F1-measure rate.Parkinson's disease (PD) is a neurodegenerative nervous system disorder that mainly affects body movement, and it is one of the most common diseases, particularly in elderly individuals. This paper proposes a new machine learning approach to predict Parkinson's disease severity using UCI's Parkinson's telemonitoring voice dataset. The proposed method analyses the patient's voice data and classifies them into “severe” and “nonsevere” classes. At first, a subset of features was selected, then a novel approach with a combination of Rotation Forest and Random Forest was applied on selected features to determine each patient's disease severity. Analysis of the experimental results shows that the proposed approach can detect the severity of PD patients in the early stages. Moreover, the proposed model is compared with several algorithms, and the results indicate that the model is highly successful in classifying records and outperformed the other methods concerning classification accuracy and  Parkinson's disease (PD) is a complex disease that mainly affects the patient's movement, and for the first time, was reported in 1817 by a physician, James Parkinson . ApproxiHence, to better control this disease and improve PD patients' life conditions, it needs many researchers from various fields such as behavioral-driven and chemical studies to computer-assisted diagnosis work together . ComputeA hybrid approach is proposed to effectively detect PD patients' severity using a combination of Rotation Forest and Random Forest algorithms.The proposed model achieved high classification accuracy in predicting the severity of PD patients.Extended experiments on the datasets demonstrate that the suggested method produces better classification accuracy than other considered algorithms.This research might help clinics predict the severity of Parkinson's disease in early stages using patients' voice data.Therefore, in past years, many researchers made use of computer-assisted systems like machine learning techniques to solve the PD diagnosis problem. These techniques are among the preferred methods to deal with imbalanced data, mostly audio and voice signals. This paper has proposed a hybrid machine learning model with a combination of Rotation Forest and Random Forest algorithms to analyze the patient's voice data and classify them into “severe” and “nonsevere” classes. This research used two scales of UPDRS  rates and total UPDRS and motor UPDRS rates for evaluation propose. The motor UPDRS evaluates the patient's motor ability on a range of 0–108, while the total UPDRS scores range is over the 0–176. Furthermore, the proposed model's usability is assessed by comparisons with different classification and ensemble techniques using the same data such as J48, Random Tree, Naive Bayes, RBF, AdaBoost, Bagging, LogitBoost, Random Forest, and Multilayer Perceptron Neural Networks, and eventually, conclusions are specified. However, the significant contributions of this research are given below:The research article is structured as follows. In recent years, various methods and techniques have been introduced to monitor and assess Parkinson's disease patients. In many cases, machine learning techniques supported experts in diagnosing Parkinson's in patients. Machine learning methods have a wide variety of applications in different domains . Many reCaramia et al.  proposedGenain et al.  demonstrGrover et al.  proposedNilashi et al.  introducTherefore, according to the literature, it is evident that machine learning techniques have a critical role in Parkinson's disease diagnoses, helping to detect PD in patients in early stage. The majority of recent studies work on the detection of Parkinson's disease using voice signals. There are a limited number of researchers that investigate the use of machine learning algorithms to detect Parkinson's disease in the early stages by the patients' voice signals. Furthermore, the many existing works suffered from high complexity and low accuracy in detection severity among Parkinson's patients. However, based on such motivation, we investigate a current state-of-the-art Rotation Forest ensemble algorithm with the Random Forest algorithm for detection severity of Parkinson's disease in different patients. Therefore, this research aims to build a novel methodological approach that can solve the complex and high-dimensional data for Parkinson's disease severity detection. The suggested method is a combination of a Rotation Forest (RTF) ensemble algorithm and the Random Forest (RF) classification approach, where it is used to classify the severity of Parkinson's patients based on their voice data.This section first describes the dataset we used for our experiments; then, we discuss the preprocessing phase and describe used features for classification purposes. Finally, we present our proposed model for predicting Parkinson's disease severity on the dataset.This paper has used Parkinson's Telemonitoring Voice Dataset from the UCI Repository . The datz-score, and decimal scaling methods; and in this research, we used the min-max method to normalize dataset values.The used dataset contains features with diverse ranges of values. Preparing these types of data for direct use on classification is takes time and increases computational complexity. In addition, the classification process could be affected and raise the rate of inaccurate results . TherefoThe 16 features have been selected for classification and values of them have been normalized. The list of selected features is presented in The classes of records are severe and nonsevere which have been identified using the range of the various metrics, and the description of defined metrics for nonsevere and severe classes is exhibited in The Random Forest is a type of ensemble algorithm that was presented by Breiman , and it N random features [n is the only parameter chosen by the experiments [The RF can be categorized as a meta-classifier algorithm that works using ensemble methodology, and it generates the trees using features . This aleriments . The RF Therefore, Breiman described the best performance of RF Algorithms as associated with the good quality of each tree and a negligible correlation between the trees are in the forest and the strength of each tree in the ensemble. On so-called out-of-bag (OOB) samples, the trees' correlation is described as the normal correlation of predictions. The OOB samples are the set of views applied to measure the prediction failure and then estimate the variable quality, and they are not used for making the current tree . TherefoK subsets, and then it uses Principal Component Analysis (PCA) on each set of features. This method also retains all principal components to maintain the variability information of data. Hence, rotations of the K axis proceed to construct new features for a base classifier. Rotation Forest is a tree-based ensemble algorithm proposed by Rodriguez et al.  in 2006.The primary steps of the model, including dividing the data into subsets of features, and bootstrap helps secure the required diversity for better accuracy. Therefore, these two methods are utilized on the original dataset to obtain diverse data in order to be used as the input of classification algorithms. The dividing the data into subsets stayed fixed for the whole diversity of data.In the produced subsets of data, the data matrix is split into subsets, including several features.Bootstrap is applied to each subset of features that includes randomly selected patterns. The ratio of patterns has stayed %75 of the entire subset, as defined in the traditional pseudocode of Rotation Forest .PCA algorithm is used to take the eigenvector coefficients performed to achieve the data with different diversity. In this method, the PCA algorithm is applied to all of the provided subsets by bootstrap process, and it produces square matrices based on the number of features in all subsets.As can be observed in In the model, the values of Total UPDRS and Motor UPDRS attributes have various ranges. The Total UPDRS values range between a minimum of 5.0377 and a maximum of 54.992, while the Motor UPDRS values are in the range between a minimum of 5.0377 and a maximum of 39.511.This section includes the experimental setup and the investigation of experiment outcomes on Parkinson's disease dataset. We have implemented the proposed model in a system with 16 GB RAM using the Weka tool. As mentioned in the previous section, before we start the classification process, feature values are normalized to the range of  to reduct maximum size of a group of attributes, and the removed percentage, which represents the percentage of information apply for building up the base classifier after PCA transform. In the current research, we set the Maxgroup feature to 11 based on series of empirical testing of different values from 2 to 30. The removed percentage was set to 40% based on the reports presented in [In our introduced model, we have classified Parkinson's dataset using RTF and Random Forest algorithm. Typically, two parameters play an essential role in the performance of the RTF method. The Maxgroup, which represents ented in , where iIn order to evaluate the performance of the proposed model, we used the confusion matrix to calculate evaluation measurements of the model; the outcome results were measured in terms of classification accuracy, precision, recall, and f-measure. The description of the confusion matrix is illustrated in To better evaluate the model performance, we have performed two sets of experiments on our PD severity prediction. We studied the proposed model performance on both Total UPDRS and Motor UPDRS scores in the first set, and we have compared the proposed model experimental result with other classification algorithms such as J48, Random Tree, Naive Bayes, KNN, MLP, and RBF neural network on the same UCI Parkinson's Telemonitoring Voice Dataset. In the second set of experiments, we analyzed and compared the difference between the proposed model and several well-known ensemble algorithms on Total UPDRS and Motor UPDRS scores. The ensemble algorithms applied as a benchmark in comparison included AdaBoost, Bagging, LogitBoost, and Random Forest. However, the model's performance and its comparison with several classification algorithms on the Total UPDRS dataset are exhibited in In the next test, we predicted PD severity based on the Motor UPDRS