Using Front Line Networks to Develop and Implement Data Driven Policy
Bibliographic record
Abstract
Introduction: Using data to drive policy development is desirable but typically leads to a top-down approach which may have limited implementation success at the clinical level. The province of Alberta, Canada, with a universal public health system, has robust, linked data sources which can inform healthcare policy.Objective: We sought to develop a mechanism whereby data could be more effectively used to create healthcare policy that would be acceptable and implementable within primary care.Targeted population: Our process was targeted to involve the approximately 3500 family doctors (representing 86% of all primary care) who practice in Primary Care Networks and who deliver primary care in the province of Alberta, population 4.2 million persons.Highlights: In our approach we used the robust, comprehensive data that comes from a universal health care system and which ties together elements such as resource utilization, physician and system contacts, diagnoses, lab and other clinical data, demographics, etcetera. Rather than having health administrators or the government interpret the data and develop policy which would then be sought to be implemented at a clinical level, we took a different approach. Instead, we shared the data with a formalized network that encompassed representation from all of Alberta's 42 primary care networks. The significance of the data was discussed, its priority and impact on various aspects of the health system agreed upon, and voluntary but binding policy decisions were made by consensus. Responsibility for implementation of the policies then fell to a joint partnership of the Alberta health care provision system (Alberta Health Services) and to the primary care networks and their individual practices. Once this process was underway, the Ministry of Health was advised and consulted so that policy could be formalized at a governmental level and be aligned with that of the health delivery system and provider organization. An important component of the approach was monitoring and tracking policy implementation and measuring its impact.Transferability: This approach can be used, in context and as appropriate, in many settings provided there are groups of organized providers and sources of data that are meaningful to those providers.Conclusions: By having an organizational structure that allows front-line providers to assess data and agree upon policy in collaboration with the healthcare system, and for the good of the population, formalization of such policy is easily carried out but, more importantly, there is buy-in across the provider community that allows for rapid and relatively easy implementation compared to traditional top-down policy implementation. However, success is dependent on having an organizational structure for the providers that can commit their constituents to a course of action; robust and comprehensive data along with the ability to interpret such data; as well as a trusting relationship between the providers, their organization, and in this case, the health care delivery system and government.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.156 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".