Prescription practices in the outpatient department of Bangladesh Medical University: A clinical audit
Authors
- Kazi Ali AftabDepartment of Internal Medicine, Bangladesh Medical University, Dhaka, Bangladesh
- Khaled Mahbub MurshedDepartment of Internal Medicine, Bangladesh Medical University, Dhaka, Bangladesh
- Abdullah Al FaisalDepartment of Internal Medicine, Bangladesh Medical University, Dhaka, Bangladesh
- Md. Hashibul Hasan ShawonDepartment of Internal Medicine, Bangladesh Medical University, Dhaka, Bangladesh
- Tasnim NafianDepartment of Internal Medicine, Bangladesh Medical University, Dhaka, Bangladesh
- Md. Abul Kalam AzadDepartment of Internal Medicine, Bangladesh Medical University, Dhaka, Bangladesh
- Shahinul AlamDepartment of Hepatology, Bangladesh Medical University, Dhaka, Bangladesh
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Published by Bangladesh Medical University (former Bangabandhu Sheikh Mujib Medical University).
Rational prescribing is a cornerstone of safe, effective clinical care. A complete prescription encodes patient identity, diagnostic reasoning, therapeutic decision-making, and continuity-of-care planning errors at any of these junctures, thereby threatening patient safety and escalating healthcare costs [1]. In outpatient departments (OPDs) of low- and middle-income countries (LMICs), prescribing deficiencies are compounded by high patient volumes, limited resources, and inconsistent use of clinical guidelines [2]. Bangladesh, a lower-middle-income country with a predominantly paper-based prescribing system, faces pronounced challenges: studies in Dhaka’s hospitals have documented high rates of polypharmacy and omission errors in both government and private facilities [3]. Clinical audit - the systematic comparison of current practice against agreed standards is a proven quality improvement strategy endorsed by the WHO and the UK National Institute for Health and Care Excellence (NICE) [4]. Repeated audit–feedback cycles have demonstrated sustained improvements in prescription completeness and rational drug use even in resource-constrained LMIC settings [5]. Bangladesh Medical University (BMU) is the country’s premier postgraduate medical institution; yet, no structured prescription audit had previously been conducted in its OPD. The objective of this audit was to identify gaps, benchmark compliance, and inform targeted interventions.
A cross-sectional clinical audit was conducted on 546 conveniently sampled outpatient prescriptions written between October and December 2025 at the OPD-1 and OPD-2 pharmacies of BMU. Duplicate prescriptions were excluded. A 20-item structured audit tool was developed by reviewing national standards (DGDA and BMDC), BMU’s own OPD prescription format, the WHO Guide to Good Prescribing, and NICE Medicines Optimisation guidelines [4]. Indicators were grouped into six domains: (i) patient demographics and identification, (ii) clinical documentation, (iii) diagnosis and therapeutic rationale, (iv) continuity of care and communication, (v) prescription legality and clarity and (vi) malpractice. Pre-defined compliance benchmarks were ≥ 90% for administrative parameters and ≥80% for core clinical parameters, consistent with published quality-assurance literature [6]. Two trained independent reviewers assessed each prescription, and disagreements were resolved by discussion and referral to a third senior reviewer. Descriptive statistics (frequencies and percentages) were computed using Microsoft Excel 365. Institutional permission was obtained on 14 September 2026.
Administrative parameters were well documented: patient age (97.1%), prescription date (97.3%), registration number (96.2%), and prescribing department (96.3%) all exceeded the 90% benchmark. By contrast, critical demographic identifiers showed alarming deficiencies: patients’ sex was recorded in only 9.2% of prescriptions, addresses in 2.0%, mobile numbers in 2.9%, and National IDs in 0.9%. Sex is a key biological determinant of pharmacokinetics and adverse drug reaction risk [7] its omission undermines personalised and safe prescribing.
