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  1. Article ; Online: Assessing Patient Safety Culture in Hospital Settings.

    Azyabi, Abdulmajeed / Karwowski, Waldemar / Davahli, Mohammad Reza

    International journal of environmental research and public health

    2021  Volume 18, Issue 5

    Abstract: The current knowledge about patient safety culture (PSC) in the healthcare industry, as well as the research tools that have been used to evaluate PSC in hospitals, is limited. Such a limitation may hamper current efforts to improve patient safety ... ...

    Abstract The current knowledge about patient safety culture (PSC) in the healthcare industry, as well as the research tools that have been used to evaluate PSC in hospitals, is limited. Such a limitation may hamper current efforts to improve patient safety worldwide. This study provides a systematic review of published research on the perception of PSC in hospitals. The research methods used to survey and evaluate PSC in healthcare settings are also explored. A list of academic databases was searched from 2006 to 2020 to form a comprehensive view of PSC's current applications. The following research instruments have been applied in the past to assess PSC: the Hospital Survey on Patient Safety Culture (HSPSC), the Safety Attitudes Questionnaire (SAQ), the Patient Safety Climate in Health Care Organizations (PSCHO), the Modified Stanford Instrument (MSI-2006), and the Scottish Hospital Safety Questionnaire (SHSQ). Some of the most critical factors that impact the PSC are teamwork and organizational and behavioral learning. Reporting errors and safety awareness, gender and demographics, work experience, and staffing levels have also been identified as essential factors. Therefore, these factors will need to be considered in future work to improve PSC. Finally, the results reveal strong evidence of growing interest among individuals in the healthcare industry to assess hospitals' general patient safety culture.
    MeSH term(s) Attitude of Health Personnel ; Cross-Sectional Studies ; Hospitals ; Humans ; Organizational Culture ; Patient Safety ; Safety Management ; Surveys and Questionnaires
    Language English
    Publishing date 2021-03-03
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review ; Systematic Review
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph18052466
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks.

    Davahli, Mohammad Reza / Karwowski, Waldemar / Fiok, Krzysztof

    PloS one

    2021  Volume 16, Issue 7, Page(s) e0253925

    Abstract: Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of ... ...

    Abstract Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of active virus at the time of vaccination. This research developed sequence-learning models to predict the behavior of the COVID-19 pandemic across the US, based on previously reported information. For this objective, we used two time-series datasets of confirmed COVID-19 cases and COVID-19 effective reproduction numbers from January 22, 2020 to November 26, 2020 for all states in the US. The datasets have 310 time-steps (days) and 50 features (US states). To avoid training the models for all states, we categorized US states on the basis of their similarity to previously reported COVID-19 behavior. For this purpose, we used an unsupervised self-organizing map to categorize all states of the US into four groups on the basis of the similarity of their effective reproduction numbers. After selecting a leading state (the state with earliest outbreaks) in each group, we developed deterministic and stochastic Long Short Term Memory (LSTM) and Mixture Density Network (MDN) models. We trained the models with data from each leading state to make predictions, then compared the models with a baseline linear regression model. We also remove seasonality and trends from a dataset of non-stationary COVID-19 cases to determine the effects on prediction. We showed that the deterministic LSTM model trained on the COVID-19 effective reproduction numbers outperforms other prediction methods.
    MeSH term(s) COVID-19/prevention & control ; COVID-19/virology ; COVID-19 Vaccines/administration & dosage ; Neural Networks, Computer ; SARS-CoV-2/isolation & purification ; United States ; Vaccination/statistics & numerical data
    Chemical Substances COVID-19 Vaccines
    Language English
    Publishing date 2021-07-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0253925
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A System Dynamics Simulation Applied to Healthcare: A Systematic Review.

    Davahli, Mohammad Reza / Karwowski, Waldemar / Taiar, Redha

    International journal of environmental research and public health

    2020  Volume 17, Issue 16

    Abstract: In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems. However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence ... ...

