Gang Li

Background: The swift shift toward internet hospitals has relied on the willingness of medical practitioners to embrace new systems and workflows. Low engagement or acceptance by medical practitioners leads to difficulties in patient access. However, few investigations have focused on barriers and facilitators of adoption of internet hospitals from the perspective of medical practitioners. Objective: This study aims to identify both enabling and inhibiting predictors associated with resistance and behavioral intentions of medical practitioners to use internet hospitals by combining the conservation of resources theory with the Unified Theory of Acceptance and Use of Technology and technostress framework. Methods: A mixed methods research design was conducted to qualitatively identify the factors that enable and inhibit resistance and behavioral intention to use internet hospitals, followed by a quantitative survey-based study that empirically tested the effects of the identified factors. The qualitative phase involved conducting in-depth interviews with 16 experts in China from June to August 2022. Thematic analysis was performed using the qualitative data analysis software NVivo version 10 (QSR International). On the basis of the findings and conceptual framework gained from the qualitative interviews, a cross-sectional, anonymous, web-based survey of 593 medical practitioners in 28 provincial administrative regions of China was conducted. The data collected were analyzed using the partial least squares method, with the assistance of SPSS 27.0 (IBM Corp) and Mplus 7.0 (Muthen and Muthen), to measure and validate the proposed model. Results: On the basis of qualitative results, this study identified 4 facilitators and inhibitors, namely performance expectancy, social influence, work overload, and role ambiguity. Of the 593 medical practitioners surveyed in the quantitative research, most were female (n=364, 61.4%), had a middle title (n=211, 35.6%) or primary title (n=212, 35.8%), and had an average use experience of 6 months every year. By conducting structural equation modeling, we found that performance expectancy (β=−.55; P<.001) and work overload (β=.16; P=.005) had the most significant impact on resistance to change. Resistance to change fully mediated the influence of performance expectancy and partially mediated the influences of social influence (variance accounted for [VAF]=43.3%; P=.002), work overload (VAF=37.2%; P=.03), and role ambiguity (VAF=12.2%; P<.001) on behavioral intentions to use internet hospitals. In addition, this study found that the sex, age, professional title, and use experience of medical practitioners significantly moderated the aforementioned influencing mechanisms. Conclusions: This study investigated the factors that facilitate or hinder medical practitioners’ resistance to change and their behavioral intentions to use internet hospitals. The findings suggest that policy makers avoid the resistance and further promote the adoption of internet hospitals by ensuring performance expectancy and social influence and eliminating work overload and role ambiguity.

BACKGROUND The swift shift toward internet hospitals has relied on the willingness of medical practitioners to embrace new systems and workflows. Low engagement or acceptance by medical practitioners leads to difficulties in patient access. However, few investigations have focused on barriers and facilitators of adoption of internet hospitals from the perspective of medical practitioners. OBJECTIVE This study aims to identify both enabling and inhibiting predictors associated with resistance and behavioral intentions of medical practitioners to use internet hospitals by combining the conservation of resources theory with the Unified Theory of Acceptance and Use of Technology and technostress framework. METHODS A mixed methods research design was conducted to qualitatively identify the factors that enable and inhibit resistance and behavioral intention to use internet hospitals, followed by a quantitative survey-based study that empirically tested the effects of the identified factors. The qualitative phase involved conducting in-depth interviews with 16 experts in China from June to August 2022. Thematic analysis was performed using the qualitative data analysis software NVivo version 10 (QSR International). On the basis of the findings and conceptual framework gained from the qualitative interviews, a cross-sectional, anonymous, web-based survey of 593 medical practitioners in 28 provincial administrative regions of China was conducted. The data collected were analyzed using the partial least squares method, with the assistance of SPSS 27.0 (IBM Corp) and Mplus 7.0 (Muthen and Muthen), to measure and validate the proposed model. RESULTS On the basis of qualitative results, this study identified 4 facilitators and inhibitors, namely performance expectancy, social influence, work overload, and role ambiguity. Of the 593 medical practitioners surveyed in the quantitative research, most were female (n=364, 61.4%), had a middle title (n=211, 35.6%) or primary title (n=212, 35.8%), and had an average use experience of 6 months every year. By conducting structural equation modeling, we found that performance expectancy (β=−.55; P<.001) and work overload (β=.16; P=.005) had the most significant impact on resistance to change. Resistance to change fully mediated the influence of performance expectancy and partially mediated the influences of social influence (variance accounted for [VAF]=43.3%; P=.002), work overload (VAF=37.2%; P=.03), and role ambiguity (VAF=12.2%; P<.001) on behavioral intentions to use internet hospitals. In addition, this study found that the sex, age, professional title, and use experience of medical practitioners significantly moderated the aforementioned influencing mechanisms. CONCLUSIONS This study investigated the factors that facilitate or hinder medical practitioners’ resistance to change and their behavioral intentions to use internet hospitals. The findings suggest that policy makers avoid the resistance and further promote the adoption of internet hospitals by ensuring performance expectancy and social influence and eliminating work overload and role ambiguity.

