The association between continuity of care and outcomes: a systematic and critical review
Bibliographic record
Abstract
BACKGROUND: Numerous studies have tried to determine the association between continuity and outcomes. Studies doing so must actually measure continuity. If continuity and outcomes are measured concurrently, their association can only be determined with time-dependent methods. OBJECTIVE: To identify and summarize all methodologically studies that measure the association between continuity of care and patient outcomes. METHODS: We searched MEDLINE database (1950-2008) and hand-searched to identify studies that tried to associate continuity and outcomes. English studies were included if they: actually measured continuity; determined the association of continuity with patient outcomes; and properly accounted for the relative timing of continuity and outcome measures. RESULTS: A total of 139 English language studies tried to measure the association between continuity and outcomes but only 18 studies (12.9%) met methodological criteria. All but two studies measured provider continuity and used health utilization or patient satisfaction as the outcome. Eight of nine high-quality studies found a significant association between increased continuity and decreased health utilization including hospitalization and emergency visits. Five of seven studies found improved patient satisfaction with increased continuity. CONCLUSIONS: These studies validate the belief that increased provider continuity is associated with improved patient outcomes and satisfaction. Further research is required to determine whether information or management continuity improves outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".