A5087242915- Emergency and Acute Care Studies
- Statistical Methods in Epidemiology
- Bayesian Methods and Mixture Models
- Healthcare Policy and Management
- COVID-19 and healthcare impacts
- Forecasting Techniques and Applications
- Trauma and Emergency Care Studies
- Healthcare Operations and Scheduling Optimization
- Analytical chemistry methods development
- Research in Social Sciences
- Mass Spectrometry Techniques and Applications
- Breast Implant and Reconstruction
- Machine Learning in Healthcare
- Breast Lesions and Carcinomas
- Breast Cancer Treatment Studies
- Ion-surface interactions and analysis
- Insurance and Financial Risk Management
- Data-Driven Disease Surveillance
- Hydrology and Drought Analysis
- Sepsis Diagnosis and Treatment
- COVID-19 epidemiological studies
Tampere University
2020-2023
Abstract Background and objective Emergency Department (ED) overcrowding is a chronic international issue that associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation could alleviate the problem. There has been continued academic interest in ED forecasting but number used explanatory variables low, limited mainly to calendar weather variables. In this study we investigate whether predictive accuracy next day...
Abstract The current evidence suggests that higher levels of crowding in the Emergency Department (ED) have a negative impact on patient outcomes, including mortality. However, only limited data are available about association between and mortality, especially for patients discharged from ED. primary objective this study was to establish ED overall 10-day mortality non-critical patients. secondary perform subgroup analysis risk separately both admitted An observational single-centre...
Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand the potential improve outcomes. Despite active research on subject, proposed forecasting models have become outdated, due quick influx of advanced machine learning because amount multivariable input data limited. In this study, we document performance set in ED occupancy 24 h ahead. We use electronic health record from...
Abstract Background Emergency departments (EDs) worldwide have been in the epicentre of novel coronavirus disease (COVID-19). However, impact pandemic and national emergency measures on number non-COVID-19 presentations assessed acuity those remain uncertain. Methods We acquired a retrospective cohort containing all ED visits Finnish secondary care hospital during years 2018, 2019 2020. compared 2020 state emergency, i.e. from March 16 to June 11, with numbers 2018 2019. Presentations were...
Abstract Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead better resource management the potential improve treatment outcomes. This logic motivated an increasing number research articles but there little no effort move these findings from theory practice. In this article, we present first results prospective early warning software, that was...
Abstract Background and Objectives Optimal margins for ductal carcinoma in situ (DCIS) remain controversial breast‐conserving surgery (BCS) mastectomy. We examine the association of positive margins, reoperations, DCIS age. Methods A retrospective study histopathological reports (4489 patients). Margin positivity was defined as ink on tumor invasive carcinoma. For DCIS, we applied 2 mm anterior side margin thresholds, posterior margin. Results The incidence 20% BCS 5% mastectomies ( p <...
Abstract Background: Emergency departments (EDs) worldwide have been in the epicentre of novel coronavirus disease (COVID-19). However, impact pandemic and national emergency measures on number non-COVID-19 presentations assessed acuity those remain uncertain. Methods: We acquired a retrospective cohort containing all ED visits Finnish secondary care hospital during years 2018, 2019 2020. compared 2020 state emergency, i.e. from March 16 to June 11, with numbers 2018 2019. Presentations were...
Abstract Objective Emergency department (ED) crowding is a global problem associated with negative patient outcomes such as mortality and prolonged length of stay. Forecasting overcrowding would enable pre-emptive strategical maneuvers subject constant academic interest. However, most studies focus on forecasting arrivals in United States ED setting. We propose novel intuitive metric called daily peak occupancy assess Nordic Combined using both established predictive algorithms. Methods All...
Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead better resource management the potential improve treatment outcomes. This logic motivated an increasing number research articles but there little no effort move these findings from theory practice. In this article, we present first results prospective early warning software, that was integrated...
Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand the potential outcomes. Despite active research on subject, several gaps remain: 1) proposed forecasting models have become outdated due quick influx of advanced machine learning (ML), 2) amount multivariable input data limited 3) discrete performance metrics rarely reported. In this study, we document set ML in ED...
Abstract Background and Objective Emergency Department (ED) overcrowding is a chronic international issue that associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation could alleviate the problem. There has been continued academic interest in ED forecasting but number used explanatory variables low, limited mainly to calendar weather variables. In this study we investigate whether predictive accuracy next day...
The association between emergency department crowding andadverse treatment outcomes has been extensively documented. To alleviate this problem, there a continued academic interest in predicting future overcrowding, but the methods applied have mostly consisted of statistical time series models and number explanatory variables used limited. Over last two years, significant advances introducing artificial intelligence (AI) to forecasting, which promises increase accuracy enables use...