Operationalising ethics in secondary health-data use: an operator-focused framework for normative governance with audit and performance metrics

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Ethical guidance is extensive, yet it rarely specifies operator-level practices or indicators that show whether governance performs well in routine use. This paper proposes an operator-focused framework that makes ethics implementable and assessable. Methods The framework synthesises international guidance, systematic reviews and other empirical work on governance gaps, and publicly available governance materials from major data infrastructures. Ethics practices were selected for conceptual distinctness and operator-level controllability, grouped into seven governance domains, and aligned with a PDCA (Plan–Do–Check–Act) cycle. For each practice, the framework provides illustrative audit items, descriptive metrics, and outcome indicators intended as adaptable references rather than prescriptive standards. Results Seven domains are covered: Informed Consent & Information, Privacy & Data Protection, Data Quality, Use & Access Governance, Incidental Findings, Stakeholder Engagement, and Project-Level Transparency. Each domain contains four PDCA-aligned practices with linked evaluation items. Discussion The framework shifts practical bioethics for secondary use from policy existence to demonstrable performance. It can support operator self-audit and proportionate oversight by funders, ethics committees, and stakeholder bodies, while remaining modular across legal and infrastructural contexts. It also provides a starting point for developing minimal, transparent reporting expectations to support trust-building governance. 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F1000Research 2026, 15 :508 ( https://doi.org/10.12688/f1000research.179326.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Opinion Article Operationalising ethics in secondary health-data use: an operator-focused framework for normative governance with audit and performance metrics [version 1; peer review: awaiting peer review] Daniel Strech https://orcid.org/0000-0002-9153-079X Daniel Strech https://orcid.org/0000-0002-9153-079X PUBLISHED 13 Apr 2026 Author details Author details QUEST Center for Responsible Research, Berlin Institute of Health at Charite, Berlin, Germany Daniel Strech Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Writing – Original Draft Preparation OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW This article is included in the Health Services gateway. Abstract Background Operators of health data hubs and registries enable secondary use by implementing consent processes, privacy safeguards, access governance, data quality management, transparency, and stakeholder engagement. Ethical guidance is extensive, yet it rarely specifies operator-level practices or indicators that show whether governance performs well in routine use. This paper proposes an operator-focused framework that makes ethics implementable and assessable. Methods The framework synthesises international guidance, systematic reviews and other empirical work on governance gaps, and publicly available governance materials from major data infrastructures. Ethics practices were selected for conceptual distinctness and operator-level controllability, grouped into seven governance domains, and aligned with a PDCA (Plan–Do–Check–Act) cycle. For each practice, the framework provides illustrative audit items, descriptive metrics, and outcome indicators intended as adaptable references rather than prescriptive standards. Results Seven domains are covered: Informed Consent & Information, Privacy & Data Protection, Data Quality, Use & Access Governance, Incidental Findings, Stakeholder Engagement, and Project-Level Transparency. Each domain contains four PDCA-aligned practices with linked evaluation items. Discussion The framework shifts practical bioethics for secondary use from policy existence to demonstrable performance. It can support operator self-audit and proportionate oversight by funders, ethics committees, and stakeholder bodies, while remaining modular across legal and infrastructural contexts. It also provides a starting point for developing minimal, transparent reporting expectations to support trust-building governance. READ ALL READ LESS Keywords secondary use; health data governance; ethics implementation; data access governance; audit and performance metrics; health data hubs Corresponding Author(s) Daniel Strech ( [email protected] ) Close Corresponding author: Daniel Strech Competing interests: The author served as spokesperson and remains a member of the German working group ‘Consent,’ which developed the broad consent model for the German Medical Informatics Initiative (MII). The author receives research funding related to the topics of this paper from MII consortia (HiGHmed, CAEHR) and from the EU-funded project More-EUROPA. The author was also part of an expert group that authored a report for the German Federal Ministry of Health outlining technical, legal, and ethical preconditions for responsible secondary use of health data. In addition, the author served as a member of two WHO working/review groups relevant to this topic: ‘Guidance for Ethical Review of Health Systems Research’ and ‘Ethics of Public Health Surveillance.’ Grant information: This work was supported by intramural funds from the Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Germany. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2026 Strech D. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Strech D. Operationalising ethics in secondary health-data use: an operator-focused framework for normative governance with audit and performance metrics [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :508 ( https://doi.org/10.12688/f1000research.179326.1 ) First published: 13 Apr 2026, 15 :508 ( https://doi.org/10.12688/f1000research.179326.1 ) Latest published: 13 Apr 2026, 15 :508 ( https://doi.org/10.12688/f1000research.179326.1 ) Introduction Over the past decade, the secondary use of health data has evolved into a highly heterogeneous field, spanning retrospective analyses of electronic health records, prospective cohort studies, real-time surveillance infrastructures, and large-scale national platforms. This diversity reflects not only differences in data types and access models, but also varying regulatory traditions, governance capacities, and stakeholder expectations. 1 Examples of long-standing infrastructures include the Clinical Practice Research Datalink (CPRD, UK), 2 claims data repositories in the US (e.g., OptumLabs), and hospital-based disease registries operated by academic institutions. Earlier national flagships such as the UK Biobank paved the way, while more recent large-scale programmes, e.g. All of Us in the United States or the German Medical Informatics Initiative (MII), explicitly aim to serve multiple user groups, including academic research, public health and policy. Regardless of label, these infrastructures depend on operators who shoulder a long list of governance duties: maintaining system availability, enforcing privacy safeguards, implementing interoperability standards, curating data quality, selecting a consent model if needed, planning access procedures, engaging stakeholders and more. While all of these tasks have normative relevance, they differ in how directly they implement ethical values. Highly technical or operational activities (e.g., uptime, load balancing, budgeting) are guided primarily by IT and finance frameworks and express values only indirectly. By contrast, tasks such as protecting privacy, obtaining informed consent, ensuring data quality, or involving patients in governance processes directly translate aspirational norms into operator-controlled rules, procedures, and verifiable actions. This paper refers to such tasks as ethics practices. Taken together, they define the domain of normative governance : the set of responsibilities through which infrastructure operators concretely implement core ethical principles. For example, “respect for persons” is expressed through consent mechanisms and privacy safeguards, while “public value” is operationalized via data quality management and transparency regarding approved secondary uses and their results reporting. Normative governance: an underdefined field The maturity of ethics practices varies considerably. Some areas, such as privacy or data quality, are guided by extensive privacy standards (GDPR, HIPAA, ISO 27701) and by well-established data-quality frameworks (FAIR principles, Kahn et al. dimensions). 3 , 4 Others, such as consent models, access governance, or stakeholder engagement, remain heterogeneous, under-institutionalized, and difficult to evaluate. Although central to trust and accountability in data infrastructures, these practices are rarely treated as a coherent field with shared structures, expectations, or evaluative tools. The missing piece: a framework for mapping and evaluation What is still lacking is a structured approach to describe, organize, and assess these ethics practices. Unlike technical domains such as data architecture or cost allocation, which are supported by specialized toolkits and professional norms, normative governance remains fragmented. No widely accepted model captures the full range of ethics practices, their connection to ethical values, or the means by which their implementation can be evaluated. This paper proposes a framework that addresses this gap in two ways: first, by categorizing ethics practices across key governance domains (e.g. consent, data protection, access control); second, by linking each domain to relevant audit questions and evaluation criteria. The framework is not a prescriptive checklist but an orientation tool, intended to support operators in adapting, combining, or omitting elements based on their institutional context. Positioning within the landscape of existing ethics guidance Numerous bioethics contributions, international statements and best-practice guidelines have laid out the ethical principles for secondary data use. A systematic review of principles and norms in this area by Kalkman et al. synthesized four stable value clusters: (i) societal benefit & public value, (ii) respect for persons, (iii) fairness & data justice, and (iv) public trust & engagement. 5 These value clusters are echoed in reviews of patient and public attitudes 6 , 7 and in principle-oriented guidance such as the WMA Declaration of Taipei, 8 the OECD Recommendation on Health Data Governance 9 and the CIOMS International Ethical Guidelines. 