Core clinical documentation was substantially below target. A clear diagnosis was documented in only 42.5% of prescriptions, well below the 80% benchmark, and evidence-based treatment was recorded in just 36.6%. Treatment concordance with diagnosis was present in 41.6%, physical examination findings in 20.7%, and follow-up plans in only 18.1%. Medical history and laboratory investigations were moderately documented (68.1%). Written patient advice appeared in 38.1% of prescriptions. Legibility of handwriting was satisfactory (85.2%), and doctors’ signatures were present in 84.1% of cases, but the prescriber's name and designation, essential for accountability, were recorded in only 21.1% of cases. Prescription of unregistered, herbal, or Ayurvedic medicines occurred in 1.8% of cases.
Table 1 Compliance with clinical audit standards for outpatient prescriptions (n=546)
Clinical audit standards | Number (%) |
Patient demographics and identification |
|
Registration number present | 525 (96.2) |
Patient’s full name written | 530 (97.1) |
Date of prescription recorded | 531 (97.3) |
Patient’s address mentioned | 11 (2) |
National Identification Number present | 5 (0.9) |
Patient’s age documented | 530 (97.1) |
Patient’s sex documented | 50 (9.2) |
Mobile phone number provided | 16 (2.9) |
Clinical documentation |
|
Department specified | 526 (96.3) |
Medical history included | 372 (68.1) |
Physical examination findings recorded | 113 (20.7) |
Laboratory investigations documented | 372 (68.1) |
Diagnosis and therapeutic rationale |
|
Diagnosis documented | 232 (42.5) |
Treatment prescribed according to diagnosis | 227 (41.6) |
Evidence-based treatment given (guideline or literature supported) | 200 (36.6) |
Continuity of care and communication |
|
Follow-up plan clearly documented | 99 (18.1) |
Advice written in prescription | 208 (38.1) |
Prescription legality and clarity |
|
Handwriting is easy to understand | 465 (85.2) |
Signature of doctor documented | 459 (84.1) |
Malpractice |
|
Any unregistered/herbal/Unani/Ayurvedic drugs prescribed | 10 (1.8) |
This audit reveals a paradox common to many LMIC OPDs. Robust administrative record-keeping coexists with critically deficient clinical documentation. The near-universal recording of age, date, and registration number reflects a well-embedded administrative culture, yet the failure to document diagnosis in more than half of prescriptions is a fundamental lapse. Diagnosis is the anchor of rational therapy; without it, the appropriateness of prescribed medications cannot be verified, monitored, or audited [1]. This gap is mirrored in findings from comparable Bangladesh hospitals, where prescription omission errors, particularly for diagnosis and indication, were prevalent across both government and private OPDs [3].
The low rate of evidence-based prescribing (36.6%) suggests that clinical decisions at BMU OPD may be influenced by habit, pharmaceutical promotion, or patient demand rather than current evidence, a pattern documented across South Asian and sub-Saharan LMICs [2]. This concern is reinforced by the treatment–diagnosis concordance rate of 41.6%, implying potential misalignment between recorded diagnoses and therapeutic choices.
The near-absence of follow-up plans (18.1%) and patient advice (38.1%) reflects an episodic, consultation-centred model of care ill-suited to the management of chronic non-communicable diseases, which constitute an increasing share of Bangladesh’s disease burden. Continuity of care is a validated predictor of improved clinical outcomes and reduced hospital re-admission [8]. Equally concerning is the poor documentation of prescriber identity (21.1%), which weakens professional accountability and hinders inter-provider communication.
Three evidence-informed interventions are recommended. First, a structured mandatory OPD prescription form with dedicated fields for patient sex, diagnosis (including ICD code), evidence-based reference, follow-up instruction, and prescriber name and designation should be introduced, with a carbon-copy duplicate retained in the patient record. Second, regular interactive prescriber training, anchored in audit data and case-based learning, should be implemented; studies in Pakistan and Sudan demonstrate that educational cycles improve prescription quality across successive audit rounds [9]. Third, an electronic OPD prescribing system should be explored as a long-term solution: e-prescribing has been shown to reduce omission errors and improve prescribing safety in both high-income and LMIC settings [10]. Institutionalised quarterly prescription audits with constructive feedback to clinicians are essential to embed a culture of continuous quality improvement.