dataset. As shown in As shown in Tables Therefore, the comparison results present a significant gap between the proposed model's performance and considered algorithms, which shows the used approach performed better than other methods in the detection of Parkinson's disease severity on both Total and Motor UPDRS datasets. This study's results also have demonstrated the best classification performance achieved by the proposed model on the Motor UPDRS dataset, which shows it is a more useful metric for predicting Parkinson's disease severity.Next, to verify the effectiveness of the proposed model, we compared the model with several other ensemble methods to analyze its performance for predicting the severity of Parkinson's disease. Figures Next, we compared the presented model performance with the same considered ensemble algorithms in the previous test on the Motor UPDRS dataset. The comparison results are illustrated in As shown in Figures Parkinson's disease is a long-term progressive disorder that affects nerve cells in the brain responsible for body movement. The progress of this disorder could be identified using motor and nonmotor symptoms, and it is so crucial for patients to diagnose the disease in early stages. In the present research, we have used an efficient machine learning method to analyze changes in patients' voices as one of PD patients' common symptoms and classify it into “severe” and “nonsevere” classes. In the presented technique, we employed the Rotation Forest to achieve better classification performance than other well-known algorithms such as J48, RBF, KNN, MLP, AdaBoost, Bagging, LogitBoost, and Random Forest. The Rotation Forest is an ensemble learning method. The primary purpose of ensemble methods is to create several models and combine them to provide enhanced results. Ensemble techniques usually provide solutions with higher classification accuracy than single models. Random Forest and Rotation Forest are two essential ensemble learning methods created explicitly known for producing forests. These two algorithms contain some similarities, such as developing multiple trees in classification. However, they are different; RTF uses different feature spaces with the help of algorithms such as PCA to produce the dataset's subset. The Rotation Forest is also designed to run by a smaller number of ensembles than the Random Forest. The combination of them in current research leads us to building a robust classification algorithm. The previous section's reported results present our idea that Rotation Forest performs better than other considered algorithms in terms of classification accuracy and F-measure.The main aim of our method is to find the severity of Parkinson's disease patients in the early stages based on their voice features using a new machine learning model. Hence, in order to present and evaluate our model's effectiveness in classification, we compared the model classification performance with state-of-the-art algorithms. Therefore, the comparison section has two parts. At first, the proposed method is compared with well-known classification algorithms. In the second part, we compared the classification performance of the proposed model with other ensemble methods to demonstrate its effectiveness over other state-of-art ensemble algorithms.In the model preprocessing phase, we employed the min-max normalization technique that helps to convert all of the values of the features in the dataset with various scales to the same range. This method avoids the dominant feature with a larger scale than other features, harming the prediction process. In terms of train-test splitting data, we conducted experiments with 80 percent of the dataset for training and the remaining 20% for testing the model. This selection is operated by Resample filter of the Weka tool. This method was similar for all conducted experiments and compared algorithms to produce a fair comparison. The different train and test splitting ratios can provide different results in various researches. However, the employed technique for splitting data in our research is one of the most common and standard ratios for splitting data in the ML domain.F-measure. The RTF encourages member diversities and individual accuracy in a classifier ensemble simultaneously. This is because of the proposed method classified instances based on the Rotation Forest ensemble technique using Random Forest as the base classifier. It is our method's uniqueness. Hence, as reported in our comparative study with considered algorithms, we also believe the Rotation Forest is an underrated ensemble classifier that deserves more attention in the literature. Additionally, the achieved result indicated that the Motor UPDRS score might be a better metric for predicting Parkinson's disease's severity since it achieved better classification accuracy.Therefore, in the results presented in F-measure rate than considered algorithms. The paper findings also indicate that performing classification using Motor UPDRS scores is more beneficial for severity prediction since it can achieve better classification accuracy than Total UPDRS. However, we have used a dataset of 5875 instances for evaluation of the proposed model, and it was one of the limitations of our research since the accuracy of the proposed method can be enhanced by investigating through other large datasets.Parkinson's disease is a progressive central nervous system disorder that affects body movement control. In recent years, some researchers have introduced various techniques for predicting Parkinson's disease in patients. However, this paper aims to develop an effective hybrid model to predict Parkinson's disease severity. The introduced model conations are a two-step process: first, we selected the optimal subset of features. Next, we applied a novel approach with a combination of Rotation Forest and Random Forest algorithms on selected features to classify every patient's records into “severe” and “nonsevere” classes. We investigated our proposed model on the two separate datasets created based on Total UPDRS and Motor UPDRS rates. The proposed method has achieved results demonstrating that the model performs significantly better than other classification and ensemble algorithms since it produces better classification accuracy and an


This study aims to identify all actors that hold some responsibility for ensuring food safety based on the complete food supply chain in the context of China's current circumstances.The study was conducted among citizens in Wuxi, Jiangsu, China. All citizens fully understood the purpose of the study and voluntarily agreed to participate. From December 10 to 14, 2020, a total of 398 valid samples were collected by the researchers using a structured questionnaire. Survey data were assessed using best–worst scaling and a mixed logit model from the perspective of citizen responsibility.In descending order, responsibility for ensuring food safety goes from food producers and traders  to the government, social organizations, news media, and finally to citizens. Food producers and traders are the actors who should take the greatest responsibility, whereas citizens bear the least responsibility.The responsibility of citizens in food safety co-governance should be recognized but it should not be arbitrarily extended. Food safety is a major public issue facing all countries around the world , 2, and Ensuring food safety is one of a government's basic responsibilities . GovernmIn fact, citizenship entails responsibilities. Participation in public affairs is an inherent duty of citizenship . HoweverCitizens can play a critical role in food safety risk management . AccordiIn this study, we attempted to incorporate citizens into the food safety social co-governance system, and scientifically identify the key responsible actors, including the government, enterprises, social organizations, and citizens, in the co-governance system. Moreover, we investigated the responsibilities of these key actors for ensuring food safety and objectively evaluated citizens' responsibilities in the food safety co-governance system in the context of China using best–worst scaling (BWS) and a mixed logit model, thereby eliciting the policy implications for enabling citizens to play their role in China, which is an innovative perspective and methodology.Many traditional stakeholders exist in food safety. Meanwhile, the continuous development of online trading has seen the emergence of trading platforms and WeChat Moments, etc., as new stakeholders. Nevertheless, as early as 1995, the Commission on Global Governance (CGG) put forward a generally accepted view based on extensive academic research, namely that the government, food producers and traders, social organizations, and citizens are the four fundamental types of responsible actors in the food safety co-governance system in Western countries . Other sThe most basic element of food safety is production, i.e., all steps of the production process before food reaches consumers' plates. Ensuring food safety thereby requires combined efforts from producers, distributors, and retailers, all of whom are indispensable. These three actors have clear boundaries and are mutually irreplaceable. Together, they constitute the whole process from farm to fork. As the actors most directly involved in ensuring food safety, they should hold primary responsibility for jointly ensuring food safety , in keepFood safety also relies on governance. As a major public health issue, food safety risks are the shared responsibility of governments around the world . In ChinSocial organizations are diverse in form and function. Based on the classification used in Western countries as well as the current situation of China, this study divides social organizations that can play a role in the co-governance system into the following four types. The first type is professional food industry organizations. In China, such organizations include food industry associations, food fermentation associations, etc., which have professional, industry-specific, and self-disciplined functions in food safety co-governance. The second type is food testing organizations. Such organizations have been entrusted with inspecting the quality of food produced by enterprises. Although an entrusted passive behavior, it has higher credibility. Especially in the case of disputes, it plays a greater role than the active