Neurotransmitter accumulation and Parkinson's disease-like phenotype caused by anion channelrhodopsin opto-controlled astrocytic mitochondrial depolarization in substantia nigra pars compacta.

Parkinson's disease (PD) is a mitochondria-related neurodegenerative disease characterized by locomotor deficits and loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc). Majority of PD research primarily focused on neuronal dysfunction, while the roles of astrocytes and their mitochondria remain largely unexplored. To bridge the gap and investigate the roles of astrocytic mitochondria in PD progression, we constructed a specialized optogenetic tool, mitochondrial-targeted anion channelrhodopsin, to manipulate mitochondrial membrane potential in astrocytes. Utilizing this tool, the depolarization of astrocytic mitochondria within the SNc in vivo led to the accumulation of γ-aminobutyric acid (GABA) and glutamate in SNc, subsequently resulting in excitatory/inhibitory imbalance and locomotor deficits. Consequently, in vivo calcium imaging and interventions of neurotransmitter antagonists demonstrated that GABA accumulation mediated movement deficits of mice. Furthermore, 1 h/day intermittent astrocytic mitochondrial depolarization for 2 weeks triggered spontaneous locomotor dysfunction, α-synuclein aggregation, and the loss of DA neurons, suggesting that astrocytic mitochondrial depolarization was sufficient to induce a PD-like phenotype. In summary, our findings suggest the maintenance of proper astrocytic mitochondrial function and the reinstatement of a balanced neurotransmitter profile may provide a new angle for mitigating neuronal dysfunction during the initial phases of PD.

Fuzzy PID control of permanent magnet synchronous motor electric steering engine by improved beetle antennae search algorithm.

The accuracy of control in permanent magnet synchronous motor system significantly affects overall mechanical structure safety. To satisfy high-performance control for the position servo of the electric steering engine, this study selects a suitable vector control model for permanent magnet synchronous motor. Additionally, an enhanced beetle antennae search algorithm is designed and employed to optimize the fuzzy proportional-integral-derivative controller. The hybrid fuzzy proportional-integral-derivative controller is then implemented in the control model of the permanent magnet synchronous motor, resulting in the establishment of a novel control model for the electric steering engine driven by the permanent magnet synchronous motor. The test results showed that root-mean-square error of this control model was 0.03 mm and 0.02 mm respectively under the conditions of sinusoidal response, square wave response and step response, which was obviously shorter than all the selected control models. In addition, the standard deviation of the control model designed in this study accounted for less than 4% of root-mean-square error of electric steering engine position under the sinusoidal response condition, so the calculation stability was high. The research results show that the designed control model has a certain reference value for improving servo control performance of permanent magnet synchronous motor.

Understanding the Antecedents of the Routine Use of Mobile Health Services: A Person-Technology-Health Framework.

Although numerous studies have been conducted to understand the antecedents of usage of mobile health (mHealth) services, most of them solely focus on characteristics of mHealth services themselves but neglect taking users' psychological and health-related factors into consideration. Besides, the comprehensive understanding of what influences users' routine use intentions regarding mHealth services is lacking. Therefore, this study proposes a person-technology-health framework that underlines how personal factors (e.g., personal innovativeness in IT), technological factors (e.g., trust), and health factors (e.g., perceived health severity) jointly influence individuals' routine use intentions regarding mHealth services. The proposed research model and related hypotheses were tested based on survey data from 270 respondents. The results indicate that personal innovativeness in IT, trust, and perceived health severity are important for enhancing routine use intention of mHealth services. Specifically, in situations of high perceived health severity, trust relates less positively to routine use intention than personal innovativeness in IT. In contrast, in situations of low perceived health severity, trust relates more positively to routine use intention than personal innovativeness in IT. The research findings extend the existing literature on routine use intention related to mHealth services and provide significant implications for practitioners.

The effect of interactive factors on online health consultation review deviation: An empirical investigation.

