Using conceptual graphs for clinical guidelines representation and knowledge visualization

The intrinsic complexity of the medical domain requires the building of some tools to assist the clinician and improve the patient’s health care. Clinical practice guidelines and protocols (CGPs) are documents with the aim of guiding decisions and criteria in specific areas of healthcare and they have been represented using several languages, but these are difficult to understand without a formal background. This paper uses conceptual graph formalism to represent CGPs. The originality here is the use of a graph-based approach in which reasoning is based on graph-theory operations to support sound logical reasoning in a visual manner. It allows users to have a maximal understanding and control over each step of the knowledge reasoning process in the CGPs exploitation. The application example concentrates on a protocol for the management of adult patients with hyperosmolar hyperglycemic state in the Intensive Care Unit

Nudging within learning health systems: next generation decision support to improve cardiovascular care

The increasing volume and richness of healthcare data collected during routine clinical practice have not yet translated into significant numbers of actionable insights that have systematically improved patient outcomes. An evidence-practice gap continues to exist in healthcare. We contest that this gap can be reduced by assessing the use of nudge theory as part of clinical decision support systems (CDSS). Deploying nudges to modify clinician behaviour and improve adherence to guideline-directed therapy represents an underused tool in bridging the evidence-practice gap. In conjunction with electronic health records (EHRs) and newer devices including artificial intelligence algorithms that are increasingly integrated within learning health systems, nudges such as CDSS alerts should be iteratively tested for all stakeholders involved in health decision-making: clinicians, researchers, and patients alike. Not only could they improve the implementation of known evidence, but the true value of nudging could lie in areas where traditional randomized controlled trials are lacking, and where clinical equipoise and variation dominate. The opportunity to test CDSS nudge alerts and their ability to standardize behaviour in the face of uncertainty may generate novel insights and improve patient outcomes in areas of clinical practice currently without a robust evidence base

The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview

Aziz Sheikh and colleagues report the findings of their systematic overview that assessed the impact of eHealth solutions on the quality and safety of health care

A Sensing Platform to Monitor Sleep Efficiency

Sleep plays a fundamental role in the human life. Sleep research is mainly focused on the understanding of the sleep patterns, stages and duration. An accurate sleep monitoring can detect early signs of sleep deprivation and insomnia consequentially implementing mechanisms for preventing and overcoming these problems. Recently, sleep monitoring has been achieved using wearable technologies, able to analyse also the body movements, but old people can encounter some difficulties in using and maintaining these devices. In this paper, we propose an unobtrusive sensing platform able to analyze body movements, infer sleep duration and awakenings occurred along the night, and evaluating the sleep efficiency index. To prove the feasibility of the suggested method we did a pilot trial in which several healthy users have been involved. The sensors were installed within the bed and, on each day, each user was administered with the Groningen Sleep Quality Scale questionnaire to evaluate the user’s perceived sleep quality. Finally, we show potential correlation between a perceived evaluation with an objective index as the sleep efficiency.</p

Analysis of Tech-Based and Telemental Health Approaches within the Adolescent and Young Adult Populations of Rural America

Background: The incidences of depression and anxiety among adolescent and young adult populations are intensifying, and resources remain limited for the management of these diseases within rural America. Objective: The purpose of this literature review is to evaluate evidence-based practices regarding telemedicine and tech-based mental health interventions. Evaluation of different interventions will demonstrate the ability of access, efficacy, and patient adherence with the hope of decreasing the incidence of disease and increase the availability of resources to rural areas. Methods: A comprehensive literature review was completed by assessing journal articles and scientific databases from the last fifteen years using a variety of keywords and MeSH terms, with limitations set among the population ranging from 13-23 years of age. Databases used included PubMed, PsycINFO, DynaMed Plus, and Cochrane Review Database. Abstracts were carefully reviewed, and then articles were furthermore assessed for quality, evidence, and proper methodology to ensure appropriate data were being represented in an unbiased way. Conclusions: Based on the current completed research, there is a predominance of evidence that has shown moderate statistical data and promising outcomes from tech-based mobile applications and telemental health. Statistical evidence supports that technology-based interventions are a potential treatment option that could assist in the management of mental health symptoms within the adolescent and young adult populations of rural America. However, due to small study sample sizes and the lack of follow up, further longitudinal research is still needed in order to determine adherence and long-term outcomes compared to other methods used in mental health treatments and in-person assessments

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges