In the context of gamification strategy, online health communities (OHCs) have introduced patient feedback mechanisms, enabling patients to contribute to doctors’ success in gamified systems by offering gifts. However, the impact of doctors’ performance in online services on patients’ gift-giving behaviour and the underlying mechanisms driving this impact are relatively under-explored. Based on the expectancy violations theory, this study constructed a research model to examine how doctors’ e-consultation performance and the progress bar influence patients’ gift-giving behaviour and investigated the moderating role of the progress bar. Using the data collected from an OHC, the proposed hypotheses were empirically tested through regression analysis. The empirical results showed that doctors’ performance—both instrumental and socio-emotional—positively affected patients’ gift-giving behaviour, whereas the length of the progress bar had a negative effect. The progress bar strengthened the effect of socio-emotional performance on patients’ gift-giving behaviour, but its interaction with instrumental performance was not statistically significant. Theoretically, this study extends EVT by demonstrating how publicly visible gamification cues systematically elevate observers’ baseline expectations, thereby compressing positive violation magnitudes. The findings offer valuable insights for OHC designers and managers to optimise gamification design in order to enhance doctors’ online services.

Politeness plays a central role in online health communication, yet its consequences for physicians’ service quality remain insufficiently understood. Drawing on social exchange theory, this study examines how patients’ linguistic politeness and impoliteness influence physicians’ informational and emotional support in a large Chinese online health community. Using computational text analysis and econometric modeling, we analyze physician–patient interactions at the consultation level. Results show that patients’ politeness is positively associated with both informational and emotional support. Interestingly, moderate levels of impoliteness are also associated with increased physician effort, suggesting compensatory responses to emotionally charged requests. Moreover, patients’ politeness exhibits spillover effects, shaping physicians’ service provision to other patients within the same time frame. By distinguishing between politeness and impoliteness and identifying both direct and cross-patient effects, this study extends research on language use in digital healthcare and highlights the relational dynamics underlying service quality in online professional communities.

PurposeThis study aims to delineate the antecedents of two divergent stress outcomes - distress and eustress - resulting from the use of smart healthcare systems (SHSs).Design/methodology/approachGrounded in the job demands-resources (JD-R) model, this study examines how perceived SHS-use hindrance demands and resources differentially impact distress and eustress, with espoused organizational cultural values positioned as a key antecedent to these perceived resources. The research model is empirically validated by analyzing survey data collected from 244 doctors at a large hospital in China.FindingsWe find that all perceived SHS-use resources are positively related to eustress, and the two perceived SHS-use demands are positively related to distress. Espoused organizational cultural values are found to be antecedents of perceived SHS-use resources. The perceived SHS-use resources are not significant to distress.Originality/valueThe findings suggest that increasing eustress is a very efficient way to achieve SHS-use satisfaction.

In the care sector, the application of artificial intelligence (AI) has become a key strategy to alleviate workforce shortages. However, systematic research is still lacking on the differential effects of different AI adoption approaches on caregivers' willingness to stay and their underlying mechanisms. This study uses job demandsresources theory as basis to develop a dual-path model to explore the differences between AI augmentation and AI substitution in influencing caregivers' willingness to stay. Results of two experiments indicate that AI augmentation has a significantly stronger positive effect on willingness to stay than AI substitution, with the resource gain pathway of 'self-efficacy-job satisfaction' playing a partial mediating role. AI substitution also significantly enhances willingness to stay (compared with scenarios without AI), and its effect is mediated by the resource gain and job demand pathways of 'identity threat-emotional exhaustion'. Additionally, AI learning anxiety significantly weakens the positive effects of both AI adoption approaches, whilst job replacement anxiety only negatively moderates the effect of AI substitution. This study provides a markedly nuanced theoretical perspective for research on organisational AI adoption and offers practical insights into the practical application of AI in the care sector.