Background: Inappropriate hospitalization day (IHD) is recognized as an important indication of the excessive demand for health-care services, especially for surgical patients. We aim to examine the degree of IHDs, predictors associated with higher incidences of IHDs, and reasons for each IHD in different periods of hospitalization. Methods: A total of 4586 hospital days from 408 cases were evaluated by a cross-sectional and retrospective audit program carried out in a tertiary hospital with 5613 beds and 9623 faculty in Wuhan, China. This study used the revised Chinese version of the Appropriateness Evaluation Protocol (C-AEP) to assess IHDs, and the Delay Tool to ascertain each reason for IHDs. A binary logistic regression model was performed to examine the predictors of higher incidences of IHDs. Results: The average frequency of IHDs was 23.24 %, and a total of 322 cases (78.92 %) were reported to have experienced at least one IHD. The multivariate analysis showed that patients at the age of 60–69 with respect to under 50, and with overlength of stay were predictors of higher incidences of preoperative IHDs, while admission from outpatient, multiple diagnosis, higher surgical incision level, and overlength of stay were predictors of higher incidence of postoperative IHDs. The most frequent reasons related to health providers for IHDs were doctor’s conservative views of patient management and delays in inspection, prescription, appointment, or result report. Patient factors gave rise to nearly a quarter of postoperative IHDs. Conclusions: Findings from this study indicate that measures including paying more attention to the construction of MDT for diagnosis and treatment in general surgery, reducing laboratory turnaround time, dispelling distrust among health-care providers and patients, setting stricter discharge standards and, providing integrated out-of-hospital services could be adopted accordingly to improve the inappropriateness of hospital stays.

Background The first step in every quality improvement process is the identification the problem and its extent. We aim to analyze the frequency and its associated factors of inappropriate hospitalization for a tertiary hospital in Wuhan and ascertain the specific reasons for each IHDs to provide evidence for further reduction of inappropriate hospital days.Results A total of 4586 days of hospital stay from 408 cases were evaluated by A cross-sectional and retrospective audit program carried out in a third-level teaching hospital with 5613 beds and 9623 faculty in Wuhan, China. 320 cases (78.43%) were reported to have experience at least one IHD and 1053 hospital days (23.31%) were found to be inappropriate. Predictors of higher levels of inappropriateness with surgery were patients at the age of 60-69(OR=2.328, 95% CI=1.359-3.989) before the surgery, and use of ventilators(OR=2.127, 95%CI=1.116-2.839), admission from outpatient(OR=5.724, 95%CI=1.579-20.744), multiple diagnosis(OR=1.604, 95%CI=1.059-2.432), and higher surgical incision level(OR=2.152, 95%CI=1.391-3.327) after surgery. The main causes of inappropriateness were medical factors that were related to the healthcare services providers, however, the patient factors gave rise to nearly a quarter of IHDs after surgery.Conclusions The prevalence of IHDs was 23.31% in this tertiary hospital in Wuhan. Findings from this study indicate that the characteristics of surgical patients from different phases(preoperative/postoperative) are supposed to be taken into account to develop efficient and feasible interventions for IHD reduction.

Background: Since the outbreak of 2019 novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS‐CoV‐2]) pneumonia, thousands of patients with fever or cough were flocked into fever clinic of designated hospitals in Wuhan, China. To date, no data have ever been reported to reflect the prevalence of coronavirus disease 2019 (COVID‐19) among these outpatients. Moreover, it is almost unknown to discriminate COVID‐19 and nucleic acid negative patients based on clinical features in the fever clinics.Methods: The infectious status of SARS‐CoV‐2 was estimated among the outpatients. The epidemiological and clinical characteristics were compared between COVID‐19 and nucleic acid negative patients.Results: The nucleic acid positive rate for SARS‐CoV‐2 in the outpatients from our fever clinic was 67·1%, while the majority of patients with COVID‐19 were mild cases. The predominant initial symptom in those patients with COVID‐19 was fever (78.2%), followed by cough (15.6%). Very significantly lower number of eosinophils was characterized in patients with COVID‐19 as compared with that of nucleic acid negative patients. More importantly, the proportion of subjects with eosinophil counts lower than normal levels in patients with COVID‐19 was much higher than that of nucleic acid negative patients. Fever combined with bilateral ground‐glass opacities in computed tomography imaging and eosinophil count below the normal level are probably a valuable indicator of COVID‐19 infection in those outpatients.Conclusions: Those findings may provide critical information for the regions, such as Europe and United States that are facing the same situation as Wuhan experienced, and could be valuable to prevent those nucleic acid negative patients from misdiagnosis before antibody testing.

