Guiding Principles for Data Architecture to Support the Pathways Community HUB Model
Bibliographic record
Abstract
INTRODUCTION: The Pathways Community HUB Model provides a unique strategy to effectively supplement health care services with social services needed to overcome barriers for those most at risk of poor health outcomes. Pathways are standardized measurement tools used to define and track health and social issues from identification through to a measurable completion point. The HUB use Pathways to coordinate agencies and service providers in the community to eliminate the inefficiencies and duplication that exist among them. PATHWAYS COMMUNITY HUB MODEL AND FORMALIZATION: Experience with the Model has brought out the need for better information technology solutions to support implementation of the Pathways themselves through decision-support tools for care coordinators and other users to track activities and outcomes, and to facilitate reporting. Here we provide a basis for discussing recommendations for such a data infrastructure by developing a conceptual model that formalizes the Pathway concept underlying current implementations. REQUIREMENTS FOR DATA ARCHITECTURE TO SUPPORT THE PATHWAYS COMMUNITY HUB MODEL: The main contribution is a set of core recommendations as a framework for developing and implementing a data architecture to support implementation of the Pathways Community HUB Model. The objective is to present a tool for communities interested in adopting the Model to learn from and to adapt in their own development and implementation efforts. PROBLEMS WITH QUALITY OF DATA EXTRACTED FROM THE CHAP DATABASE: Experience with the Community Health Access Project (CHAP) data base system (the core implementation of the Model) has identified several issues and remedies that have been developed to address these issues. Based on analysis of issues and remedies, we present several key features for a data architecture meeting the just mentioned recommendations. IMPLEMENTATION OF FEATURES: Presentation of features is followed by a practical guide to their implementation allowing an organization to consider either tailoring off-the-shelf generic systems to meet the requirements or offerings that are specialized for community-based care coordination. DISCUSSION: Looking to future extensions, we discuss the utility and prospects for an ontology to include care coordination in the Unified Medical Language System (UMLS) of the National Library of Medicine and other existing medical and nursing taxonomies. CONCLUSIONS AND RECOMMENDATIONS: Pathways structures are an important principle, not only for organizing the care coordination activities, but also for structuring the data stored in electronic form in the conduct of such care. We showed how the proposed architecture encourages design of effective decision support systems for coordinated care and suggested how interested organizations can set about acquiring such systems. Although the presentation focuses on the Pathways Community HUB Model, the principles for data architecture are stated in generic form and are applicable to any health information system for improving care coordination services and population health.
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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 | 0.069 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".