Prescription practices in the outpatient department of Bangladesh Medical University: A clinical audit

Authors

DOI:

Keywords

clinical audit, prescription practices, outpatient department, evidence-based medicine, quality improvement

Correspondence

Kazi Ali Aftab
Email: aftabk63@bsmmu.edu.bd

Publication history

Received: 18 Apr 2026
Accepted: 28 May 2026
Published online: 1 June 2026

Responsible editor

Reviewers

B: Abdullah Md Abu Ayub Ansary
C: Mohammad Rashal Chowdhury

Funding

None

Ethical approval

Ethical approval was not required as the clinical audit was done solely on outdoor prescriptions, and the patient did not provide any data. We have taken permission from the authority of BMU hospital before conducting the clinical audit. All data were handled confidentially, and patient identifiers were removed prior to analysis.

Trial registration number

Not applicable

Copyright

© The Author(s) 2026; all rights reserved. 
Published by Bangladesh Medical University (former Bangabandhu Sheikh Mujib Medical University).
Key messages
Accurate and complete prescription writing is essential for patient safety, continuity of care, and rational prescribing. Fewer than half of prescriptions from outpatients at Bangladesh Medical University Hospital met the required standards, highlighting a major gap in documentation. By introducing an electronic outpatient prescription system, implementing evidence-based prescription training, and conducting periodic evaluations, Bangladesh Medical University can improve prescription quality.

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
marks (%)

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

 

 

 

 

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)

-

(1.7)

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)

Acknowledgements
We are grateful to the authorities, employees, and the stuff of various department of outpatient department of Bangladesh Medical University for their wholehearted cooperation.
Author contributions
Manuscript drafting and revising it critically: KAA, SA, MAKA, KMM, AAF, MHHS, TN. Approval of the final version of the manuscript: KAA, SA, MAKA, KMM, AAF, MHHS, TN. Guarantor of accuracy and integrity of the work: KAA, SA, MAKA, KMM, AAF, MHHS, TN.
Conflict of interest
We do not have any conflict of interest.
Data availability statement
We confirm that the data supporting the findings of the study will be shared upon reasonable request.
AI disclosure
We acknowledge the use of Grammarly AI to assist with English language editing, improving sentence structure, grammar and vocabulary for greater clarity. We critically reviewed and revised all generated suggestions to ensure the manuscript’s readability. We take full responsibility for the content of this article.
Supplementary file
The BMU OPD prescription format may be obtained from the corresponding author.
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