Understanding and alleviating informal caregiver burden through the development and validation of a caregiver strain index-based model in Taiwan.

BACKGROUND: Quantifying the informal caregiver burden is important for understanding the risk factors associated with caregiver overload and for evaluating the effectiveness of services provided in Long-term Care (LTC). OBJECTIVE: This study aimed to develop and validate a Caregiver Strain Index (CSI)-based score for quantifying the informal caregiver burden, while the original dataset did not fully cover evaluation items commonly included in international assessments. Subsequently, we utilized the CSI-based score to pinpoint key caregiver burden risk factors, examine the initial timing of LTC services adoption, and assess the impact of LTC services on reducing caregiver burden. METHODS: The study analyzed over 28,000 LTC cases in Southern Taiwan from August 2019 to December 2022. Through multiple regression analysis, we identified significant risk factors associated with caregiver burden and examined changes in this burden after utilizing various services. Survival analysis was employed to explore the relationship between adopting the first LTC services and varying levels of caregiver burden. RESULTS: We identified 126 significant risk factors for caregiver burden. The most critical factors included caregiving for other disabled family members or children under the age of three (ß = 0.74, p < 0.001), the employment status of the caregiver (ß = 0.30-0.53, p < 0.001), the frailty of the care recipient (ß = 0.28-0.31, p < 0.001), and the behavioral symptoms of dementia in care recipients (ß = 0.28-2.60, p < 0.05). Generally, caregivers facing higher burdens sought LTC services earlier, and providing home care services alleviated the caregiver's burden. CONCLUSION: This comprehensive study suggests policy refinements to recognize high-risk caregivers better early and provide timely support to improve the overall well-being of both informal caregivers and care recipients.

Apolipoprotein A-1 protected hepatic ischaemia-reperfusion injury through suppressing macrophage pyroptosis via TLR4-NF-κB pathway.

BACKGROUND AND AIMS: Apolipoprotein A-1 (ApoA-1), the major apolipoprotein of high-density lipoprotein, plays anti-atherogenic role in cardiovascular diseases and exerts anti-inflammation effect in various inflammatory and infectious diseases. However, the role and mechanism of ApoA-1 in hepatic ischaemia-reperfusion (I/R) injury is unknown. METHODS: In this study, we measured ApoA-1 expression in human liver grafts after transplantation. Mice partial hepatic I/R injury model was made in ApoA-1 knockout mice, ApoA-1 mimetic peptide D-4F treatment mice and corresponding control mice to examine the effect of ApoA-1 on liver damage, inflammation response and cell death. Primary hepatocytes and macrophages were isolated for in vitro study. RESULTS: The results showed that ApoA-1 expression was down-regulated in human liver grafts after transplantation and mice livers subjected to hepatic I/R injury. ApoA-1 deficiency aggravated liver damage and inflammation response induced by hepatic I/R injury. Interestingly, we found that ApoA-1 deficiency increased pyroptosis instead of apoptosis during acute phase of hepatic I/R injury, which mainly occurred in macrophages rather than hepatocytes. The inhibition of pyroptosis compensated for the adverse impact of ApoA-1 deficiency. Furthermore, the up-regulated pyroptosis process was testified to be mediated by ApoA-1 through TLR4-NF-κB pathway and TLR4 inhibition significantly improved hepatic I/R injury. In addition, we confirmed that D-4F ameliorated hepatic I/R injury. CONCLUSIONS: Our study has identified the protective role of ApoA-1 in hepatic I/R injury through inhibiting pyroptosis in macrophages via TLR4-NF-κB pathway. The effect of ApoA-1 may provide a novel therapeutic approach for hepatic I/R injury.

Exploring concepts and trends in informal caregiver burden: systematic review using citation network and content analysis.

BACKGROUND: With the increase in the aging population, informal caregivers have become an essential pillar for the long-term care of older individuals. However, providing care can have a negative impact and increase the burden on caregivers, which is a cause for concern. OBJECTIVE: This study aimed to comprehensively depict the concept of "informal caregiver burden" through bibliometric and content analyses. METHODS: We searched the Web of Science (WoS) database to obtain bibliometric data and included only papers published between 2013 and 2022. We used content analysis to extract and identify the core concepts within the text systematically. RESULTS: Altogether, 934 papers were included in the bibliometric analysis, from which we selected 19 highly impactful papers for content analysis. The results indicate that researchers have focused on exploring the factors that impact informal caregiver burden. Meanwhile, there has been a widespread discussion regarding the caregiver burden among those caring for recipients with specific illnesses, such as dementia, Alzheimer's disease, and cancer, as these illnesses can contribute to varying levels of burden on informal caregivers. In addition, questionnaires and interviews emerged as the predominant methods for data collection in the realm of informal caregiver research. Furthermore, we identified 26 distinct assessment tools specifically tailored for evaluating burden, such as caregiver strain index (CSI). CONCLUSION: For future studies, we suggest considering the intersectionality of factors contributing to the burden on informal caregivers. This approach could enhance the well-being of both caregivers and older care recipients.