Although emerging data demonstrated mortality of young COVID‐19 patients, no data have reported the risk factors of mortality for these young patients, and whether obesity is a risk for young COVID‐19 patients remains unknown. We conducted a retrospective study including 13 young patients who died of COVID‐19 and 40 matched survivors. Logistic regression was employed to characterize the risk factors of mortality in young obese COVID‐19 patients. Most of the young deceased COVID‐19 patients were mild cases at the time of admission, but the disease progressed rapidly featured by a higher severity of patchy shadows (100.00% vs 48.70%; P = .006), pleural thickening (61.50% vs 12.80%; P = .012), and mild pericardial effusion (76.90% vs 0.00%; P < .001). Most importantly, the deceased patients manifested higher body mass index (odds ratio [OR] = 1.354; 95% confidence interval [CI] = 1.075‐1.704; P = .010), inflammation‐related index C‐reactive protein (OR = 1.014; 95% CI = 1.003‐1.025; P = .014), cardiac injury biomarker hs‐cTnI (OR = 1.420; 95% CI = 1.112‐1.814; P = .005), and increased coagulation activity biomarker D‐dimer (OR = 418.7; P = .047), as compared with that of survivors. Our data support that obesity could be a risk factor associated with high mortality in young COVID‐19 patients, whereas aggravated inflammatory response, enhanced cardiac injury, and increased coagulation activity are likely to be the mechanisms contributing to the high mortality.

Background: The last decade has witnessed many achievements in China’s health care industry, but the industry still faces major challenges among which the uneven distribution of medical resources and the imbalance between supply and demand are the most pressing problems. Although mobile health (mHealth) services play a significant role in mitigating problems associated with health care delivery, their adoption rates have been low. Objective: The objective of this study was to explore the impact of resource scarcity and resource accessibility on the adoption of mHealth from the perspective of resource competition, to examine the concerning factors, and to provide a theoretical basis for promoting mHealth in China. Methods: We used 229,516 original registration records of outpatients to conduct an empirical analysis to examine the adoption of mHealth services from the perspective of resource competition. Results: The adoption rate of mobile services for outpatients was low, accounting for only 31.5% (N=71,707). The empirical results indicated that resource scarcity (beta=.435, P=.01) and accessibility (beta=−.134, P=.02) have a significant impact on the adoption of mHealth. In addition, gender (beta=.073, P=.01) and age (beta=−.009, P<.001) are significantly related to adoption of mHealth. Experience with mHealth has a moderating role in the relationship between resource scarcity (beta=−.129, P=.02), accessibility (beta=.138, P=.04), and adoption of mHealth. Conclusions: In this study we demonstrate that the external environment (resource scarcity and resource accessibility) has a significant impact on the adoption of mHealth. This study also demonstrates that experience with mHealth has a moderating role in the relationship between the elements of the external environment. Finally, we confirm that mHealth is a key factor in the delivery and allocation of medical resources and provide a theoretical basis for government agencies to develop policies on mHealth.

BACKGROUND The last decade has witnessed many achievements in China’s health care industry, but the industry still faces major challenges among which the uneven distribution of medical resources and the imbalance between supply and demand are the most pressing problems. Although mobile health (mHealth) services play a significant role in mitigating problems associated with health care delivery, their adoption rates have been low. OBJECTIVE The objective of this study was to explore the impact of resource scarcity and resource accessibility on the adoption of mHealth from the perspective of resource competition, to examine the concerning factors, and to provide a theoretical basis for promoting mHealth in China. METHODS We used 229,516 original registration records of outpatients to conduct an empirical analysis to examine the adoption of mHealth services from the perspective of resource competition. RESULTS The adoption rate of mobile services for outpatients was low, accounting for only 31.5% (N=71,707). The empirical results indicated that resource scarcity (beta=.435, P=.01) and accessibility (beta=−.134, P=.02) have a significant impact on the adoption of mHealth. In addition, gender (beta=.073, P=.01) and age (beta=−.009, P<.001) are significantly related to adoption of mHealth. Experience with mHealth has a moderating role in the relationship between resource scarcity (beta=−.129, P=.02), accessibility (beta=.138, P=.04), and adoption of mHealth. CONCLUSIONS In this study we demonstrate that the external environment (resource scarcity and resource accessibility) has a significant impact on the adoption of mHealth. This study also demonstrates that experience with mHealth has a moderating role in the relationship between the elements of the external environment. Finally, we confirm that mHealth is a key factor in the delivery and allocation of medical resources and provide a theoretical basis for government agencies to develop policies on mHealth.