Search Results (5)

Modern healthcare generates significant amounts of greenhouse gas emissions and waste, which pollute the global environment and damage human health. Healthcare firms could reduce these environmental emissions and waste by developing environmentally friendly technologies and production processes. However, the implementation of green innovations requires significant investments. Healthcare equity mutual funds could provide them financial resources whether this allows fund managers to comply with their fiduciary duties. Previous literature has examined the financial performance of healthcare mutual funds without considering the environmental practices that investees adopt. To understand this issue, we examined the effect of investees’ environmental business practices on healthcare fund financial performance by considering different states of the economy. To this end, we obtained a sample of 148 global healthcare equity mutual funds from December 2015 to December 2022. Adopting the Fama–French model, our findings indicate that mutual funds improve financial performance when investee firms are in the initial phase of greening their processes and activities. However, the mutual funds invested in healthcare firms with advanced environmental practices achieve risk-adjusted returns similar to those invested in healthcare firms that implement conventional business management strategies. Furthermore, the financial performance of healthcare mutual funds is not significantly affected by the COVID-19 pandemic crisis at the aggregate level. Therefore, adopting environmental practices in the healthcare sector will not result in a loss of investor wealth from 2016 to 2022. Full article

Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing research on predicting future prices of healthcare securities has been predominantly reliant on historical trading data, limiting predictive accuracy and scope. This study aims to overcome these constraints by integrating a diverse set of twelve external factors spanning economic, industrial, and company-specific domains to enhance predictive models. Employing Long Short-Term Memory (LSTM) and Multiple Linear Regression (MLR) techniques, the study evaluates the effectiveness of this multifaceted approach. Results indicate that incorporating various influencing factors beyond historical data significantly improves price prediction accuracy. Moreover, the utilization of LSTM alongside this comprehensive dataset yields comparable predictive outcomes to those obtained solely from historical data. Thus, this study highlights the potential of leveraging diverse external factors for more robust forecasting of mutual fund prices within the healthcare sector. Full article

Digitization has become involved in every aspect of life, including the healthcare sector with its healthcare professionals (HCPs), citizens (patients and their families), and services. This complex process is supported by policies: however, to date, no policy analysis on healthcare digitalization has been conducted in European countries to identify the main goals of digital transformation and its practical implementation. This research aimed to describe and compare the digital health policies across four European countries; namely, their priorities, their implementation in practice, and the digital competencies expected by HCPs. A multiple-case study was performed. Participants were the members of the Digital EducationaL programme invoLVing hEalth profEssionals (DELIVER), a project funded by the European Union under the Erasmus+ programme, involving three countries (Denmark, Italy, and Slovenia) and one autonomous region (Catalonia—Spain). Data were collected using two approaches: (a) a written interview with open-ended questions involving the members of the DELIVER project as key informants; and (b) a policy-document analysis. Interviews were analysed using the textual narrative synthesis and the word cloud policy analysis was conducted according to the Ready, Extract, Analyse and Distil approach. Results showed that all countries had established recent policies at the national level to address the development of digital health and specific governmental bodies were addressing the implementation of the digital transformation with specific ramifications at the regional and local levels. The words “health” and “care” characterized the policy documents of Denmark and Italy (309 and 56 times, 114 and 24 times, respectively), while “development” and “digital” (497 and 478 times, respectively) were common in the Slovenia document. The most used words in the Catalonia policy document were “data” and “system” (570 and 523 times, respectively). The HCP competencies expected are not clearly delineated among countries, and there is no formal plan for their development at the undergraduate, postgraduate, and continuing educational levels. Mutual understanding and exchange of good practices between countries may facilitate the digitalization processes; moreover, concrete actions in the context of HCP migration across Europe for employment purposes, as well as in the context of citizens’ migration for healthcare-seeking purposes are needed to consider the differences emerged across the countries. Full article

The Center for Epidemiologic Studies Depression Scale (CES-D) performs well in screening depression in primary care. However, people are looking for alternatives because it screens for too many items. With the popularity of social media platforms, facial movement can be recorded ecologically. Considering that there are nonverbal behaviors, including facial movement, associated with a depressive state, this study aims to establish an automatic depression recognition model to be easily used in primary healthcare. We integrated facial activities and gaze behaviors to establish a machine learning algorithm (Kernal Ridge Regression, KRR). We compared different algorithms and different features to achieve the best model. The results showed that the prediction effect of facial and gaze features was higher than that of only facial features. In all of the models we tried, the ridge model with a periodic kernel showed the best performance. The model showed a mutual fund R-squared (R2) value of 0.43 and a Pearson correlation coefficient (r) value of 0.69 (p < 0.001). Then, the most relevant variables (e.g., gaze directions and facial action units) were revealed in the present study. Full article

Measures favoring healthy lives among populations around the world are essential to reduce social inequalities. Mutual funds could play an important role funding these measures if they are able to attract socially concerned investors by improving their wealth. This study analyzes the financial performance of mutual funds focused on the biotechnology and healthcare sectors related to UN sustainable development goal 3 (SDG 3), comparing their risk-adjusted return with that achieved by conventional mutual funds. This study implements Carhart’s multifactor model and Bollen and Busse’s timing multifactor model on a sample of 34 biotechnology and 178 healthcare mutual funds and 4352 conventional mutual funds. The results show that biotechnology and healthcare mutual funds perform similarly, while both of them outperform conventional mutual funds. This outperformance of biotechnology and healthcare funds is driven by the superior stock-picking skills of their managers with regards to those of conventional fund managers, while managers of biotechnology, healthcare, and conventional mutual funds present similar poor market timing ability. Mutual funds specialized in biotechnology and healthcare sectors related to sustainable development goal 3 (SDG 3) outperform conventional mutual funds. Full article