A Data-Enabled Business Model for a Smart Healthcare Information Service Platform in the Era of Digital Transformation

Su, Yu; Hou, Fei; Qi, Mingde; Li, Wanxuan; Ji, Ying

doi:10.1155/2021/5519891

Cited by 17 publications

(13 citation statements)

References 21 publications

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“…Smart healthcare services refer to embedding the new generation of intelligent technologies into the traditional healthcare industry to provide patients with more convenient, flexible, personalized, and diversified healthcare services (5,6). The application of intelligent technologies has changed the way of value creation of service systems by improving medical devices, optimizing treatment processes, and empowering disease diagnosis and prediction, which leads to the transformation of the organizational structure, ecosystem, and innovative model of the modern healthcare service industry (7)(8)(9).…”

Section: Introductionmentioning

confidence: 99%

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

Chen

Zhang

Wei

2022

Front. Public Health

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With the emergence of the digital age, smart healthcare services based on the new generation of information technologies play an increasingly important role in improving the quality of resident health. This study empirically examined the impact of regional smart healthcare services on resident health as well as the underlying mechanism by employing a two-way fixed effects model. We constructed a Regional Smart Healthcare Service Development Index and matched it with survey data from the China Health and Retirement Longitudinal Study to validate the model. The results showed that (1) smart healthcare services have a significant positive impact on resident health. (2) The availability of outpatient services and inpatient services plays a mediating role in the relationship between regional smart healthcare services and resident health. (3) The influence of regional smart healthcare services on resident health is heterogeneous among different regions. Specifically, the effect of smart healthcare services on resident health is significant in the eastern regions, while it is not significant in the central, western, and northeastern regions. The effect of smart healthcare services on resident health is significant in rural regions but not in urban regions. This study enriches the nascent research stream of smart healthcare services. This study offers useful insights for practitioners and the government to guide them in formulating smart healthcare strategies.

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Section: Introductionmentioning

confidence: 99%

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

Chen

Zhang

Wei

2022

Front. Public Health

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“…Effective nursing intervention is conducive to reducing maternal mortality, which makes it of great significance to grasp the health status of high-risk pregnant women [ 3 ]. With the progress of medical technology, data services have been effectively and widely applied [ 4 ]. In the context of the rapid formation of smart medical services, it is necessary to pay timely attention to high-risk pregnant women through data analysis in order to strengthen the protection of themselves and their fetuses [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care

et al. 2022

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Objective. In order to improve the refined management of hospitals, promote the scientific development of smart hospitals in medical institutions, and solve the problem of data filling and reporting that is increasing year by year in the country, province, and city. Methods. A total of 84 high-risk pregnant women admitted to our hospital from January 2020 to October 2021 were selected and screened for high-risk pregnant women. Risk pregnant women were divided into a routine intervention group and a DS medical group, with 42 cases in each group. High-risk pregnant women in the routine intervention group received routine intervention, and the DS medical group applied data to serve smart medical services on the basis of routine intervention. The scores of self-care, anxiety, and depression were compared between the two groups, the coping styles were analyzed, the satisfaction rate and incidence of adverse conditions of the high-risk puerperae were recorded, and the delivery methods of the two groups were compared. Results. After the intervention, the activities of daily living, follow-up, fetal monitoring, and self-protection behaviors in the DS medical group were higher than those in the routine intervention group, and the difference was statistically significant ( P < 0.05 ). The scores of anxiety and depression in the group were lower, with statistical significance ( P < 0.05 ); after the intervention, the scores of negative coping styles in the DS medical group were lower than those in the conventional intervention group, while the scores for positive coping styles were higher than those in the conventional intervention group; the DS medical group had higher risk. The satisfaction of pregnant women was significantly higher than that of the routine intervention group, and the difference was statistically significant ( P < 0.05 ); the overall incidence of adverse maternal outcomes among high-risk pregnant women in the DS medical group was lower than that of the routine intervention group, and the difference was not statistically significant ( P > 0.05 ). Compared with the routine group, the DS medical group had a higher number of vaginal deliveries and a lower number of cesarean deliveries, and the difference was statistically significant ( P < 0.05 ). Conclusion. The application of data services in a smart medical high-risk maternity-related data management platform enables the promotion of high-risk pregnant women’s self-care behaviors and improves negative emotions, enables them to cooperate in delivery with positive behaviors, and reduces the number of cases of cesarean delivery.

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“…Through the improvement of medical equipment, the optimization of treatment processes, and the ability to diagnose and predict diseases, the application of smart technology has changed the method of value creation in the service system, which has led to the transformation of organizational structures, ecosystems, and innovation models of modern healthcare services [ 57 , 58 , 59 ]. In the context of smart health, while some small healthcare institutions may not be able to provide certain healthcare services due to limited medical equipment or the skills and capabilities of medical staff, these issues can be overcome with the help of telemedicinal services [ 60 ].…”

Section: Results and Analysismentioning

confidence: 99%

Conceptual Framework for Smart Health: A Multi-Dimensional Model Using IPO Logic to Link Drivers and Outcomes

Deng

Huang

Wang

et al. 2022

IJERPH

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Smart health is considered to be a new phase in the application of information and communication technologies (ICT) in healthcare that can improve its efficiency and sustainability. However, based on our literature review on the concept of smart health, there is a lack of a comprehensive perspective on the concept of smart health and a framework for how to link the drivers and outcomes of smart health. This paper aims to interweave the drivers and outcomes in a multi-dimensional framework under the input–process–output (IPO) logic of the “system view” so as to promote a deeper understanding of the model of smart health. In addition to the collection of studies, we used the modified Delphi method (MDM) to invite 10 experts from different fields, and the views of the panelists were analyzed and integrated through a three-round iterative process to reach a consensus on the elements included in the conceptual framework. The study revealed that smart health contains five drivers (community, technology, policy, service, and management) and eight outcomes (efficient, smart, sustainable, planned, trustworthy, safe, equitable, health-beneficial, and economic). They all represent a unique aspect of smart health. This paper expands the research horizon of smart health, shifting from a single technology to multiple perspectives, such as community and management, to guide the development of policies and plans in order to promote smart health.

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A Data-Enabled Business Model for a Smart Healthcare Information Service Platform in the Era of Digital Transformation

Cited by 17 publications

References 21 publications

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

How Does Smart Healthcare Service Affect Resident Health in the Digital Age? Empirical Evidence From 105 Cities of China

Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care

Conceptual Framework for Smart Health: A Multi-Dimensional Model Using IPO Logic to Link Drivers and Outcomes

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