inspection of industry and consumer associations . The thiNews media can take advantage of their strong influence and rapid information dissemination capabilities to alleviate food safety information asymmetry to a certain extent. For example, the melamine-tainted milk powder crisis was widely reported by different types of news media within 4 months of its outbreak in August 2008. The incident's huge scale not only caused a sensation in China, but also attracted extensive attention from the international community. On the one hand, the strong public opinion motivated government regulators to enhance food safety regulation. On the other hand, online public opinion began to be noticed by a wide range of citizens at different levels in China, which initiated the process of mobilizing citizens to participate in food safety risk management. As such, the news media can act as a “vanguard” and “watchmen” in the food safety social co-governance system in China , 49. NewAs indicated in the literature review, citizens capable of independent behavior are not simply food consumers. They not only should assume the responsibility of self-protection, but can also act as the best participant and regulator of food safety. As such, they are an important force in social co-governance, playing an irreplaceable role. In the context of China, a citizen is a person who has citizenship and thus has certain rights as well as obligations. It represents the concept of an individual. However, in this study, citizens included in the co-governance system to assume the corresponding responsibility are a collective concept, representing a collection of individual citizens.To sum up, this study develops an analytical framework shown in The data for this study were collected by a questionnaire survey. Wuxi in Jiangsu Province has been among the leading large and medium-sized cities in China in terms of economic and social development. In 2020, Wuxi had the highest per capita GDP among all cities in China. Ordinary people generally live in good conditions and show relatively high satisfaction with food safety. This area therefore provides a good foundation for this study. Sample data were collected from a field questionnaire survey in the five administrative districts of Wuxi, including Liangxi, Xishan, Huishan, Binhu, and Xinwu districts. The sample size for each district was proportional to the respective resident population. The survey was carried out among citizens aged over 18 years (hereinafter referred to as respondents) in farmers' markets, chain supermarkets, and food stores with a large flow of customers in each district. Investigators were instructed to select the third person coming into view as a respondent to maximize sample randomness . The queBWS, also known as maximum difference scaling (MaxDiff), is an extension of pairwise comparison . As an iAccording to Louviere et al. , offerinThe mixed logit model is based on random utility maximization and is a generalization of the traditional multinomial logit model. Compared with the traditional multinomial logit model, the mixed logit model relaxes the strict assumption of independence of irrelevant alternatives, allows coefficients to differ between samples with the same observable characteristics, and does not assume that error terms follow a normal distribution , 63. TheUnkt obtained by individual n  from choosing the kth  alternative in situation t  can be divided into two parts. The first part is the observable fixed utility, which depends on the observable attributes of the alternative and individual characteristics. The second part is the unobservable random utility, which represents the effect of unobservable factors on an individual's choice. The utility expression is as follows:According to the random utility maximization theory, it is assumed that the utility Unkt is the utility obtained by individual n from choosing k in situation t; Vnkt is the observable utility; Xnkt is the attribute variable of actor k, which is the observable explanatory vector; and βn and εnkt are the random-effect variables that cannot be directly measured.where i and j to bear the greatest and least responsibility, respectively, the utility difference between Unit and Unjt is then greater than that of all other choice sets M, where M = K(K−1)−1. By assuming εnkt follows an independent and identically distributed type I extreme-value distribution, the following choice probability of the conditional logit model can be derived:In the BWS study, respondents were asked to choose the pair of actors with the largest utility difference, i.e., the actors that should bear the greatest and least responsibility. For example, assuming that respondents chose actors i is the actor chosen to bear the greatest responsibility, and j is the actor chosen to bear the least responsibility. When respondents choose a range of actor combinations that should bear the greatest and least responsibility in situation t, the choice probability of Equation (2) can be expressed as follows:where f. The choice probability of Equation (3) can then be expressed asThe mixed logit probability is a weighted average of each logit variable in the estimation of different β values. The weights are given by the density function Respondent demographics are shown in The assignment of responsibilities to each responsible actor in the food safety social co-governance system for ensuring food safety was performed using the utility code .The mixed logit model estimates are presented in Among the seven actors of the five types, food producers, distributors, and retailers together constitute a combined actor, namely food producers and traders. The results in The government should bear 28.19% of the responsibility, which is the second greatest share of responsibility after that of food producers and traders. In addition, social organizations and news media should bear 13.90 and 9.30% of responsibility, respectively, which are considerable shares. As explained above, the news media is regarded as an individual actor because most news media outlets in China have some attributes of government organizations despite being different from government regulators; they also have a unique function that is different from those of any other types of social organizations in the co-governance system. However, news media is also a social organization with an independent legal status. In this sense, the responsibility share of all social organizations, including news media, for food safety co-governance, was 13.90%, which is the third greatest share.In addition, the responsibility share of citizens was 6.54%, which is the smallest share in the co-governance system. This is consistent with the conclusions of Erdem et al.  and is rHowever, some controversy still exists in the literature regarding whether citizens are the actor with the least responsibility in the food safety social co-governance system. Redmond and Griffith  and Van There are limitations in this study. First, the survey for this study is geographically limited to Wuxi and is not nationwide due to time and fund constraints. Moreover, the demographics of the sample do not perfectly match the overall demographics of Wuxi. Future research should expand the survey area to examine the differences in the public's responsibility perception in different areas to make improvement in depth and breadth. Second, there is no clear division of power and responsibility in the entire food supply chain between the government, market, and society. The role of any individual actor has spatial and temporal boundaries, which, however, are relative and limited, leading to the ambiguity of responsibilities. Therefore, there is still some overlap in the functions undertaken by the seven actors identified in this study in ensuring food safety. This is not only a limitation of this study, but also an unbreakable fact that exists in all countries. Third and lastly, some controversy still exists in the literature regarding whether citizens are the actor with the least responsibility in the food safety social co-governance system. As the present study is based on data obtained from a citizen survey, it is possible that the conclusions may suffer impaired objectivity because citizens may consciously ignore or shirk their responsibility due to their own interest demands. Therefore, future research should include a survey of all responsible actors using new methods to allow a more objective assessment of the responsibilities of all responsible actors, including citizens, in ensuring food safety.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Jiangnan University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.LW conceptualized the study. KQ processed and analyzed the relevant data, as well as wrote the manuscript. XC collected and analyzed the relevant data and revised the manuscript. All authors contributed to the study design, interpretation the results and manuscript revision, and have approved the final manuscript.This study was supported by the National Social Science Fund of China: Research on social co-governance of food safety risks and cross-border cooperative governance mechanism (20&ZD117).