Background: Since the outbreak of COVID-19, the development of dashboards as dynamic, visual tools for communicating COVID-19 data has surged worldwide. Dashboards can inform decision-making and support behavior change. To do so, they must be actionable. The features that constitute an actionable dashboard in the context of the COVID-19 pandemic have not been rigorously assessed. Objective: The aim of this study is to explore the characteristics of public web-based COVID-19 dashboards by assessing their purpose and users (“why”), content and data (“what”), and analyses and displays (“how” they communicate COVID-19 data), and ultimately to appraise the common features of highly actionable dashboards. Methods: We conducted a descriptive assessment and scoring using nominal group technique with an international panel of experts (n=17) on a global sample of COVID-19 dashboards in July 2020. The sequence of steps included multimethod sampling of dashboards; development and piloting of an assessment tool; data extraction and an initial round of actionability scoring; a workshop based on a preliminary analysis of the results; and reconsideration of actionability scores followed by joint determination of common features of highly actionable dashboards. We used descriptive statistics and thematic analysis to explore the findings by research question. Results: A total of 158 dashboards from 53 countries were assessed. Dashboards were predominately developed by government authorities (100/158, 63.0%) and were national (93/158, 58.9%) in scope. We found that only 20 of the 158 dashboards (12.7%) stated both their primary purpose and intended audience. Nearly all dashboards reported epidemiological indicators (155/158, 98.1%), followed by health system management indicators (85/158, 53.8%), whereas indicators on social and economic impact and behavioral insights were the least reported (7/158, 4.4% and 2/158, 1.3%, respectively). Approximately a quarter of the dashboards (39/158, 24.7%) did not report their data sources. The dashboards predominately reported time trends and disaggregated data by two geographic levels and by age and sex. The dashboards used an average of 2.2 types of displays (SD 0.86); these were mostly graphs and maps, followed by tables. To support data interpretation, color-coding was common (93/158, 89.4%), although only one-fifth of the dashboards (31/158, 19.6%) included text explaining the quality and meaning of the data. In total, 20/158 dashboards (12.7%) were appraised as highly actionable, and seven common features were identified between them. Actionable COVID-19 dashboards (1) know their audience and information needs; (2) manage the type, volume, and flow of displayed information; (3) report data sources and methods clearly; (4) link time trends to policy decisions; (5) provide data that are “close to home”; (6) break down the population into relevant subgroups; and (7) use storytelling and visual cues. Conclusions: COVID-19 dashboards are diverse in the why, what, and how by which they communicate insights on the pandemic and support data-driven decision-making. To leverage their full potential, dashboard developers should consider adopting the seven actionability features identified.

The health workers in rural primary care systems are at the increasing risk of job burnout. To explore the prevalence and associated factors of the job burnout among the primary healthcare worker in rural China, a cross-sectional survey was conducted among 15,627 participants in 459 township hospitals from six provinces. A combination of stratified multi-stage sampling and cluster sampling method, and a self-administrated questionnaire with the Chinese version of the Maslach Burnout Inventory-General Scale (MBI-GS) were used in the investigation. Multilevel regression analyses were used to examine the potential associated factors on both individual and organisational levels. 47.6% of respondents were experiencing moderate burnout, and 3.3% were in severe burnout. Professionals working for over 40 h per week, at young age, with a college degree, and with professional titles at medium or high rank reported a higher degree of job burnout. At the institutional level, the high ratio of performance-based salary was associated with a higher level of depersonalization. Burnout has become prevalent among the primary healthcare workers in rural China, and multiple strategies are needed to reduce the work stress and some high-risk groups’ vulnerability to job burnout.

The increasing concerns of the geographical maldistribution of medical resources have sparked worldwide interests in exploring the potential of telemedicine in the rural health system. This study aimed to investigate the application and effect of telemedicine as well as their regional heterogeneity in the primary care centres in rural China. Based on the stratified multistage cluster sampling, a cross-sectional study was conducted among 358 township health centres (THCs) from eastern, central and western China. A self-administered questionnaire was used and the data of the Health Statistical Annual Reports in 2017 were collected to investigate the implication of telemedicine as well as the performance and other characteristics of each THCs. Propensity score matching was used to estimate the effect of telemedicine application on the bed occupancy rate and the number of annual outpatient visits of the THCs, with comparison among the regions. The overall prevalence of telemedicine application was 58.66% in 2017, and it was found to increase the bed occupancy rate of the THCs in the national range (p < 0.1). When divided into different regions, telemedicine was found to improve the number of annual outpatient visits in western China (p < 0.05) and the bed occupancy rate in eastern China (p < 0.1). Disparities in the degree of remoteness and the capability of THCs among the regions were also found in this study, which may be the reasons for the regional heterogeneous effects of telemedicine. These findings suggested the potential of telemedicine in improving the utilization of primary care centres in rural areas. Further studies were needed to investigate the underlying reasons for its regional heterogeneous effects.