10 Practice-oriented documents go a step further. The ISBER Best Practices 11 or the WHO policy and implementation guide on health-data reuse 12 provide detailed operational recommendations on biospecimen handling, data security, access procedures and quality control. Similarly, frameworks such as the UK Biobank Ethics & Governance Framework, 13 , 14 the All of Us Data-Access Framework 15 and the GA4GH Framework for Responsible Sharing of Genomic and Health-Related Data 16 set concrete standards for consent, privacy or tiered access. However, each addresses only selected ethics-practice domains and rarely embeds evaluation metrics. As a result, even the more detailed best-practice documents offer valuable guidance but do not yet provide an integrative, evaluable framework for ethics practices as a field in its own right. Methods Conceptual orientation The goal was to construct an operator-focused framework that couples widely accepted ethical values to actionable practices and a lean but meaningful evaluation tier. Knowledge inputs and scope Six knowledge streams informed the design: 1. International guidance or frameworks for secondary data use or its specific ethics practices 8 , 10 , 12 – 22 2. Systematic reviews on values and public attitudes anchoring the four aspirational norms 5 – 7 , 23 3. Governance-gap studies that document heterogeneous or missing practices 1 , 24 – 29 4. Practice-focused case literature on the implementation of specific ethics practices for secondary use 30 – 37 5. An expert report for the German Federal Ministry of Health that described technical, legal and ethical preconditions and success requirements for responsible secondary use. 38 6. Conceptual work on ethics implementation that stresses measurability. 39 – 41 Practice selection Source documents were scanned for recurring but distinct ethics practices such as consent, data protection or stakeholder engagement. Candidate practices were retained if they met two filters: 1) Conceptual distinctness – no thematic overlap with other candidates, and 2) Operator controllability – the platform can implement, mandate, or audit the activity. Each governance domain was then structured into four PDCA (Plan–Do–Check–Act)-aligned activities to reflect the dual goal of implementation and evaluation 42 : • Norm-Setting (Plan): Define and justify the ethics practice; • Operational (Do): Implement the practice and capture primary data; • Assurance (Check): Monitor implementation with metrics and judge effectiveness and quality; • Improvement (Act): Revise standards or processes when assurance findings or new requirements demand change. The list of ethics practices across the PDCA structure is deliberately illustrative, not comprehensive. An all-inclusive catalogue would be unmanageable, ignore context-specific tailoring and age rapidly. The framework therefore provides anchor practices in each domain—enough to guide gap analysis and stakeholder dialogue, while leaving space for operators to add, refine or merge practices as required. Evaluation layer For every ethics practice, the framework specifies: • an audit item verifying the presence and currency of policies or logs, and • descriptive and/or outcome metrics that feed the Assurance tier (e.g. completeness of key variables, consent-decline rate, access-review turnaround, follow-up completion for incidental findings). Plausibility check To verify the completeness and real-world applicability of the resulting list of ethics practices, the following plausibility checks were conducted. In a practice-guideline triangulation the list was cross-matched with five benchmark guidelines for secondary health-data use, namely. 8 – 12 In a real-world implementation scan the practices were mapped to governance artefacts from five well-documented platforms (UK Biobank, All of Us, PCORnet, German MII, GA4GH) and to 51 European patient registries reviewed by van den Akker et al. 25 Results Overview of the three-layer framework The framework translates four aspirational norms into illustrative ethics practices across seven governance domains ( Table 1 ) and couples each practice to evaluation items ( Table 2 ). Practices are organised along the Plan–Do–Check–Act (PDCA) logic, labelled Norm-Setting, Operational, Assurance and, where applicable, Improvement. Table 1. Seven governance domains with illustrative ethics practices aligned to the PDCA cycle. Domain Norm-setting practice (Plan) 1 Operational practice (Do) Assurance practice (Check) Improvement practice (Act) 2 Informed Consent & Information Select and justify the consent model (broad, waiver/opt-out); draft and approve participant information, consent documents, and guidance e.g. for consent procedures Collect consent and withdrawals; publish (version-controlled) documents Monitor consent/decline/withdrawal rates; test comprehension of participant information on a representative sample Revise materials and procedures Privacy & Data Protection Adopt a privacy & data-protection policy compliant with GDPR (or equivalent) and complete a DPIA/PIA Implement pseudonymisation, encryption and audit-log controls; maintain breach-response plan Audit logs