Integrating Patient Reported Data into Primary Care Networks in Saskatchewan
Bibliographic record
Abstract
To ensure patient priorities are driving health system decision making, a team co-led by patient and family partners developed and implemented systems to collect patient experience data in four Saskatchewan health networks. The Saskatchewan Health Authority (SHA) is committed to collecting People-Centred Measures (PCMs) at all levels of the health system. PCMs focus on measures that matter to patients. PCMs include Patient Reported Experience Measures (PREMs), Patient Reported Outcome Measures (PROMs) and qualitative ways of hearing the voice of patients. Work was completed to begin collecting and using PCMs in four of the province primary care networks. Primary care networks connect teams of health-care professionals and community partners within a given geographic area to better meet the needs of the people they serve; they allow the SHA to better organize services and resources internally to deliver more reliable and consistent team‐based care as close to home as possible. To begin collecting PCM data, a PCM Implementation team that included primary care directors, patient and family partners, SHA support staff and academic researchers: ) established a team charter outlining our concensus decision making model, 2) developed a set of survey questions that captured important aspects of care that could inform planning and improvement, 3) used these questions to create an online survey, 4) tested the survey with home care clients and continuing care aids, 5) ensured all questions were written in plain language, 6) tailored survey distribution plans for each network to collect a representative sample of each network, 7) presented data in meaningful way to patients, community members and network directors and 8) made recommendations regarding spread and scale of the processes. Results included adoption of a consensus decision making process, creation of survey that collected meaningful data in a way that was accessible to patients and clients, the creation of network specific distribution plans, the collection of PCMs, the identification of common themes and creation of knowledge translation materials for each network. Different distribution strategies were used to varying degrees in each primary care network. These included: social media, traditional media, collaboration with community organizations, promotion in health facility using printed material and a focus on phone interviews. Emphasizing different strategies led to different response rates between networks. None resulted in a strong response from under-recognized populations. Survey responses for each network were summarized using frequencies and themes identified for open-ended responses. Themes included: access to care, continuity of care, interpersonal processes of care, and experiences of discrimination. This data was discussed and interpreted with the team to identify opportunities for improvement. Work is currently underway to action those opportunities. Experience collecting data in these networks is informing future planning around PCM collection. This project will inform future distribution strategies, network-specific quality improvement initiatives, integrated knowledge translation processes and plans for scale and spread. Quality improvement initiatives will be presented in a separate abstract. Responses resulting from different distribution strategies will help inform how data is collected and indicates the need to utilize strategies beyond surveys to hear from under-recognized populations. Network staff found the data reflected their experiences with care delivery; validating many quality improvement initiatives that are currently underway. The team is developing -page summaries indicating what we did what we heard and what we do to share back to the public. The integrated knowledge translation strategy and the resulting commitment to the project are seen as important steps to collecting actionable data and similar processes are recommended moving forward. Finally, the initiative as a whole, is being reviewed at leadership tables and will inform strategies for expansion across all health networks.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".