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In this paper we present the results of a systematic review aimed at investigating what the literature reports on cyberbullying and cyberhate, whether and to what extent the connection between the two phenomena is made explicit, and whether it is possible to identify overlapping factors in the description of the phenomena. Specifically, for each of the 24 selected papers, we have identified the predictors of cyberbullying behaviors and the consequences of cyberbullying acts on the victims; the same analysis has been carried out with reference to cyberhate. Then, by comparing what emerged from the literature on cyberbullying with what emerged from the literature on cyberhate, we verify to what extent the two phenomena overlap in terms of predictors and consequences. Results show that the cyberhate issue related to adolescents is less investigated than cyberbullying, and most of the papers focusing on one of them do not refer to the other. Nevertheless, by comparing the predictors and outcomes of cyberbullying and cyberhate as reported in the literature, an overlap between the two concepts emerges, with reference to: the parent-child relationship to reduce the risk of cyber-aggression; the link between sexuality and cyber-attacks; the protective role of the families and of good quality friendship relationships; the impact of cyberbullying and cyberhate on adolescents' individuals' well-being and emotions; meaningful analogies between the coping strategies put in practice by victims of cyberbullying and cyberhate. We argue that the results of this review can stimulate a holistic approach for future studies on cyberbullying and cyberhate where the two phenomena are analyzed as two interlinked instances of cyber-aggression. Similarly, prevention and intervention programs on a responsible and safe use of social media should refer to both cyberbullying and cyberhate issues, as they share many predictors as well as consequences on adolescents' wellbeing, thus making it diminishing to afford them separately.http://www.crd.york.ac.uk/PROSPERO, identifier: CRD42021239461. Social media have become online environments in which face-to-face activities of everyday life are transferred in the network mediated world with a wider audience  and no time constraints (they are open 24 h a day). According to the Digital 2020 July Global Statshot reportThe share of adolescents contributing to these numbers is impressive: 90% of US teens aged 13-17 years use social media AACAP, 3; a simi2020According to these statistics, adolescents are among the most frequent users of social media. Following Shapiro and Margolin , the maiDespite these perceived or real benefits, social media can become a place in which antisocial behaviors such as bullying, harassment and hate speech, proliferate and evolve leveraging the peculiarity of the online world  is rapidly increasing, and the risk for adolescents to be exposed to hateful online material is increasing accordingly  have access to a smartphone and 45% of them reported that they were online “almost constantly”  by another youth or group of youths who are not siblings or current dating partners that involves an observed or perceived power imbalance and is repeated multiple times or is highly likely to be repeated.”Cyberbullying does not simply refer to the transition from 'traditional' bullying in face-to-face contexts to bullying in online contexts, where, according to the Centers for Disease Control and Prevention, bullying is defined as “Other characteristics of the virtual environment affect the proliferation of cyberbullying and differentiate cyberbullying from traditional bullying. In the virtual environment cyberbullies can use anonymous accounts to attack their victims. There is another relevant difference between cyberbullying and bullying as stated by Englander  who statMoving to the cyberhate concept, there are not universally accepted definitions of hate speech and cyberhate  the common language used by the perpetrators. Then, school-aged children, adolescents and young people are particularly exposed to cyberbullying and cyberhate, both as victims and perpetrators: Li et colleagues  have illIn addition to these general factors that characterize the nature of the two phenomena, the aim of this review is, as anticipated in the introduction, to analyze in more detail the individual factors that—based on the results of the empirical studies presented in the selected papers—allow us to understand the level of overlap between cyberbullying and cyberhate. This will make it possible to better target interventions aimed at preventing the phenomena of cyber-aggression committed by or directed against adolescents.http://www.crd.york.ac.uk/PROSPERO).The PRISMA method was followed for the review methodology and data extraction  from a book, Ph.D. theses, reports on ID guidelines.The initial database search identified a total of 1296 records, after a careful selection, according to the PRISMA checklist and the inclusion and exclusion criteria, 24 papers were analyzed for the data extraction. The flow diagram following the models of Page et al.  is incluPapers were firstly reviewed through the title and abstract to determine if they could be included or excluded. The final papers were organized for the data extraction according to the following variables: digital object ID, article title, abstract, journal title, journal year, authors, social media as the subject of investigation, which kind of cyberbullying/cyberhate, content of the studies, method and procedure participants, age participants, sex, stage of life, country, aim of the study, result.The synthesis of the main variables considered essential to the topic of the systematic review process is reported in To systematically report and compare the heterogeneous findings of the articles included, a coding scheme was created, with categories derived from the reported results. Since some papers contained a large number of different worth reporting findings, the results were not exclusively assigned to one category, but are presented in the context of different dimensions. Given that the focus of this review was on cyberbullying and cyberhate as two interlinked instances of cyber-aggression, both phenomena contributed to the main distinction of the results, with 19 papers referring to cyberbullying and 5 papers referring to cyberhate. Next, the results were categorized according to whether they related to the predictors or the impact of cyberbullying or cyberhate. Thus, a 2 × 2 matrix was created, which served as a grid for the analysis of the results. The grid was further divided into subcategories as follows: as predictors, we considered (1) socio-demographic variables , (2) individual and contextual factors  and (3) the overlap between traditional and cyberbullying as well as the overlap of cyberhate with other forms of aggressive behavior. Similarly, the effects of both phenomena were contrasted and considered categories such as effects on health and well-being  and coping strategies.Numerous studies investigated the potential impact of various socio-demographic variables on cyberbullying behaviors. Age seems to play a substantial role in the prevalence of these behaviors. For example, Ybarra et al.  found evAge (school grade level) also seemed to moderate the association between cyberbullying victimization and students' engagement. While a positive association between cyberbullying victimization and emotional engagement was stronger for high school students than for middle school students, a negative association between cyberbullying victimization and cognitive-behavioral engagement was stronger for middle school students than for high school students  and via the Internet  and Schneider et al. . Emotions cited by victims included anger, stress, or fear. Although Low and Espelage  and cyberbullying is also assumed by Ševčíková and Šmahel , who hypIn their study involving more than 700 Finnish youth aged 15–18 who used Facebook as a social medium, Oksanen et al.  analyzedThese results are consistent with those found by Wachs et al. , who repAlthough socio-demographic data do not appear to be influential in the context of cyberhate, it is worth noting that, first, in the study by Oksanen et al.  online hStudy findings presented by Oksanen et al.  suggest According to the results of Oksanen et al. , the hatThe role of the family was again addressed in the study by Räsänen et al. , but theMoreover, the authors report that the likelihood of becoming a victim of hate online is higher among youth who visit harmful sites on the Internet, the likelihood increases for those who deliberately search for this type of content, and the odds of victimization are almost four times higher for those producing hate materials. In line with this, Pauwels and Schils  found thLooking at individual and contextual factors, Wachs et al.  analyzedvia a link from a friend.Finally, Räsänen et al.  found thAs Wachs et al.  showed, Based on the results of their study involving 1,900 French students, Blaya et al.  noted thIn addition, they found that as victimization at schools (offline behavior) increased, the likelihood of exposure to hate material online (online behavior) also increased. Furthermore, their findings indicate that students who insult or threaten others at school also spread hate messages against others online. The way cyberhate is related to other forms of aggressive behavior is shown by the further results of the study. Thus, a weaker relationship was observed between cyberhate victimization and cyberhate perpetration, a moderate relationship was observed between school bullying victimization and cyberhate perpetration, and a moderate relationship was observed between cyberhate victimization and school bullying perpetration.That exposure to hate material can be associated with offline physical victimization was also noted by Oksanen et al. . In theiVictimization through cyberbullying can result in very different effects. For example, some studies linked online victimization to psychological distress  felt more stressed than occasional victims. In the case of cyberbullying via the Internet, more women (13.7%) than men (2%) reported feeling stressed. This finding is in line with the conclusion drawn by Baldry et al.  represent a further element of overlapping between cyberbullying and cyberhate, being predictors of perpetration, victimization and exposure to online hate material . Previous research on the consequences of discrimination (online and offline) on adolescents' mental well-being can support this assertion. Discrimination is, in fact, a transversal theme to cyberbullying and cyberhate, where in the first case it is a tool aimed at hurting the individual, while in the second case it is a manifestation of hatred against a group of individuals , with which the adolescent may or may not recognize himself. Studies analyzing the effects of discrimination against individuals confirm how the risk of adjustment problems and the number of suicidal ideations and attempts increase for adolescents who have been discriminated against  can be predictors of cyberbullying victimization . In fact, the literature on cyberhate clearly indicates that race is a predictor of cyberhate exposure and victimization  as predictors of both phenomena; the protective role of the families against cyberbullying and cyberhate attacks, provided that parents offer instructive mediation while restrictive parental mediation determines the opposite effect; the role of good quality friendship relationships as deterrent against cyberbullying and cyberhate attacks; the impact of