Renin-Angiotensin-Aldosterone System Inhibitors and Development of Gynecologic Cancers: A 23 Million Individual Population-Based Study.

The chronic receipt of renin-angiotensin-aldosterone system (RAAS) inhibitors including angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been assumed to be associated with a significant decrease in overall gynecologic cancer risks. This study aimed to investigate the associations of long-term RAAS inhibitors use with gynecologic cancer risks. A large population-based case-control study was conducted from claim databases of Taiwan's Health and Welfare Data Science Center (2000-2016) and linked with Taiwan Cancer Registry (1979-2016). Each eligible case was matched with four controls using propensity matching score method for age, sex, month, and year of diagnosis. We applied conditional logistic regression with 95% confidence intervals to identify the associations of RAAS inhibitors use with gynecologic cancer risks. The statistical significance threshold was p < 0.05. A total of 97,736 gynecologic cancer cases were identified and matched with 390,944 controls. The adjusted odds ratio for RAAS inhibitors use and overall gynecologic cancer was 0.87 (95% CI: 0.85-0.89). Cervical cancer risk was found to be significantly decreased in the groups aged 20-39 years (aOR: 0.70, 95% CI: 0.58-0.85), 40-64 years (aOR: 0.77, 95% CI: 0.74-0.81), ≥65 years (aOR: 0.87, 95% CI: 0.83-0.91), and overall (aOR: 0.81, 95% CI: 0.79-0.84). Ovarian cancer risk was significantly lower in the groups aged 40-64 years (aOR: 0.76, 95% CI: 0.69-0.82), ≥65 years (aOR: 0.83, 95% CI: 0.75-092), and overall (aOR: 0.79, 95% CI: 0.74-0.84). However, a significantly increased endometrial cancer risk was observed in users aged 20-39 years (aOR: 2.54, 95% CI: 1.79-3.61), 40-64 years (aOR: 1.08, 95% CI: 1.02-1.14), and overall (aOR: 1.06, 95% CI: 1.01-1.11). There were significantly reduced risks of gynecologic cancers with ACEIs users in the groups aged 40-64 years (aOR: 0.88, 95% CI: 0.84-0.91), ≥65 years (aOR: 0.87, 95% CI: 0.83-0.90), and overall (aOR: 0.88, 95% CI: 0.85-0.80), and ARBs users aged 40-64 years (aOR: 0.91, 95% CI: 0.86-0.95). Our case-control study demonstrated that RAAS inhibitors use was associated with a significant decrease in overall gynecologic cancer risks. RAAS inhibitors exposure had lower associations with cervical and ovarian cancer risks, and increased endometrial cancer risk. ACEIs/ARBs use was found to have a preventive effect against gynecologic cancers. Future clinical research is needed to establish causality.

Voice-based control system for smart hospital wards: a pilot study of patient acceptance.

BACKGROUND: The smart hospital's concept of using the Internet of Things (IoT) to reduce human resources demand has become more popular in the aging society. OBJECTIVE: To implement the voice smart care (VSC) system in hospital wards and explore patient acceptance via the Technology Acceptance Model (TAM). METHODS: A structured questionnaire based on TAM was developed and validated as a research tool. Only the patients hospitalized in the VSC wards and who used it for more than two days were invited to fill the questionnaire. Statistical variables were analyzed using SPSS version 24.0. A total of 30 valid questionnaires were finally obtained after excluding two incomplete questionnaires. Cronbach's α values for all study constructs were above 0.84. RESULT: We observed that perceived ease of use on perceived usefulness, perceived usefulness on user satisfaction and attitude toward using, and attitude toward using on behavioral intention to use had statistical significance (p < .01), respectively. CONCLUSION: We have successfully developed the VSC system in a Taiwanese academic medical center. Our study indicated that perceived usefulness was a crucial factor, which means the system function should precisely meet the patients' demands. Additionally, a clever system design is important since perceived ease of use positively affects perceived usefulness. The insight generated from this study could be beneficial to hospitals when implementing similar systems to their wards.

