The Healthcare and Technology Synergy (HATS) Model for Practice and Research
Bibliographic record
Abstract
Background: Research models that include a focus on technologies or products are critical in today,s healthcare environment. The Healthcare and Technology Synergy (HATS) model represents a synergy between three major variables, patient, product and practice, with each one affecting and being affected by the other. These variables exist within a total healthcare environment and are applicable to research in the professions of allied health, medicine and nursing. Problem: To understand and review the use of the Healthcare and Technology Synergy (HATS) model in research and practice. Approach: Models that include products can aid in evidence-based research that is translatable to patient care, patient outcomes and cost effectiveness. Outcome: In the past 5 years, five countries (Australia, Canada, China, Ireland, United States) showed interest in the model, with 143 total views of the seminal article, and 5 research studies from medicine and nursing have used the HATS model. Conclusion: Patient, product, and practice are of paramount importance in many areas of research such as bloodstream infections, urinary infections, ventilator assisted pneumonia, safety, and patient and product outcomes. Using the HATS model can strengthen research outcomes, aid in the prioritization of research agendas, establish evidencebased practice guidelines, and help in evaluating patient care outcomes.
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How this classification was reachedexpand
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 | 0.012 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".