monthly; conduct an annual penetration test Revise privacy and data protection measures Data Quality Adopt a data-quality framework aligned with Kahn et al. dimensions (completeness, plausibility, uniqueness, temporal consistency, bias) and FAIR metadata principles Run automated profiling & quality checks on each load; publish quarterly quality dashboard Benchmark key indicators; trigger bias review if coverage or completeness falls below threshold Remediate quality gaps (re-extract data or update curation rules) Use & Access Governance Specify eligibility, fairness and rejection criteria in a use&access policy, publish a data-transfer agreement template, adopt a conflicts-of-interest (COI) policy Execute a structured access checklist and sign data-transfer agreements, maintain an up-to-date COI register Track turnaround; analyse rejection reasons, review the COI register annually Revise policy and template Incidental-Finding Management Establish clinical-significance thresholds and minimum communication and follow-up guidances Run the IF workflow; record detection, disclosure and follow-up Calculate % actionable IFs communicated and follow-up completion Update thresholds and guidances Patient & Stakeholder Engagement Specify engagement measures (e.g. seats for patient representatives in U&A committee, annual consultation forums, user satisfaction surveys) Hold consultation forums and post summaries of feedback received Survey user/stakeholder satisfaction Update engagement measures Project-Level Transparency Define transparency rules for approved secondary-use projects such as prospective registration, public result reporting, and code sharing Maintain or cooperate with a public project register; collect result summaries and code uploads Measure % projects registered prospectively, % results posted, % code shared Revise transparency rules 1 Abbreviations: COI = conflict of interest; DPIA/PIA = (Data) Privacy Impact Assessment; IF = incidental finding; FAIR = Findable, Accessible, Interoperable, Reusable; 2 Executed when assurance results or legal/ethical updates indicate a need for change Table 2. Evaluation layer: Illustrative examples for audit, descriptive and outcome metrics for each ethics practice. Domain Audit items 1 Descriptive metrics 2 Quality/outcome metrics 3 Informed Consent & Information Consent-model file approved; latest info sheet online; staff training records Consent/decline/withdrawal rates; median dialogue duration Comprehension score ≥ 80% correct Privacy & Data Protection DPIA on file & reviewed annually; data-processing-agreement template current Number of log-exception alerts per month; encryption key rotations per year No critical pen-test findings open >30 days Data Quality Data-quality framework document available; latest dashboard ≤3 months old Completeness rate of key variables; number of plausibility fails Completeness ≥95% and plausibility error < 1% Use & Access Governance Use-and-access policy online; DTA template online Median turnaround days; number of requests & rejections 100% of veto-based rejections independently reviewed and documented quarterly Incidental-Finding Management Guidelines for IF communication and follow-up available Actionable IFs detected/disclosed; follow-ups initiated Follow-up completion ≥90% Patient & Stakeholder Engagement List of patient/stakeholder representatives online; engagement plan posted; forum minutes archived Attendance count at consultation forum Mean user-satisfaction score ≥ 3 Transparency Public project register operational; result-summary template available; code-sharing policy published Percent projects registered prospectively; percent results posted ≤12 m Percent projects with linked code/data (threshold set by operator) 1 Audit metrics verify policy existence or a documented “not-applicable” rationale, 2 Descriptive metrics characterise workload or process features without value judgement, 3 Outcome metrics enable normative assessment by comparing results with predefined thresholds Seven domains proved sufficient when tested against the contents of multiple international guidance documents and governance artefacts from five major data platforms and 33 registries. No further domain emerged, indicating a breadth that is “complete enough to start a gap analysis” yet concise. Ethics practices Each domain includes four action-oriented, conceptually distinct practices (one per PDCA step). The domains and examples are as follows: • Informed Consent & Information distinguishes: (i) choosing and justifying an opt-in or opt-out (or no-consent) model; (ii) maintaining participant-facing documents; (iii) monitoring decline and comprehension; (iv) revising materials when thresholds are missed. • Privacy & Data Protection lists DPIA completion, implementation of pseudonymisation/encryption, annual pen-test review and remediation of critical findings. • Data Quality includes adoption of a Kahn/FAIR framework, automated profiling with quality dashboards, bias review if thresholds are missed and data recuration as needed. • Use & Access Governance covers eligibility criteria, a structured review checklist with data-transfer agreements, analysis of veto-based rejections and criterion adjustment when bottlenecks appear. • Incidental-Finding Management