This study has notable strengths, including a comprehensive 20-item, multi-domain audit tool and benchmarks anchored in national and international standards. Limitations include a reliance on documented rather than verbally communicated information; clinical appropriateness of individual prescriptions was not assessed. In cases where no diagnosis was documented, the appropriateness of the treatment could not be fully verified from the prescription alone. Two OPD departments might not represent all the outpatient services at BMU.
This clinical audit at BMU OPD identified critical deficiencies in diagnosis documentation, evidence-based prescribing, and follow-up planning, against a backdrop of strong administrative compliance. These findings highlight an urgent need for structured prescription reform, prescriber education, and institutionalised rigorous audit to improve prescribing quality and patient care at BMU.


Variables | Frequency (%) |
Indication of colposcopy |
|
Visual inspection of the cervix with acetic acid positive | 200 (66.7) |
Abnormal pap test | 13 (4.3) |
Human papilloma virus DNA positive | 4 (1.3) |
Suspicious looking cervix | 14 (4.7) |
Others (per vaginal discharge, post-coital bleeding) | 69 (23.0) |
Histopathological diagnosis | |
Cervical Intraepithelial Neoplasia 1 | 193 (64.3) |
Cervical Intraepithelial Neoplasia 2 | 26 (8.7) |
Cervical Intraepithelial Neoplasia 3 | 32 (10.7) |
Invasive cervical cancer | 27 (9.0) |
Chronic cervicitis | 17 (5.6) |
Squamous metaplasia | 5 (1.7) |
Groups based on pre-test marks | Pretest | Posttest Marks (%) | Difference in pre and post-test marks (mean improvement) | P |
Didactic lecture classes | ||||
<50% | 36.6 (4.8) | 63.2 (9.4) | 26.6 | <0.001 |
≥50% | 52.8 (4.5) | 72.4 (14.9) | 19.6 | <0.001 |
Flipped classes | ||||
<50% | 36.9 (4.7) | 82.2 (10.8) | 45.4 | <0.001 |
≥50% | 52.8 (4.6) | 84.2 (10.3) | 31.4 | <0.001 |
Data presented as mean (standard deviation) | ||||
Background characteristics | Number (%) |
Age at presentation (weeks)a | 14.3 (9.2) |
Gestational age at birth (weeks)a | 37.5 (2.8) |
Birth weight (grams)a | 2,975.0 (825.0) |
Sex |
|
Male | 82 (41) |
Female | 118 (59) |
Affected side |
|
Right | 140 (70) |
Left | 54 (27) |
Bilateral | 6 (3) |
Delivery type |
|
Normal vaginal delivery | 152 (76) |
Instrumental delivery | 40 (20) |
Cesarean section | 8 (4) |
Place of delivery |
|
Home delivery by traditional birth attendant | 30 (15) |
Hospital delivery by midwife | 120 (60) |
Hospital delivery by doctor | 50 (25) |
Prolonged labor | 136 (68) |
Presentation |
|
Cephalic | 144 (72) |
Breech | 40 (20) |
Transverse | 16 (8) |
Shoulder dystocia | 136 (68) |
Maternal diabetes | 40 (20) |
Maternal age (years)a | 27.5 (6.8) |
Parity of mother |
