Patient- and Family-Centered Care Recommendations in Cardiology Guidelines: An AI-Driven Systematic Review
Bibliographic record
Abstract
Background Patient- and family-centered care (PFCC) is recognized as a critical component of cardiovascular care, but its integration into cardiology society guidelines has not been described. The objective of this study is to review PFCC language use and recommendations within major cardiology society guidelines. Methods We conducted a systematic review of guidelines and statements from the American College of Cardiology (ACC), American Heart Association (AHA), Canadian Cardiovascular Society (CCS), and the European Society of Cardiology (ESC) from 2013 to 2023. PFCC-related key terms were identified using an AI-based natural language processing algorithm, and recommendations were categorized into eight dimensions of PFCC. The inclusion of PFCC recommendations across societies and trends over time were examined. Results There were 260 guidelines and statements analyzed. The most frequent PFCC dimensions overall were Health Transitions (23.5/100 pages), Shared Decision-Making (11.1/100 pages), and Care Access (9.9/100 pages). The least commonly identified dimensions across all journals were Care Coordination (6.5/100 pages), Emotional Support (4.0/100 pages), and Familial Support (1.0/100 pages). CCS, ACC, and AHA had more recommendations using PFCC key terms than ESC per 100 pages (17.3, 12.0, 10.3 vs. 4.6, respectively, p<0.01). PFCC language usage increased markedly over the ten-year period for ACC, AHA, and ESC, but decreased for CCS (all p<0.05). Conclusion PFCC language and recommendations are increasingly being included in cardiology society guidelines. Societal differences in PFCC language use exist. Future research is needed to evaluate the impact of these guideline recommendations on clinical practice.
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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 | 0.073 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".