moves from threshold policy, through workflow logging, to follow-up auditing and policy update if completion falls below a certain threshold. • Patient & Stakeholder Engagement progresses from formal inclusion, to consultation forums, satisfaction surveying and redesign if scores drop below a certain threshold. • Project-Level Transparency includes prospective registration, timely result reporting, and reproducibility through code and data sharing. The practices are illustrative, not exhaustive. They are intended to provide a shared vocabulary and structure, while enabling tailoring or merging in line with platform scope, objectives and resources. Special context for Privacy & Data Protection and Data Quality For Privacy & Data Protection and Data Quality these examples are deliberately concise, because detailed operational playbooks already exist e.g. GDPR and ISO 27701 for privacy safeguards; the Kahn data-quality dimensions together with FAIR and further guides for profiling and validation. In the other five domains, Informed Consent & Information, Use & Access Governance, Incidental-Finding Management, Patient & Stakeholder Engagement, and Project-Level Transparency, no universally adopted standards are available. The framework therefore offers additional illustrative examples to help operators translate abstract norms into actionable and auditable tasks. Division of labour between operators and data users Not all practices must be executed by the operator’s internal staff. In domains such as incidental findings or project-level transparency, tasks may be i) handled centrally by the operator, ii) delegated to researchers, or iii) executed via a hybrid model, where researchers must follow operator-defined standards and report back. The framework includes practices that the operator can mandate, monitor or audit, even when operational execution is partly decentralised. Whether platforms opt for centralised, decentralised or hybrid models will depend on legal, scientific and logistical context. In all cases, operators should retain oversight to ensure that the aspirational norms are met. Evaluation items Table 2 links each ethics practice to three evaluation items: 1. Audit items verify whether each practice is formally addressed and up to date, either through an operative artefact (policy, SOP, dashboard, log) or through a documented justification that the practice is not applicable (e.g. no consent required, no incidental findings expected). These can often be assessed externally by funders, reviewers or patient representatives. 2. Descriptive metrics characterise workloads and basic process features (e.g. consent-decline rate, number of log exceptions, median turnaround time for access requests). They enable trend analysis without value judgement. 3. Quality/outcome metrics enable normative assessment, for instance: comprehension of consent materials ≥80%, data completeness ≥95%, no critical pen-test findings unresolved after 30 days, or independent review of all veto-based access rejections, Currently, most platforms satisfy only the audit item: they can point to a policy or SOP, but descriptive and especially outcome metrics are still uncommon. Consent materials are seldom comprehension-tested, IF workflows rarely audited, and stakeholder engagement rarely evaluated for satisfaction or perceived impact. The proposed structure aims to shift normative governance culture from “existence” to “performance.” Plausibility results The list of ethics practices was cross-matched with five benchmark guidelines (ISBER 2023, WHO 2022, OECD 2022, CIOMS 2016, WMA 2016). All seven governance domains appeared at least once across the set, no additional domain emerged. Coverage, however, was uneven: domains such as Informed Consent & Information and Privacy & Data Protection were present (and often detailed) in every guideline, whereas Stakeholder Engagement and Incidental-Finding Management were mentioned in only two or three, typically at a high level. A real-world scan linked the seven domains to governance artefacts from UK Biobank, All of Us, PCORnet, GA4GH, and the German MII. Each domain had at least one concrete implementation artefact (policy, template, SOP), demonstrating that every practice on the list is in active use somewhere in the field. The registry review by van den Akker et al. (2024) analysed 51 European patient registries. Again, every domain surfaced at least once, yet implementation was highly variable. For example, only 1 of 20 consent/information forms (5%) described a procedure for incidental findings, and fewer than one-third of registries detailed how data were anonymised or pseudonymised. These findings confirm the framework’s completeness while illustrating the heterogeneity it is meant to address. Discussion This paper advances the governance debate on secondary use of health data by moving beyond aspirational value statements toward a system in which ethics practices are made explicit and linked to verifiable evaluation items. In doing so, it complements earlier work that catalogued norms 5 or surveyed public attitudes 7 and extends implementation-science proposals to the ethics domain. 