    Abstract In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems. However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence on the effectiveness of system dynamics simulation in this area. The present paper draws on a systematic selection of published literature from 2000 to 2019, in order to form a comprehensive view of current applications of system dynamics methodology that address complex healthcare issues. The results indicate that the application of system dynamics has attracted significant attention from healthcare researchers since 2013. To date, articles on system dynamics have focused on a variety of healthcare topics. The most popular research areas among the reviewed papers included the topics of patient flow, obesity, workforce demand, and HIV/AIDS. Finally, the quality of the included papers was assessed based on a proposed ranking system, and ways to improve the system dynamics models' quality were discussed.
    MeSH term(s) Delivery of Health Care ; Humans ; Models, Theoretical ; Systems Analysis
    Keywords covid19
    Language English
    Publishing date 2020-08-08
    Publishing country Switzerland
    Document type Journal Article ; Systematic Review
    ISSN 1660-4601
    ISSN (online) 1660-4601
    DOI 10.3390/ijerph17165741
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Assessing Patient Safety Culture in Hospital Settings

    Abdulmajeed Azyabi / Waldemar Karwowski / Mohammad Reza Davahli

    International Journal of Environmental Research and Public Health, Vol 18, Iss 2466, p

    2021  Volume 2466

    Abstract: The current knowledge about patient safety culture (PSC) in the healthcare industry, as well as the research tools that have been used to evaluate PSC in hospitals, is limited. Such a limitation may hamper current efforts to improve patient safety ... ...

    Abstract The current knowledge about patient safety culture (PSC) in the healthcare industry, as well as the research tools that have been used to evaluate PSC in hospitals, is limited. Such a limitation may hamper current efforts to improve patient safety worldwide. This study provides a systematic review of published research on the perception of PSC in hospitals. The research methods used to survey and evaluate PSC in healthcare settings are also explored. A list of academic databases was searched from 2006 to 2020 to form a comprehensive view of PSC’s current applications. The following research instruments have been applied in the past to assess PSC: the Hospital Survey on Patient Safety Culture (HSPSC), the Safety Attitudes Questionnaire (SAQ), the Patient Safety Climate in Health Care Organizations (PSCHO), the Modified Stanford Instrument (MSI-2006), and the Scottish Hospital Safety Questionnaire (SHSQ). Some of the most critical factors that impact the PSC are teamwork and organizational and behavioral learning. Reporting errors and safety awareness, gender and demographics, work experience, and staffing levels have also been identified as essential factors. Therefore, these factors will need to be considered in future work to improve PSC. Finally, the results reveal strong evidence of growing interest among individuals in the healthcare industry to assess hospitals’ general patient safety culture.
    Keywords patient safety culture ; safety climate ; behavioral learning ; healthcare ; Medicine ; R
    Subject code 690
    Language English
    Publishing date 2021-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  5. Article ; Online: Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks.

    Mohammad Reza Davahli / Waldemar Karwowski / Krzysztof Fiok

    PLoS ONE, Vol 16, Iss 7, p e

    2021  Volume 0253925

    Abstract: Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of ... ...

    Abstract Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of active virus at the time of vaccination. This research developed sequence-learning models to predict the behavior of the COVID-19 pandemic across the US, based on previously reported information. For this objective, we used two time-series datasets of confirmed COVID-19 cases and COVID-19 effective reproduction numbers from January 22, 2020 to November 26, 2020 for all states in the US. The datasets have 310 time-steps (days) and 50 features (US states). To avoid training the models for all states, we categorized US states on the basis of their similarity to previously reported COVID-19 behavior. For this purpose, we used an unsupervised self-organizing map to categorize all states of the US into four groups on the basis of the similarity of their effective reproduction numbers. After selecting a leading state (the state with earliest outbreaks) in each group, we developed deterministic and stochastic Long Short Term Memory (LSTM) and Mixture Density Network (MDN) models. We trained the models with data from each leading state to make predictions, then compared the models with a baseline linear regression model. We also remove seasonality and trends from a dataset of non-stationary COVID-19 cases to determine the effects on prediction. We showed that the deterministic LSTM model trained on the COVID-19 effective reproduction numbers outperforms other prediction methods.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  6. Article ; Online: A System Dynamics Simulation Applied to Healthcare

