Abstract
OBJECTIVES:
The analysis of prescriptions plays a crucial role in promoting rational drug use, minimizing medication errors, and enabling effective antimicrobial surveillance. Manual analyzing is time-consuming, expensive, and error-prone. This study aimed to evaluate prescribing patterns and antimicrobial surveillance using a novel digital analytical platform in a tertiary care hospital.
METHODOLOGY:
A descriptive observational study was conducted in a tertiary care hospital, in India, between June and August 2024. Prescription data were collected from outpatient departments (general medicine, surgery, pediatrics, pulmonology, and orthopedics) and analyzed using the VaidyaRx digital analytic platform. World Health Organization Core Prescribing Indicators were applied to assess prescribing trends, generic drug usage, antibiotic prescribing patterns, fixed-dose combination (FDC), and adherence to the National List of Essential Medicines (NLEM). Data were analyzed using MS Excel and VaidyaRx.
RESULTS:
Mean patient age was 35.8 ± 19.2 years, with pediatrics (21.8%), adult (73.3%), and geriatric (4.9%). The average drugs per prescription were 3.2, and generic prescriptions were 37.5%. Antibiotics were prescribed in 24.9% of prescriptions, highest in surgery (46.9%), with ofloxacin + ornidazole and amoxicillin + potassium clavulanate being the most common. NLEM drug use was 36.7%, with more FDCs from non-NLEM (40.9%) than NLEM (8.3%) drugs. Only 64.5% of prescriptions mentioned dosage, raising concerns about completeness.
CONCLUSIONS:
Using the VaidyaRx digital platform, real-time prescription analysis was possible which helped in enhancing medication safety and antimicrobial stewardship. Suitable interventions are needed to reduce polypharmacy, increase generic prescribing, ensure rational antibiotic use, and improve prescribing practices.
Keywords: Antimicrobial surveillance, digital platform, medication error, National List of Essential Medicines, prescription analytics
Introduction
Prescription analysis in hospitals provides crucial insights into prescribing patterns among healthcare professionals. Challenges such as illegible handwriting, incomplete instructions, and the use of Latin abbreviations such as “BD,” “TDS,” and “QID” may not be understood by patients. A prescription analysis study of 67 countries demonstrated huge variations in consultation length, with global population having only a few minutes with their primary care physicians. Few Indian studies showed that the patient–doctor contact time ranges from 1.9 to 3 min.[1] Such short doctor–patient interaction time further affects patient healthcare and medication error. Globally, medication errors are estimated at 5%–10% in the US, with varied rates in Europe.[2] No data are available from India.
India’s National List of Essential Medicines (NLEM) 2022, by the Ministry of Health and Family Welfare, has 384 medicines with objective to ensure safe, efficacious, and affordable medications. It aims to promote rational therapeutics in resource-limited settings. The medicines in this list are price-controlled by the National Pharmaceutical Pricing Authority, which is important in the Indian context, given that 39.4% of healthcare expenses in India are out-of-pocket.[3]
Antimicrobial resistance (AMR), a global health crisis dubbed a “silent pandemic,” causes an estimated 4.1 million deaths annually, associated with AMR.[4] Antibiotic awareness and stewardship strategies, can be better planned with the help of prescription analytics and be useful in assessing the quality of health care.
Extraction of data manually from handwritten prescriptions is challenging due to time, effort, cost, and possibility of error.[5] Digitalization of prescription and its analysis helps to facilitate real-time insights into prescribing.[6] Mobile-based digital platform VaidyaRx is a tool for prescription and its statistical analysis. The use of mobile technology in healthcare settings represents a contemporary approach for medical data management.
The objectives of this study were to assess the following: (1) the number of drugs per prescription, (2) prescribing trends in generic and brand names, (3) prescribing antibiotics, and (4) drugs prescribed from NLEM, including FDCs.
The evaluation provides insights into current prescribing practices, identifying areas of improvement to align with standard treatment workflow suggested by the Indian Council of Medical Research.
Methodology
A descriptive observational study was conducted in a tertiary care hospital, in India, from June to August 2024. Informed consent from patients and prescribing doctors were obtained. Prescriptions of 29 doctors were digitized. The prescriptions were anonymized and the patient’s data ownership lies with the organization. Prescriptions from outpatient department patients of General Medicine, General Surgery, Pediatrics, Orthopedics, and Pulmonary Medicine departments were collected. Only the prescriptions given during a patient’s first visit to the hospital were included in the study.
Prescription data were collected using the SaaS-based VaidyaRx digital platform, which connects patients, physicians, and pharmacists through its mobile application. Trained pharmacy interns and nurses scanned and uploaded prescriptions and stored securely on an encrypted cloud service and underwent a two-tier transcription and verification process to ensure accuracy. One in five prescriptions was randomly reviewed by the prescribing doctor for quality assurance.
