The Paediatric Admission Quality of Care (PAQC) score: designing a tool to measure the quality of early inpatient paediatric care in a low‐income setting
Bibliographic record
Abstract
BACKGROUND: Evaluating clinician compliance with recommended steps in clinical guidelines provides one measure of quality of process of care but can result in a multiplicity of indicators across illnesses, making it problematic to produce any summative picture of process quality, information that may be most useful to policy-makers and managers. OBJECTIVE: We set out to develop a clinically logical summative measure of the quality of care provided to children admitted to hospital in Kenya spanning the three diagnoses present in 60% or more of admissions that would provide a patient-level measure of quality of care in the face of comorbidity. METHODS: We developed a conceptual model of care based on three domains: assessment, diagnosis and treatment of illnesses. Individual items within domains correspond to recommended processes of care within national clinical practice guidelines. Summative scores were created to reduce redundancy and enable aggregation across illnesses while maintaining a clear link to clinical domains and our conceptual model. The potential application of the score was explored using data from more than 12 000 children from eight hospitals included in a prior intervention study in Kenya. RESULTS: Summative scores obtained from items representing discrete clinical decision points reduced redundancy, aided balance of score contribution across domains and enabled direct comparison of disease-specific scores and the calculation of scores for children with comorbidity. CONCLUSION: This work describes the development of a summative Paediatric Admission Quality of Care score measured at the patient level that spans three common diseases. The score may be an efficient tool for assessing quality with an ability to adjust for case mix or other patient-level factors if needed. The score principles may have applicability to multiple illnesses and settings. Future analysis will be needed to validate the score.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".