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.

Background: Digital health, which encompasses the use of information and communications technology in support of health, is a key driving force behind the cultural transformation of medicine toward people-centeredness. Thus, eHealth literacy, assisted by innovative digital health solutions, may support better experiences of care. Objective: The purpose of this study is to explore the relationship between eHealth literacy and patient-reported experience measures (PREMs) among users of outpatient care in Hungary. Methods: In early 2019, we conducted a cross-sectional survey on a large representative online sample recruited from the Hungarian general population. eHealth literacy was measured with the eHealth Literacy Scale (eHEALS). PREMs with outpatient care were measured with a set of questions recommended by the Organisation for Economic Co-operation and Development (OECD) for respondents who attended outpatient visit within 12 months preceding the survey. Bivariate relationships were explored via polychoric correlation, the Kruskal–Wallis test, and chi-square test. To capture nonlinear associations, after controlling covariates, we analyzed the relationship between eHEALS quartiles and PREMs using multivariate probit, ordinary least squares, ordered logit, and logistic regression models. Results: From 1000 survey respondents, 666 individuals (364 females, 54.7%) were included in the study with mean age of 48.9 (SD 17.6) years and mean eHEALS score of 29.3 (SD 4.9). Respondents with higher eHEALS scores were more likely to understand the health care professionals’ (HCPs’) explanations (χ29=24.2, P=.002) and to be involved in decision making about care and treatment (χ29=18.2, P=.03). In multivariate regression, respondents with lowest (first quartile) and moderately high (third quartile) eHEALS scores differed significantly, where the latter were more likely to have an overall positive experience (P=.02) and experience fewer problems (P=.02). In addition, those respondents had better experiences in terms of how easy it was to understand the HCPs’ explanations (P<.001) and being able to ask questions during their last consultation (P=.04). Patient-reported experiences of individuals with highest (fourth quartile) and lowest (first quartile) eHEALS levels did not differ significantly in any items of the PREM instrument, and neither did composite PREM scores generated from the PREM items (P>.05 in all models). Conclusions: We demonstrated the association between eHealth literacy and PREMs. The potential patient-, physician-, and system-related factors explaining the negative experiences among people with highest levels of eHealth literacy warrant further investigation, which may contribute to the development of efficient eHealth literacy interventions. Further research is needed to establish causal relationship between eHealth literacy and patient-reported experiences.

Background The objective of this paper is to explore unmet health care needs in Hungary in ambulatory care due to costs and difficulties in travelling, and to analyze how unmet needs relate to socio-demographic characteristics. Methods The quantitative analysis is based on a national, representative online survey carried out in Hungary on a sample of 1000 respondents in early 2019 using a proposed set of questions developed by the OECD. We present and compare unmet medical needs in different socio-demographic groups, and we use multivariate logistic regression analysis to identify the main determinants of unmet medical needs. Results Among responders who had medical problems in the last 12 months, 27.3% reported forgone medical visit due to difficulties in travelling, 24.2% had unfilled prescription for medicine due to costs, 21.4% reported forgone medical visit or follow-up visit due to costs and 16.6% reported skipped medical test, treatment or other follow-up due to costs. These shares are much higher than presented previously in international databases. The logistic model indicates that respondents were significantly more likely to report unmet needs if they were women, younger or belonged to first and second income quintiles. Conclusions Policy makers need to address the issue of high prevalence of forgone medical care among the Hungarian population to avoid deterioration of population health and inequalities in access. As a first step, policies should try to decrease financial burden of vulnerable groups to improve access.

Background: Governments across the World Health Organization (WHO) European Region have prioritised dashboards for reporting COVID-19 data. The ubiquitous use of dashboards for public reporting is a novel phenomenon. Objective: This study explores the development of COVID-19 dashboards during the first year of the pandemic and identifies common barriers, enablers and lessons from the experiences of teams responsible for their development. Methods: We applied multiple methods to identify and recruit COVID-19 dashboard teams, using a purposive, quota sampling approach. Semi-structured group interviews were conducted from April to June 2021. Using elaborative coding and thematic analysis, we derived descriptive and explanatory themes from the interview data. A validation workshop was held with study participants in June 2021. Results: Eighty informants participated, representing 33 national COVID-19 dashboard teams across the WHO European Region. Most dashboards were launched swiftly during the first months of the pandemic, February to May 2020. The urgency, intense workload, limited human resources, data and privacy constraints and public scrutiny were common challenges in the initial development stage. Themes related to barriers or enablers were identified, pertaining to the pre-pandemic context, pandemic itself, people and processes and software, data and users. Lessons emerged around the themes of simplicity, trust, partnership, software and data and change. Conclusions: COVID-19 dashboards were developed in a learning-by-doing approach. The experiences of teams reveal that initial underpreparedness was offset by high-level political endorsement, the professionalism of teams, accelerated data improvements and immediate support with commercial software solutions. To leverage the full potential of dashboards for health data reporting, investments are needed at the team, national and pan-European levels.

Vertical integration is expected to improve communication and coordination between inpatient care and care after discharge. Despite being used across health systems worldwide, evidence about its impact on readmissions is sparse and contradictory. To assess the impact of vertical integration on hospital readmissions. Using difference-in-differences we compared readmissions before and after vertical integration in 6 Portuguese hospitals for years 2004–2013. A control group with 6 similar hospitals not integrated was utilized. Considered outcome was 30-day unplanned readmission. We used logistic regression at the admission level and accounted for patients’ risk factors using claims data. Analyses for each hospital and selected conditions were also run. Our results suggest that readmissions decreased overall after vertical integration [odds ratio (OR)=0.900; 95% confidence interval (CI), 0.812–0.997]. Hospital analysis indicated that there was no impact for 2 hospitals (OR=0.960; 95% CI, 0.848–1.087 and OR=0.944; 95% CI, 0.857–1.038), and a positive effect in 4 hospitals (greatest effect: OR=0.811; 95% CI, 0.736–0.894). A positive evolution was observed for a limited number of conditions, with better results for diabetes with complications (OR=0.689; 95% CI, 0.525–0.904), but no impact regarding congestive heart failure (OR=1.067; 95% CI, 0.827–1.377). Merging acute and primary care providers was associated with reduced readmissions, even though improvements were not found for all institutions or condition-specific groups. There are still challenges to be addressed regarding the success of vertical integration in reducing 30-day hospital readmissions.

This study aims to assess the impact of the COVID-19 pandemic on hospital cardiac care, as assessed by performance indicators. Scoping review methodology: performance indicators were extracted to inform on changes in care during January–June 2020. Database searches yielded 6277 articles, of which 838 met the inclusion criteria. After full-text screening, 94 articles were included and 1637 indicators were retrieved. Most of the indicators that provided information on changes in the number of admissions (n = 118, 88%) signaled a decrease in admissions; 88% (n = 15) of the indicators showed patients’ delayed presentation and 40% (n = 54) showed patients in a worse clinical condition. A reduction in diagnostic and treatment procedures was signaled by 95% (n = 18) and 81% (n = 64) of the indicators, respectively. Length of stay decreased in 58% (n = 21) of the indicators, acute coronary syndromes treatment times increased in 61% (n = 65) of the indicators, and outpatient activity decreased in 94% (n = 17) of the indicators related to outpatient care. Telehealth utilization increased in 100% (n = 6). Outcomes worsened in 40% (n = 35) of the indicators, and mortality rates increased in 52% (n = 31). All phases of the pathway were affected. This information could support the planning of care during the ongoing pandemic and in future events.