BACKGROUND: One-to-one online consultation is a common type of online health consultation. In choosing doctors for these consultations, patients rely on online reviews. Yet the deviation between online doctor reviews and the true quality of doctor-provided online services calls the usefulness of online doctor reviews into question, and the methods for reducing this deviation via doctor-patient communication remain unclear. OBJECTIVES: The purpose of this study is to test the effects of interactive factors on online doctor review deviation and to further explore deviation across doctor specialties in the context of one-to-one online health consultations. METHODS: We collect our data from a well-known Chinese online health consultation platform. The dataset includes 60,693 one-to-one online health consultation communication flows and corresponding online doctor reviews. We construct an online doctor review deviation matrix and use logistic regression and multinomial logistic regression models to examine the effects of interactive factors on online doctor review deviation. RESULTS: Our findings indicate that, in the context of a one-to-one online health consultation, a quicker response time and a lower response-question ratio could reduce deviation in online doctor reviews. Single modalities, such as the use of voice messages and uploading of photos, could reduce online doctor review deviation, especially in terms of patient overestimation. Medical information, including structural medical history and prescription information, could decrease online doctor review deviation. Moreover, the use of voice messages in surgery patient treatment can reduce online doctor review deviation more than in internal medicine. CONCLUSION: Interaction frequency, message delivery methods, and medical information can influence the deviation of online doctor reviews. Furthermore, the effects of voice messages vary across doctor specialties. This study offers theoretical and practical implications for the design of online health consultation platforms and the usage of online doctor reviews.

Building social identity-based groups to enhance online peer support for patients with chronic disease: a pilot study using mixed-methods evaluation.

Online peer support is increasingly important to encourage patients with chronic diseases to engage in successful self-management. However, studies mainly focus on individual-level participation and have not fully explored how to maximize the impact of online peer support through group identification. In this study, we aim to build an online social identity-based group to examine the impact of group identity on peer support. Twenty-five participants who completed the first phase of a larger study were randomly assigned either to the treatment group (identity-based group level, n = 15, three subgroups, five members in each subgroup) or to the control group (individual-level, n = 10). All participants in both treatment and control groups received the same tasks and incentives. Peer support behavior (informational support and emotional support), task completion (knowledge learning, self-tracking behavior), and health-related outcomes (self-efficacy [SE] and HbA1c) were collected for qualitative and quantitative analysis. Results from a 3-month pilot experiment showed that the treatment group offered substantial enhancement in peer support compared to the control group. It also significantly promoted improvement in SE. However, there was no significant difference in task completion or changes in HbA1c between the two groups. The results of the content analysis suggest that having a team leader, timely responsiveness, and intergroup competition played important roles in building social identity-based online groups and subsequently generating peer support. We provide some encouraging results that indicate how online groups may be effectively designed to promote peer support.

Addressing elderly loneliness with ICT Use: the role of ICT self-efficacy and health consciousness.

With an increasing aging population worldwide, loneliness among elderly individuals has become a salient societal problem. Fortunately, the last decade has also witnessed an upsurge in information and communication technology (ICT), which is ubiquitously deployed and integrated into our daily lives, including the lives of elderly people. This research investigates the potential exploitation of well-developed ICT to mitigate loneliness among the elderly. Specifically, we examined the effects of two dimensions of ICT use: communication use and information use. Moreover, we examined the moderating effects of two relevant features in the elderly population, namely, ICT self-efficacy and health consciousness. We applied structural equation modeling (SEM) to evaluate survey data from mainland China comprising 436 effective responses from the elderly population. We find that ICT use has a positive effect on loneliness among the elderly, and our results support and deepen this understanding, indicating that ICT self-efficacy and health consciousness can moderate the relationship between ICT use and loneliness. Our findings suggest that ICT use plays a significant role in mitigating elderly loneliness. Moreover, it is also suggested that the characteristics of ICT self-efficacy and health consciousness for the elderly can influence the relationship between their ICT use and loneliness. This gives a more accurate description, as compared with the main findings in prior literature, that ICT can help mitigate loneliness in the elderly. Finally, by adopting social cognitive theory, our research explains the moderating effect of ICT self-efficacy and health consciousness between the use of ICT by the elderly and their loneliness.

Exploring the Shift in International Trends in Mobile Health Research From 2000 to 2020: Bibliometric Analysis.