Prescription data included patient vitals, presenting complaints, diagnosis, medication history, and current prescribed drugs (name, dosage, frequency, duration, and route of administration). Analysis focused on therapeutic drug categories, generic or branded, drugs from NLEM, fixed-dose combination (FDCs), and antimicrobials.
World Health Organization (WHO) prescribing indicators were applied, including the average number of drugs per prescription, percentage of generic prescriptions, antibiotic encounters, injection use, and NLEM-listed drugs. A discipline-wise analysis was conducted to assess the prescribing patterns of antimicrobial agents.
The study highlights the utility of digital platforms like VaidyaRx for accurate data collection and analysis, providing insights into prescribing trends and identifying areas for improvement in rational drug use.
Data analysis
The collected data were tabulated in MS Excel and analyzed using the VaidyaRx digital platform. Results were presented as mean ± standard deviation or as numbers and percentages, as appropriate.
Results
A total of 3158 prescriptions were collected and digitalized, the age ranged from 6 months to 75 years (median 35 years [interquartile range: 21–50 years]), 21.8% were from the pediatric age group whereas 73.3% were adult and 4.9% were geriatric age group who visited the various departments participating in the study. The male and female ratio was 51.3% and 48.7%.
The average number of medications per prescription was 3.2, with 3.6 drugs prescribed to geriatrics followed by 3.3 to adult group and 2.8 to the pediatrics group, and 37.5% of medications were prescribed by generic names. The number of medicines prescribed from NLEM was 36.7% and the percentage encounter with antibiotics was 24.9%. The percentage of encounters with injections was 11.3%. Table 1 gives the figures of prescribing indicators of our study and the WHO reference range.
Table 1.
WHO prescribing indicators and the findings from our study
| Prescribing indicators | WHO reference range[7] | Findings of this study |
|---|---|---|
| Average number of medicines per prescription | 1.6–1.8 | 3.2 |
| Medicines prescribed by generic name (%) | 100 | 37.5 |
| Encounter with injections (%) | 13.4–24.1 | 11.3 |
| Encounter with antibiotics (%) | 20.0–26.8 | 24.9 |
| Medicines prescribed from the NLEM (%) | 100 | 36.7 |
NLEM=National list of essential medicines
The maximum prescriptions were from the general medicine followed by general surgery, pediatrics, pulmonology, and orthopedic department.
The maximum medicines prescribed by the brand name were pantoprazole and pantoprazole + domperidone combination, followed by vitamin B complex and multivitamins. Whereas, paracetamol and sucralfate + oxetacaine were the most commonly prescribed generic medications.
Of the 3158 prescriptions, antibiotics were given in 24.9% of prescriptions. The encounters with antibiotics were maximum in surgery which was higher than the WHO’s reference range followed by orthopedics, general medicine, pulmonology, and pediatrics [Table 2].
Table 2.
Department-wise number of prescriptions, encounter with antibiotics, and injections
| Departments | Number of prescriptions, n (%) | Number of encounters with antibiotics, n (%) | Number of encounters with injections, n (%) |
|---|---|---|---|
| General medicine | 1339 (42.4) | 181 (13.5) | 151 (11.3) |
| General surgery | 1146 (36.3) | 537 (46.9) | 101 (8.8) |
| Pediatrics | 419 (13.3) | 34 (8.1) | 87 (20.8) |
| Pulmonology | 227 (7.2) | 28 (12.3) | 15 (6.6) |
| Orthopedics | 27 (0.9) | 6 (22.2) | 2 (7.4) |
| Total | 3158 | 786 | 356 |
Quinolone + nitroimidazole was the most prevalent class of antibiotics (249 [29.0%]), followed by penicillin + beta-lactamase inhibitor (178 [20.8%]) and third-generation cephalosporins (90 [10.5%]). Ofloxacin + ornidazole and amoxicillin + potassium clavulanate were the most commonly prescribed FDC antibiotics. Cefixime, nitrofurantoin, and metronidazole were the most often prescribed single-drug antibiotics.
The highest percentage of injection encounters were seen in pediatrics followed by general medicine, general surgery, orthopedics, and pulmonology [Table 2].
Most frequently prescribed antibiotics for pediatrics and adults was ofloxacin plus ornidazole, whereas geriatrics were prescribed amoxicillin plus clavulanic acid. Table 3 gives the frequency of the top 10 prescribed antibiotics in three age groups.
Table 3.
