Assessing primary healthcare quality through avoidable hospitalizations: a systematic review
Bibliographic record
Abstract
Abstract Background Avoidable hospitalization (AH) for ambulatory care sensitive conditions (ACSC) has been widely used as a measure of the performance of primary health care. However, the lack of a standardized definition of these conditions and the absence of conclusive evidence regarding their efficacy across diverse healthcare settings remain significant challenges. This review aims to systematically identify and synthesize the utilization of AH for ACSCs as a metric for evaluating the quality of primary healthcare (PHC) and factors influencing such quality. Methods We conducted a systematic search for peer-reviewed studies on 3 electronic databases. Studies that used AH for ACSCs to investigate the quality of PHC were included. The following characteristics were extracted: study design, ACSCs definition, AH rates, intervention if applicable, PHC and or patients’ characteristics associated to AH rates, such as accessibility, continuity of care, demographics, socio-economic status, and comorbidities. Results 73 relevant articles were identified, published between 1994 and 2023 and mostly in USA. Preliminary findings based on 56 articles indicate that the majority (48.2%) employed the list of ACSCs from the AHRQ (i.e. the prevention quality indicators) (48.2%). Meanwhile, 12 studies (17.9%) used lists developed or modified by the authors, and another 7 (12.5%) adopted lists previously utilized in other research. Studies conducted in Brazil, Canada and USA employed lists issued by relevant national institutions. Conclusions Although preliminary, these findings highlight two main trends. Firstly, in countries where health authorities have established a standardized set of ACSCs, a notable uniformity is observed. Conversely, in the absence of such standards, variability in defining these conditions is likely to increase. This variation could hinder the comparability across different Primary Health Care (PHC) service organizations and affect the analysis of care quality. Key messages • Most studies used established indicators for the measuring of primary healthcare quality. • Diverse ACSC definitions across countries impair the generalizability of healthcare quality analysis.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".