• The developed framework is easily adjustable to other (clinical) situations.
Abstract
Purpose: The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI systems by using available data and simulations. Therefore, we developed a generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients.
Materials and methods: Our generic health-economic model simulates mechanically ventilated patients from their hospitalisation until their death. The model simulates two scenarios, care as usual and care with the AI system, and compares these scenarios to estimate their cost-effectiveness.
Results: The generic health-economic model we developed is suitable for estimating the cost-effectiveness of various AI systems. By varying input parameters and assumptions, the model can examine the cost-effectiveness of AI systems across a wide range of different clinical settings.
Conclusions: Using the proposed generic health-economic model, investors and innovators can easily assess whether implementing a certain AI system is likely to be cost-effective before an exact clinical impact is determined. The results of the early HTA can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.
Background: SARS-CoV-2 has massively changed the care situation in hospitals worldwide. Although tumour care should not be affected, initial reports from European countries were suggestive for a decrease in skin cancer during the first pandemic wave and only limited data are available thereafter.
Objectives: The aim of this study was to investigate skin cancer cases and surgeries in a nationwide inpatient dataset in Germany.
Methods: Comparative analyses were performed in a prepandemic (18 March 2019 until 17 March 2020) and a pandemic cohort (18 March 2020 until 17 March 2021). Cases were identified and analysed using the WHO international classification of diseases codes (ICDs) and process key codes (OPSs).
Results: Comparing the first year of the pandemic with the same period 1 year before, a persistent decrease of 14% in skin cancer cases (n = 19 063) was observed. The largest decrease of 24% was seen in non-invasive in situ tumours (n = 1665), followed by non-melanoma skin cancer (NMSC) with a decrease of 16% (n = 15 310) and malignant melanoma (MM) with a reduction of 7% (n = 2088). Subgroup analysis showed significant differences in the distribution of sex, age, hospital carrier type and hospital volume. There was a decrease of 17% in surgical procedures (n = 22 548), which was more pronounced in minor surgical procedures with a decrease of 24.6% compared to extended skin surgery including micrographic surgery with a decrease of 15.9%.
Conclusions: Hospital admissions and surgical procedures decreased persistently since the beginning of the pandemic in Germany for skin cancer patients. The higher decrease in NMSC cases compared to MM might reflect a prioritization effect. Further evidence from tumour registries is needed to investigate the consequences of the therapy delay and identify the upcoming challenges in skin cancer care.
The ongoing SARS-CoV-2 pandemic is characterized by poor outcome and a high mortality especially in the older patient cohort. Up to this point there is a lack of data characterising COVID-19 patients in Germany admitted to intensive care (ICU) vs. non-ICU patients. German Reimbursement inpatient data covering the period in Germany from January 1st, 2020 to December 31th, 2021 were analyzed. 561,379 patients were hospitalized with COVID-19. 24.54% (n = 137,750) were admitted to ICU. Overall hospital mortality was 16.69% (n = 93,668) and 33.36% (n = 45,947) in the ICU group. 28.66% (n = 160,881) of all patients suffer from Cardiac arrhythmia and 17.98% (n = 100,926) developed renal failure. Obesity showed an odds-ratio ranging from 0.83 (0.79–0.87) for WHO grade I to 1.13 (1.08–1.19) for grade III. Mortality-rates peaked in April 2020 and January 2021 being 21.23% (n = 4539) and 22.99% (n = 15,724). A third peak was observed November and December 2021 (16.82%, n = 7173 and 16.54%, n = 9416). Hospitalized COVID-19 patient mortality in Germany is lower than previously shown in other studies. 24.54% of all patients had to be treated in the ICU with a mortality rate of 33.36%. Congestive heart failure was associated with a higher risk of death whereas low grade obesity might have a protective effect on patient survival. High admission numbers are accompanied by a higher mortality rate.