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The purpose of this review is to identify how health professionals perceive participation in implementation of new technology in healthcare organizations.

A qualitative systematic review based on the PRISMA diagram, was conducted using qualitative synthesis. NVivo software was used for thematic analysis. The searches were performed in PubMed, CINAHL and Scopus.

A total of 15 articles were included in the review, four themes describing how participation of health professionals in digital transformation affects the outcomes were identified, and three themes describing the factors that are necessary to promote participation. The underlying latent theme of an unmet desire to participate in the digital transformation was also identified in the analysis.

The digital transformation of healthcare is complex and faces many obstacles if not managed correctly. Professional participation in the implementation seems to be essential for success. Focus on increased resources and planning during early stages, as well as teamwork and ethical reflection is important addressing the challenges that professionals face in digital transformation of healthcare.

The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

This study develops a novel method for mitigating credit risk through the use of structured derivatives, focusing in particular on the use of European put options as a strategic hedging tool. Inspired by the work of Merton (1974), our approach introduces the concept of default triggered by the stock price S_T breaching a predefined barrier B. By establishing a distributional equivalence between an existing default model and P(ST<B) for a given time T, we demonstrate the potential for reducing the necessary capital allocation for a projected loss X(T) by partially hedging with a European put option. We formulate and solve an optimization problem w.r.t. a specific risk measure to determine the optimal strike price for the option, and our numerical analysis confirms a reduction in the Solvency Capital Requirement (SCR) in markets with and without jumps. Our findings provide (insurance) companies with a pragmatic approach to mitigating losses while maintaining their current risk management framework.