Assessment of knowledge, attitudes, and practices towards Zika virus among healthcare workers in St. Kitts.

BACKGROUND: Healthcare workers are usually the first responders during outbreaks and are instrumental in educating the populace about the prevention of different diseases and illnesses. The aim of this study was to assess the association between healthcare workers' characteristics and knowledge, attitudes and practices toward Zika virus. METHODS: This was a cross-sectional study that collected data from healthcare workers at 3 medical facilities using a validated self-administered questionnaire between July 2017 - September 2017. Logistic regression models were used to examine the association between sociodemographic and knowledge, attitudes, and practices. RESULTS: A total of 190 healthcare workers were analyzed. Of these, 60, 72.6 and 64.7% had good knowledge, positive attitudes, and good practices toward Zika virus, respectively. Healthcare workers without a formal degree were less likely to have good knowledge of Zika virus (adjusted odds ratio (AOR) = 0:49; 95% confidence interval (CI) = 0.24-0.99) compared to those with a formal degree. Reduced odds for positive attitude towards Zika virus were observed in healthcare workers with low income as compared to those with high income (AOR = 0.31; 95% CI =0.13-0.75). Being younger than 40 years old was associated with poor Zika virus practices (AOR = 0:34; 95% CI = 0.15-0.79). CONCLUSIONS: Significant association between healthcare workers' sociodemographic characteristics and Zika virus knowledge, attitudes and practices were observed. Public health interventions that seek to increase Zika virus awareness should aim to train healthcare workers who are younger, without formal degree and those earning low income.

Enhancing CO2 storage and marine carbon sink based on seawater mineral carbonation.

Human activities emitting carbon dioxide (CO2) have caused severe greenhouse effects and accelerated climate change, making carbon neutrality urgent. Seawater mineral carbonation technology offers a promising negative emission strategy. This work investigates current advancements in proposed seawater mineral carbonation technologies, including CO2 storage and ocean chemical carbon sequestration. CO2 storage technology relies on indirect mineral carbonation to fix CO2, involving CO2 dissolution, Ca/Mg extraction, and carbonate precipitation, optimized by adding alkaline substances or using electrochemical methods. Ocean chemical carbon sequestration uses natural seawater for direct mineral carbonation, enhanced by adding specific materials to promote carbonate precipitation and increase CO2 absorption, thus enhancing marine carbon sinks. This study evaluates these technologies' advantages and challenges, including reaction rates, costs, and ecological impacts, and analyzes representative materials' carbon fixation potential. Literature indicates that seawater mineral carbonation can play a significant role in CO2 storage and enhancing marine carbon sinks in the coming decades.

A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications.

Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.

Correction: A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications.

[This corrects the article DOI: 10.1371/journal.pone.0296939.].

Corrigendum to "Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study" [Asia-Pacific J Oncol Nurs 10 (2023) 100195].

[This corrects the article DOI: 10.1016/j.apjon.2023.100195.].

Apolipoprotein A-1 Accelerated Liver Regeneration Through Regulating Autophagy Via AMPK-ULK1 Pathway.

BACKGROUND & AIMS: Apolipoprotein A-1 (ApoA-1), the main apolipoprotein of high-density lipoprotein, has been well studied in the area of lipid metabolism and cardiovascular diseases. In this project, we clarify the function and mechanism of ApoA-1 in liver regeneration. METHODS: Seventy percent of partial hepatectomy was applied in male ApoA-1 knockout mice and wild-type mice to investigate the effects of ApoA-1 on liver regeneration. D-4F (ApoA-1 mimetic peptide), autophagy activator, and AMPK activator were used to explore the mechanism of ApoA-1 on liver regeneration. RESULTS: We demonstrated that ApoA-1 levels were highly expressed during the early stage of liver regeneration. ApoA-1 deficiency greatly impaired liver regeneration after hepatectomy. Meanwhile, we found that ApoA-1 deficiency inhibited autophagy during liver regeneration. The activation of autophagy protected against ApoA-1 deficiency in inhibiting liver regeneration. Furthermore, ApoA-1 deficiency impaired autophagy through AMPK-ULK1 pathway, and AMPK activation significantly improved liver regeneration. The administration of D-4F could accelerated liver regeneration after hepatectomy. CONCLUSIONS: These findings suggested that ApoA-1 played an essential role in liver regeneration through promoting autophagy in hepatocytes via AMPK-ULK1 pathway. Our findings enrich the understanding of the underlying mechanism of liver regeneration and provide a potential therapeutic strategy for liver injury.

