Prescribing Pattern in a Medicine OPD Using WHO Core Prescribing Indicators and ATC Codes

Irrational prescribing — too many drugs per prescription, brand names instead of generics, unnecessary antibiotics and injections, and drugs outside the essential-medicines list — raises costs, adverse reactions and antimicrobial resistance. The World Health Organization and the International Network for Rational Use of Drugs (INRUD) developed a small set of core prescribing indicators so that any facility can measure its prescribing quality in a standard, comparable way.

This ICMR Short-Term Studentship (STS)-style drug-utilisation study applies those indicators to 600 outpatient prescriptions from the Medicine OPD of a tertiary-care teaching hospital. Prescription copies retained by the hospital pharmacy over a defined six-month period are selected by systematic random sampling, and every drug is recorded by generic name, dosage form and WHO Anatomical Therapeutic Chemical (ATC) code. The five core indicators are calculated: average number of drugs per encounter, percentage prescribed by generic name, percentage of encounters with an antibiotic, percentage with an injection, and percentage of drugs from the National List of Essential Medicines (NLEM) 2022. Results are compared with WHO-derived optimal values and with published Indian studies.

Additional analyses cover fixed-dose combinations, the commonest ATC groups, antibiotic choice by WHO AWaRe category and potential drug–drug interactions in polypharmacy prescriptions. Because the study uses existing records, it fits comfortably within the two-month STS window while teaching the core pharmacology concept every MBBS student is examined on: rational prescribing.

Abstract

A retrospective, record-based cross-sectional study will analyse 600 prescriptions from the Medicine outpatient department of a tertiary-care teaching hospital using WHO core prescribing indicators. Drugs will be classified by the ATC system and checked against NLEM 2022 and the WHO Model List. Indicator values will be compared with WHO-derived optimal values; secondary analyses will describe fixed-dose combinations, antibiotic use by AWaRe category and potential drug–drug interactions.

Introduction

WHO defines rational use of medicines as patients receiving medications appropriate to their clinical needs, in doses that meet their individual requirements, for an adequate period, at the lowest cost to them and their community. India's NLEM, the Jan Aushadhi generic-medicine scheme and National Medical Commission guidance to prescribe generic names all aim at this goal, but actual OPD prescribing is rarely audited.

Review and gap

Indian drug-utilisation studies commonly report more than two drugs per encounter, low generic prescribing and antibiotic use above WHO-derived optimal values. Many older studies predate NLEM 2022 and the WHO AWaRe classification, and few examine fixed-dose combinations or interactions alongside the core indicators. Local data for the study hospital do not exist.

Proposed work

• Obtain six months of Medicine OPD prescription copies and select 600 by systematic random sampling.
• Extract data on a structured form; assign ATC codes; check NLEM status.
• Compute the five core indicators with 95% confidence intervals.
• Describe secondary outcomes and share results with the hospital Drug and Therapeutics Committee.

Feasibility

• Data: the hospital pharmacy retains prescription copies, so no patient contact is needed.
• Time: extraction of 600 prescriptions at about 40 per day takes three weeks.
• Cost: negligible; the ATC Index, NLEM and interaction checkers are freely accessible.
• Ethics: IEC approval with waiver of consent for anonymised retrospective records.

The Medicine OPD of a teaching hospital sees a very large number of patients each day, and prescriptions are often written quickly by different residents and consultants. There is no routine audit of how many drugs are prescribed, whether generic names are used, how often antibiotics and injections are given, or whether prescribed drugs belong to the National List of Essential Medicines. Without measurement, irrational prescribing — polypharmacy, brand-name prescribing, unnecessary antibiotics and irrational fixed-dose combinations — cannot be identified or corrected.

This study aims to measure prescribing quality in the Medicine OPD using WHO core prescribing indicators, to describe the pattern of drugs by ATC class, and to compare the findings with WHO-derived optimal values, so that the Drug and Therapeutics Committee has baseline evidence for targeted educational and policy interventions.

In scope

• Adult Medicine OPD prescriptions retained by the hospital pharmacy over six months.
• WHO core prescribing indicators, ATC classification, NLEM status, AWaRe category, fixed-dose combinations and potential interactions.
• Descriptive statistics and comparison with reference values.

Out of scope

• Patient-care and facility indicators (consultation time, dispensing time, labelling) — these need prospective observation.
• Judging whether each prescription was clinically appropriate for the diagnosis (diagnoses are often incomplete on OPD slips).
• In-patient and paediatric prescriptions.

Study design: retrospective, record-based, cross-sectional drug-utilisation study.

Setting and duration: Medicine OPD and hospital pharmacy of a tertiary-care teaching hospital; records from a defined six-month period; two months for approvals, extraction, analysis and report.

Sample size: WHO's manual How to Investigate Drug Use in Health Facilities recommends at least 600 encounters for a prescribing-indicator survey; this study therefore uses 600 prescriptions, 100 from each of six months to balance seasonal variation.

Sampling: systematic random sampling within each month — sampling interval k = total prescriptions in the month / 100, with a random start.

Inclusion criteria: legible prescriptions of patients aged ≥ 18 years from the Medicine OPD with at least one drug.

Exclusion criteria: illegible or incomplete prescriptions, prescriptions containing only investigations or dietary advice, and repeat photocopies.

Data-extraction form: prescription serial number (study code), age group, gender, diagnosis if written, each drug's name as written (generic or brand), dosage form, route, dose, frequency, duration, ATC code, NLEM 2022 listing, FDC status, AWaRe category for antibiotics.

Operational definitions (WHO):

• Average drugs per encounter = total drugs / total encounters (each FDC counted as one drug).
• % generic = drugs prescribed by generic name / total drugs × 100.
• % encounters with antibiotic = encounters with ≥ 1 antibiotic / total encounters × 100.
• % encounters with injection = encounters with ≥ 1 injection / total encounters × 100 (vaccines excluded).
• % from NLEM = drugs listed in NLEM 2022 / total drugs × 100.

Interaction screening: prescriptions with ≥ 5 drugs are checked with a standard drug-interaction reference and interactions graded as major, moderate or minor.

Ethics: IEC approval with waiver of informed consent (retrospective anonymised records), permission from the Medical Superintendent and pharmacy in-charge; no patient names, hospital numbers or identity documents are transcribed; ICMR National Ethical Guidelines (2017).

Statistical analysis: Excel for entry and indicator formulas; Jamovi/SPSS for descriptive statistics, 95% CI for proportions and chi-square test for differences between age groups and genders; p < 0.05.