Achieving digital-driven patient agility in the era of big data
- URL: http://arxiv.org/abs/2106.08204v1
- Date: Tue, 15 Jun 2021 15:08:26 GMT
- Title: Achieving digital-driven patient agility in the era of big data
- Authors: Rogier van de Wetering
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: There is still a limited understanding of the necessary skill, talent, and
expertise to manage digital technologies as a crucial enabler of the hospitals
ability to adequately sense and respond to patient needs and wishes, i.e.,
patient agility. Therefore, this investigates how hospital departments can
leverage a digital dy-namic capability to enable the departments patient
agility. This study embraces the dynamic capabilities theory, develops a
research model, and tests it accordingly using data from 90 clinical hospital
departments from the Netherlands through an online survey. The model's
hypothesized relationships are tested using structural equation modeling (SEM).
The outcomes demonstrate the significance of digital dynamic capability in
developing patient sensing and responding capabili-ties that, in turn,
positively influence patient service performance. Outcomes are very relevant
for the hospital practice now, as hospitals worldwide need to trans-form
healthcare delivery processes using digital technologies and increase clinical
productivity.
Related papers
- Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era [0.0]
This study proposes a novel, integrative framework for patient-centered data science in the digital health era.
We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social determinants of health, and multi-omic data to create comprehensive digital patient representations.
arXiv Detail & Related papers (2024-07-31T02:36:17Z) - TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design.
We provide basic validation methods for each task to ensure the datasets' usability and reliability.
We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
arXiv Detail & Related papers (2024-06-30T09:13:10Z) - 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) - When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning
Framework in Classification of Medical Images on Limited Data: A COVID-19
Case Study [77.34726150561087]
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources.
CNN has been widely utilized and verified in analyzing medical images.
arXiv Detail & Related papers (2022-03-24T02:09:41Z) - Rationale production to support clinical decision-making [31.66739991129112]
We apply InfoCal to the task of predicting hospital readmission using hospital discharge notes.
We find each presented model with selected interpretability or feature importance methods yield varying results.
arXiv Detail & Related papers (2021-11-15T09:02:10Z) - SANSformers: Self-Supervised Forecasting in Electronic Health Records
with Attention-Free Models [48.07469930813923]
This work aims to forecast the demand for healthcare services, by predicting the number of patient visits to healthcare facilities.
We introduce SANSformer, an attention-free sequential model designed with specific inductive biases to cater for the unique characteristics of EHR data.
Our results illuminate the promising potential of tailored attention-free models and self-supervised pretraining in refining healthcare utilization predictions across various patient demographics.
arXiv Detail & Related papers (2021-08-31T08:23:56Z) - 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) - 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) - BiteNet: Bidirectional Temporal Encoder Network to Predict Medical
Outcomes [53.163089893876645]
We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey.
An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys.
We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
arXiv Detail & Related papers (2020-09-24T00:42:36Z) - Learning Dynamic and Personalized Comorbidity Networks from Event Data
using Deep Diffusion Processes [102.02672176520382]
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition.
We develop deep diffusion processes to model "dynamic comorbidity networks"
arXiv Detail & Related papers (2020-01-08T15:47:08Z)
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.