Improving the Healthcare Quality Measurement System Using Attribute Agreement Analysis Assessing the Presence and Stage of Pressure Ulcers
Bibliographic record
Abstract
Measurement Systems Analysis has recently been used in healthcare service processes mainly to assess the accuracy and use of equipment and devices. However, thirty-seven percent of emergency department healthcare malpractice claims were related to diagnosis errors. Diagnosis is heavily dependent upon human assessment and decisions. The paper describes the application of a healthcare case study that applied Measurement Systems Analysis Attribute Agreement Analysis and Gage R&R studies to assess the accuracy of the human element in a healthcare service process. The study was used to assess the accuracy of the diagnosis of pressure ulcers when patients are admitted to the hospital, either through the emergency department or directly through inpatient admitting. Creating an accurate and precise measurement system aided the hospital by standardizing the assessment of the pressure ulcer healthcare diagnosis process. Initial Attribute Agreement Analysis of whether a pressure ulcer was present resulted in a 94% assessor repeatability accuracy rate, and a 40% within assessor reproducibility accuracy. The within appraiser accuracy to the standard was 92%, and across assessors’ assessment to the standard was 40%. The measurement system was poorer for assessing the pressure ulcer stages, resulting in 82% within assessor repeatability accuracy and an 8% overall accuracy to standard. This study is extremely important to 1) identify a method for healthcare providers to assess and improve the measurement system related to human diagnoses in healthcare processes; and 2) to demonstrate the usefulness of expanding gage R&R and attribute agreement analysis to human diagnosis in healthcare settings.
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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 | 0.090 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".