The role of IT ambidexterity, digital dynamic capability and knowledge processes as enablers of patient agility: an empirical study

• URL: http://arxiv.org/abs/2107.09419v1
• Date: Tue, 20 Jul 2021 11:30:56 GMT
• Title: The role of IT ambidexterity, digital dynamic capability and knowledge processes as enablers of patient agility: an empirical study
• Authors: Rogier van de Wetering and Johan Versendaal
• Abstract summary: This study conveniently sampled data from 107 clinical hospital departments in the Netherlands. IT ambidexterity positively enhances the development of a digital dynamic capability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: There is a limited understanding of IT's role as a crucial enabler of patient agility and the department's ability to respond to patient's needs and wishes adequately. This study's objective is to contribute to the insights of the validity of the hypothesized relationship between IT resources, practices and capabilities, and hospital departments' knowledge processes and the department's ability to adequately sense and respond to patient needs and wishes, i.e., patient agility. This study conveniently sampled data from 107 clinical hospital departments in the Netherlands and uses structural equation modeling for model assessment. IT ambidexterity positively enhances the development of a digital dynamic capability. Likewise, IT ambidexterity also positively impacts the hospital department's knowledge processes. Both digital dynamic capability and knowledge processes positively influence patient agility. IT ambidexterity promotes taking advantage of IT resources and experiments to reshape patient services and enhance patient agility.

Related papers

• Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care [1.950650243134358]
  Thematic analysis revealed three core themes: (T1) ICU decision-making relies on a wide range of factors, (T2) the complexity of patient state is challenging for shared decision-making, and (T3) requirements and capabilities of AI decision support systems. We include design recommendations from clinical input, providing insights to inform future AI systems for intensive care.
  arXiv  Detail & Related papers  (2024-11-18T17:53:07Z)
• CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
  We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance. We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions. Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
  arXiv  Detail & Related papers  (2024-10-17T04:52:57Z)
• 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)
• 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)
• 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)
• POETREE: Interpretable Policy Learning with Adaptive Decision Trees [78.6363825307044]
  POETREE is a novel framework for interpretable policy learning. It builds probabilistic tree policies determining physician actions based on patients' observations and medical history. It outperforms the state-of-the-art on real and synthetic medical datasets.
  arXiv  Detail & Related papers  (2022-03-15T16:50:52Z)
• 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)
• 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)
• IT ambidexterity and patient agility: the mediating role of digital dynamic capability [0.0]
  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.
  arXiv  Detail & Related papers  (2021-05-19T09:27:27Z)

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.