Design and Implementation of Electronic Health Record Tools for Integrated Primary Care in Pediatrics
Bibliographic record
Abstract
Objective: Pediatric Integrated Primary Care (IPC) models include various practice elements, including shared electronic health records (EHRs). Although shared EHR systems provide collaboration opportunities and can be resources for program evaluation and quality improvement initiatives, to be used effectively, EHR tools need to be user-informed and capture the complexities and heterogeneity of behavioral healthcare. The aim of this study was to evaluate the implementation of adapted EHR tools designed to collect relevant, routine data, including presenting concerns, patient history, specific intervention components administered, patient goals and progress, and adherence to protocols. Methods: We describe the design and implementation of three interconnected EHR tools developed to promote the use of practice-based data within an established IPC program. The tools included: (1) a data flowsheet, (2) customizable documentation template, and (3) a real-time data dashboard. The RE-AIM framework guided the identification and evaluation of implementation outcomes including patient reach, provider adoption, and implementation. Behavioral Health Provider (BHP) use of the tools was examined via EHR chart review, and Tableau© tracked access to the data dashboards. Results: Six months after the introduction of the flowsheet and template to BHPs, all utilized the tools in most of their patient encounters. High use rates were sustained three years later (87.56% of encounters). The dashboard tool was never adopted for clinical purposes. Conclusions: While documentation tools were readily adopted, challenges exist in BHP uptake of data visualization tools. Further exploration of factors influencing BHP use of clinical data is essential for advancing practice-based research in pediatric IPC.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".