Health Policy

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings. We conducted a s...

Economic Evaluation of Robot-Based Telemedicine Consultation Services.

Through information and communication technology, telemedicine can deliver medical care without time and place restrictions, increasing health care accessibility in medically underdeveloped regions. Although there is growing interest in using robots in telemedicine, there are few economic evaluation studies of robot-based telemedicine. Robots developed for use in telemedicine consultation servic...

First-in-human evaluation of a hand-held automated venipuncture device for rapid venous blood draws.

Obtaining venous access for blood sampling or intravenous (IV) fluid delivery is an essential first step in patient care. However, success rates rely ...

Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm.

As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-predic...

The Digital Health Revolution and People with Disabilities: Perspective from the United States.

This article serves as the introduction to this special issue on Mobile Health and Mobile Rehabilitation for People with Disabilities. Social, technol...

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

OBJECTIVE: We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and no...

Distributed learning on 20 000+ lung cancer patients - The Personal Health Train.

BACKGROUND AND PURPOSE: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming a...

State-of-the-art methods in healthcare text classification system: AI paradigm.

Machine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual conte...

The Times they Are a-Changin' - Healthcare 4.0 Is Coming!

The Industrial Revolution brought new economics and new epidemic patterns to the people, which formed the healthcare 1.0 that focused on public health...

A Parser to Support the Definition of Access Control Policies and Rules Using Natural Languages.

As a consequence of the epidemiological transition towards non-communicable diseases, integrated care approaches are required, not solely focused on m...

Ratiometric fluorescent sensing system for drug residue analysis: Highly sensitive immunosensor using dual-emission quantum dots hybrid and compact smartphone based-device.

Immunoassays such as the enzyme-linked immunosorbent assay (ELISA) are utilized extensively for detecting protein biomarkers and small molecules in he...

Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System.

Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for or...

Artificial Intelligence: Power for Civilisation - and for Better Healthcare.

Artificial intelligence (AI) is changing the world we live in, and it has the potential to transform struggling healthcare systems with new efficienci...

Developing an Artificial Intelligence-Enabled Health Care Practice: Rewiring Health Care Professions for Better Care.

Artificial intelligence (AI) has the potential to impact almost every aspect of health care, from detection to prediction and prevention. The adoption...

A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods.

Digital care management programs can reduce health care costs and improve quality of care. However, it is unclear how to target patients who are most ...

Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects.

Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. A...

Exploiting open source 3D printer architecture for laboratory robotics to automate high-throughput time-lapse imaging for analytical microbiology.

Growth in open-source hardware designs combined with the low-cost of high performance optoelectronic and robotics components has supported a resurgenc...

Energy enhanced tissue texture in spectral computed tomography for lesion classification.

Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the tissue heterogeneity, and has been recognized as i...

Using machine learning approaches to predict high-cost chronic obstructive pulmonary disease patients in China.

The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic...

Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease.

While machine learning approaches can enhance prediction ability, little is known about their ability to predict 30-day readmission after hospitalizat...