eHealth Data Standards and Data Stewardship for Social Determinants of Health in Support of Ontario Learning Health Systems: a State-of-the-Art Review of the Literature
Bibliographic record
Abstract
Background: Health equity exists when everyone can attain their full potential for health and well-being. It is an important aim across multiple healthcare jurisdictions and a central focus of Ontario Health’s innovation plan. Health inequities have a strong relationship to social determinants of health (SDoH), those factors such as income, social status, race, gender, education, and physical environment. The healthcare quality framework the Quadruple Aim supports innovation to improve population health outcomes, patient care and experience, and provider satisfaction with lower costs with better value. To achieve this transformation, upstream social determinants of health must be addressed. The new Quintuple Aim framework embeds health equity in all four quality aims. Currently, there is a dearth of structured and actionable SDoH data in Ontario’s healthcare systems. Calls for standardization of the SDoH data collection, exchange, and use are being voiced across policymakers, academics, clinicians, and the public. Purpose: This report examines the SDoH data standards and data stewardship in the context of addressing health inequity in individuals and populations in Ontario. It will examine Ontario’s current state and progress in defining, testing, and using eHealth data standards along with principles of data stewardship, toward collection, use, and sharing of SDoH data within electronic health records. Method: OVID Medline and grey literature sources were searched to gather evidence to support the objective of this report, applying a state-of-the-art review methodology. Results: Sixty-nine academic systematic reviews and original papers and 42 grey literature reports (white papers, policy documents, guidelines, websites, personal communication) contributed to the findings of this review. Publication trends in academic literature show increased rates from 2017-2022, with more from the US than Canada, signaling more research activity and further progress spurred by US government mandates for SDoH data collection and use. Current eHealth standards serve as a basis from which to adapt SDoH-specific standards. Canadian national standards and health information agencies serve as stewards to SDoH data standardization for Ontario. Ontario has a foundation of equity frameworks and guidance documents to guide SDoH data standard initiatives as well as two prominent SDoH data collection and use programs: the SPARK study (Toronto) and the Alliance for Healthier Communities. Other literature demonstrates SDoH data standardization facilitators (frameworks, models of practice (Gravity and OCHIN), leadership, stakeholder engagement, and demonstration of value with SDoH data applied to interventions). Challenges include the difficulties in producing complete and consistent SDoH data and the resource requirements to do so. Discussion: A series of recommendations can be made related to fitting Ontario with eHealth SDoH data standards, drawn from the information collected in this report. First, the foundational work from Ontario-based early adopters of SDoH data collection can be leveraged for a spread-and-scale approach. This work needs to be shepherded with formal SDoH data standards development processes driven by key involvement of national, provincial, and standards development experts. Planning, resourcing, and enacting pilot projects to test processes of SDoH data collection and data use in interventions, should inform larger-scale program rollout. To achieve standards in SDoH data collection, exchange, and use, meaningful incentives need to support efforts to enact robust systems newly embedded within healthcare. New roles of data stewardship within organizations will foster coordinated and collaborative work ensuring the promotion and use of SDoH standards to enact robust, secure data collection and use, to the benefit of patients and populations. Taken together, the multiple and complex facets of SDoH data standardization and data use toward health equity initiatives align with the aims of a Learning Health System, where research, informatics, incentives, and culture are combined for continuous improvement and innovation. Finally, fostering and funding Canadian research in SDoH data standardization toward innovation in system design, data processes, and health outcome evaluation will be an important steering component to Ontario’s health equity deliverables. Conclusion: Standardization of SDoH along with supporting data stewardship is the path forward for the creation of high-quality SDoH data inputs that are critical to Ontario’s health equity and learning health system plan. Ontario has some early development on this front and appears poised to make further progress in the future, aided by a well-laid plan and sufficient resourcing to execute it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".