Administrative claims data analysis of nurse practitioner prescribing for older adults
Bibliographic record
Abstract
AIM: This paper is a report of a study to identify the patterns of prescribing by primary health care nurse practitioners for a cohort of older adults. BACKGROUND: The older adult population is known to receive complex pharmacotherapy. Monitoring prescribing to older adults can inform quality improvement initiatives. In comparison to other countries, research examining nurse practitioner prescribing in Canada is limited. Nurse practitioner prescribing for older adults is relatively unexplored in the international literature. Although commonly used to study physician prescribing, few studies have used claims data from drug insurance programmes to investigate nurse practitioner prescribing. METHOD: Drug claims for prescriptions written by nurse practitioners from fiscal years 2004/05 to 2006/07 for beneficiaries of the Nova Scotia Seniors' Pharmacare programme were analysed. Data were retrieved and analysed in May 2008. Prescribing was described for each drug using the World Health Organization Anatomical Therapeutic Chemical code classification system by usage and costs for each fiscal year. RESULTS: Antimicrobials and non-steroidal anti-inflammatory drugs consistently represented the top ranked groups for prescription volume and cost. Over the three fiscal years, antimicrobial prescription rates declined relative to rates of other groups of medications. Prescription volume per nurse doubled and cost per prescription increased by approximately 20%. CONCLUSION: Prescription claims data can be used to characterize the prescribing trends of nurse practitioners. Research linking patient characteristics, including diagnoses, to prescriptions is needed to assess prescribing quality. Some potential areas of improvement were identified with antimicrobial and non-steroidal antiinflammatory selection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".