cyberbullying and cyberhate on adolescents' emotions as well as their consequences on individuals' wellbeing, mainly consisting in depressive symptoms, somatic symptoms, post-traumatic stress symptoms and psychological distress; the same coping strategies put in practice by victims of the two phenomena.In addition to the factors common to cyberbullying and cyberhate, the literature highlights some of the characteristics that distinguish each of the two phenomena. In particular, differences concern the adjustment problems and the ideation of suicide, which have been found in studies on cyberbullying but not on cyberhate; individual and personal characteristics, often related to physical appearance , as predictors of cyberbullying victimization only; the gender as a predictor for cyberbullying perpetration and victimization, while it does not appear to be a predictor for cyberhate exposure and victimization; the lack of a well-defined overlap between cyberbullying and cyberhate with regard to race/ethnicity, cultural context, language spoken at home (here considered as a proxy of ethnicity); the impact of self-esteem, empathy and high levels of relational aggression on cyber-aggression, even though this issue has not been sufficiently analyzed in the literature on cyberhate.We argue that the results of this review can stimulate future research on cyberbullying and cyberhate where the two phenomena are analyzed as two interlinked instances of cyber-aggression, while respecting their distinctive features. Moreover, further research should investigate the effectiveness of prevention and intervention programs based on the shared commonalities and reciprocal influence of cyberbullying and cyberhate , according to a holistic approach to the general problem of cyber-aggression in adolescence.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.GF: conceptualization, formal analysis, and paper drafting and revising. DT: conceptualization and paper drafting and revising. LS: conceptualization, formal analysis, data curation, methodology, and paper drafting. VS: formal analysis, data curation, methodology, and paper drafting and revising. SE: conceptualization and paper revising. All authors contributed to the article and approved the submitted version.This work has been developed in the framework of the project COURAGE—A social media companion safeguarding and educating students (No. 95567), funded by the Volkswagen Foundation in the topic Artificial Intelligence and the Society of the Future.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Follow-up comparisons found greater increases for gender relative to race/ethnicity.Despite increasing attention to lack of diversity among medical education faculty, those traditionally underrepresented in medicine remain so. In 2017, the University of Nevada, Reno School of Medicine approved a new policy to increase diversity in the faculty search process, which includes a mandatory 2-h workshop on best practices in search processes and implicit bias training. Workshop participants were 179 search committee members making up 55 committees from February 2017 to March 2020. Participants completed two separate social validity surveys, one immediately following the workshop and another following the close of their search, and rated various aspects of the workshop. Each search committee completed a Diversity Checklist (DCL) of various mandatory and best practices to be implemented during each search. Historical data on diversity of job applicants, interviewees, and hires over the 5-year period immediately preceding workshop implementation were compared with corresponding diversity data from the participant search committees for a 3-year period following implementation of the workshop. Social validity surveys indicated high ratings pertaining to the benefits of the workshop (means 3.82–4.39 out of 5). Implementation of practices outlined in the DCL were high (94% of mandatory and 87% of best practices). Chi-square analyses of diversity data before and after implementation revealed significant increases in overall diversity (both race and gender) of applicants ( The AAMC , 2 advocIn order to address the potential negative effects of bias on search committees, the AAMC  and otheGiven the aforementioned strategies are reported as “best” and “proven” practices , academiIn addition, intentional focus on communication of organizational changes  and complementary training are critical. Research suggests that diversity and implicit bias training may contribute to diversity resistance among those who identify with non-marginalized groups . DiversiIn February 2017, The Faculty Search Policy to Create Excellence and Diversity was passed and implemented at the University of Nevada, Reno School of Medicine (UNR Med). The policy addresses both systemic and interpersonal racism by mandating that all members of search committees complete a 2-h, in-person workshop on best practices, including training on implicit bias, to increase diversity equity. The workshop incorporates the best practices noted above, as described in the literature. Values alignment, imagery, and goal setting are also incorporated to address motivation and promote values-based behaviors , 34. It The purpose of this study was to evaluate the participatory influence of the search committee diversity training package on committee members' perceived utility and on the diversity of applicants hired at UNR Med for all faculty positions, including leadership positions. We predicted that the workshop would be well-received by participants and feasible for an institution to adopt as a mandatory component of the search committee process. We also anticipated that participants would report high levels of intent to implement the practices upon completion of the workshop and high levels of actual implementation of the practices upon completion of their respective searches. Finally, we hypothesized that in the time following implementation of the workshop, the diversity (race and gender) of the pool of candidates at each stage of the search process would be greater than during the years prior to implementation of the workshop.The 2-h, in-person search committee workshop was provided separately for each search committee, at the outset of that respective search. The workshop included content on (1) How diversity fits into the vision, mission, and core values at UNR Med; (2) The relationship between diversity and excellence, including a review of data on how diversity enhances medical education, influences access and outcomes in health care, advances research, and improves management decisions , 18, 20;Search committee member participants included classified staff, faculty, and community partners. Two different social validity surveys  communicated with each respective search coordinator, in order to ensure compliance with the mandatory items in the Diversity Checklist see . Thus, tMoreover, historical data on the diversity of candidates for the 5-year period (1 January 2012 through 31 January 2017) prior to implementation of the new workshop were gathered and compared to similar data for the immediate 3-year period (February 2017 to March 2020) following implementation of the workshop, in order to determine if the new workshop corresponded with an increase in the diversity of applicants, candidates interviewed, and hires.A total of 179 participants completed the workshop and were therefore asked to complete the first social validity survey following the workshop. The vast majority of these participants ultimately participated in at least one search, however, some participants were part of multiple searches, while others may have left the institution before or during a respective search, given that searches were typically 6 months in duration. It is important to note that every search committee was a unique combination of members, although a specific committee member may have participated in more than one unique search committee over the entire 8-year span of the study period. If a search committee member participated in more than one search, they were only required to complete the workshop once, as the Faculty Search Policy to Create Excellence and Diversity mandates renewal of the training every 3 years. The 179 participants who received the training package constituted 89% (179/201) of individuals who were mandated to attend, suggesting a positive response to mandatory training and indicating the feasibility of making such training mandatory for all search committee members. As noted previously, if a search member did not complete the mandatory training, they were removed from the search committee.The first social validity survey SV1, see  was compData from the DCL indicated high levels of implementation of the practices taught in the workshop, based on yes/no responses of whether specific practices listed in the DCL were implemented during each respective search. Only one DCL was required per search committee and was submitted by the search coordinator. Of the total search committees that participated, 91% (50/55) completed and submitted the DCL. Compliance with both mandatory practices (94% average) and best practices (88% average) listed in the DCL was high.The most important outcome variable was whether the diversity of the pools of candidates improved once we began implementing the workshop, which was evaluated through a pre-post design. Percentages of diverse candidates pre- and post-workshop throughout the search process are depicted by category in 2(1) = 21.0, p < 0.001], applicants being offered an interview , and those eventually offered a position . Follow-up comparison looking at race (Black and/or Latinx) only found a significant increase from pre to post workshop only in applicants to positions, , but not interviews, , or offers, . Follow-up comparisons of a further breakdown into specifically Black or Latinx categories indicated the observed increase in applicants was a function of an increase in Black applicants and not Latinx applicants. The follow-up comparison looking only at gender  revealed significant increases from pre to post workshop in number of applicants , interviews extended to applicants , and eventual positions offered . Findings indicated improvements were greater for gender than for race.With regard to overall diversity (race and/or gender combined) of candidates, data revealed a significant increase from pre to post