Using large language model (LLM) to identify high-burden informal caregivers in long-term care.

BACKGROUND: The rising global elderly population increases the demand for caregiving, yet traditional methods may not fully assess the challenges faced by vital informal caregivers. OBJECTIVE: To investigate the efficacy of Large Language Model (LLM) in detecting overburdened informal caregivers, benchmarking against rule-based and machine learning methods. METHODS: 1,791 eligible informal caregivers from Southern Taiwan and utilized their textual case summary reports for the LLM. We also employed structured questionnaire results for machine learning models. Furthermore, we leveraged the visualization of the LLM's attention mechanisms to enhance our understanding of the model's interpretative capabilities. RESULTS: The LLM achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.84 and an Area Under the Precision-Recall Curve (AUPRC) of 0.70, marking an 8% and 14% improvement over traditional methods. The visualization of the attention mechanism accurately reflected the evaluations of human experts, concentrating on descriptions of high-burden descriptions and the relationships between caregivers and recipients. CONCLUSION: This research demonstrates the notable capability of LLM to accurately identify high-burden caregivers in Long-term Care (LTC) settings. Compared to traditional approaches, LLM offers an opportunity for the future of LTC research and policymaking.

CircUGP2 Suppresses Intrahepatic Cholangiocarcinoma Progression via p53 Signaling Through Interacting With PURB to Regulate ADGRB1 Transcription and Sponging miR-3191-5p.

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver cancer and its prognosis remains poor. Although growing numbers of studies have verified the involvement of circular RNAs (circRNAs) in various cancer types, their specific functions in ICC remain elusive. Herein, a circRNA, circUGP2 is identified by circRNA sequencing, which is downregulated in ICC tissues and correlated with patients' prognosis. Moreover, circUGP2 overexpression suppresses tumor progression in vitro and in vivo. Mechanistically, circUGP2 functions as a transcriptional co-activator of PURB over the expression of ADGRB1. It can also upregulate ADGRB1 expression by sponging miR-3191-5p. As a result, ADGRB1 prevents MDM2-mediated p53 polyubiquitination and thereby activates p53 signaling to inhibit ICC progression. Based on these findings, circUGP2 plasmid is encapsulated into a lipid nanoparticle (LNP) system, which has successfully targeted tumor site and shows superior anti-tumor effects. In summary, the present study has identified the role of circUGP2 as a tumor suppressor in ICC through regulating ADGRB1/p53 axis, and the application of LNP provides a promising translational strategy for ICC treatment.

Using alert dwell time to filter universal clinical alerts: A machine learning approach.

BACKGROUND: Alerts in computerized physician order entry (CPOE) systems can improve patient safety. However, alerts in rule-based systems cannot be customized based on individual patient or user characteristics. This limitation can lead to the presentation of irrelevant alerts and subsequent alert fatigue. OBJECTIVE: We used machine learning approaches with alert dwell time to filter out irrelevant alerts for physicians based on contextual factors. METHODS: We utilized five machine learning algorithms and a total of 1,120 features grouped into six categories: alert, demographic, environment, diagnosis, prescription, and laboratory results. The output of the models was the alert dwell time within a specified time window to determine the optimal range by the sensitivity analysis. RESULTS: We used 813,026 records (19 categories) from the hospital's outpatient clinic data from 2020 to 2021. The sensitivity analysis showed that a time window with a range of 0.3-4.0 s had the best performance, with an area under the receiver operating characteristic (AUROC) curve of 0.73 and an area under the precision-recall curve (AUPRC) of 0.97. The model built with alert and demographic feature groups showed the best performance, with an AUROC of 0.73. The most significant individual feature groups were alert and demographic, with AUROCs of 0.66 and 0.62, respectively. CONCLUSION: Our study found that alerts and user and patient demographic features are more crucial than clinical features when constructing universal context-aware alerts. Using alert dwell time in combination with a time window is an effective way to determine the trigger status of an alert. The findings of this study can provide useful insights for researchers working on specific and universal context-aware alerts.

Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study.