    Mohammad Reza Davahli / Waldemar Karwowski / Redha Taiar

    International Journal of Environmental Research and Public Health, Vol 17, Iss 5741, p

    A Systematic Review

    2020  Volume 5741

    Abstract: In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems. However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence ... ...

    Abstract In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems. However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence on the effectiveness of system dynamics simulation in this area. The present paper draws on a systematic selection of published literature from 2000 to 2019, in order to form a comprehensive view of current applications of system dynamics methodology that address complex healthcare issues. The results indicate that the application of system dynamics has attracted significant attention from healthcare researchers since 2013. To date, articles on system dynamics have focused on a variety of healthcare topics. The most popular research areas among the reviewed papers included the topics of patient flow, obesity, workforce demand, and HIV/AIDS. Finally, the quality of the included papers was assessed based on a proposed ranking system, and ways to improve the system dynamics models’ quality were discussed.
    Keywords simulation modeling ; system dynamics ; healthcare ; Medicine ; R
    Subject code 690
    Language English
    Publishing date 2020-08-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  7. Article ; Online: The Combination of Artificial Intelligence and Extended Reality

    Dirk Reiners / Mohammad Reza Davahli / Waldemar Karwowski / Carolina Cruz-Neira

    Frontiers in Virtual Reality, Vol

    A Systematic Review

    2021  Volume 2

    Abstract: Artificial intelligence (AI) and extended reality (XR) differ in their origin and primary objectives. However, their combination is emerging as a powerful tool for addressing prominent AI and XR challenges and opportunities for cross-development. To ... ...

    Abstract Artificial intelligence (AI) and extended reality (XR) differ in their origin and primary objectives. However, their combination is emerging as a powerful tool for addressing prominent AI and XR challenges and opportunities for cross-development. To investigate the AI-XR combination, we mapped and analyzed published articles through a multi-stage screening strategy. We identified the main applications of the AI-XR combination, including autonomous cars, robotics, military, medical training, cancer diagnosis, entertainment, and gaming applications, advanced visualization methods, smart homes, affective computing, and driver education and training. In addition, we found that the primary motivation for developing the AI-XR applications include 1) training AI, 2) conferring intelligence on XR, and 3) interpreting XR- generated data. Finally, our results highlight the advancements and future perspectives of the AI-XR combination.
    Keywords artificial intelligence ; extended reality ; learning environment ; autonomous cars ; robotics ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 401
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: The Hospitality Industry in the Face of the COVID-19 Pandemic: Current Topics and Research Methods.

    Davahli, Mohammad Reza / Karwowski, Waldemar / Sonmez, Sevil / Apostolopoulos, Yorghos

    International journal of environmental research and public health

    2020  Volume 17, Issue 20

    Abstract: This study reports on a systematic review of the published literature used to reveal the current research investigating the hospitality industry in the face of the COVID-19 pandemic. The presented review identified relevant papers using Google Scholar, ... ...

