Evolution of Regulatory Models for Public Health Data Ecosystems From a Linked Democracy Perspective

Abstract

Public healthcare is a data-intensive environment that manages ever-increasing volumes of biomedical data resulting from medical data-generating technologies. In this paper, the authors discuss strategies to regulate the collection and use of biomedical data and metadata to build sustainable public health data ecosystems; this can assist citizens to get control of dataflows by defining identity in the public domain and shaping the capacity to use the web of data: get access to healthcare services and receive benefits and appropriate care. The authors suggest that a strategy based on the linked democracy governance model and safeguards, implemented through the meta-rule of law, enables better design of regulatory tools to handle semantically driven data flows. This strategy ties well in with models of deliberative and epistemic democracy, focused on relationships between people, data, and institutions. The authors investigate privacy, security, and data protection issues, applying existing ethical and legal frameworks for public health data and the theory of justice; they discuss the implementation of strategies to articulate the public domain and propose intermediate, anchoring institutions at the meso-level by building ontologies, selecting technical functionalities and algorithms, and embedding protections of the rule of law into specific public health data ecosystems.

The Tethered Pathway to Organizational Adaptation in Collaboration

“…The value above each observed variable is the squared multiple correlations (SMC, R 2 ), the proportion of the observed variable's variance explained by the latent variable. The IBM SPSS AMOS 27 graphic output does not show error variance (Clark et al, 2021;Khuwaja et al, 2020;Kline, 2016;Lokshina & Lanting, 2023). Error variance associated with each observed variable equals 1 minus SMC.…”

“…Jones (2023) expressed that power issues often result in inhibited success of collaboration. Another aspect to consider is the effect of power imbalances on proper policy engagement (Lokshina & Lanting, 2023).…”

“…The AT framework of the study was tested using IBM SPSS AMOS 27 and IBM SPSS Statistics 27. Select SEM-related reliability and validity tests were conducted to ensure reliability of the instruments (Clark et al, 2021;Khuwaja et al, 2020;Kline, 2016;Lokshina & Lanting, 2023). Composite reliability (CR), factor analysis, and average variance extracted (AVE) of the variables, as presented in Table 6, were tested to ensure overall model validity and reliability (Clark et al, 2021;Khuwaja et al, 2020;Kline, 2016;Lokshina & Lanting, 2023).…”

“…Table 6 provides the constructs, indicators, and psychometric properties of the construct items (Hair et al, 2019;Henseler et al, 2015). That said, recent research has advanced the notion that the HTMT criteria for discriminant validity, involving variance-based SEM, depend on the model construct and on the conservative disposition of the researcher's assessment of discriminant validity as such (Clark et al, 2021;Khuwaja et al, 2020;Kline, 2016;Lokshina & Lanting, 2023).…”

“…The factors depicted in ellipses represent latent variables, while the observed variables (i.e., items) are reflected as rectangles (Clark et al, 2021;Khuwaja et al, 2020;Kline, 2016;Lokshina & Lanting, 2023). The value beside the three-headed arrows stands for the correlation coefficient of three latent variables or three error terms.…”