|
Primipara | 156 (78) |
Multipara | 156 (78) |
aMean (standard deviation), all others are n (%) | |
Background characteristics | Number (%) |
Age at presentation (weeks)a | 14.3 (9.2) |
Gestational age at birth (weeks)a | 37.5 (2.8) |
Birth weight (grams)a | 2,975.0 (825.0) |
Sex |
|
Male | 82 (41) |
Female | 118 (59) |
Affected side |
|
Right | 140 (70) |
Left | 54 (27) |
Bilateral | 6 (3) |
Delivery type |
|
Normal vaginal delivery | 152 (76) |
Instrumental delivery | 40 (20) |
Cesarean section | 8 (4) |
Place of delivery |
|
Home delivery by traditional birth attendant | 30 (15) |
Hospital delivery by midwife | 120 (60) |
Hospital delivery by doctor | 50 (25) |
Prolonged labor | 136 (68) |
Presentation |
|
Cephalic | 144 (72) |
Breech | 40 (20) |
Transverse | 16 (8) |
Shoulder dystocia | 136 (68) |
Maternal diabetes | 40 (20) |
Maternal age (years)a | 27.5 (6.8) |
Parity of mother |
|
Primipara | 156 (78) |
Multipara | 156 (78) |
aMean (standard deviation), all others are n (%) | |
Mean escape latency of acquisition day | Groups | ||||
NC | SC | ColC | Pre-SwE Exp | Post-SwE Exp | |
Days |
|
|
|
|
|
1st | 26.2 (2.3) | 30.6 (2.4) | 60.0 (0.0)b | 43.2 (1.8)b | 43.8 (1.6)b |
2nd | 22.6 (1.0) | 25.4 (0.6) | 58.9 (0.5)b | 38.6 (2.0)b | 40.5 (1.2)b |
3rd | 14.5 (1.8) | 18.9 (0.4) | 56.5 (1.2)b | 34.2 (1.9)b | 33.8 (1.0)b |
4th | 13.1 (1.7) | 17.5 (0.8) | 53.9 (0.7)b | 35.0 (1.6)b | 34.9 (1.6)b |
5th | 13.0 (1.2) | 15.9 (0.7) | 51.7 (2.0)b | 25.9 (0.7)b | 27.7 (0.9)b |
6th | 12.2 (1.0) | 13.3 (0.4) | 49.5 (2.0)b | 16.8 (1.1)b | 16.8 (0.8)b |
Average of acquisition days | |||||
5th and 6th | 12.6 (0.2) | 14.6 (0.8) | 50.6 (0.7)b | 20.4 (2.1)a | 22.4 (3.2)a |
NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure. aP <0.05; bP <0.01. | |||||
Categories | Number (%) |
Sex |
|
Male | 36 (60.0) |
Female | 24 (40.0) |
Age in yearsa | 8.8 (4.2) |
Education |
|
Pre-school | 20 (33.3) |
Elementary school | 24 (40.0) |
Junior high school | 16 (26.7) |
Cancer diagnoses |
|
Acute lymphoblastic leukemia | 33 (55) |
Retinoblastoma | 5 (8.3) |
Acute myeloid leukemia | 4 (6.7) |
Non-Hodgkins lymphoma | 4 (6.7) |
Osteosarcoma | 3 (5) |
Hepatoblastoma | 2 (3.3) |
Lymphoma | 2 (3.3) |
Neuroblastoma | 2 (3.3) |
Medulloblastoma | 1 (1.7) |
Neurofibroma | 1 (1.7) |
Ovarian tumour | 1 (1.7) |
Pancreatic cancer | 1 (1.7) |
Rhabdomyosarcoma | 1 (1.7) |
aMean (standard deviation) | |



Test results | Disease | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | ||
Yes | No | ||||||
Reid’s score ≥ 5 | Positive | 10 | 15 | 37.0 | 94.5 | 40.1 | 93.8 |
Negative | 17 | 258 |
|
|
|
| |
Swede score ≥ 5 | Positive | 20 | 150 | 74.1 | 45.0 | 11.8 | 94.6 |
Negative | 7 | 123 |
|
|
|
| |
Swede score ≥ 8 | Positive | 3 | 21 | 11.1 | 92.3 | 12.5 | 91.3 |