39 The framework enables registry operators to conduct quick self-audits, select context-relevant metrics, and monitor progress over time. Researchers, funders, patient representatives, and policymakers can use the same structure as a reference for what constitutes good normative governance in this area and what types of evidence should be requested or scrutinized. From value to verifiability Current guidance for biobanks and data platforms, such as the WMA Declaration of Taipei, 8 typically stops at high-level imperatives such as “ensure consent” or “justify data access,” without indicating which concrete ethics practices are required or how performance should be judged. Checklists such as the ISBER Best Practices go further, 11 but they rarely span all seven domains identified here and seldom distinguish between the mere existence of a document and the effectiveness of its implementation. By pairing each practice with audit items and performance metrics, the present framework fills this gap. Its breadth is intentional but lean: complete enough to initiate internal gap analysis, yet not so prescriptive that initiatives are paralyzed by dozens of indicators. The framework moves governance from a trust-based logic of presumed compliance to an audit-informed logic that demands not only written policies but also evidence of their real-world performance. Contextual adaptability and scalable use A key feature of the framework is its modularity. It allows differentiated use across stakeholder groups, from self-assessment by operators to oversight by funders or advocacy by civil society actors. Equally important, it is scalable: the domains and practices are structured in a way that applies to small-scale registries as well as national or multi-jurisdictional platforms. Its illustrative nature enables context-sensitive specification. Operators are not expected to implement every practice identically, but to transparently justify how they adapt the framework to their operational, legal, and social environment. Such justification may take two forms: a positive specification (e.g. “we implement X by doing Y”) or an explicit non-adoption with rationale (e.g. “this domain is not relevant because our data are fully anonymised” or “this metric is currently infeasible but under development”). This practice of context-based declaration makes the framework practical and normatively accountable at once. From policies to performance As the plausibility check showed, most large data platforms (e.g. UK Biobank, All of Us, PCORnet,) and many patient registries already satisfy the audit layer by publishing policies or SOPs that cover at least some of the seven ethics-practice domains. Publicly disclosed descriptive or outcome metrics, however, remain rare. The framework draws attention to this blind spot. Tracking consent comprehension, veto-based access rejections, or IF follow-up adds workload, but these data are essential for demonstrating respect, fairness, and public value in practice. Future work should therefore develop lightweight tooling for routine metric collection and explore how external stakeholders (ethics committees, funders, patient groups) can access and interpret such data without over-burdening operators. Although systematic evaluation remains the exception, published case studies show that it is feasible. Zenker et al. report on broad consent uptake across 27 university hospitals participating in the German Medical Informatics Initiative (MII), providing implementation-level insights into consent processes at scale. 43 While substantial variation in consent rates across sites was observed, the authors emphasize that interpretation remains difficult due to inconsistent definitions and documentation standards. Fischer-Rosinsky et al. complement these findings with detailed uptake data from four emergency departments 44 : among 1,138 approached patients, only 28% ultimately consented, while over 40% declined or could not complete the process, often due to contextual or situational factors. These studies illustrate that capturing consent dynamics is feasible, but depends on clear procedural standards and routine monitoring. Bossert et al. used participatory feedback and cognitive interviews to iteratively revise a broad consent form. 45 While no quantitative comprehension testing was reported, patient feedback led to substantial revisions across three document versions. These case examples for the domain “Consent&Information” show that descriptive and outcome metrics can be embedded into routine operations or implemented by independent third parties. Beyond consent, recent audits illustrate how further ethics practices can be assessed in practice. A pan-European survey of 41 health-data hubs showed that 83% apply some form of data-quality control and 65% report anonymisation procedures, but only about half publish Data-Access or Data-Processing Agreements, and fewer still enforce minimum quality thresholds before ingesting data. 1 The cross-sectional audit of 51 European patient registries showed that although 31 (61%) claimed to have a use-and-access policy, only 17 made the full document publicly available and just four explicitly prohibited re-identification. 