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The purpose of this paper is to promote the corrosion resistance of the 5083-111H aluminum alloy by laser cleaning.

Laser with 2 ns pulse width was adopted in this project and the corrosion resistance of cleaned samples was tested by copper-accelerated salt spray (CASS). The surface morphology, elemental composition and distribution were then characterized by SEM. Moreover, surface morphology, elemental composition and distribution were also tested.

Results suggested a higher corrosion resistance was successfully obtained by laser cleaning. Compared with samples cleaned by 2000 grit sandpaper, mechanical cleaning resulted in a 53% larger height difference between the peak and valley. The content of the oxygen is 8.85% on the surface cleaned mechanically and the distribution is dependent on the distribution of aluminum whereas that of the laser cleaning sample is 24.41% and the distribution existed even in the Al-poor area.

In this project, the 2-ns laser cleaning was proved to have the capability to remove the oxide layer on the aluminum alloy surface while retaining an excellent corrosion resistance and smooth surface. Meanwhile, a thorough elemental distribution and smaller grain size lead to a smaller difference in elemental concentration. This retards the diffusion of oxygen into the substrate and hence increases the corrosion resistance of the surface.

Sharing patient health information (PHI) among hospitals has been much slower than the adoption of health record systems. This paper aims to investigate if privacy regulation (PR) or security measures (SMs) influence hospitals’ use of health information exchange (HIE) to share PHI with other providers (e.g. physicians, labs, hospitals). The study specifically focuses on how multiple PRs can impede and a strong national security infrastructure (NSI) can support HIE.

The study uses secondary data from a multi-national and multi-hospital survey administered by the European Union. The multi-level structure of the cross-sectional panel data is used to test the influence of both hospital-level (e.g. PR) and national-level variables (e.g. NSI) on HIE. A total of nine types of HIE, three types of PRs, nine SMs and other relevant control variables are considered. This study uses a two-level random intercept generalized linear model to test the hypothesis proposed in the study.

The study finds that national-level PRs (NLPR) have the strongest positive influence on HIE in comparison to regional (RLPR) and hospital-level (HLPR) PRs. Moreover, the study finds evidence that the presence of RLPR and HLPR, on average, decreases the positive impact of NLPR by 264%. The SMs also have a significant and positive impact on HIE. Adoption of an additional SM can increase the odds of engaging in a certain type of HIE between 21% and 61%. On the other hand, a strong NSI can also amplify the positive impact of SM on certain types of HIE.

This study extends prior research on the role of PRs in enabling HIE by considering the complexities brought up by adopting multiple PRs. NLPRs have the strongest impact on HIE in comparison to RLPRs or HLPRs. Moreover, public infrastructure initiatives such as those related to secure communications can also complement SMs adopted by the providers by encouraging HIE.

Health information exchange (HIE) initiatives utilize sharing mechanisms through which health information is mostly transmitted without a patient's close supervision; thus, patient trust in the HIE is the core in this setting. Existing technology acceptance theories mainly consider cognitive beliefs resulting in adoption behavior. The study argues that existing theories should be expanded to cover not only cognitive beliefs but also the emotion provoked by the sharing nature of the technology. Based on the theory of reasoned action, the technology adoption literature, and the trust literature, we theoretically explain and empirically test the impact of perceived transparency of privacy policy on cognitive trust and emotional trust in HIEs. Moreover, the study analyzes the effects of cognitive trust and emotional trust on the intention to opt in to HIEs and willingness to disclose health information.

An online survey was conducted using data from individuals who were aware of HIEs through experience with at least one provider participating in an HIE network. Data were collected from a wide range of adult population groups in the United States.

The structural equation modeling analysis results provide empirical support for the proposed model. The model highlights the strategic role of the perceived transparency of the privacy policy in building trust in HIEs. When patients know more about HIE security measures, sharing procedures, and privacy terms, they feel more in control, more assured, and less at risk. The results also show that patient trust in HIEs may take the forms of intention to opt in to an HIE and willingness to disclose health information exchanged through HIE networks.

The findings of this study should be of interest to both academics and practitioners. The research highlights the importance of developing and using a transparent privacy policy in the diffusion of HIEs. The findings provide a deep understanding of dimensions of HIE privacy policy that should be addressed by health-care organizations to exchange personal health information in a secure and private manner.

Patient-centric exchanges, a major type of Health Information Exchange (HIE), empower patients to aggregate and manage their health information. This exchange model helps patients access, modify and share their medical information with multiple healthcare organizations. Although existing studies examine patient engagement, more research is required to investigate patients' attitudes and willingness to play an active role in patient-centered information exchange. The study's main objective is to develop a model based on the belief-attitude-intention paradigm to empirically examine the effects of patients' attitudes toward engagement in care on their willingness to participate in patient-centric HIE.

The authors conducted an online survey study to identify the antecedents and consequences of patients' attitudes toward engagement in care. To empirically test the research model, the authors collected data from a national sample (n = 357) of individuals in the United States. The data were analyzed using structural equation modeling (SEM).

The proposed model categorizes the antecedents to patients' attitudes toward engagement in patient-related and healthcare system factors. The results show that patient-related factors (perceived health literacy and perceived coping ability) and health system factors (perceived experience with the healthcare organization and perceived patient-provider interaction) significantly shape patient attitude toward care management engagement. The results indicate that patients' attitudes toward engaging in their healthcare significantly contribute to their willingness to participate in medical information sharing through patient-centric HIE initiatives. Moreover, the authors’ findings also demonstrate that the link between patient engagement and willingness to participate in HIE is stronger for individuals who perceive lower levels of privacy and security concerns.

The authors validate the proposed model explaining patients' perceptions about their characteristics and the healthcare system significantly influence their attitude toward engaging in their care. This study also suggests that patients' favorable attitude toward engagement can bring patient-centric HIE efforts onto a path to success. The authors’ research attempts to shed light on the importance of patients' roles in adopting patient-centric HIE initiatives. Theoretical and practical contributions of this study are noticeable since they could result in a deeper understanding of the concept of patient engagement and how it may affect healthcare services in an evolving digital world. The authors’ findings can help healthcare organizations provide public citizen-centric services by introducing user-oriented approaches in healthcare delivery systems.

The purpose of this paper is to investigate user satisfaction with digital health solutions by identifying and prioritizing different service attributes on the basis of their impact on improving user satisfaction.

Through a literature review and interviews with health professionals and patients, 20 attributes of digital health services provided in Italy have been identified. User satisfaction with these attributes has been evaluated by adopting the Kano model’s continuous and discrete analyses.

The findings reveal the essential attributes of digital health services that meet users' expectations, identify the attributes that users appreciate or dislike having and highlight unexpected attributes that lead to a significant boost in satisfaction when provided.

This study demonstrates the efficacy of the Kano model in assessing the nonlinear correlation between user satisfaction and the quality of digital health services, thus contributing to fill a gap in the literature in this area. The main limitation of this work is the use of a non-probabilistic sampling method.

This research suggests healthcare institutions and organizations consider user preferences when designing digital health solutions to increase their satisfaction. The results indicate different effects on user satisfaction and dissatisfaction for different categories of attributes in the Italian context.

Previous works studied customer satisfaction with digital health, assuming a linear relationship with service quality, or investigated consumer adoption intentions focusing on the technological factors. This work advances available knowledge by analyzing the nonlinear relationship between digital health attributes and users’ satisfaction and dissatisfaction.