workshop in applicants to positions [χA depiction of the year-by-year trends for the 5 years prior to and 3 years following implementation of the search committee workshop is presented for each diversity category in Many best practices aimed at diversifying the academic medicine workforce are touted in the literature as gold-standard, however, there is a lack of evidence to demonstrate their effectiveness. In the present study, we combined several best practices into one training package and observed a corresponding improvement in diversity of applicants, candidates interviewed, and faculty hired in the period of time after workshop implementation began. The present findings are preliminary in nature because the applied research design employed could not rule out the possible influence of extraneous factors on the significant improvements observed. In addition, our findings also indicate implementation of such a training package is feasible, perceived to be socially valid by participants, and therefore likely worth the investment of time and resources by medical schools.via mandated implicit bias assessment and training for all members of search committees.The training package addresses two of three levels of racism , heedingWe found an increase in diversity of applicants for gender and race combined as well as when examined separately. However, in terms of diversity of those applicants who were then offered interviews and positions, we observed an increase in overall diversity, when race and gender were combined, as well as with gender only, but not for race when analyzed separately. The significant increase in overall diversity of interviewees and hires was largely driven by the increase in gender as compared with race.via implicit bias training but also potential institutional and systemic barriers through the practices outlined in the DCL.There are several possibilities that could account for the lack of statistically significant increases in racial diversity among those candidates who were selected for both interviews and offers. First, it is possibly due to smaller sample sizes among the Black/Latinx category only, relative to those categories which included gender, that may have precluded the possibility to detect anything other than a very large effect. Additionally, while we are not aware of any empirical data that can be used to support the following notion, it may be possible that increased national awareness of cultural movements such as #MeToo and #TimesUp exerted some level of influence during the timeframe of the present study and may have therefore drawn additional attention to issues of gender equity during that time. It is worth noting these data were collected prior to the more recent focus on systemic racism following high-profile instances of police brutality toward communities of color in 2020. In this vein, another strength of this study is that the training package addressed not only interpersonal bias Based on our finding that greater improvements were observed among gender relative to race, the workshop has been revised to include a greater emphasis on racial diversity and intersectionality between race and gender. Now that we have demonstrated the feasibility of the workshop, next steps include collaboration and implementation at other medical schools to increase generalizability of results and establish greater experimental control of the workshop intervention. In addition, other data collected during the workshop rollout but not presented here will also be analyzed, in order to determine which best practices contributed most  to the diversity of candidate pools and applicants hired, as well as determine the participatory influence of implicit race and gender bias among search committee members on various outcomes of the search process.Although the findings are promising, this study was limited by lack of a control group, as the newly adopted Faculty Search Policy to Create Excellence and Diversity mandated participation of all search committees in the newly established process. As such, it is possible the demonstrated changes in diversity of applicants hired could have been due to variables other than the workshop itself, such as the pro-diversity culture at our institution, as reflected in the willingness of our school to adopt the Faculty Search Policy to Create Excellence and Diversity and mandate best practices to increase diversity. Another limitation includes our inability to stratify results by type of position  due to threats to confidentiality if results were broken down in such a way. Similarly, we were unable to disaggregate results by department due to privacy concerns, so results may have been skewed by departments who were more diverse prior to the search and continued to be after workshop implementation. Lack of ability to further disaggregate data also precluded an evaluation of intersectionality. In addition, generalizability was limited since this study was conducted at only one site. Our low response rate for SV1 and SV2 surveys may have reflected a response bias and specific steps to increase response rates to workshop evaluations should be incorporated in future studies.Despite the limitations of this naturalistic study, we were able to demonstrate participatory influence of a diversity training package that incorporated best practices to promote diversity at each stage of the search process. Following implementation of the workshops, we noted a significant increase in the diversity of applicants, candidates interviewed, and faculty hired, though we cannot attribute this improvement solely to the workshop at this time. In addition, we demonstrated that the mandatory training package was feasible and well received by participants. Based on these findings, other institutions are informed that requiring search committee members to participate in diversity training and mandating best practices is well worth their investment of time, effort, and resources.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the University of Nevada, Reno Institutional Review Board reviewed this protocol and deemed the study to be Exempt from IRB Review on 6 March 2017, Reference # 980480-1. The patients/participants provided their written informed consent to participate in this study.NJ wrote the introduction and discussion, developed and carried out the trainings, and oversaw data collection. JE-L helped to develop and carry out the trainings, directly collected data, and oversaw data analysis along with our office of continuous institutional assessment. GS wrote the methods and results section, helped with data analysis, contributed significantly to writing of the introduction, and methods as well as edits to the overall manuscript. MP provided substantive edits to the overall manuscript. RH oversaw the project, had input on methods, and helped to edit the overall manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Multiple attempts aimed at highlighting the relationship between big data analytics and benefits for healthcare organizations have been raised in the literature. The big data impact on health organization management is still not clear due to the relationship’s multi-disciplinary nature. This study aims to answer three research questions: a) What is the state of art of big data analytics adopted by healthcare organizations? b) What about the benefits for both health managers and healthcare organizations? c) What about future directions on big data analytics research in healthcare?Through a systematic literature review the impact of big data analytics on healthcare management has been examined. The study aims to map extant literature and present a framework for future scholars to further build on, and executives to be guided by.The positive relationship between big data analytics and healthcare organization management has emerged. To find out common elements in the studies reviewed, 16 studies have been selected and clustered into 4 research areas: 1) Potentialities of big data analytics. 2) Resource management. 3) Big data analytics and management of health surveillance systems. 4) Big data analytics and technology for healthcare organization.In conclusion is identified how the big data analytics solutions are considered a milestone for managerial studies applied to healthcare organizations, although scientific research needs to investigate standardization and integration of the devices as well as the protocol in data analysis to improve the performance of the healthcare organization.The online version contains supplementary material available at 10.1186/s12913-022-08167-z. Big data is transforming and will transform the healthcare organizations in the near future , 2. ScieTechnological applications in healthcare BDA-related are rapidly increasing  and willNowadays, the amount of data is no longer an issue. Internet traffic reports from Cisco and other network operators have estimated the entire digital universe to be 44 zettabytes and 463 exabytes will be the daily information could be generated by 2025. A new era took place in which the processes of production and management of human knowledge will no longer be the exclusive preserve of humans; machines will also play their part as knowledge producers . From phThe market size is a useful indicator of how much the healthcare organizations are turning their attention to new management models based on the use of big data. By 2025, the big data market in healthcare will touch $70 billion with a record 568% growth in 10 years. The use of such a tool not only represents a complex challenge , but alsProvide specific services to patients, from diagnostics to preventive medicine passing through therapeutic adherence.Detect the onset and spread of diseases in advance.Observe parameters inherent to hospital quality standards, promoting control and prevention actions.Modify treatment techniques.Facilitate research and development in pharmacology, reducing the time to market of drugs.Facilitate research and development of new and specific medical devices.The BDA satisfies multiple needs that, on the one hand, influence the quality of the healthcare organization’s performance and, on the other hand, are useful in directing management strategies to improve the supply of healthcare services. Below there are some strategies, which aim to:modus operandi of healthcare organizations will be launched.The main aim of this research is, therefore, to