We systematically analyze the evolutionary characteristics of China’s public health policies, focusing on the dynamic changes in policy content, stage-specific differences, and inter-subject collaborative relationships. Based on 137 public health policy documents issued by the central government, the analysis is conducted from a dual perspective: first, the BERTopic model is employed to identify prominent policy themes and track their evolutionary paths; second, Social Network Analysis (SNA) is utilized to deconstruct the collaborative mechanisms and network structural characteristics among policy actors, goals, and tools. The findings indicate: (1) Collaboration among core policy actors is close, yet inter-departmental transparency and collaborative inclusivity remain limited for certain organizations. (2) Policy goals show a diversifying trend, with the strategic focus shifting from infectious disease prevention and control to comprehensive public health services. (3) There are significant preferences in the selection of policy tools for balancing rapid emergency response with sustainable long-term health governance. These findings reveal the evolutionary laws of the public health policy system and provide a theoretical basis for optimizing the policy framework and enhancing governance efficacy.

Purpose The landscape of health information acquisition has shifted from offline to online, and online question-and-answer (Q&A) communities have emerged as prominent sources of health information; however, it is unclear how users identify satisfactory health information. This paper identifies factors that influence users’ adoption of health information in the context of online Q&A communities. Design/methodology/approach Based on the elaboration likelihood model (ELM) and opinion leader theory, we construct a research model to examine how information quality (complexity, image structure and emotional change) and source credibility (authentication status, follower number) affect health information adoption behavior. We verify the hypotheses by Poisson regression and zero-inflation Poisson regression using the data collected from an online Q&A community. Findings The empirical results indicate that both information quality and source credibility positively affect users’ adoption of health information. Originality/value This research can assist designers and managers of online Q&A communities to better comprehend users’ health information needs and their preferences for adoption. This enhanced understanding can facilitate the provision of superior online health information.

This study investigates the factors influencing the frequency of generative AI usage among 315 investors, categorized into three groups: stock-only, crypto-only, and diversified. Through surveys and regression analyses, we explore how investment type, personality traits, and perceptions of AI affect adoption. The results reveal that diversified investors use AI most frequently. Narcissism and perceived performance significantly predict AI usage, whereas perceived ease of use does not play a notable role. These findings emphasize the interplay of investor diversity and personality in AI engagement, suggesting the need for tailored AI tools for investors.

The COVID-19 pandemic has markedly exacerbated the complexities surrounding the diagnosis and prognosis of diverse severe pneumonia types, posing extraordinary challenges to healthcare systems worldwide. While previous AI-based approaches primarily targeted COVID-19 severe pneumonia and sought to enhance machine learning accuracy, they often neglected critical aspects such as distinguishing diagnostic and prognostic features among COVID-19 infectious, non-COVID infectious, and non-infectious severe pneumonia, as well as the explainability and fairness of user-centric AI assist decisions. This study es the need for robust, fair, and reliable diagnosis and prognosis of severe pneumonia within the context of the COVID-19 pandemic. This paper introduces a user-centric framework that first employs a GaussianCopula-based data augmentation method to enhance fairness by addressing small imbalanced sample sets. Following this, the framework introduces an explainable AI system designed to classify three types of severe pneumonia using demographic and physiological indicators, offering transparent decision-making processes and an understandable analysis of prognosis risk factors. Our fair system utilizes transparent models exclusively, which enables healthcare practitioners to access intelligent and reliable medical services such as pre-diagnosis and prognosis analysis (the likelihood of death) of severe pneumonia. The results show the data augmentation method efficiently reduces data bias and enhances fairness, reaching 70.70% distribution similarity. Our transparent model-based severe pneumonia classification module achieves 98.88% F1-scores on a real-world dataset. The transparent mechanism reveals that the four most significant features for classifying severe pneumonia types are ‘Interleukin_6’, ‘Albumin’, ‘D_Dimer’, and ‘CD4_absolute_count’. Meanwhile, the explainable statistical analysis identifies critical mortality risk factors for each pneumonia category: ‘Blood platelet’ and ‘Creatinine’ for COVID-19 severe pneumonia, ‘Hemameba’, ‘Interleukin-6’, and ‘Uric Acid’ for non-COVID-19 infectious severe pneumonia, and ‘Hemameba’, ‘BNP’, ‘Cholesterol’, and ‘PT’ for non-infectious severe pneumonia. Our study highlights the potential of transparent machine learning algorithms for accurate diagnosis and Cox proportional regression for transparent risk trend prediction. These analytical tools and medical results can facilitate early and appropriate management of pneumonia patients for doctors, potentially revolutionizing diagnostic processes and patient care strategies to improve clinical outcomes.

