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: The COVID-19 pandemic is a complex global public health crisis presenting clinical, organisational and system-wide challenges. Different research perspectives on health are needed in order to manage and monitor this crisis. Performance intelligence is an approach that emphasises the need for different research perspectives in supporting health systems' decision-makers to determine policies based on well-informed choices. In this paper, we present the viewpoint of the Innovative Training Network for Healthcare Performance Intelligence Professionals (HealthPros) on how performance intelligence can be used during and after the COVID-19 pandemic. Discussion: A lack of standardised information, paired with limited discussion and alignment between countries contribute to uncertainty in decision-making in all countries. Consequently, a plethora of different non-data-driven and uncoordinated approaches to address the outbreak are noted worldwide. Comparative health system research is needed to help countries shape their response models in social care, public health, primary care, hospital care and long-term care through the different phases of the pandemic. There is a need in each phase to compare context-specific bundles of measures where the impact on health outcomes can be modelled using targeted data and advanced statistical methods. Performance intelligence can be pursued to compare data, construct indicators and identify optimal strategies. Embracing a system perspective will allow countries to take coordinated strategic decisions while mitigating the risk of system collapse.A framework for the development and implementation of performance intelligence has been outlined by the HealthPros Network and is of pertinence. Health systems need better and more timely data to govern through a pandemic-induced transition period where tensions between care needs, demand and capacity are exceptionally high worldwide. Health systems are challenged to ensure essential levels of healthcare towards all patients, including those who need routine assistance.

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.