Prescription Audit in General Outpatient Department of a Tertiary Care Teaching Hospital-A Prospective Observational Study
Bibliographic record
Abstract
Introduction: The prescribing habits are of critical importance since therapeutic efficacy and safety depends on rationality of the prescriptions. Prescription audit helps to detect any effective changes that would help HCPs to offer superior quality of care to the patients. Aims & Objective: Identify the gaps in current prescribing practice and support HCPs to boost rational prescribing. Methodology: A prospective observational study was conducted on 1188 prescriptions for a span of two month in the general outpatient department of a tertiary care teaching hospital in western Rajasthan, India. A total of 1188 prescriptions were sampled based on the MOHFW Govt. of India “Prescription Audit guideline” recommendation. All the prescriptions were analyzed based on WHO prescribing indicators and were evaluated for errors in prescription writing. Data were entered and analyzed using microsoft excel. Results: 1188 prescription comprising of 4876 drugs were analyzed. The average number of drugs per prescription was four. The study encompassed 39.81% males and 60.19% females. Around 50% prescriptions were written in legible handwriting & recorded salient feature of clinical examinations. Presumptive diagnosis was mentioned in 95% however clear medicine doses & schedule were mentioned in just 75% prescriptions. None of the prescriptions mentioned next date of visit of the patients however just 0.5% prescription included allergy status of the patient. Approx. 85% of prescription didn’t mention any medical history of the patient. Follow-up advice and precautions (do’s and don’ts) as well as relevant clinical details and reason in case of referral were given in less than 1% of audited prescriptions. Polypharmacy (more than 5 medicines) was observed in 35% and about 1% prescriptions contain more than 10 medicines. Vitamins, Tonics or Enzymes and Antibiotics were prescribed in approx. 30% of audited prescriptions of which only 1/3rd of antibiotics were prescribed as per facility’s Antibiotic Policy. Conclusion: Prescription audit can be helpful to plan appropriate intervention to ensure the rational drug therapy and to evaluate the existing drug use pattern. It also reflects the perspectives of current prescribing pattern in hospitals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".