PurposeGamification has been widely applied in mobile fitness apps to motivate users to exercise continuously. Based on the affordances–psychological outcomes–behavioral outcomes framework, this study explores the roles of three specific gamification affordances (competition, visibility of achievement and interactivity) in self-health management (continuous use behavior and health behavior) from the perspectives of achievement satisfaction and gamification exhaustion.Design/methodology/approachWe test the research model using a structural equation model (SEM) with 505 self-reported data points. Furthermore, we apply fuzzy-set qualitative comparative analysis (fsQCA) to explore configurations of gamification affordances associated with self-health management behavior, reinforcing the SEM results.FindingsResults indicate that competition, visibility of achievement and interactivity can enhance achievement satisfaction, which further boosts self-health management behavior. However, competition and interactivity can also cause gamification exhaustion, which undermines self-health management behavior to some extent. Overall, the positive impacts of the three affordances outweigh the negative impacts.Practical implicationsThis study provides new insights for relevant practitioners on designing gamification affordances, aiding the sustainable development of mobile fitness apps and their long-term effects on self-health management. Visibility of achievement should be emphasized, and competition and interactivity should be thoughtfully designed to minimize their negative effects.Originality/valueThis study extends the affordances–psychological outcomes–behavioral outcomes framework and the literature on gamification and health management by applying both SEM and fsQCA methodologies to examine the relationship between specific gamification affordances and self-health management behavior.

In online medical consultations, patients convey their medical condition through self-disclosure, and the linguistic features of this disclosure, as signals, may significantly impact doctors’ diagnostic behavior and service quality. Based on signaling theory, this paper collects consultation data from a large online medical platform in China, employs text mining and classification techniques to extract relevant variables, and applies econometric models to empirically examine the effect of patients’ self-disclosure linguistic features on the quality of online medical services. The results indicate that the completeness and readability of patients’ self-disclosure have a significant positive impact on the quality of doctors’ services, while the expertise and positive sentiment of the disclosure have a significant negative effect. From the perspective of signaling theory, this study reveals the mechanism through which patients’ self-disclosure linguistic features influence doctors’ online consultation behavior, providing an important theoretical foundation for promoting online doctor–patient interaction and enhancing patient well-being.

Pre-positioned inventory of relief materials is crucial for the rapid response to potential disasters. Storing relief materials near disaster-prone areas facilitates quick delivery after a disaster. Consequently, governments often collaborate with local enterprises and stores to store materials. However, the proximity to disaster sites can result in the damage of relief materials, exacerbating shortages, particularly during major disasters. Despite this, the literature has not thoroughly addressed how governments pre-position materials considering damage risk. Therefore, this paper constructs a model for the relief supply chains, including a local government, a local supplier and a non-local supplier to determine the government's optimal reserve strategy and quantity. The demand and damage to materials depend on disaster intensity. We derive the conditions under which a specific reserve strategy (no-external reserve, local reserve, non-local reserve, bi-site reserve) is optimal and corresponding reserve quantity. The impacts of supplier characteristics, government-owned reserves, emergency procurement price, disaster intensity distribution, demand-intensity and supply-intensity relationships on optimal prepositioned reserves are identified. Several management insights are drawn from extensive numerical experiments.

