Key Components of Traditional Consultation Letters and Their Relevance to Electronic Consultation Replies: A Systematic Review
Bibliographic record
Abstract
Background: Effective communication between primary care providers (PCPs) and specialists plays a key role in providing high-quality patient care. A high-quality referral process should involve referral letters containing all information that is necessary to support shared care between primary and specialty care. Introduction: There is no consensus on the optimal components of specialist-to-PCP communication after a face-to-face patient encounter or in the context of the emerging field of electronic consultations (eConsult). In this study, we aimed at synthesizing the evidence on key components of a traditional consultation letter and at determining whether they can be applied to eConsult replies. Methods: We conducted a systematic review by using a narrative synthesis approach. We searched Pubmed and Embase from inception to January/March 2016 (English). Included studies focused on features of specialists' responses to PCPs. We extracted components of a consultation letter that were identified to be of importance to PCPs and attempted to relate their applicability to eConsult replies. Results: The search revealed 744 potentially relevant citations, of which 65 were deemed eligible for full-text review. Forty-one papers were excluded on full-text review, resulting in 24 studies included in the final synthesis. Important components of consultation letters that were applicable to eConsults included: answering a direct question, providing a diagnosis, providing treatment options, providing education around the case, providing a prognosis, and arranging follow-up, clarity, and organization. Key differences between traditional and eConsult replies included the history and physical investigations, impression, plan, and rationale for plan/education. Conclusion: When seeking to improve the quality of specialist reply letters in both traditional and eConsult replies, one should consider differences in how information is collected and accessed, the role of each provider, and factors that impact specialist-to-PCP communication.
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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 | 0.045 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".