Negative | 24 | 252 |
|
|
|
| |
a High-grade indicates a score of ≥5 in both tests; PPV indicates positive predictive value; NPV, negative predictive value | |||||||
Test | Sensitivity (%) | Specificity (%) | Positive predictive value (%) | Negative predictive value (%) |
Reid’s score ≥ 5 | 37.0 | 94.5 | 40.0 | 93.8 |
Swede score ≥ 5 | 74.1 | 45 | 11.8 | 94.6 |
Swede score ≥ 8 | 11.1 | 92.3 | 12.5 | 91.3 |
Test | Sensitivity (%) | Specificity (%) | Positive predictive value (%) | Negative predictive value (%) |
Reid’s score ≥ 5 | 37.0 | 94.5 | 40.0 | 93.8 |
Swede score ≥ 5 | 74.1 | 45 | 11.8 | 94.6 |
Swede score ≥ 8 | 11.1 | 92.3 | 12.5 | 91.3 |
Narakas classification | Total 200 (100%) | Grade 1 72 (36%) | Grade 2 64 (32%) | Grade 3 50 (25%) | Grade 4 14 (7%) |
Complete recoverya | 107 (54) | 60 (83) | 40 (63) | 7 (14) | - |
Near complete functional recovery but partial deformitya | 22 (11) | 5 (7) | 10 (16) | 6 (12) | 1 (7) |
Partial recovery with gross functional defect and deformity | 31 (16) | 7 (10) | 13 (20) | 10 (20) | 1 (7) |
No significant improvement | 40 (20) | - | 1 (1.5) | 27 (54) | 12 (86) |
aSatisfactory recovery bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7, 8, 9, Grade 4, panpalsy with Hornon’s syndrome. | |||||
Narakas classification | Total 200 (100%) | Grade-1 72 (36%) | Grade-2 64 (32%) | Grade-3 50 (25%) | Grade-4 14 (7%) |
Complete recoverya | 107 (54) | 60 (83) | 40 (63) | 7 (14) | - |
Near complete functional recovery but partial deformitya | 22 (11) | 5 (7) | 10 (16) | 6 (12) | 1 (7) |
Partial recovery with gross functional defect and deformity | 31 (16) | 7 (10) | 13 (20) | 10 (20) | 1 (7) |
No significant improvement | 40 (20) | - | 1 (1.5) | 27 (54) | 12 (86) |
aSatisfactory recovery bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7,8,9, Grade 4, panpalsy with Hornon’s syndrome. | |||||
Variables in probe trial day | Groups | ||||
NC | SC | ColC | Pre-SwE Exp | Post-SwE Exp | |
Target crossings | 8.0 (0.3) | 7.3 (0.3) | 1.7 (0.2)a | 6.0 (0.3)a | 5.8 (0.4)a |
Time spent in target | 18.0 (0.4) | 16.2 (0.7) | 5.8 (0.8)a | 15.3 (0.7)a | 15.2 (0.9)a |
NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure. aP <0.01. | |||||
Pain level | Number (%) | P | ||
Pre | Post 1 | Post 2 | ||
Mean (SD)a pain score | 4.7 (1.9) | 2.7 (1.6) | 0.8 (1.1) | <0.001 |
Pain categories | ||||
No pain (0) | - | 1 (1.7) | 31 (51.7) | <0.001 |
Mild pain (1-3) | 15 (25.0) | 43 (70.0) | 27 (45.0) | |
Moderete pain (4-6) | 37 (61.7) | 15 (25.0) | 2 (3.3) | |
Severe pain (7-10) | 8 (13.3) | 2 (3.3) | - | |
aPain scores according to the visual analogue scale ranging from 0 to 10; SD indicates standard deviation | ||||
Surgeries | Number (%) | Satisfactory outcomes n (%) |