BACKGROUND: Smartphones have become an integral part of our lives with unprecedented popularity and a diverse selection of apps. The continuous upgrading of information technology has also enabled smartphones to display great potential in the field of health care. OBJECTIVE: We aimed to determine the future research direction of mobile health (mHealth) by analyzing its research trends and latest research hotspots. METHODS: This study collected mHealth-related literature published between 2000 and 2020 from the Web of Science database. Descriptive statistics of publication trends of mHealth research were determined by analyzing the annual number of publications in the literature and annual number of publications by country. We constructed visualization network maps of country (or regional) collaborations and author-provided keyword co-occurrences, as well as overlay visualization maps of the average publication year of author-provided keywords to analyze the hotspots and research trends in mHealth research. RESULTS: In total, 12,593 mHealth-related research papers published between 2000 and 2020 were found. The results showed an exponential growth trend in the number of annual publications in mHealth literature. JMIR mHealth and uHealth, the Journal of Medical Internet Research, and JMIR Research Protocols were the 3 top journals with respect to number of publications. The United States remained the leading contributor to the literature in this area (5294/12,593, 42.0%), well ahead of other countries and regions. Other countries and regions also showed a clear trend of annual increases in the number of mHealth publications. The 4 countries with the largest number of publications-the United States, the United Kingdom, Canada, and Australia-were found to cooperate more closely. The rest of the countries and regions showed a clear geographic pattern of cooperation. The keyword co-occurrence analysis of the top 100 authors demonstrated 5 clusters, namely, development of mHealth medical technology and its application to various diseases, use of mHealth technology to improve basic public health and health policy, mHealth self-health testing and management in daily life, adolescent use of mHealth, and mHealth in mental health. The research trends revealed a gradual shift in mHealth research from health policy and improving public health care to the development and social application of mHealth technologies. CONCLUSIONS: To the best of our knowledge, the most current bibliometric analysis dates back to 2016. However, the number of mHealth research published between 2017 and 2020 exceeds the previous total. The results of this study shed light on the latest hotspots and trends in mHealth research. These findings provide a useful overview of the development of the field; they may also serve as a valuable reference and provide guidance for researchers in the digital health field.

Information and Communication Technology Use for Life Satisfaction among the Elderly: A Motivation Perspective.

Objectives: Information and communication technology (ICT) has emerged as an appealing approach to address the changing life satisfaction of the aging population. This study explores the influence of perceived benefit and social support on the life satisfaction of elderly individuals via the mediation of their ICT use, from a motivation perspective. Additionally, we explore the moderating effect of health consciousness on the relationship between perceived benefit, social support and ICT use for the elderly. Methods: Using 237 valid samples from elderly individuals in China, we conducted a survey to evaluate their ICT use, perceived benefit, social support and life satisfaction. Results: Perceived benefit and social support both can influence life satisfaction of elderly individuals, and the effects of these factors are mediated by elderly ICT use. Furthermore, health consciousness can moderate the relationship among perceived benefit, social support and ICT use for the elderly. Conclusions: Both intrinsic and extrinsic motivations can influence ICT use by elderly individuals as well as their life satisfaction. ICT use plays an important role in life satisfaction for elderly people, and their individual health consciousness is a crucial factor.

Clinical decision-making framework against over-testing based on modeling implicit evaluation criteria.

Different statistical methods include various subjective criteria that can prevent over-testing. However, no unified framework that defines generalized objective criteria for various diseases is available to determine the appropriateness of diagnostic tests recommended by doctors. We present the clinical decision-making framework against over-testing based on modeling the implicit evaluation criteria (CDFO-MIEC). The CDFO-MIEC quantifies the subjective evaluation process using statistics-based methods to identify over-testing. Furthermore, it determines the test's appropriateness with extracted entities obtained via named entity recognition and entity alignment. More specifically, implicit evaluation criteria are defined-namely, the correlation among the diagnostic tests, symptoms, and diseases, confirmation function, and exclusion function. Additionally, four evaluation strategies are implemented by applying statistical methods, including the multi-label k-nearest neighbor and the conditional probability algorithms, to model the implicit evaluation criteria. Finally, they are combined into a classification and regression tree to make the final decision. The CDFO-MIEC also provides interpretability by decision conditions for supporting each clinical decision of over-testing. We tested the CDFO-MIEC on 2,860 clinical texts obtained from a single respiratory medicine department in China with the appropriate confirmation by physicians. The dataset was supplemented with random inappropriate tests. The proposed framework excelled against the best competing text classification methods with a Mean_F1 of 0.9167. This determined whether the appropriate and inappropriate tests were properly classified. The four evaluation strategies captured the features effectively, and they were imperative. Therefore, the proposed CDFO-MIEC is feasible because it exhibits high performance and can prevent over-testing.