25 Relation to adjacent ethics domains While this framework centers on operator-level governance of health data infrastructures, it intersects with adjacent ethics discourses that warrant distinction. One such discourse concerns Learning Health Care Systems (LHCS). These systems depend on many of the same infrastructures and may presuppose several of the ethics practices addressed here. However, LHCS ethics typically focus on downstream questions at the point of care or project level, such as minimal-risk research without explicit consent, point-of-care randomisation, or obligations to implement findings. 46 , 47 In contrast, the present framework addresses upstream responsibilities: the governance of data access, protection, and provisioning that enables such learning activities in the first place. A parallel situation arises with AI ethics. Much AI development, especially in medical contexts, relies on access to large, well-governed datasets managed by health data platforms. Yet AI ethics debates often focus on downstream issues such as algorithmic explainability, fairness, or accountability. 48 , 49 These are important but distinct from the operator-level responsibilities that determine whether and how data become available for AI development at all. While this framework does not seek to resolve domain-specific concerns in LHCS or AI ethics, it may help clarify what responsible infrastructure entails in practice, especially where operator decisions shape the scope, quality, and legitimacy of downstream research and innovation. Strengths, limitations, and future directions A strength of the framework lies in its integrative structure: it brings together ethical concepts, normative considerations, and insights from existing governance materials across multiple health data platforms. Rather than proposing a fixed model, it offers adaptable components that can be aligned with different institutional contexts and levels of maturity. At the same time, several limitations apply. First, the proposed metrics are illustrative: they have not been broadly discussed or endorsed across the data governance community, and no shared standards exist regarding their use, relevance, or thresholds. Routine data collection for many domains, such as consent comprehension, veto-based access rejections, or incidental finding follow-up, remains rare. Implementation will therefore require context-sensitive prioritization and negotiation of feasibility. Second, while the framework is grounded in a broad range of conceptual and documentary sources, it does not result from a systematic or exhaustive scan of all existing platforms and policies. Rather, it reflects a structured synthesis of operator-level responsibilities, developed through iterative comparison with international ethics guidance and governance practices. While the seven domains are intended to offer a comprehensive account of ethically relevant tasks at the infrastructure level, future work may test their completeness and applicability across additional settings. Building on these limitations, two lines of inquiry suggest themselves. First, adaptation studies at platform level could explore how the framework is interpreted and specified in different legal, infrastructural, and ethical environments, and what rationales are offered for adaptation, postponement, or omission. Second, indicator research should refine and test proposed metrics, distinguishing between norm-referenced targets and descriptive monitors. Consensus-building methods may help establish minimal reporting standards, including metadata on evidentiary strength and resource requirements. Conclusion Governing secondary use of health data requires both normative robustness and empirical accountability. By coupling widely accepted values to a compact but comprehensive set of ethics practices and evaluation items, this framework offers a practical starting point. 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PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Apr 2026 ADD YOUR COMMENT Comment Author details Author details QUEST Center for Responsible Research, Berlin Institute of Health at Charite, Berlin, Germany Daniel Strech Roles: Conceptualization, Formal Analysis, Methodology, Project Administration, Writing – Original Draft Preparation Competing interests The author served as spokesperson and remains a member of the German working group ‘Consent,’ which developed the broad consent model for the German Medical Informatics Initiative (MII). The author receives research funding related to the topics of this paper from MII consortia (HiGHmed, CAEHR) and from the EU-funded project More-EUROPA. The author was also part of an expert group that authored a report for the German Federal Ministry of Health outlining technical, legal, and ethical preconditions for responsible secondary use of health data. In addition, the author served as a member of two WHO working/review groups relevant to this topic: ‘Guidance for Ethical Review of Health Systems Research’ and ‘Ethics of Public Health Surveillance.’ Grant information This work was supported by intramural funds from the Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Germany. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 13 Apr 2026, 15:508 https://doi.org/10.12688/f1000research.179326.1 Copyright © 2026 Strech D. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. 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