Drug-Utilisation Evaluation Using WHO/INRUD Core Prescribing Indicators in a Medicine OPD (SPSS)

Irrational prescribing — too many drugs per encounter, needless antibiotics and injections, brands written where generic names should be — drains household income and fuels resistance in India. The World Health Organization, with the International Network for Rational Use of Drugs (INRUD), published a standard, comparable way to measure this: the WHO/INRUD core drug-use indicators, split into prescribing, patient-care and facility indicators.

This project is a prospective, cross-sectional drug-utilisation evaluation of 600 prescriptions from the Medicine outpatient department (OPD) of a tertiary-care teaching hospital over six months, audited against the twelve WHO/INRUD indicators: average number of drugs per encounter, percentage of drugs prescribed by generic name, percentage of encounters with an antibiotic, percentage with an injection, percentage of drugs from the essential medicines list, plus consultation time, dispensing time, drugs actually dispensed, adequacy of labelling, patient knowledge of dosage, and the availability of a formulary and key medicines.

Beyond the core indicators, the study adds an ATC/DDD-based utilisation profile of the ten most prescribed drugs and tests whether prescribing behaviour differs by prescriber grade (professor, assistant professor, junior resident) using chi-square and non-parametric tests in SPSS. The output is not only a marks-earning report but a baseline the hospital's Pharmacy and Therapeutics Committee can act on, with focused deliverable: generic-writing drives, a stewardship reminder stamp, better labelling at the OPD pharmacy.

The design uses only aggregate, anonymised data — no patient names or identifiers are recorded — under Institutional Ethics Committee approval: an achievable, defensible individual Pharm.D Year V project.

Medicine OPDs in Indian teaching hospitals prescribe to hundreds of patients a day, and prescribing habits there shape both household medicine spending and future antibiotic resistance. The hospital, however, has no measured baseline: nobody can say what the average drugs per encounter is, what share of drugs are written by generic name, how often antibiotics or injections are prescribed, whether patients leave the pharmacy knowing their dose, or how long they spend with the prescriber. Without numbers, the Pharmacy and Therapeutics Committee cannot target rational-use interventions or measure later improvement.

This study fills that gap by auditing prescriptions against the WHO/INRUD core indicators — the international standard for exactly this measurement — and by testing whether prescribing differs by prescriber grade, so that any intervention (formulary enforcement, generic-writing campaigns, stewardship reminders) is aimed at the right group and can be re-measured with the same instrument.

Abstract

A prospective, cross-sectional drug-utilisation evaluation will audit 600 Medicine OPD prescriptions of a tertiary-care teaching hospital over six months against the WHO/INRUD core prescribing, patient-care and facility indicators, with an ATC/DDD profile of the most-prescribed drugs and comparison by prescriber grade. Data will be anonymised at collection, analysed descriptively and inferentially in SPSS, and reported with recommendations to the Pharmacy and Therapeutics Committee.

Introduction

More than half of the medicines consumed in India are prescribed in outpatient departments, yet OPD prescribing habits are rarely measured. The WHO/INRUD method (1993) standardises measurement so that a teaching hospital can compare itself with WHO reference values and with published Indian studies — the same twelve indicators, the same definitions, comparable denominators. Pharm.D Year V includes Pharmacoepidemiology and Biostatistics, and this project is their natural field exercise.

Existing evidence and gap

• Indian WHO/INRUD studies exist, but most audit 100–300 prescriptions, mix wards with OPDs, or report indicators without testing associations with prescriber characteristics.
• Many omit the patient-care indicators (consultation and dispensing time, labelling, patient knowledge) because they need an observer in the pharmacy — yet these are the indicators patients actually feel.
• ATC/DDD quantification of what is actually consumed is rarely reported alongside the core indicators.
• Gap: a complete twelve-indicator evaluation with prescriber-grade association and DDD profiling in one OPD, as a reproducible Year V individual study.

Proposed work

• Develop and pilot a structured prescription-audit form; obtain IEC approval and departmental permissions.
• Audit 600 prescriptions by systematic random sampling on two fixed OPD days per month, and observe consultation and dispensing for the patient-care indicators using the WHO/INRUD summary forms.
• Compute the twelve indicators, profile the top ten drugs by ATC class with defined daily doses, and test associations between prescriber grade and polypharmacy, antibiotic and injection prescribing.
• Compare findings with WHO reference values and recent Indian OPD studies; present recommendations.

Feasibility

• Setting: the Medicine OPD registers 150–250 patients per clinic day, so two days per month yield the sample comfortably within six months.
• Resources: only a validated audit form, a stopwatch, WHO/INRUD summary forms, Excel and SPSS — all available in the Department of Pharmacy Practice.
• Ethics: data are aggregate and anonymised; the design was chosen so that no patient record is identified and no therapy is altered, keeping IEC review straightforward.

In scope

• 600 prescriptions from the general Medicine OPD (adults) of one tertiary-care teaching hospital, sampled on two fixed clinic days per month for six months.
• All twelve WHO/INRUD core indicators, computed with the manual's definitions and summary forms.
• ATC/DDD profiling of the top ten drugs; association analysis with prescriber grade; comparison with WHO reference values and Indian literature.

