Journal of evaluation in clinical practice
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Healthcare systems remain disease oriented despite growing sustainability concerns caused by inadequate management of patients with multimorbidity. Comprehensive care programmes (CCPs) can play an important role in streamlining care delivery, but large differences in setup and results hinder firm conclusions on their effectiveness. Many elements for successful implementation of CCPs are identified, but strategies to overcome barriers and embed programmes within health systems remain unknown. ⋯ The introduction of a CCP is feasible, and exploratory analysis on effectiveness shows lower hospital care use without decreasing patient satisfaction. However, this is accompanied by several challenges that show current fragmented systems still do not support implementation of integrated care initiatives. Overcoming those comes with substantial costs and may require a strong bottom-up implementation within a motivated team and actions on all levels of healthcare systems.
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Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to classifiers with low accuracy and high error rates. Traditional feature-engineered models struggle with this task, and class imbalance is a known factor that reduces the performance of neural network techniques. ⋯ Deep neural network methods, particularly Bi-LSTM, offer promising alternatives to traditional feature-engineered models for clinical abbreviation disambiguation. By employing data generation techniques, we can address the challenges posed by limited-resource and imbalanced clinical datasets. This approach leads to a significant improvement in model accuracy for clinical abbreviation disambiguation tasks.
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We aimed to demonstrate the use of quantitative bias analysis (QBA), which reveals the effects of systematic error, including confounding, misclassification and selection bias, on study results in epidemiological studies published in the period from 2010 to mid-23. ⋯ The application of QBA is rare in the literature but is increasing over time. Future researchers should include detailed analyzes such as QBA analysis to obtain inferences with higher evidence value, taking into account systematic errors.
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Electronic health records (EHR) are frequently used for epidemiological research including drug utilisation studies in a defined population such as the population with knee osteoarthritis (KOA). We sought to describe the process of defining a cohort of patients with KOA from a large UK primary care database and estimate the annual incidence of diagnosed KOA between 2000 and 2015. ⋯ This study logically/sensibly defined a cohort of patients with diagnosed KOA through the application of several strategies. This was an essential step to avoid subsequent over or underestimation of the prevalence of drug utilisation and the associated adverse clinical outcomes within primary care patients with KAO.
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The underreporting of occupational diseases in many countries significantly hampers the development of intervention programs, posing a significant public health problem. Our study aimed to contribute to the occupational diseases surveillance by examining the data of hospitals authorized to issue reports throughout Turkey. ⋯ This study is reflecting national data in Turkey and is the country's first nationwide study. The number of occupational diseases in Turkey is lower than expected. It would be more accurate to express the data in a way that includes medical diagnoses instead of using the number of compensated files corresponding to legal diagnoses.