IT ambidexterity and patient agility: the mediating role of digital
dynamic capability
- URL: http://arxiv.org/abs/2105.09013v1
- Date: Wed, 19 May 2021 09:27:27 GMT
- Title: IT ambidexterity and patient agility: the mediating role of digital
dynamic capability
- Authors: Rogier van de Wetering
- Abstract summary: This paper investigates how hospital departments can leverage the equivocal capacity to explore and exploit IT resources and practices.
The study outcomes can be used to transform clinical practice and contribute to the current IS knowledge base.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Despite a wealth of attention for information technology (IT)-enabled
transformation in healthcare research, limited attention has been given to ITs
role in developing specific organizational capabilities to respond to patients
their needs and wishes adequately. This paper investigates how hospital
departments can leverage the equivocal capacity to explore and exploit IT
resources and practices, i.e., IT ambidexterity, to adequately sense and
respond to patients their needs and demands, i.e., patient agility. Following
the dynamic capabilities view, this research develops a research model and
tests it accordingly using data obtained from 107 clinical hospital departments
from the Netherlands through an online survey. The hypothesized relationships
are tested using structural equation modeling (SEM). The outcomes demonstrate
the significance of IT ambidexterity in developing a digital dynamic capability
that, in turn, positively influences patient agility. The study outcomes can be
used to transform clinical practice and contribute to the current IS knowledge
base.
Related papers
- Latent-Predictive Empowerment: Measuring Empowerment without a Simulator [56.53777237504011]
We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner.
LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states.
arXiv Detail & Related papers (2024-10-15T00:41:18Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
A major challenge for AI-driven drug discovery is the scarcity of high-quality data.
We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously.
Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
arXiv Detail & Related papers (2024-04-16T07:42:55Z) - Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts [1.9374282535132377]
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare.
We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes.
There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures.
arXiv Detail & Related papers (2024-03-14T15:58:13Z) - Deployment of a Robust and Explainable Mortality Prediction Model: The
COVID-19 Pandemic and Beyond [0.59374762912328]
This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and beyond.
arXiv Detail & Related papers (2023-11-28T18:15:53Z) - Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
Time-series learning is the bread and butter of data-driven *clinical decision support*
Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit.
Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
arXiv Detail & Related papers (2023-10-28T12:08:03Z) - TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic
Tree-Based Memory Network [54.332862955411656]
Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment.
In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials.
We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
arXiv Detail & Related papers (2023-07-19T12:35:09Z) - The role of IT ambidexterity, digital dynamic capability and knowledge
processes as enablers of patient agility: an empirical study [0.0]
This study conveniently sampled data from 107 clinical hospital departments in the Netherlands.
IT ambidexterity positively enhances the development of a digital dynamic capability.
arXiv Detail & Related papers (2021-07-20T11:30:56Z) - IT ambidexterity driven patient agility and hospital patient service
performance: a variance approach [0.0]
This paper investigates how hospital departments can leverage the ability of firms to simultaneously explore new IT resources and practices.
It develops a research model and tests it accordingly using cross-sectional data from 90 clinical hospital departments from the Netherlands.
The study outcomes support the theorized model can the outcomes shed light on how to transform clinical practice and drive patient agility.
arXiv Detail & Related papers (2021-07-20T11:23:22Z) - MIMO: Mutual Integration of Patient Journey and Medical Ontology for
Healthcare Representation Learning [49.57261599776167]
We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
arXiv Detail & Related papers (2021-07-20T07:04:52Z) - Achieving digital-driven patient agility in the era of big data [0.0]
This study investigates how hospital departments can leverage a digital dy-namic capability to enable the departments patient agility.
The outcomes demonstrate the significance of digital dynamic capability in developing patient sensing and responding capabilities.
arXiv Detail & Related papers (2021-06-15T15:08:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.