Privacy Concerns About Health Information Disclosure in Mobile Health: Questionnaire Study Investigating the Moderation Effect of Social Support.

BACKGROUND: Mobile health (mHealth) provides a new opportunity for disease prediction and patient health self-management. However, privacy problems in mHealth have drawn significant attention to patients' online health information disclosure and to the possibility that privacy concerns may hinder mHealth development. OBJECTIVE: Privacy calculus theory (PCT) has been widely used to understand personal information disclosure behaviors with the basic assumption of a rational and linear decision-making process. However, cognitive behavior processes are complex and mutual. In an attempt to gain a fuller understanding of information disclosure behavior, we further optimize a PCT-based information disclosure model by identifying the mutual relationship between costs (privacy concerns) and benefits. Social support, which has been proven to be a distinct and significant disclosure benefit of mHealth, was chosen as the representative benefit of information disclosure. METHODS: We examine a structural equation model that incorporates privacy concerns, health information disclosure intention in mHealth, and social support from mHealth, all at the individual level. RESULTS: A validated questionnaire was completed by 253 randomly selected participants. The result indicated that perceived health information sensitivity positively enhances patients' privacy concern (beta path coefficient 0.505, P<.001), and higher privacy concern levels will decrease their health information disclosure intention (beta path coefficient -0.338, P<.001). Various individual characteristics influence perceived health information sensitivity in different ways. One dimension of social support, informational support, negatively moderates the effect of the relationship between perceived health information sensitivity and privacy concerns (beta path coefficient -0.171, P=.092) and the effect of the relationship between privacy concerns and health information disclosure intention (beta path coefficient -0.105, P=.092). However, another dimension, emotional support, has no direct moderation effect on the relationship between privacy concerns and health information disclosure intention. CONCLUSIONS: The results indicate that social support can be regarded as a disutility reducer. That is, on the one hand, it reduces patients' privacy concerns; on the other hand, it also reduces the negative impact of privacy concerns on information disclosure intention. Moreover, the moderation effect of social support is partially supported. Informational support, one dimension of social support, is significant (beta path coefficient -0.171, P=.092), while the other dimension, emotional support, is not significant (beta path coefficient -0.137, P=.146), in mHealth. Furthermore, the results are different among patients with different individual characteristics. This study also provides specific theoretical and practical implications to enhance the development of mHealth.

Social Capital and Digital Divide: Implications for Mobile Health Policy in Developing Countries.

Digital divide has been a major obstacle for mobile health services for the elderly in developing countries; to assess the potential solution to narrow digital divide among the elderly, we use data from the China Health and Retirement Longitudinal Study (CHARLS) and test for a causal role of social capital in digital access among elderly individuals in China. To handle endogenous problems associated with social capital, we introduce instrumental variable (IV) estimates in our models. Our data analysis shows that social capital facilitates increased digital access. We distinguish between two digital access patterns, an infrastructure pattern and a personal device pattern, and find that the causal effect of social capital is determined by the personal device pattern. Therefore, since family members and relatives increase digital access among elderly people, we propose a family-centered mobile health policy in developing countries.

Influence of Personality on mHealth Use in Patients with Diabetes: Prospective Pilot Study.

