Open health data: Mapping the ecosystem

Abstract

Background Governments around the world own multiple datasets related to the policy domain of health. Datasets range from vaccination rates to the availability of health care practitioners in a region to the outcomes of certain surgeries. Health is believed to be a promising subject in the case of open government data policies. However, the specific properties of health data such as its sensibilities regarding privacy, ethics, and ownership encompass particular conditions either enabling or preventing datasets to become freely and easily accessible for everyone. Objective and methods This paper aims to map the ecosystem of open health data. By analyzing the foundations of health data and the commonalities of open data ecosystems via literature analysis, the socio-technical environment in which health data managed by governments are opened up or potentially stay closed is created. After its theoretical development, the open health data ecosystem is tested via a case study concerning the Data for Better Health initiative from the government of Belgium. Results Creation and assessment of an open health data ecosystem consisting of stakeholders, interests, information policies, and data preparation activities. Conclusions The policy domain of health includes de-identification activities, bioethical assessments, and the specific role of data providers within its open data ecosystem. However, the concept of open data does not always fully apply to the topic of health. Such several health datasets may be findable via government portals but not directly accessible. Differentiation within types of health data and data user capacities are recommendable for future research.

Barriers to Openly Sharing Government Data: Towards an Open Data-adapted Innovation Resistance Theory

“…The findings reveal that the category with the most frequently encountered barriers is the risk barrier category. Risks of violating data protection legislation, sensitive data exposure, data misuse, and misinterpretation are barriers faced by more than half of the organizations interviewed, which is also in line with some previous research (Heijlen & Crompvoets, 2021;Kleiman et al, 2020;Shepherd et al, 2020;Wang et al, 2019;Zhao & Fan, 2021). In addition, complexity of data preparation for their publication so that they comply with OGD principles and the amount of resources to be spent on this (Kleiman et al, 2020) were the most frequent barriers, indicating significant challenges that need to be addressed.…”

International eHealth ecosystems and the quest for the winning value proposition: findings from a survey study

“…In these projects, many of the same value propositions are found as in this study, albeit on a smaller scale, connecting regionally instead of internationally. Examples include data gathering for geographical analysis ( García Holgado et al , 2019 ), a national open data ecosystem ( Heijlen & Crompvoets, 2021 ), or the ability to access new technologies ( Botti & Monda, 2020 ). Schiza and colleagues (2019) describe a national level ecosystem that is aligned with EU standards (e.g., related to data protection and privacy), to allow for exchanging and connecting with other national ecosystems.…”

Using eHealth Data to Inform CPD for Medical Practitioners: A Scoping Review with a Consultation Exercise with International Experts

“…Even though a shared definition is lacking, eHealth data can be considered as any piece of personal information in digital or electronic format concerning the health status, risk, or outcome of an individual and/or pertaining to the provision health care interventions and services to the individual. 1–4…”

“…Even though a shared definition is lacking, eHealth data can be considered as any piece of personal information in digital or electronic format concerning the health status, risk, or outcome of an individual and/or pertaining to the provision health care interventions and services to the individual. [1][2][3][4] To date, this type of information has been used for epidemiologic and clinical research, 5,6 clinical decision-making support systems, 7 clinical behavior change, 8 quality assurance reporting, 9 and Quality Improvement and Patient Safety purposes. 10,11 However, there is limited research on the role of eHealth data analysis in health professions education research, and in particular for the development of Continuing Professional Development (CPD) activities, programs, and plans for medical practitioners.…”