Health Information management
mohammad reza ahmadi varzaneh; ali rashidpour; Hamid Reza Peikari; amir reza naghsh
Abstract
Introduction: Mobile value-added services in health encompass all services beyond voice calls and their implementation carries many benefits. The aim of the present study is to rank the factors related to mobile value-added services in the health sector.Research Method: This research is of the applied ...
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Introduction: Mobile value-added services in health encompass all services beyond voice calls and their implementation carries many benefits. The aim of the present study is to rank the factors related to mobile value-added services in the health sector.Research Method: This research is of the applied and descriptive-cross-sectional type with the statistical population of all information technology experts in the Social Security Organization of Tehran province, including 84 people. The measurement tool, with 64 items in 18 components, and its reliability was obtained with Cronbach’s alpha of 0.916. The validity of the questionnaire was confirmed by 5 experts. For data analysis, confirmatory factor analysis method and SmartPLS software were used. For ranking related factors, a pairwise comparison questionnaire was designed and made available to 15 specialized experts and their opinions were calculated and ranked using Expert choice software.Findings: The indices and coefficients obtained from the model of implementing mobile value-added services in the health sector have sufficient validity. The themes of effects and outcomes with a weight of 0.558, user understanding with a weight of 0.165, reliability with a weight of 0.115, mentality and expectations with a weight of 0.071, effective environmental conditions with a weight of 0.054, technology development with a weight of 0.037 have the most impact on the implementation of mobile value-added services in the health sector.Conclusion: Organizations providing health services can implement by considering effective factors such as effects and consequences for using these services and other factors based on priority, in order to improve the acceptance rate, in order to improve processes and increase satisfaction
Samaneh Pourmehr; Hamid Reza Peikari; Parastoo Golshiri
Abstract
Introduction: Protecting patients’ privacy by psychiatry hospitals’ staff has a high priority due to possible negative effects of the disclosure of their information on patients’ reputation. This research aimed to assess the relationship between perceived organizational justice and privacy observation ...
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Introduction: Protecting patients’ privacy by psychiatry hospitals’ staff has a high priority due to possible negative effects of the disclosure of their information on patients’ reputation. This research aimed to assess the relationship between perceived organizational justice and privacy observation with the moderating role of deterrent factors in psychiatry hospitals in Isfahan City, Iran.Methods: This was a descriptive-correlation research, and study population consisted of 445 medical staff in two psychiatry hospitals in Isfahan City (Farabi and Modarres). Sample size was set to 205 staff members, following Morgan table, and classified sampling method was used to select 126 and 79 participants from Farabi and Modarres hospitals, respectively. The questionnaire was a five-point Likert scale, adapted from four different questionnaires and the face, content, and confirmatory factor analyses were used to determine its validity, and Cronbach’s alpha for its reliability. Descriptive analyses were performed and partial least square modeling was employed to test the hypotheses.Results: There was a positive significant correlation between distributive (P < 0.05), procedural (P < 0.01), and interactional organizational justice (P < 0.01) with intention of privacy observation. Moreover, the correlation of distributive (P < 0.01), procedural (P < 0.05), and interactional organizational justice (P < 0.05) with intention of privacy observation found to be moderated by deterrent.Conclusion: This model can be used to predict privacy breach in hospitals, and make policies regarding patients’ privacy. Implementing a fair system for supervision of treatment staff in hospitals, as well as compilation and execution of instructions regarding the certainty and severity of sanctions against violation of patients’ privacy can reduce the likelihood of privacy breaches in hospitals.
