Operationalizing a learning health system: A self‐assessment tool for interprofessional teams
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The operationalization of learning health system (LHS) principles remains challenging, with minimal guidance currently available to support interprofessional teams on the ground. Consequently, LHS initiatives often fall short of their intended objectives, resulting in wasted resources, delays, and mounting frustration among key stakeholders. Methods: = 20) from an academic health system and a pragmatic literature review. Using these data sources, we conducted three design iterations until a final version was reached. Results: The resulting roadmap specifies processes to be performed during project-based LHS initiatives, and provides a self-assessment tool that enables team members to quantitatively evaluate progress. For generalizability and standardization across settings, we used clinically neutral terminology to describe all elements in the roadmap. We demonstrated content validity through multiple rounds of data collection and analyses with stakeholders. A simulated demonstration is provided to illustrate how the roadmap may be used for team assessments in practice. Conclusions: Participants considered the roadmap to be an effective tool to assist project management and highly useful for evaluating teams' progress for planning and communication purposes. As a reference model, the roadmap may be re-utilized across multiple LHS initiatives in any given health system to standardize and streamline LHS development. This research was conducted within a single department in an academic health system, and future research is needed to assess the roadmap's generalizability in other settings. To facilitate development of similar or complementary instruments, the detailed design methodology used in this research may be replicated and/or tailored in other contexts.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it