Primary surgery (n=24) |
|
|
Upper plexus | 6 (25) | 5 (83) |
Pan-palsy | 18 (75) | 6 (33) |
All | 24 (100) | 11 (46) |
Secondary Surgery (n=26) |
|
|
Shoulder deformity | 15 (58) | 13 (87) |
Wrist and forearm deformity | 11 (42) | 6 (54) |
All | 26 (100) | 19 (73) |
Primary and secondary surgery | 50 (100) | 30 (60) |
Mallet score 14 to 25 or Raimondi score 2-3 or Medical Research grading >3 to 5. | ||
Narakas classification | Total 200 (100%) | Grade-1 72 (36%) | Grade-2 64 (32%) | Grade-3 50 (25%) | Grade-4 14 (7%) |
Complete recoverya | 107 (54) | 60 (83) | 40 (63) | 7 (14) | - |
Near complete functional recovery but partial deformitya | 22 (11) | 5 (7) | 10 (16) | 6 (12) | 1 (7) |
Partial recovery with gross functional defect and deformity | 31 (16) | 7 (10) | 13 (20) | 10 (20) | 1 (7) |
No significant improvement | 40 (20) | - | 1 (1.5) | 27 (54) | 12 (86) |
aSatisfactory recovery bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7,8,9, Grade 4, panpalsy with Hornon’s syndrome. | |||||
Trials | Groups | ||||
NC | SC | ColC | Pre-SwE Exp | Post-SwE Exp | |
1 | 20.8 (0.6) | 22.1 (1.8) | 41.1 (1.3)b | 31.9 (1.9)b | 32.9 (1.8)a, b |
2 | 10.9 (0.6) | 14.9 (1.7) | 37.4 (1.1)b | 24.9 (2.0)b | 26.8 (2.5)b |
3 | 8.4 (0.5) | 9.9 (2.0) | 32.8 (1.2)b | 22.0 (1.4)b | 21.0 (1.4)b |
4 | 7.8 (0.5) | 10.4 (1.3) | 27.6(1.1)b | 12.8 (1.2)b | 13.0 (1.4)b |
Savings (%)c | 47.7 (3.0) | 33.0 (3.0) | 10.0 (0.9)b | 23.6 (2.7)b | 18.9 (5.3)b |
NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure. aP <0.05; bP <0.01. cThe difference in latency scores between trials 1 and 2, expressed as the percentage of savings increased from trial 1 to trial 2 | |||||


Lesion-size | Histopathology report | Total | |||||
CIN1 | CIN2 | CIN3 | ICC | CC | SM | ||
0–5 mm | 73 | 0 | 0 | 0 | 5 | 5 | 83 |
6–15 mm | 119 | 18 | 1 | 4 | 0 | 0 | 142 |
>15 mm | 1 | 8 | 31 | 23 | 12 | 0 | 75 |
Total | 193 | 26 | 32 | 27 | 17 | 5 | 300 |
CIN indicates cervical intraepithelial neoplasia; ICC, invasive cervical cancer; CC, chronic cervicitis; SM, squamous metaplasia | |||||||
| Histopathology report | Total | ||||||
CIN1 | CIN2 | CIN3 | ICC | CC | SM | |||
Lesion -Size | 0-5 mm | 73 | 0 | 0 | 0 | 5 | 5 | 83 |
6-15 mm | 119 | 18 | 1 | 4 | 0 | 0 | 142 | |
>15 mm | 1 | 8 | 31 | 23 | 12 | 0 | 75 | |
Total | 193 | 26 | 32 | 27 | 17 | 5 | 300 | |
CIN indicates Cervical intraepithelial neoplasia; ICC, Invasive cervical cancer; CC, Chronic cervicitis; SM, Squamous metaplasia | ||||||||
Group | Didactic posttest marks (%) | Flipped posttest marks (%) | Difference in marks (mean improvement) | P |
<50% | 63.2 (9.4) | 82.2 (10.8) | 19.0 | <0.001 |
≥50% | 72.4 (14.9) | 84.2 ( 10.3) | 11.8 | <0.001 |
Data presented as mean (standard deviation) | ||||








