Zhening Liu, Alistair Brandon-Jones and Christos Vasilakis
The purpose of this paper is to examine patient engagement in remote consultation services, an increasingly important issue facing Healthcare Operations Management (HOM) given the…
Abstract
Purpose
The purpose of this paper is to examine patient engagement in remote consultation services, an increasingly important issue facing Healthcare Operations Management (HOM) given the significant expansion in this and other forms of telehealth worldwide over the last decade. We use our analysis of the literature to develop a comprehensive framework that incorporates the patient journey, multidimensionality, antecedents and consequences, interventions and improvement options, as well as the cyclic nature of patient engagement. We also propose measures suitable for empirical assessment of different aspects of our framework.
Design/methodology/approach
We undertook a comprehensive review of the extant literature using a systematic review approach. We identified and analysed 63 articles published in peer-reviewed scientific journals between 2003 and 2022.
Findings
We conceptualise patient engagement with remote consultation across three key aspects: dimensions, process, and the antecedents and consequences of engagement. We identify nine contextual categories that influence such engagement. We propose several possible metrics for measuring patient engagement during three stages (before service, at/during service and after service) of remote consultation, as well as interventions and possible options for improving patient engagement therein.
Originality/value
The primary contribution of our research is the development of a comprehensive framework for patient engagement in remote consultation that draws on insights from literature in several disciplines. In addition, we have linked the three dimensions of engagement with the clinical process to create a structure for future engagement assessment. Furthermore, we have identified impact factors and outcomes of engagement in remote consultation by understanding which can help to improve levels of adoption, application and satisfaction, and reduce healthcare inequality. Finally, we have adopted a “cyclic” perspective and identified potential interventions that can be combined to further improve patient engagement in remote consultation.
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Tumbwene Elieza Mwansisya, Anne H. Outwater and Zhening Liu
The purpose of this paper is to determine perceived barriers to utilization of mental health services among adults in Dodoma Municipality, Tanzania. To improve the use of mental…
Abstract
Purpose
The purpose of this paper is to determine perceived barriers to utilization of mental health services among adults in Dodoma Municipality, Tanzania. To improve the use of mental health services, identifying related perceived barriers is a key step.
Design/methodology/approach
A concurrent mixed method model was used. Data were collected through face-to-face interviews (n=152) using a structured survey questionnaire. In addition in-depth interviews were conducted (n=10). The quantitative data were analyzed by using Epi info version 2002. Content analysis was used for analyzing qualitative data.
Findings
The majority of respondents opted to use modern mental health facilities for mental illness treatment. They also used spiritual healing and other forms traditional methods including herbal medicines. The most frequently identified causes of mental illness were: drug abuse, being cursed and witchcraft, demons or evil spirit possession. The reported significant perceived barriers were stigma, economic, lack of transport, witchcraft, lack of awareness of mental health services, unemployment, and negative believes about professional cure.
Originality/value
The option for mental health service utilization is influenced by the existing barriers on community and clients’ perception. There is a need for mental health professionals and policy makers to integrate mental health into primary care. Mutual sharing of knowledge between mental health professionals and tradition healers is warranted. Further research on the attitudes toward mental health professional services and on effectiveness of traditional healers’ services is indicated.
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Gang Yao, Xiaojian Hu, Liangcheng Xu and Zhening Wu
Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction…
Abstract
Purpose
Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.
Design/methodology/approach
The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.
Findings
The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.
Practical implications
The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.
Originality/value
The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.