Background: The previous “one-size-fits-all” practice in resource allocation can no longer adapt to the spatial variation in population and health needs. This study aimed to investigate the spatially heterogeneous effect of medical equipment investment in the township health centres in rural China to optimize the investment strategies. Methods: Based on the national-scale stratified multistage cluster sampling, 319 township health centres from six provinces were included in the study. The retrospective data from 2013 to 2017 were collected for each sampled township health centres and the corresponding township community. The panel threshold regression model was applied to estimate the nonlinear effect of medical equipment increment on the service utilization due to the township communities’ urbanization degree. The influence of township community remoteness on the effects of equipment increment was investigated through subgroup analysis. Results: Among the township health centres in the neighbouring towns of the county seat (travel time to the county seat < 1 h), the significant effect of medical equipment increment was only found in the township health centres of the towns with high urbanization degrees (the proportion of the residents living in the built-up area > 69.89%), of which the effect size was 774.81 (95% CI 495.63, 1053.98, p < 0.05). Among the township health centres in the remote towns (travel time ≥ 1 h), the effect of medical equipment increment in the township health centres of the low urbanized towns (urban ≤ 5.99%, β = 1052.54, p < 0.01) was around four times the size of that of the counterparts (urban > 5.99%, β = 237.00, p < 0.01). Conclusion: This study demonstrated the spatially heterogeneous effect of medical equipment investment in the primary care centres in rural China. The priority of the equipment investment was suggested to be given to the township health centres in the remote towns with a low urbanization degree and those in the highly-urbanized neighbouring towns of the county seats.

Background: Healthcare-associated infections (HAIs) are still a major health threats worldwide. Traditional surveillance methods involving manual surveillance by infection control practitioners (ICPs) for data collection processes are laborious, inefficient, and generate data of variable quality. In this study, we sought to evaluate the impact of surveillance and interaction platform system (SIPS) for HAIs surveillance compared to manual survey in tertiary general hospitals. Methods: A large multi-center study including 21 tertiary general hospitals and 63 wards were performed to evaluate the impact of electronic SIPS for HAIs. Results: We collected 4,098 consecutive patients and found that the hospitals installed with SIPS significantly increased work efficiency of ICPs achieving satisfactory diagnostic performance of HAIs with 0.73 for sensitivity, 0.81 for specificity and 0.81 area under the curve (AUC). However, there were significant heterogeneity own to regions, time of SIPS installation, departments and sample size. Conclusions: SIPS significantly improved ICPs efficiency and HAIs monitoring effectiveness, but there were shortcomings such as untimely maintenance and high cost.

Based on the embeddedness theory, the present study aims to explore the association between patients’ trust in family doctors (Interpersonal Trust), trust in community health centers (Organizational Trust), and trust in society as a whole (Social Trust). A cross-sectional survey was conducted in six community health centers in China, using partial least squares structural equation modeling to assess trust scale reliability and validity while testing hypotheses. The scores of Interpersonal Trust, Organizational Trust, and Social Trust were 3.761, 3.636, and 3.635, respectively. Our findings confirm that Interpersonal Trust is positively associated with Organizational Trust (β = 0.916, p < 0.001), Organizational Trust is positively associated with Social Trust (β = 0.617, p < 0.001) and Interpersonal Trust (β = 0.864, p < 0.001), and Social Trust is positively associated with Organizational Trust (β = 0.784, p < 0.001). However, the relationship between Interpersonal Trust and Social Trust was not demonstrated. This study enhances our understanding of patient trust in family doctors. Applying embeddedness theory, we reveal positive correlations between Social Trust and Organizational Trust, as well as between Organizational Trust and Interpersonal Trust. Moreover, we have identified Organizational Trust as a crucial mediator in the relationship between Interpersonal Trust and Social Trust.