This article presents a comprehensive dataset from the annual reports of China's public-listed companies, the China Stock Market and Accounting Research Database, and the Wind database, focusing on digital transformation and strategic risk taking. This dataset covers 14 years from 2008 to 2021 with 17,089 firm-year observations. Digital transformation is calculated using text mining techniques and keyword frequency analyses based on the firms' annual reports. Then, strategic risk taking is a composite strategic index that combines long-term debt, R&D expenditure, and capital expenditure. The dynamic capability is measured by a comprehensive index that includes three dimensions: absorptive capacity, adaptive capability, and innovation capability. This dataset can serve as a reference base for future studies on the effect of digital transformation on corporate strategic behavior. It can also be integrated into building core competencies to assist managers in identifying the role of dynamic capability.
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http://dx.doi.org/10.1016/j.dib.2024.110511 | DOI Listing |
Front Digit Health
December 2024
MOH Office for Healthcare Transformation, Singapore, Singapore.
The COVID-19 pandemic in Singapore led to limited access to mental health services, resulting in increased distress among the population. This study explores the potential benefits of offering a digital mental health intervention (DMHI), Wysa, as a brief and longitudinal intervention as part of the mindline.sg initiative launched by the MOH Office for Healthcare Transformation in Singapore.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Department of Microbiology Dr D. Y. Patil Medical College, Hospital and Research Centre, Dr D. Y. Patil Vidyapeeth (Deemed-to-be-University) Pune Maharashtra India.
Background And Aims: Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.
View Article and Find Full Text PDFJ Korean Med Sci
January 2025
Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings.
Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals.
Clin Transl Med
January 2025
Department of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria.
The editorial, "Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell," introduces the innovative clinical artificial intelligence single-cell (caiSC) system, which merges AI with single-cell informatics to advance real-time diagnostics, disease monitoring, and treatment prediction. By combining clinical data and multimodal molecular inputs, caiSC facilitates personalized medicine, promising enhanced diagnostic precision and tailored therapeutic approaches. Despite its potential, caiSC lacks comprehensive data coverage across cell types and diseases, presenting challenges in data quality and model robustness.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Center for Health Services Research, Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, Rüdersdorf bei Berlin, Germany.
Background: In recent years, health care has undergone a rapid and unprecedented digital transformation. In many fields of specialty care, such as rheumatology, this shift is driven by the growing number of patients and limited resources, leading to increased use of digital health technologies (DHTs) to maintain high-quality clinical care. Previous studies examined user acceptance of individual DHTs in rheumatology, such as telemedicine, video consultations, and mHealth.
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