An Empirical Investigation into Physician Preferences in Drug Prescription: An Integrated Methodology of AHP and QFD
Bibliographic record
Abstract
In order to sustain in a competitive market like pharmaceutical in Bangladesh, it is important to get an insight into physicians’ preferences in prescribing the drugs. The aim of this work is to investigate and address the physician requirements through an integrated methodology of Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD). In this research, an expert panel has been interviewed to recognize the criteria affecting physicians’ decisions. The results from AHP derived through Expert Choice software demonstrate that from the viewpoint of physicians, out of the five criteria, quality of product offering is ranked highest in prescribing the drugs followed by the reputation of the company, relationship enjoyed with the company, etc. As for the technical aspects, derived from the relationship matrix of AHP and QFD, out of the sixteen, brand image is ranked first followed by the quality of raw and packaging materials, skilled production personnel etc. The contribution of this research is expected to enable the managers in the pharmaceutical companies to recognize the factors that influence physicians in prescribing drugs for the patients and help them find out challenging items with preeminent alternatives. Few suggestions for future research are also put forward.
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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 | 0.041 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".