Quality of congestive heart failure care: assessing measurement of care using electronic medical records.
Bibliographic record
Abstract
OBJECTIVE: To study the feasibility of using electronic medical record (EMR) data from the Deliver Primary Healthcare Information (DELPHI) database to measure quality of care for patients with congestive heart failure (CHF) in primary care and to determine the percentage of patients with CHF receiving the recommended care. DESIGN: Items listed on the Ontario Ministry of Health and Long-Term Care Heart Failure Patient Care Flow Sheet (CHF flow sheet) were assessed and measured using EMRs of patients diagnosed with CHF between October 1, 2005, and September 30, 2008. SETTING: Ten primary health care practices in southwestern Ontario. PARTICIPANTS: Four hundred eighty-eight patients who were considered to have CHF because at least 1 of the following was indicated in their EMRs: an International Classification of Diseases billing code for CHF (category 428), an International Classification of Primary Care diagnosis code for heart failure (ie, K77), or "CHF" reported on the problem list. MAIN OUTCOME MEASURES: Number of CHF flow sheet items that were measurable using EMR data from the DELPHI database. Percentage of patients with CHF receiving required quality-of-care items since the date of diagnosis. RESULTS: The DELPHI database contained information on 60 (65.9%) of the 91 items identified using the CHF flow sheet. The recommended tests and procedures were recorded infrequently: 55.5% of patients with CHF had chest radiographs; 32.6% had electrocardiograms; 32.2% had echocardiograms; 30.5% were prescribed angiotensin-converting enzyme inhibitors; 20.9% were prescribed β-blockers; and 15.8% were prescribed angiotensin II receptor blockers. CONCLUSION: Low frequencies of recommended care items for patients with CHF were recorded in the EMR. Physicians explained that CHF care was documented in areas of the EMR that contained patient identifiers, such as the encounter notes, and was therefore not part of the DELPHI database. Extractable information from the EMR does not provide a complete picture of the quality of care provided to patients with CHF.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".