Objective: The popularity of the â€‹"bring your own device (BYOD)" â€‹concept has grown in recent years, and its application has extended to the healthcare field. This study was aimed at examining nurses' acceptance of a BYOD-supported system after a 9-month implementation period. Methods: We used the technology acceptance model to develop and validate a structured questionnaire as a research tool. All nurses (n â€‹= â€‹18) responsible for the BYOD-supported wards during the study period were included in our study. A 5-point Likert scale was used to assess the degree of disagreement and agreement. Statistical analysis was performed in SPSS version 24.0. Results: The questionnaire was determined to be reliable and well constructed, on the basis of the item-level content validity index and Cronbach α values above 0.95 and 0.87, respectively. The mean constant values for all items were above 3.95, thus suggesting that nurses had a positive attitude toward the BYOD-supported system, driven by the characteristics of the tasks involved. Conclusions: We successfully developed a BYOD-supported system. Our study results suggested that nursing staff satisfaction with BYOD-supported systems could be effectively increased by providing practical functionalities and reducing clinical burden. Hospitals could benefit from the insights generated by this study when implementing similar systems.

Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach.

BACKGROUND: Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking. OBJECTIVE: This study aimed to predict LTC service demands for cancer patients and identify the crucial factors. METHODS: 3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases. RESULTS: Service usage differences in lung, liver, brain, and pancreatic cancers were significant. For the UPM, the top 20 performance model cutoff points were adopted, such as through Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and XGBoost (XGB), achieving an AUROC range of 0.707 to 0.728. The CSPM demonstrated performance with an AUROC ranging from 0.777 to 0.837 for the top five most frequently used services. The most critical predictive factors were the types of cancer, patients' age and female caregivers, and specific health needs. CONCLUSION: The results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients.

Seed priming with selenium: Effects on germination, seedling growth, biochemical attributes, and grain yield in rice growing under flooding conditions.

Prevalent irregular rainfall, flooding for weed control, and unleveled fields in the middle and lower reaches of the Yangtze River all contribute to flooding stress on germination and growth of direct-seeded rice (Oryza sativa L.). Herein, some experiments were conducted so as to assess the effects of seed priming with selenium (Se) on the germination and growth of rice under hypoxia. The experiment was arranged in a completely randomized factorial design with two factors and five replicates. Factors included Se concentration (0, 30, and 60 µmol/L) and duration of flooding stress (0, 2, 4, and 8 days). The experimental results showed that Se accelerated seed germination and increased emergence index and final emergence percentage. Additionally, Se increased shoot and root lengths and dry weights, but high Se concentration (60 µmol/L) reduced 18-day-old seedling dry weight under long-term flooding (8 days). Furthermore, Se reduced malondialdehyde content and increased starch hydrolysis efficiency in seeds, superoxide dismutase, peroxidase, catalase, and glutathione peroxidase activities and seedling soluble protein and total chlorophyll contents. Se improved seedling total Se and organic Se contents while increasing total dry weight and yield. Notably, the highest yield was obtained after a 4-day flooding period. Although Se priming favored rice seedling emergence and growth under flooding conditions, Se concentrations equal or above 60 µmol/L increased the risk of seedling death during long-term flooding (≥8 days).

Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis.

A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.

Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis.

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.

A novel method to retrieve alerts from a homegrown Computerized Physician Order Entry (CPOE) system of an academic medical center: Comprehensive alert characteristic analysis.

BACKGROUND: The collection and analysis of alert logs are necessary for hospital administrators to understand the types and distribution of alert categories within the organization and reduce alert fatigue. However, this is not readily available in most homegrown Computerized Physician Order Entry (CPOE) systems. OBJECTIVE: To present a novel method that can collect alert information from a homegrown CPOE system (at an academic medical center in Taiwan) and conduct a comprehensive analysis of the number of alerts triggered and alert characteristics. METHODS: An alert log collector was developed using the Golang programming language and was implemented to collect all triggered interruptive alerts from a homegrown CPOE system of a 726-bed academic medical center from November 2017 to June 2018. Two physicians categorized the alerts from the log collector as either clinical or non-clinical (administrative). RESULTS: Overall, 1,625,341 interruptive alerts were collected and classified into 1,474 different categories based on message content. The sum of the top 20, 50, and 100 categories of most frequently triggered alerts accounted for approximately 80, 90 and 97 percent of the total triggered alerts, respectively. Among alerts from the 100 most frequently triggered categories, 1,266,818 (80.2%) were administrative and 312,593 (19.8%) were clinical alerts. CONCLUSION: We have successfully developed an alert log collector that can serve as an extended function to retrieve alerts from a homegrown CPOE system. The insight generated from the present study could also potentially bring value to hospital system designers and hospital administrators when redesigning their CPOE system.