BACKGROUND: Mobile technology for health (mHealth) interventions are increasingly being used to help improve self-management among patients with diabetes; however, these interventions have not been adopted by a large number of patients and often have high dropout rates. Patient personality characteristics may play a critical role in app adoption and active utilization, but few studies have focused on addressing this question. OBJECTIVE: This study aims to address a gap in understanding of the relationship between personality traits and mHealth treatment for patients with diabetes. We tested the role of the five-factor model of personality traits (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) in mHealth adoption preference and active utilization. METHODS: We developed an mHealth app (DiaSocial) aimed to encourage diabetes self-management. We recruited 98 patients with diabetes-each patient freely chose whether to receive the standard care or the mHealth app intervention. Patient demographic information and patient personality characteristics were assessed at baseline. App usage data were collected to measure user utilization of the app. Patient health outcomes were assessed with lab measures of glycated hemoglobin (HbA1c level). Logistic regression models and linear regression were employed to explore factors predicting the relationship between mHealth use (adoption and active utilization) and changes in health outcome. RESULTS: Of 98 study participants, 46 (47%) downloaded and used the app. Relatively younger patients with diabetes were 9% more likely to try and use the app (P=.02, odds ratio [OR] 0.91, 95% CI 0.85-0.98) than older patients with diabetes were. Extraversion was negatively associated with adoption of the mHealth app (P=.04, OR 0.71, 95% CI 0.51-0.98), and openness to experience was positively associated with adoption of the app (P=.03, OR 1.73, 95% CI 1.07-2.80). Gender (P=.43, OR 0.66, 95% CI 0.23-1.88), education (senior: P=.99, OR 1.00, 95% CI 0.32-3.11; higher: P=.21, OR 2.51, 95% CI 0.59-10.66), and baseline HbA1c level (P=.36, OR 0.79, 95% CI 0.47-1.31) were not associated with app adoption. Among those who adopted the app, a low education level (senior versus primary P=.003; higher versus primary P=.03) and a high level of openness to experience (P=.048, OR 2.01, 95% CI 1.01-4.00) were associated with active app utilization. Active users showed a significantly greater decrease in HbA1c level than other users (ΔHbA1c=-0.64, P=.05). CONCLUSIONS: This is one of the first studies to investigate how different personality traits influence the adoption and active utilization of an mHealth app among patients with diabetes. The research findings suggest that personality is a factor that should be considered when trying to identify patients who would benefit the most from apps for diabetes management.

Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis.

Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made by MKN-based clinical decision support system depends on the accuracy of medical knowledge and the completeness of the network. However, collecting knowledge is a long-lasting and cumulative process, which means it's hard to construct a complete MKN with limited data. This study was conducted with the objective of developing an expandable EMR-based MKN to enhance capabilities in making an initial clinical diagnosis. A network of symptom-indicate-disease knowledge in 992 Chinese EMRs (CEMRs) was manually constructed as Original-MKN, and an incremental expansion framework was applied to it to obtain an expandable MKN based on new CEMRs. The framework was composed by: (1) integrating external knowledge extracted from the medical information websites and (2) mining potential knowledge with new EMRs. The framework also adopts a diagnosis-driven learning method to estimate the effectiveness of each knowledge in clinical practice. Experimental results indicate that our expanded MKN achieves a precision of 0.837 for a recall of 0.719 in clinical diagnosis, which outperforms Original-MKN and four classical machine learning methods. Furthermore, both external medical knowledge and potential medical knowledge benefit MKN expansion and disease diagnosis. The proposed incremental expansion framework sustains the MKN learning new knowledge.

Privacy Protection in Online Health Communities: Natural Experimental Empirical Study.