Out of scope

• Paediatric, specialty and emergency OPDs; inpatient and ICU prescribing (a natural second study).
• Prescribing appropriateness for individual diagnoses (needs chart review and is beyond the indicator method), cost analysis, and patient outcomes after discharge.
• Any intervention — this is a baseline evaluation; re-audit after intervention is future scope.

Study design: prospective, cross-sectional, indicator-based drug-utilisation evaluation. Setting and duration: general Medicine OPD of a tertiary-care teaching hospital; six months of Project Work (Year V) at 20 hours per week, two OPD audit days per month. Sample size: 600 prescriptions — the WHO/INRUD manual's recommended minimum of 600 encounters per facility for reliable indicator estimates — collected by systematic random sampling of every fifth prescription per session until the day's quota.

Instruments:

• A structured prescription-audit form (serial number only; no patient identifiers): encounter date, prescriber grade, number of drugs, generic versus brand, antibiotic, injection, essential-list status, diagnosis category.
• WHO/INRUD patient-care summary forms with a stopwatch: consultation time (prescriber enters to leaves), dispensing time (pharmacy counter hand-over), drugs dispensed versus prescribed, label adequacy (drug name, strength, dose, frequency, duration), and a brief exit interview on dosage knowledge.
• Facility checklist: printed essential-drugs list/formulary presence and availability of the twelve WHO key medicines.

Procedure: the form is piloted on 20 prescriptions and refined; data are entered into Excel as anonymised serial numbers and analysed in SPSS.

Statistical analysis: descriptive statistics for all indicators (mean, SD, percentages with 95% confidence intervals); Shapiro–Wilk test for normality of times; chi-square test (or Fisher's exact test where cells are small) for association of prescriber grade with polypharmacy, antibiotic and injection prescribing; Mann–Whitney U or Kruskal–Wallis tests for non-normal time variables across grades; p < 0.05 as significant. The test for each question is stated in advance in the analysis plan — the justification examiners ask for.

Ethics: approval of the Institutional Ethics Committee (IEC) of the college–hospital, with the IEC reference number quoted in the report; written permissions from the Head of Medicine and the Medical Superintendent; anonymised, aggregate reporting; waiver of documented consent for the prescription review and verbal consent for exit interviews, per ICMR National Ethical Guidelines (2017).

Timeline: month 1 protocol, IEC and pilot; months 2–5 data collection; month 5 analysis; month 6 seminars, report and PTC presentation.

The study is designed as a one-way measurement pipeline: prescribe → observe → audit → compute → compare → recommend. Every arrow below is documented on a standard form, so a second Pharm.D student can repeat the audit and the numbers will be comparable.

flowchart TD
  A["IEC approval and departmental permissions"] --> B["Pilot form on 20 prescriptions and refine"]
  B --> C["Audit days: 2 per month x 6 months"]
  C --> D["Systematic random sampling: every 5th prescription"]
  D --> E["Prescribing indicators (5) from audit form"]
  D --> F["Patient-care indicators (5): stopwatch + exit interview"]
  D --> G["Facility indicators (2): checklist"]
  E --> H["Excel: anonymised serial-number data"]
  F --> H
  G --> H
  H --> I["SPSS: descriptives, chi-square, non-parametric tests"]
  I --> J["Compare with WHO reference values and Indian studies"]
  J --> K["Recommendations to Pharmacy & Therapeutics Committee"]

Indicator computation flow

flowchart LR
  P["600 anonymised prescriptions"] --> Q1["Average drugs per encounter"]
  P --> Q2["% generic, % antibiotic, % injection, % from EDL"]
  O["Observed encounters"] --> Q3["Consultation & dispensing time, dispensed, labelled, dosage knowledge"]
  F["Pharmacy checklist"] --> Q4["Formulary and key-drug availability"]
  T["Top 10 drugs"] --> Q5["ATC class + defined daily doses"]
  Q1 & Q2 & Q3 & Q4 & Q5 --> R["Indicator summary tables with 95% CI"]

The twelve indicators and the ATC/DDD block map one-to-one onto the data-collection forms, which keeps the methods chapter, the results tables and the viva answers mutually consistent — a point external examiners check carefully.

• Re-audit after intervention — repeat the identical instrument six months after the PTC acts on the recommendations, and report change per indicator.
• Extension to paediatric and specialty OPDs and to inpatient wards, building a hospital-wide prescribing profile.
• Prescribing-appropriateness layer — linking indicators to diagnoses and to standard treatment guidelines for conditions like acute respiratory infection.
• Cost analysis — what generic prescribing at reference prices would save patients per 100 encounters.
• Antibiotic-stewardship linkage — feeding the antibiotic-encounter indicator into the hospital's antimicrobial stewardship programme.
• Academic detailing — a structured prescriber-feedback session and its measured effect on the generic-prescribing indicator.