Abstract: Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.
Abstract: Background: To improve the delivery of paediatric
healthcare in low resource settings, Community Health Workers
(CHW) have been provided with a paper-based set of protocols
known as Community Case Management (CCM). Yet research has
shown that CHW adherence to CCM guidelines is poor, ultimately
impacting health service delivery. Digitising the CCM guidelines via
mobile technology is argued in extant literature to improve CHW
adherence. However, little research exist which outlines how (a) this
process can be digitised and (b) adherence could be improved as a
result. Aim: To explore how an electronic mobile version of CCM
(eCCM) can overcome issues associated with the paper-based CCM
protocol (inadequate adherence to guidelines) vis-à-vis service
blueprinting. This service blueprint will outline how (a) the CCM
process can be digitised using mobile Clinical Decision Support
Systems software to support clinical decision-making and (b)
adherence can be improved as a result. Method: Development of a
single service blueprint for a standalone application which visually
depicts the service processes (eCCM) when supporting the CHWs,
using an application known as Supporting LIFE (SL eCCM app) as
an exemplar. Results: A service blueprint is developed which
illustrates how the SL eCCM app can be utilised by CHWs to assist
with the delivery of healthcare services to children. Leveraging
smartphone technologies can (a) provide CHWs with just-in-time
data to assist with their decision making at the point-of-care and (b)
improve CHW adherence to CCM guidelines. Conclusions: The
development of the eCCM opens up opportunities for the CHWs to
leverage the inherent benefit of mobile devices to assist them with
health service delivery in rural settings. To ensure that benefits are
achieved, it is imperative to comprehend the functionality and form
of the eCCM service process. By creating such a service blueprint for
an eCCM approach, CHWs are provided with a clear picture
regarding the role of the eCCM solution, often resulting in buy-in
from the end-users.
Abstract: Automated intelligent, clinical decision support systems generally promote to help or to assist physicians and patients regarding to prevention of diseases or treatment of illnesses using computer represented knowledge and information. In this paper, assessment factors affecting the proper design of clinical decision support system were investigated. The required procedure steps for gathering the data from clinical trial and extracting the information from large volume of healthcare repositories were listed, which are necessary for validation and verification of evidence-based implementation of clinical decision support system. The goal of this paper is to extract useful evaluation factors affecting the quality of the clinical decision support system in the design, development, and implementation of a computer-based decision support system.
Abstract: The structure of retinal vessels is a prominent feature,
that reveals information on the state of disease that are reflected in
the form of measurable abnormalities in thickness and colour.
Vascular structures of retina, for implementation of clinical diabetic
retinopathy decision making system is presented in this paper.
Retinal Vascular structure is with thin blood vessel, whose accuracy
is highly dependent upon the vessel segmentation. In this paper the
blood vessel thickness is automatically detected using preprocessing
techniques and vessel segmentation algorithm. First the capture
image is binarized to get the blood vessel structure clearly, then it is
skeletonised to get the overall structure of all the terminal and
branching nodes of the blood vessels. By identifying the terminal
node and the branching points automatically, the main and branching
blood vessel thickness is estimated. Results are presented and
compared with those provided by clinical classification on 50 vessels
collected from Bejan Singh Eye hospital..