    Abstract This study reports on a systematic review of the published literature used to reveal the current research investigating the hospitality industry in the face of the COVID-19 pandemic. The presented review identified relevant papers using Google Scholar, Web of Science, and Science Direct databases. Of the 175 articles found, 50 papers met the predefined inclusion criteria. The included papers were classified concerning the following dimensions: the source of publication, hospitality industry domain, and methodology. The reviewed articles focused on different aspects of the hospitality industry, including hospitality workers' issues, loss of jobs, revenue impact, the COVID-19 spreading patterns in the industry, market demand, prospects for recovery of the hospitality industry, safety and health, travel behavior, and preference of customers. The results revealed a variety of research approaches that have been used to investigate the hospitality industry at the time of the pandemic. The reported approaches include simulation and scenario modeling for discovering the COVID-19 spreading patterns, field surveys, secondary data analysis, discussing the resumption of activities during and after the pandemic, comparing the COVID-19 pandemic with previous public health crises, and measuring the impact of the pandemic in terms of economics.
    MeSH term(s) COVID-19 ; Coronavirus Infections/epidemiology ; Humans ; Industry ; Pandemics ; Pneumonia, Viral/epidemiology ; Research Design ; Restaurants
    Keywords covid19
    Language English
    Publishing date 2020-10-09
    Publishing country Switzerland
    Document type Journal Article ; Systematic Review
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph17207366
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks.

    Davahli, Mohammad Reza / Fiok, Krzysztof / Karwowski, Waldemar / Aljuaid, Awad M / Taiar, Redha

    International journal of environmental research and public health

    2021  Volume 18, Issue 7

    Abstract: The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions ... ...

    Abstract The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the United States. While the GTNN model's edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with R
    MeSH term(s) COVID-19 ; COVID-19 Vaccines ; Humans ; Neural Networks, Computer ; Pandemics ; SARS-CoV-2 ; United States/epidemiology
    Chemical Substances COVID-19 Vaccines
    Language English
    Publishing date 2021-04-06
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph18073834
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: The Chaotic Behavior of the Spread of Infection during the COVID-19 Pandemic in Japan.

    Sapkota, Nabin / Murata, Atsuo / Karwowski, Waldemar / Davahli, Mohammad Reza / Fiok, Krzysztof / Aljuaid, Awad M / Marek, Tadeusz / Ahram, Tareq

    International journal of environmental research and public health

    2022  Volume 19, Issue 19

    Abstract: In December 2019, China reported a new virus identified as SARS-CoV-2, causing COVID-19, which soon spread to other countries and led to a global pandemic. Although many countries imposed strict actions to control the spread of the virus, the COVID-19 ... ...

    Abstract In December 2019, China reported a new virus identified as SARS-CoV-2, causing COVID-19, which soon spread to other countries and led to a global pandemic. Although many countries imposed strict actions to control the spread of the virus, the COVID-19 pandemic resulted in unprecedented economic and social consequences in 2020 and early 2021. To understand the dynamics of the spread of the virus, we evaluated its chaotic behavior in Japan. A 0-1 test was applied to the time-series data of daily COVID-19 cases from January 26, 2020 to August 5, 2021 (3 days before the end of the Tokyo Olympic Games). Additionally, the influence of hosting the Olympic Games in Tokyo was assessed in data including the post-Olympic period until October 8, 2021. Even with these extended time period data, although the time-series data for the daily infections across Japan were not found to be chaotic, more than 76.6% and 55.3% of the prefectures in Japan showed chaotic behavior in the pre- and post-Olympic Games periods, respectively. Notably, Tokyo and Kanagawa, the two most populous cities in Japan, did not show chaotic behavior in their time-series data of daily COVID-19 confirmed cases. Overall, the prefectures with the largest population centers showed non-chaotic behavior, whereas the prefectures with smaller populations showed chaotic behavior. This phenomenon was observed in both of the analyzed time periods (pre- and post-Olympic Games); therefore, more attention should be paid to prefectures with smaller populations, in which controlling and preventing the current pandemic is more difficult.
    MeSH term(s) COVID-19/epidemiology ; Humans ; Japan/epidemiology ; Pandemics/prevention & control ; SARS-CoV-2 ; Tokyo/epidemiology
    Language English
    Publishing date 2022-10-06
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2175195-X
    ISSN 1660-4601 ; 1661-7827
    ISSN (online) 1660-4601
    ISSN 1661-7827
    DOI 10.3390/ijerph191912804
    Database MEDical Literature Analysis and Retrieval System OnLINE

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