BACKGROUND: An online health community (OHC) is a novel sharing channel through which doctors share professional health care knowledge with patients. While doctors have the authority to protect their patients' privacy in OHCs, we have limited information on how doctors' privacy protection choices affect their professional health care knowledge sharing with patients. OBJECTIVE: We examined the relationship between privacy protection and professional health care knowledge sharing in OHCs. Specifically, we examined the effects of privacy protection settings in an OHC on doctors' interactive professional health care knowledge sharing and searching professional health care knowledge sharing (two dimensions of professional health care knowledge sharing). Moreover, we explored how such effects differ across different levels of disease stigma. METHODS: We collected the monthly panel data of 19,456 doctors from Good Doctor, one of the largest OHCs in China, from January 2008 to April 2016. A natural experimental empirical study with difference-in-difference analysis was conducted to test our hypotheses. The time fixed effect and the individual fixed effect were both considered to better identify the effects of a privacy protection setting on professional health care knowledge sharing. Additionally, a cross-sectional analysis was performed for a robust check. RESULTS: The results indicate that the privacy protection setting has a significant positive effect on interactive professional health care knowledge sharing (ß=.123, P<.001). However, the privacy protection setting has a significant negative effect on searching professional health care knowledge sharing (ß=-.225, P=.05). Moreover, we found that high disease stigma positively impacts the effect of privacy protection on interactive professional health care knowledge sharing (coefficients are in the same valence) and negatively impacts the effects of privacy protection on searching professional health care knowledge sharing (coefficients are in the reverse valence). CONCLUSIONS: Privacy protection has a bilateral effect on professional health care knowledge sharing (ie, a positive effect on interactive professional health care knowledge sharing and a negative effect on searching professional health care knowledge sharing). Such bilateral switches of professional health care knowledge sharing call for a balanced state in conjunction with practical implications. This research also identifies a moderate effect of disease stigma on privacy protection settings and professional health care knowledge sharing.

Exploring Patients' Intentions for Continuous Usage of mHealth Services: Elaboration-Likelihood Perspective Study.

BACKGROUND: With the increasingly rapid development of Web 2.0 technologies, the application of mobile health (mHealth) care in the field of health management has become popular. Accordingly, patients are able to access consulting services and effective health information online without temporal and geographical constraints. The elaboration-likelihood model (ELM) is a dual-process persuasion theory that describes the change of attitudes and behavior. OBJECTIVE: In this study, we drew on the ELM to investigate patients' continuous usage intentions regarding mHealth services. In addition, we further examined which route-central or peripheral-has a stronger impact on a patient's usage of health care management. METHODS: To meet these objectives, five hypotheses were developed and empirically validated using a field survey to test the direct and indirect effects, via attitude, of the two routes on continuous usage intention. RESULTS: We found that patients' perceived mHealth information quality and perceived mHealth system quality had a positive effect on their personal attitudes. The results revealed that social media influence had a positive effect on a patient's attitude toward mHealth services. In particular, our findings suggest that a patient's health consciousness has a positive effect on the relationship between social media influence and attitude. CONCLUSIONS: This study contributes to the mHealth services literature by introducing the ELM as a referent theory for research, as well as by specifying the moderating role of health consciousness. For practitioners, this study introduces influence processes as policy tools that managers can employ to motivate the uptake of mHealth services within their organizations.

Direct and indirect optical absorptions of cubic BAs and BSb.

Recently, boron arsenide (BAs) has been measured with high thermal conductivity in the experiments, great encouragement for low-power photoelectric devices. Hence we systematically investigate the direct and indirect optical absorptions of BAs and BSb by using first-principles calculations. We obtain the absorption onset corresponding to the value of indirect bandgap by considering the phonon-assisted second-order indirect optical absorption. The temperature-dependent calculations also capture the redshift of absorption onset, enhancement, and smoothness of optical absorption spectra. Moreover, in order to introduce the first-order absorption in the visible range, the doping effect of congeners is studied without the assist of phonon. It is found that the decrease of local direct bandgap derives from either the decrease of bonding-antibonding repulsion of p orbital states by the heavier III group elements or the similar influence of lighter V group elements on the s orbital states. Thus, the doping of congeners can improve the visible optical absorptions.

Medical knowledge embedding based on recursive neural network for multi-disease diagnosis.

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.e., high-dimensional and continuous vectors) is a feasible approach to complex reasoning that can not only retain the semantic information of knowledge, but also establish the quantifiable relationship among embeddings. In this paper, we propose a recursive neural knowledge network (RNKN), which combines medical knowledge based on first-order logic with a recursive neural network for multi-disease diagnosis. After the RNKN is efficiently trained using manually annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented knowledge embeddings and weight matrixes are learned. The experimental results confirm that the diagnostic accuracy of the RNKN is superior to those of four machine learning models, four classical neural networks and Markov logic network. The results also demonstrate that the more explicit the evidence extracted from CEMRs, the better the performance. The RNKN gradually reveals the interpretation of knowledge embeddings as the number of training epochs increases.