provide both an integrative framework on the state of art, and perspectives on how the BDA can be useful for the management of the healthcare organization. Considering the results, food-for-thought on how this technological and cultural revolution will affect the Through an overview of recent scientific studies, this research aims to raise awareness among both practitioners and managers about BDA tools applied to healthcare management to address more effectively and efficiently the challenges imposed by an increasing demand for healthcare services.In this regard, the study provides a systematic literature review (SLR) to explore the effect of BDA on the healthcare management by analyzing articles from the Scopus database during a period of 5 years (2016 – 2021).Furthermore, the result through a content analysis, aspires to be a privileged starting point to find out potential barriers and opportunities provided by BDA-based management systems for smarter healthcare organization. Specifically, the study answers different research questions (RQs) as different levels of analysis have been performed. By analyzing the relationship between BDA-based management systems and the benefits delivered to the organizations, the research could not be conducted without exploring the state of art of BDA tools deployed in the field of healthcare. Thus, starting from this background the discussion on the future perspectives on BDA development in the healthcare organizations appears as a need.Why use BDA and how to exploit its potential for healthcare organization management? This is the main question asked by managers and decision makers working in the healthcare sector. In recent years there have been multiple attempts in the literature aimed at highlighting the relationship between implementation of BDA and benefits for healthcare organizations, in terms of both resource efficiency and process management.organizational facility with tools, techniques, and processes that enable a firm to process, organize, visualize, and analyze data, thereby producing insights that enable data-driven operational planning, decision-making, and execution”. In the healthcare organization, BDAC represents the ability to collect, store, analyze, and process huge volume variety, and velocity of health data come from various sources to improve data-driven decisions [In 2017, a study by Wang and Hajli  has propecisions , 19. Indecisions , validatResearchers have found that BDA capabilities primarily stem from the implementation of various tools and features. Specifically, in order of importance, BDA capabilities are firstly triggered by processing tools , followed by aggregation tools , and, secondly, by data visualization tools and capabilities .predictive analytics capability, i.e., the ability to explore data and identify useful correlations, patterns and trends, and extrapolate them to predict what is likely to occur in the future , 25;interoperability capability, i.e., the ability to integrate data and processes to support management, collaboration, and sharing across different healthcare departments, managers, and facilities , and fintraceability capability, i.e., the ability to integrate and track all patient history data from different IT facilities and different healthcare units.Among the potentials triggered by the implementation of BDA in the healthcare organization, the analytical one was the main capability, that is the ability to process clinical data characterized by immense volume, variety (from text to graph), and speed (from batch to streaming), using descriptive analysis techniques , 22. In In terms of expected benefits from the BDA implementation, the study of Wang and Hajli  has showIn another study carried out to identify BDA benefits and supports, and to drive organizational strategies, Wang, Kung, and Byrd , throughThe first successful strategy is to implement governance based on the use of big data, starting with a definition of objectives, procedures, and key performance indicators (KPIs). Once again, one of the discriminating factors for success in implementing such a strategy remains the integration of information systems and the standardization of data protocols that often come from heterogeneous sources already existing in healthcare organizations. The second strategy is related to developing a culture of data sharing. The third one considers the training of healthcare managers, who cannot ignore knowledge related to BDA, for example on the use of data mining and business intelligence tools. The fourth strategy is related to the storage of big data, often available in heterogeneous formats, and is identified in the transition from the more expensive traditional storage systems (NAS) to more efficient and effective systems such as cloud computing solutions. The last strategic driver involves pathways related to the implementation of predictive BDA models. The mastery of KPIs, interactive visualization and data aggregation tools such as dashboards and reports should be acquired instruments for healthcare managers and in general for healthcare organizations oriented to BDA driven process management strategies.More recent studies focus attention on the management practices supply chain in healthcare. In the study performed by Yu et al. , the autThe scholars have delivered an important contribution in demonstrating the relationship between BDAC, SCI, and operational flexibility from multiple perspectives, by providing useful management guidance for healthcare executives and managers involved in the supply chain. By analyzing and processing medical and managerial data with advanced analytical techniques, Chinese healthcare organizations were able to facilitate decision-making process with timely and appropriate actions, for example, tracking people's movements during the lockdown caused by the Coronavirus, understanding ongoing health trends, and managing pharmaceutical supplies , 31.This theoretical framework provides a key to interpreting the benefits offered by good practices deriving from the use of the BDA in the healthcare organization.At the same time, the rigorous scientific method allows the validation of empirical experiences in relation to clear theoretical references. In the next paragraph projects that demonstrate what is stated in the literature are shown.N(ursing) + Care App is an mHealth application that supports the work of frontline health workers (FHW) in developing countries [ountries . The sysFor healthcare managers, predicting the number of emergency department accesses is a critical issue which complicates the optimization of the human resource management. To this end, Intel, and Assistance Publique-Hôpitaux de Paris (AP-HP), the largest hospital university in Europe, leveraging datasets from multiple sources, worked together to build a cloud-based solution to predict the number of patient visits to emergency rooms and hospital admissions. This predictive analytics tool, will enable healthcare managers at AP-HP hospitals to know the number of emergency room visits and hospital admissions at 15 days in order to reduce wait times, optimize human resource (HR) levels based on anticipated needs, accurately plan patient loads, including by pathology, and overall improve the quality and efficiency of services provided by the healthcare organization .Enerzair Breezhaler. It was the first drug for the treatment of asthma co-packaged and co-prescribed with the Propeller digital platform. The app sends a reminder to comply with therapeutic adherence and maintains a record of the data, which the patient shares with him or her physician. Studies have demonstrated that the Propeller platform increases the degree of asthma control by up to 63%, therapeutic adherence by up to 58% [Chronic conditions, if not kept under control through a rigorous program of therapeutic adherence, can become a source of both more serious physical problems for patients and economic burdens for healthcare organizations. Another project that actively introduced BDA tools into healthcare management was carried out by the European Commission to launch production of the drug p to 58% , and redp to 58% .The practical framework described, aided by some empirical experience, only partially reveals the potential offered by BDA. The diffusion of BDA-based management systems in the healthcare organization will trigger a virtuous circle, allowing soon to accumulate increasingly accurate medical data. By exploiting the most advanced AI technologies, BDA will support predictive analysis, allow physicians to make more accurate and faster diagnostic pathways and managers to use results. It will help health practitioners in the decision-making process, optimize the use of resources with a consequent costs reduction and, overall, improve the quality of services provided by healthcare organizations.The main aim of this study is to update the state of art about the BDA-based management systems adopted in the healthcare organization, underlining management advantages for both the organizations and managers. BDA has the potential to reduce the cost of care, prevent disease outbreaks, and improve the patients’ quality of life. Through its ability to process and cross-reference massive amounts of both management, and clinical information, BDA promises to be an effective support tool for both healthcare managers and patients.To achieve this aim, a Systematic Literature Review (SLR) was performed. This method identifies, evaluates, and summarizes the updates that raise from the literature about the BDA tools used to improve both the healthcare organizations performance and patients’ quality of life. The method takes inspiration from the protocol used by Khanra S., et al.  which coWhat is the state of art of BDA adopted by healthcare organizations?What about the benefits for both health managers and healthcare organization?What about future directions on BDA research in healthcare?The present study aims to add a contribute to the literature by addressing three RQs:To answer the RQs, as widespread electronic database Scopus has been selected. To obtain an international validity of studies, the research only considers papers in English. Utilizing the Boolean operator “AND”, the following keywords have been searched: “big data analytics” AND “healthcare” AND “management”. As inclusion criteria, only papers published from 2016 to 2021 have been considered. As subject areas, “medicine” and “business, management and accounting” have been selected. Instead, as exclusion criteria, article in press and the following documents type: “review”, “book”, “conference review”, “letter” and “note” have not been taken into account. Also, to avoid a dispersal of the study, conference proceedings have been excluded. Following the searching protocol, 34 results have been obtained  is an ambitious project of the European Commission. It will lead member states to share an efficient infrastructure for both exchange and management health data by providing citizens with equal treatment, free access to clinical data, and quality healthcare services.In the area “Others” all the other countries contributing marginally to research have been included.The next step of the study is focused on a content analysis to show the experiences of applying BDA in healthcare organizations.Starting from the 34 articles selected for the descriptive analysis, to identify in detail the core issue of the study, a second screening was performed. 