ORCID Profile
0000-0002-8897-4840
Current Organisations
UNSW Sydney
,
Shanghai Jiao Tong University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: JMIR Publications Inc.
Date: 12-05-2017
Publisher: Elsevier BV
Date: 09-2016
DOI: 10.1016/J.IJMEDINF.2016.05.010
Abstract: There are increasing numbers of mHealth initiatives in middle and low income countries aimed at improving health outcomes. Bangladesh is no exception with more than 20 mobile health (mHealth) initiatives in place. A recent study in Bangladesh examined community readiness for mHealth using a framework based on quantitative data. Given the importance of a framework and the complementary role of qualitative exploration, this paper presents data from a qualitative study which complements findings from the quantitative study. The study was conducted in the Chakaria sub-district of Bangladesh. In total, 37 in-depth interviews were conducted between December 2012 and March 2013. Participants included the general public, students, community leaders, school teachers, and formal and informal healthcare providers. Thematic analysis was used to develop a logical and relevant framework to examine community readiness. As in the quantitative exploration, this study approached the investigation with four types of readiness in mind: core readiness, technological readiness, human resource readiness and motivational readiness. Community members, community leaders and healthcare providers expressed their interest in the use of mHealth in rural Bangladesh. Awareness of mHealth and its advantages was low among uneducated people. Participants who have used mHealth were attracted to the speed of access to qualified healthcare providers, time savings and low cost. Some participants did not see the value of using mobile phones for healthcare compared to a face-to-face consultation. Illiteracy, lack of English language proficiency, lack of trust and technological incapability were identified as barriers to mHealth use. However, a sense of ownership, evidence of utility, a positive attitude to the use of mHealth, and intentions towards future use of mHealth were driving forces in the adoption of mHealth services. This study re-affirmed the mHealth readiness conceptual framework with different dimensions of readiness and identified potential barriers and possible solutions for mHealth. Moving forward, emphasis should be placed on training users, providing low-cost services and improving trust of users.
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 11-2016
Publisher: IGI Global
Date: 2015
DOI: 10.4018/978-1-4666-8756-1.CH023
Abstract: The role of ontologies in chronic disease management and associated challenges such as defining data quality (DQ) and its specification is a current topic of interest. In domains such as Diabetes Management, a robust Data Quality Ontology (DQO) is required to support the automation of data extraction semantically from Electronic Health Record (EHR) and access and manage DQ, so that the data set is fit for purpose. A five steps strategy is proposed in this paper to create the DQO which captures the semantics of clinical data. It consists of: (1) Knowledge acquisition (2) Conceptualization (3) Semantic modeling (4) Knowledge representation and (5) Validation. The DQO was applied to the identification of patients with Type 2 Diabetes Mellitus (T2DM) in EHRs, which included an assessment of the DQ of the EHR. The five steps methodology is generalizable and reusable in other domains.
Publisher: IGI Global
Date: 2014
DOI: 10.4018/978-1-4666-6316-9.CH016
Abstract: Improved Data Quality (DQ) can improve the quality of decisions and lead to better policy in health organizations. Ontologies can support automated tools to assess DQ. This chapter examines ontology-based approaches to conceptualization and specification of DQ based on “fitness for purpose” within the health context. English language studies that addressed DQ, fitness for purpose, ontology-based approaches, and implementations were included. The authors screened 315 papers excluded 36 duplicates, 182 on abstract review, and 46 on full-text review leaving 52 papers. These were appraised with a realist “context-mechanism-impacts/outcomes” template. The authors found a lack of consensus frameworks or definitions for DQ and comprehensive ontological approaches to DQ or fitness for purpose. The majority of papers described the processes of the development of DQ tools. Some assessed the impact of implementing ontology-based specifications for DQ. There were few evaluative studies of the performance of DQ assessment tools developed none compared ontological with non-ontological approaches.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
No related grants have been discovered for Pradeep Ray.