管理学最新文献

This paper explores the evolution of academic consumer research on Facebook by consolidating the existing body of literature. The study consists of 336 papers on the topic of Facebook and consumers, published in top journals between 2008 and 2023, sourced via Scopus. The data collection followed the PRISMA framework, and bibliometric analysis was conducted using descriptive and performance analyses with the aid of data tabulation software. Additionally, natural language processing (NLP) and thematic analysis of the data were conducted via text mining, topic modelling and data visualisation with Leximancer—an artificial intelligence (AI)-based programme. The results revealed that, over the 15-year time period, and most prominently in the last 5 years, there has been a noticeable shift in consumer research on Facebook in line with the evolution of the social media platform itself. The paper identifies evident gaps in the literature via thematic analysis of future research suggestions and managerial implications emergent from the data. It proposes specific future research directions for academic researchers to explore. Practitioners are provided insights corresponding to consumer-centric and effective social media marketing strategies.

Objective: Our study aimed to expedite data sharing requests of Limited Data Sets (LDS) through the development of a streamlined platform that allows distributed, immutable management of network activities, provides transparent and intuitive auditing of data access history, and systematically evaluated it on a multi-capacity network setting for meaningful efficiency metrics.

Materials and methods: We developed a blockchain-based system with six types of smart contracts to automate the LDS sharing process among major stakeholders. Our workflow included metadata initialization, access-request processing, and audit-log querying. We evaluated our system using synthetic data on three machines with varying specifications to emulate real-world scenarios. The data employed included ∼1000 researcher requests and ∼360 000 log queries.

Results: On average, it took ∼2.5 s to register and respond to a researcher access request. The average runtime for an audit-log query with non-empty output was ∼3 ms. The runtime metrics at each institution showed general trends affiliated with their computational capacity.

Discussion: Our system can reduce the LDS sharing request time from potentially hours to seconds, while enhancing data access transparency in a multi-institutional setting. There were variations in performance across sites that could be attributed to differences in hardware specifications. The performance gains became marginal beyond certain hardware thresholds, pointing to the influence of external factors such as network speeds.

Conclusion: Our blockchain-based system can potentially accelerate clinical research by strengthening the data access process, expediting access and delivery of data links, increasing transparency with clear audit trails, and reinforcing trust in medical data management. Our smart contracts are available at: https://github.com/graceyufei/LDS-Request-Management.

Objectives: To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).

Materials and methods: This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS.

Results: The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development.

Discussion: Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications.

Conclusion: This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.

Objective: To identify stigmatizing language in obstetric clinical notes using natural language processing (NLP).

Materials and methods: We analyzed electronic health records from birth admissions in the Northeast United States in 2017. We annotated 1771 clinical notes to generate the initial gold standard dataset. Annotators labeled for exemplars of 5 stigmatizing and 1 positive/preferred language categories. We used a semantic similarity-based search approach to expand the initial dataset by adding additional exemplars, composing an enhanced dataset. We employed traditional classifiers (Support Vector Machine, Decision Trees, and Random Forest) and a transformer-based model, ClinicalBERT (Bidirectional Encoder Representations from Transformers) and BERT base. Models were trained and validated on initial and enhanced datasets and were tested on enhanced testing dataset.

Results: In the initial dataset, we annotated 963 exemplars as stigmatizing or positive/preferred. The most frequently identified category was marginalized language/identities (n = 397, 41%), and the least frequent was questioning patient credibility (n = 51, 5%). After employing a semantic similarity-based search approach, 502 additional exemplars were added, increasing the number of low-frequency categories. All NLP models also showed improved performance, with Decision Trees demonstrating the greatest improvement (21%). ClinicalBERT outperformed other models, with the highest average F1-score of 0.78.

Discussion: Clinical BERT seems to most effectively capture the nuanced and context-dependent stigmatizing language found in obstetric clinical notes, demonstrating its potential clinical applications for real-time monitoring and alerts to prevent usages of stigmatizing language use and reduce healthcare bias. Future research should explore stigmatizing language in diverse geographic locations and clinical settings to further contribute to high-quality and equitable perinatal care.

Conclusion: ClinicalBERT effectively captures the nuanced stigmatizing language in obstetric clinical notes. Our semantic similarity-based search approach to rapidly extract additional exemplars enhanced the performances while reducing the need for labor-intensive annotation.