18 articles were excluded because weakly focused on the research objective which concerns specifically how BDA can be used for healthcare organization management. Thus, after an in-depth reading of abstracts and full papers, the scholars have identified 16 papers closer targeted on the mentioned research objective. The 16 studies selected through a content analysis were clustered into 4 research areas (RAs) as showed in the following table , is necessary for R&D to be focused on data filtering mechanisms in order to obtain good-quality reliable information . The heaIn light of the above, the RA1 includes studies for which the quality of data and the need for high performance filtering mechanisms are becoming keys factor for the success of BDA-based management systems in the healthcare organizations. For example, the study carried out by Maglaveras et al., , includeAnother important research direction emerged from the literature review, concerns positive impact of the BDA on the resource management. Insufficient policy for managing medical materials waste, energy use and environmental burden, restricts the resources conservation. The BDA is extremely useful in this aspect; it could provide in the next future an important contribution to implement the circular economy processes and to support sustainable development initiatives in the healthcare organizations . To thisThe impact on the performance has been also investigated by studies that have linked benefits of BDA and artificial intelligence with green supply chain integration process . DigitalNowadays, many scholars are focusing on BDA-driven decision support systems to sustain the healthcare managers . These tIn this RA, two other important topics investigated by BDA are: high quality healthcare service, and healthcare costs. Optimize the supply chain activities is an imperative to keep lower the healthcare costs. The data generated by medical equipment and devices can be successfully used in forecasting, decision-making process, and to make more efficient the healthcare supply chain management . The stuAs already asserted, a good implementation of BDA in the healthcare organization will play a fundamental role in improving the clinical outcomes management, giving helpful insights for decision makers and managers, in order to avoiding diseases, reducing healthcare expenses, and improving the performance of the healthcare organization . HoweverThe rise of BDA promises to solve many healthcare challenges in the developing countries. The BDA applied to healthcare organization help managers to rationalize the resources, and health system to better delivery treatments to the patients . In thisThe wearable devices and different kind of sensors, able to collect clinical data, in combination with BDA, will constitute the basis of personalized medicine and will be crucial tools to improve the performance of healthcare organizations . The sciA fruitful field of interest for the application of BDA in healthcare organization is the diagnostic imaging. To take out maximum benefits from it and to be useful for managers of healthcare organizations, it is necessary to implement digital platforms and applications . Indeed,In light of these results, it is possible to state that IoT, big data, and artificial intelligence as machine-learning algorithms, are three of the most significative innovations in the healthcare organization. These types of organizations are implementing home-centric data collection networks and intelligent BDA systems based on machine learning technologies. For example, a high-level implementation of these systems has been efficiently implemented in Cartagena, Colombia, for hypertensive patients by using an e-Health sensor and Amazon Web Services components . The autAs highlighted by the results, in Europe, as well as in the rest of the world, a significant trend is emerging among healthcare organizations in adopting BDA-based management systems . Among tAs emerged by the RAs, some studies explore business value for healthcare organizations and the concept of potentialities of BDA (RA1) to explain the evidence of precise path-to-value chains leading to specific benefits . These pIn general, BDA-based management systems make personalized care programs possible. However, considering the enormous amount and heterogeneity of information available nowadays, it emerges the necessity to address R&D pathways towards data filtering mechanisms and engineering new intelligent decision support systems within the healthcare organizations .Circular economy (CE) and sustainability concepts are becoming important key drivers in healthcare organizations to reduce negative impact on the environment (RA2). Some study directions look at BDA as tool to provide solution for barriers related to CE and support sustainable development initiatives in the healthcare organizations . EmpiricIn developing countries, as well as in the rest of the world, the management of health surveillance is a sensitive issue (RA3). Therefore, authors have studied main key factors that hind BDA access in the healthcare organization . TechnolThe rising of AI, IoT, machine learning –51, and By exploring the relationship between BDA-based management systems and the benefits delivered to the healthcare organizations, this study replies to 3 RQs: 1) What is the state of art of BDA adopted by healthcare organizations, 2) What are the benefits for both health managers and healthcare organizations and 3) What are the future directions on BDA research in healthcare.To answer the RQs the SLR has started from an investigation on the recent literature BDA about the BDA in healthcare organizations. Descriptive analysis has been performed on a sample of 34 studies coming from all over the world. The second stage shows a detailed content analysis on 16 studies which better answer to research question about the relationship between benefits for the healthcare organization and BDA solutions.By analyzing the successful BDA strategies in healthcare context, some authors focus their attention on the BDA potentialities applied in the healthcare organizations , 37. IndIn the literature, other scholars highlight the positive impact of BDA on resource management. The BDA solutions are analyzed as tools to sustain CE initiatives , 39 as wAccording to the recent literature, BDA is transforming the healthcare organizations. The SLR has showed how the BDA solutions are now quite considered a milestone for managerial studies applied to healthcare organizations. The Coronavirus pandemic has been a good test run for using BDA to design healthcare policy strategies. Although an extensive literature on BDA to support healthcare management is being produced, the classification into four RAs proposed is an attempt to examine precise key research directions. About that, the limitations of the present research can be detected as the difficulty to review a field of literature constantly evolving. To date, the amount of data is no longer an issue. To be useful in the healthcare context, is necessary to validate their quality and then find the right correlations. In other words, the data should be processed, analyzed, and interpreted correctly. For this reason, emerges the need to address research pathways towards filtering mechanisms, by converting data from big to smart, and engineering new decision support systems within the healthcare organizations .The content analysis carried out in this research has shown that studies are addressed to find out new models for both predictive and personalized medicine by exploiting BDA technologies . The resThus, considering the results obtained, it is possible to state that BDA can effectively help healthcare managers to detect common patterns and turn high volumes of data into usable knowledges. Investments on human capital become a priority to exploit the potential of BDA .To achieve these objectives the future research should provide usable insights and standardized procedures for training healthcare managers and practitioners. AI, machines learning, as well as management strategies, will also play their part as knowledge producers in the healthcare organization. Privacy issues related to healthcare data and also the necessity to make sensor data homogeneous, are becoming crucial research topics to be faced. Finally, due to the heterogeneity of information sources, the future direction of research should investigate the standardization and integration of the protocol in data analysis, as well as the techniques useful for the managerial sector to implement increasingly BDA-based management systems in future healthcare organizations .Nowadays the challenge for healthcare organizations is the development of useful applications BDA-based. According with the circular economy view, the future research directions should be addressed considering the relationship between digitalization and management resources consumption. The data centralization combined with a BDA approach can effectively support circular economy processes in healthcare supply chain by reducing waste and resource consumptions.Exploiting the BDA’s capabilities will also be a key factor in forecasting and monitoring outbreaks. Future studies will need to focus on developing more efficient models for sharing data in order to improve the performance of healthcare organizations around the world.Additional file 1. List of articles.