An Integrated Care Approach to Health Equity Data Collection and Use
Bibliographic record
Abstract
Background: Inequities in healthcare have consistently been demonstrated to impact the health of patient populations. The East Toronto Health Partners (ETHP), a collection of more than 00 cross-sectional care delivery organizations in Ontario, Canada, have committed to addressing health inequities. In this study, we sought to understand how organizations within the ETHP define health equity and equity-seeking populations, how they currently collect and utilize health equity data, and their recommendations for system-wide collaboration to share and use the collected data to address the needs of the population they serve. This evaluation is a first step to equity-informed healthcare decision-making, to reduce inequities in care delivery and planning. Approach: In this exploratory qualitative descriptive study, we conducted 60- to 90-minute semi-structured interviews with ETHP anchor-partner decision-makers who were responsible for decision-making around the collection and use of health equity data in their respective organizations. Participants were purposively sampled and identified through snowball sampling. Concurrent content analysis was performed. Results: Study participants (n=6), from different organizations, included ETHP leaders holding various positions such as Director, CEO, Vice President, Board Chair, Quality Officer, Physicians and others. Seven unique equity data collection tools were identified. Some common data collected are language, birth country, ethnic/racial background, disability status, gender identity, sexual orientation and family income, among others. The interviews revealed a lack of common definition among ETHP partners; additionally, while there is an acknowledgement that equity data is beyond sociodemographic information, the current practice is limited to the collection of sociodemographic data in most settings. Currently, a scattered, top-down approach (directed by Ontario Health) is driving the data collection within ETHP. However, there seems to be a lack of planning for the use of such data. Additionally, a patient-centeredness approach to the collection and use of equity data is not a priority. A patient-centered approach to health equity data and use is an approach in which patients are actively present at the decision-making table, resources are dedicated to building trust within the community, and providing variety of options for patients to share their data at the right time and place. The interviews revealed that an ideal future integrated state envisions a patient-centered and collaborative endeavor where the collection and use of data are intertwined. Implications: Moving from the current to the future state requires understanding each organization needs for equity data collection and use, addressing barriers such as size, staff shortage, and resources, and ensuring efforts are centered around the patient. Emphasizing on shared-values and vision the collaboration will be a collective effort to create a sustainable impact on reducing inequalities. Creating pooled resources, standardizing data collection while maintaining flexibility, and establishing a shared vision for data use are crucial steps. The study underscores the critical need for policies and investments with system-wide governance and leadership to encourage healthcare organizations to more effectively collect and utilize health equity data. Policies that mandate collection of health equity data should also require organizations to develop clear and comprehensive plans for utilizing these data for healthcare improvement. Additionally, promoting collaboration between healthcare settings may enhance the overall effectiveness of health equity initiatives to improve public health. More needs to be done to effectively use the data being collected to address the needs of the clients, improve population health, and decrease health inequities.
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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 | 0.277 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".