• 한국멀티미디어학회논문지
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• 제17권8호
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• pp.995-1011
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• 2014

In consideration of the rapid changes in the so-called information society of the $21^{st}$ century, about 80% of a total population in Korea has used the Internet. However, the social effect of the Internet and related devices has not been yet systematically studied in the literature. In healthcare as well, consumers' efficient use of the Internet for their positive health outcomes is becoming an issue. The purpose of this study was to analyze the medical subject headings keywords of the selected studies on consumers' use of Internet and mobile health information. For the analysis, social network analysis was used to provide basic information to present directions for future research on the field of interest.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.313-324
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• 2021

This study empirically investigates the factors influencing the intention to accept mobile technology in Saudi healthcare service delivery using the extended unified theory of acceptance and use of technology model (UTAUT) with perceived reliability and price value. Accordingly, a conceptual model combining behavioral constructs with those linked to the technology acceptance model is developed. This model aims to identify factors that predict patients' acceptance of mobile technology healthcare service delivery. The developed model is examined using responses obtained from a survey on 545 participants receiving healthcare services in Saudi Arabia. Thus, we have conceptualized the developed model and validated seven hypotheses involving key constructs. Results suggest that performance expectancy, effort expectancy, social influence, facilitating conditions, price value, and perceived reliability are direct predictors of user behavior to accept mobile technology in healthcare service delivery. The results provide empirical evidence to the literature on the effect of facilitating conditions and effort expectancy on mobile health (mHealth) adoption. The results show that the COVID-19 pandemic has significantly increased the adoption of mHealth services in Saudi Arabia.

• Journal of Communications and Networks
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• 제13권2호
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• pp.102-112
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• 2011

In this paper, we propose a privacy-preserving emergency call scheme, called PEC, enabling patients in life-threatening emergencies to fast and accurately transmit emergency data to the nearby helpers via mobile healthcare social networks (MHSNs). Once an emergency happens, the personal digital assistant (PDA) of the patient runs the PEC to collect the emergency data including emergency location, patient health record, as well as patient physiological condition. The PEC then generates an emergency call with the emergency data inside and epidemically disseminates it to every user in the patient's neighborhood. If a physician happens to be nearby, the PEC ensures the time used to notify the physician of the emergency is the shortest. We show via theoretical analysis that the PEC is able to provide fine-grained access control on the emergency data, where the access policy is set by patients themselves. Moreover, the PEC can withstandmultiple types of attacks, such as identity theft attack, forgery attack, and collusion attack. We also devise an effective revocation mechanism to make the revocable PEC (rPEC) resistant to inside attacks. In addition, we demonstrate via simulation that the PEC can significantly reduce the response time of emergency care in MHSNs.

• 지능정보연구
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• 제16권3호
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• pp.181-199
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• 2010

인구의 고령화 및 건강에 대한 관심이 증가됨에 따라 유헬스케어 서비스는 발병 후 관리관점에서 발병 전의 예방 관점으로 그 목적이 점차 이동하고 있다. 그러나 기존의 유헬스케어 서비스는 원격진료 차원의 의료 서비스 성격이 강하여, 만성 성인병과 같은 대사 증후군을 예방 및 관리하기에는 한계가 있을 뿐만 아니라, 관리자 중심의 단방향 서비스를 제공함으로 인해 사용들이 중도에 이용을 포기하는 비율이 높았다. 이와 같은 문제를 해결하기 위하여, 본 연구에서는 사회 네트워크를 이용한 사용자 기반 유헬스케어 서비스 추천 시스템을 제안하였으며, 실세계에서 유헬스케어 서비스 추천 시스템의 활용 가능성을 제시하기 위하여 실제 의료원에서 대사 증후군 예방 및 관리를 위해 처방한 식단 및 운동 정보를 기반으로 유비쿼터스 컴퓨팅 환경에서 적용가능한 시스템을 구현하였다. 본 연구에서 제안한 시스템은 사용자가 선호하지 않는 서비스가 네트워크를 통해 확산될 가능성을 낮추는 동시에 추천의 신뢰성 제고를 위해 네이버들이 이용한 서비스를 공유함으로써 전체적인 추천 품질을 높인다. 즉, 사용자의 식습관 및 운동습관 등과 같은 생활습관을 개선하기 위하여 사회 네트워크를 활용함으로써 사용자간의 자율협업을 통한 개인화된 추천이 가능하다. 따라서 본 연구에서 제안하는 유헬스케어 서비스 추천 시스템은 생활습관 개선을 위하여 사용자에게 적합한 식단 및 운동을 제공하고, 생활습관의 개선을 통해 만성 성인병과 같은 대사증후군을 사전에 예방할 수 있을 것으로 기대된다.

• 지역사회간호학회지
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• 제29권3호
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• pp.267-278
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• 2018

Purpose: This study is to investigate working conditions including job stress among visiting nurses in public health centers in Korea. Methods: An social network based mobile survey was conducted in May 2017 (N=936, response rate: 47.0%). Results: The visiting nurses in this study had their average total career as a nurse is 13.7 years. The 68.3% of them were employed in an indefinite term, 17.0% were hired in a fixed term, and 11.0% came from outsourcing. They responded as high job-stress level including inadequate compensation (71.22/100) and job demands (71.91/100). They experienced down-talk (63.4%), swearwords (32.9%), being made a dirty face (39.9%), sexual jokes (30.8%), or being likened or evaluated with their appearance sexually (14.3%). Among the causes of job related conflicts and discrimination, deprived salary level was the most frequent reason (83.4%). The conflicts and discrimination were incurred by government officers (52.4%). There were no significant differences in overall job stress, emotional labor, organizational commitment, violence, and discrimination experience based on their employment types. Conclusion: The differences in working conditions among the non-regular nurses were trivial, and their overall working conditions were poor. It is necessary to improve non-regular nurses' working conditions in order to make up the limitations of the Korean healthcare system which is centered on hospitals.

• Healthcare Informatics Research
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• 제24권4호
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• pp.253-262
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• 2018

Objectives: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. Methods: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. Results: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. Conclusions: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.