The impact mechanism of the controlling system in hospitals on quality of care: A study on clinical practice in China.

BACKGROUND: Quality control system is one of the hospital information systems. The adoption of quality control system increases the work efficiency; however, to some extent, it also increases the workload for physicians. OBJECTIVE: The purpose of this study is to investigate the impacts of the quality control system on quality of care (e.g., process and outcome performance). METHODS: Our study collected physicians' behavior information from a large urban hospital in China. We constructed the fixed-effect model to examine the relationship between the quality control system adoption and quality of care. RESULTS: Using the quality control system has a significant (p< 0.001) and negative effect on patients' stay length in the hospital (process performance). Furthermore, using the quality control system has a significant (p< 0.001) and positive effect on the trends of cure rate in the hospital (outcome performance). The coefficient of the dependent variable from the patients' stay length (process performance) is lower than the trends of cure rate (outcome performance). CONCLUSIONS: The controlling system can improve medical quality even though it limits physician behavior to some extent. The controlling system improves both the process performance and outcome performance, and it brings more benefits to outcome performance rather than process performance which means the reflection of the new technology may have more evident on outcome variables.

Exploring the Impact of the Rational Antibiotic Use System on Hospital Performance: The Direct Effect and the Spillover Effect.

Irrational antibiotic usage not only causes an increase in antibiotic-borne diseases, but also inflicts pain on patients, as a result of inappropriate treatment. In order to resolve the hazards caused by irrational antibiotic usage, a kind of e-health service, the Rational Antibiotic Use System (RAUS), has been incorporated into the hospital information system. The RAUS provides doctors and patients with the functions of antibiotic usage monitoring, antibiotic information consultation and antibiotic prescription support. Though existing literature has already proved the usefulness of the RAUS on monitoring doctors' behavior, the effects on hospital performance from an organizational perspective has rarely been measured by empirical data. Therefore, our study has explored the effects of the RAUS on the performance of a large Chinese hospital, which has implemented the RAUS since March 2014. Through empirical research, we quantified the effects of the implementation of the RAUS on a hospital's performance from both the direct effects on the "drug income" and the spillover effect on the "treatment income". The results indicate a significant positive spillover effect on the treatment incomes of a hospital in its inpatient activities (seen as significant in the long term) and in its outpatient activities (seen as significant in both the short and long terms). In addition, this research provides certain theoretical and practical implications for the dilemma of e-health services application in irrational antibiotic usage.

Exploring the Role of Online Health Community Information in Patients' Decisions to Switch from Online to Offline Medical Services.

BACKGROUND: Online healthcare has become an important part of healthcare services. Patients can access unlimited information reflecting a doctor's service quality in online health communities (OHCs). This information can reduce information asymmetry between patients and doctors, and further promote patient decisions. In this study, we focus on the patient's decision to switch from a doctor's online medical service to the same doctor's offline service. OBJECTIVES: We classify OHC information into objective information and subjective information. Following this, we investigate how these two types of information influence patient decisions to switch from a doctor's online to offline medical service, and explore the nature of interaction between the two types of influence. METHODS: We collected data from a leading OHC in China and constructed a longitudinal dataset to examine our research questions. To control for doctor heterogeneity and common trends across time, we leveraged fixed effects at the doctor level and monthly fixed effects in our models, respectively. RESULTS: Our findings demonstrate a strong relationship between OHC information and patient decisions to switch from a doctor's online to offline medical service. Patients are more likely to switch from online to offline medical services with doctors whose subjective and objective information on their homepages indicate a higher quality (ßSubjective = 0.001, p < 0.001; αObjective = 0.019, p < 0.002). We also observe that patients prefer doctors who do not exhibit objective information that indicates low-quality service over those who show such information on their homepages, even though the chosen doctor provides a lower-quality service (αLow_Objective = -0.032, p < 0.006). Further, we find that the influence of subjective information on patient decisions crowds out the influence of objective information. CONCLUSION: This study investigates the role of subjective and objective information on patient decisions to switch from a doctor's online to offline medical service and the interaction effect between these two types of information. This study provides valuable theoretical and practical implications.