Background:Lack of personalized hypertension management (PHM) is the main driver of low treatment and control rates of blood pressure (BP). Digital intervention has great potential in realizing PHM. Methods:This two-arm cluster randomized controlled trial aimed at assessing the added value of personalized information for leveraging commonly recognized objective behaviors facilitated by pragmatic digital applications. Implemented in Jieshou of Anhui Province, China, the intervention lasted for 12 months starting from August 2021. The primary outcome measure was systolic BP (SBP); and the secondary measures, diastolic BP (DBP), BP control rate, quality adjusted of life years (QALYs), deaths of and admissions for all-causes and hypertension complications and behavior scores. These measures were analyzed using mainly generalized linear mixed modeling controlled for clustering with or without imputation. Findings:The trial recruited 2392 patients at a response rate of 96.84%. At 12-month follow-up, the intervention group demonstrated (via intent-to treat analysis) superior SBP, -5.60 mmHg (95% CI: -8.10, -3.09; p < 0.001). Compared with 24-months follow-up, the adjusted difference decreased moderately to -3.93 mmHg (95% CI: -5.80, -2.05; p < 0.001). The intervention arm demonstrated non-greater risks of all the adverse effects observed. Interpretation:The study provides evidence to support the use of the personalized information support in controlling hypertension and its complications. The next step is an implementation strategy to incorporate the intervention into daily practice and realize its benefits. Funding:The trial was supported by National Natural Science Foundation of China (No. 72004002) and Scientific Research Foundation of Education Department of Anhui Province of China (No. KJ2021A0259).

Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply‐side and demand‐side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease‐related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy‐making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.

Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply-side and demand-side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease-related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy-making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.

PurposeComplex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.Design/methodology/approachThis article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.FindingsThe proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.Originality/valueThe proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

Medical information extraction is a crucial task in the governance of healthcare data within medical information systems in the medical internet network, aimed at extracting vital information from existing content. However, structuring this key information into a table is currently a challenge, hindering the development of AI-driven smart health. In this study, we study the medical text-to-table task based on a new generative perspective. To address the challenges of ineffective numerical embedding, flexible table formats, and dense medical terminology and numerical entities in an end-to-end manner, we present the innovative medical text-to-table model called MedT2T. This model, built on the BART backbone, operates in an end-to-end manner and comprises three essential modules: Encoder, Decoder, and Adapter. The Encoder utilizes an innovative adaptive medical numerical constraint to facilitate precise embedding and generation of medical numerical data. The generated output of the Decoder adheres to relational constraints and table formats, ensuring the desired structure and organization. Additionally, the Adapter incorporates an adaptive pointer generation mechanism, allowing for dynamic referencing of medical terminology and numerical information either from the source text or generated through the vocabulary distribution of the Decoder. Our method outperforms existing baselines in terms of exact match, character level match, and BERTScore. We also proved that MedT2T can serve as an essential table extraction tool to bring informative gains for medical downstream classifiers and predictors. This study not only achieved accurate entity generation for tables from lengthy medical texts to improve physician efficiency in accessing critical information for decision-making, but also provided large-scale structured training table data for downstream tasks such as AI-driven smart healthcare.

PurposeThis study aims to investigate the process of developing loyalty in the Chinese mobile health community from the information seeking perspective.Design/methodology/approachA covariance-based structural equation model was developed to explore the mobile health community loyalty development process from information seeking perspective and tested with LISREL 9.30 for the 191 mobile health platform user samples.FindingsThe empirical results demonstrate that the information seeking perspective offers an interesting explanation for the mobile health community loyalty development process. All hypotheses in the proposed research model are supported except the relationship between privacy and trust. The two types of mobile health community loyalty-attitudal loyalty and behavioral loyalty are explained with 58 and 37% variance.Originality/valueThis paper has brought out the information seeking perspective in the loyalty formation process in mobile health community and identified several important constructs for this perspective for the loyalty formation process including information quality, communication with doctors and communication with patients.

Gamification is used to increase the engagement of doctors on online health platforms. We track 2,106 doctors from a leading online health community (Good Doctor) over one year to investigate how gamification features (i.e., how actively doctors embellish their profiles and how they perform in popularity indicators via points and virtual gifts received) affect doctors’ future health service provision. The results showed that gamification can lead to high long-term doctor engagement. Specifically, virtual gifts and points were positively associated with doctors’ instrumental and socio-emotional participation, whereas customization was positively associated with doctors’ instrumental participation.

BackgroundAllergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information. ObjectiveThis study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR? MethodsThis study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR. ResultsOur classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1% and a recall rate of 96.3%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body’s immune system and lead to the development of allergies. ConclusionsOur approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR.

ABSTRACT Urban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.