Frequency of antibiotics prescribed in pediatric, adult, and geriatric age group
| Antibiotics in different age groups | |||||
|---|---|---|---|---|---|
|
| |||||
| Pediatric (0–18 years) (n=187) | Adult (19–65 years) (n=634) | Geriatric (more than 65 years) (n=37) | |||
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| Antibiotic formulation | Percentage | Antibiotic formulation | Percentage | Antibiotic formulation | Percentage |
| Ofloxacin + ornidazole | 38.0 | Ofloxacin + ornidazole | 27.6 | Amoxicillin + potassium clavulanate | 24.3 |
| Amoxicillin + potassium clavulanate | 23.5 | Amoxicillin + potassium clavulanate | 20.0 | Cefixime | 13.5 |
| Cefixime | 8.0 | Nitrofurantoin | 7.7 | Cefpodoxime + potassium clavulanate | 13.5 |
| Cefpodoxime + potassium clavulanate | 3.7 | Cefixime | 6.9 | Linezolid | 8.1 |
| Azithromycin | 3.2 | Metronidazole | 5.7 | Ofloxacin + ornidazole | 8.1 |
| Nitrofurantoin | 3.2 | Cefpodoxime + potassium clavulanate | 5.7 | Nitrofurantoin | 5.4 |
| Benzathine penicillin | 2.1 | Azithromycin | 3.8 | Metronidazole | 5.4 |
| Metronidazole | 1.6 | Doxycycline | 3.5 | Clindamycin | 5.4 |
| Linezolid | 1.6 | Ciprofloxacin + ornidazole | 2.4 | Azithromycin | 2.7 |
| Cefuroxime | 1.6 | Linezolid | 2.0 | Sulfamethoxazole + trimethoprim | 2.7 |
Overall, 36.7% of prescribed medications (3758/10,242) were from NLEM. The maximum number of medicinal items from NLEM were prescribed by the pediatrics department followed by orthopedics, general surgery, general medicine, and pulmonology. The maximum number of NLEM medicines were from general medicine followed by general surgery, pediatrics, pulmonology, and orthopedics. From NLEM, 8.3% (312/3758) medications were FDCs, whereas 40.9% (2649/6484) were from non-NLEM. In terms of FDC prescriptions, pulmonology was followed by general surgery, general medicine, orthopedics, and pediatrics [Table 4].
Table 4.
Department-wise distribution of medicinal items prescribed from the National List of Essential Medicines and Fixed Dose Combination
| Departmentwise medicinal items (n=10,242) | Number of medicinal items from NLEM*, n (%) | Number of NLEM medicines** | Number of FDC, n (%) |
|---|---|---|---|
| General medicine (n=4332) | 1379 (31.8) | 92 | 1202 (27.7) |
| General surgery (n=4011) | 1502 (37.4) | 58 | 1217 (30.3) |
| Pediatrics (n=996) | 582 (58.4) | 48 | 70 (7.0) |
| Pulmonology (n=811) | 258 (31.8) | 35 | 449 (55.4) |
| Orthopedics (n=92) | 37 (40.2) | 11 | 23 (25.0) |
*Where a medicine is prescribed in two or more prescriptions it was counted as two or more medicinal items, **Represents the count of number of medicines from the NLEM list. NLEM=National list of essential medicines, FDC=Fixed-dose combination
The top 3 prescribed NLEM drugs were pantoprazole, paracetamol, and vitamin C, whereas non-NLEM drugs were vitamin B complex, multivitamins, and the combination of sucralfate + oxetacaine. The prescription frequency is given in Table 5.
Table 5.
10 commonly prescribed medicines from National List of Essential Medicines and non- National List of Essential Medicines
| NLEM | Non-NLEM | ||
|---|---|---|---|
|
|
|
||
| Medicine name | Percentage | Medicine name | Percentage |
| Pantoprazole | 21.3 | Vitamin B complex | 6.9 |
| Paracetamol | 14.3 | Multivitamin | 5.8 |
| Vitamin C | 9.9 | Sucralfate + oxetacaine | 4.2 |
| Amoxicillin + potassium clavulanate | 4.9 | Esomeprazole + domperidone | 4.0 |
| Formoterol fumarate + budesonide | 3.3 | Ofloxacin + ornidazole | 3.9 |
| Ondansetron | 2.8 | Tramadol hydrochloride + acetaminophen | 3.5 |
| Levothyroxine | 2.8 | Trypsin + chymotrypsin | 3.0 |
| Oral rehydration salt | 2.0 | Aceclofenac + paracetamol | 3.0 |
| Telmisartan | 1.8 | Calcium carbonate + Vitamin D3 | 2.7 |
| Sodium chloride | 1.8 | Aceclofenac + serratiopeptidase + paracetamol | 2.6 |
NLEM=National list of essential medicines
The number of drugs per prescription analysis showed that 25.2% of prescriptions had 3 drugs, 21.2% of prescriptions had 4 drugs followed by 20.8% of prescriptions consisting of 2 drugs, 13.6% of prescriptions consisting of 1 drug, and 8.9% of prescription consisting of more than 5 drugs.
Analysis of prescriptions revealed that in total of 65.3% (6683) medicines were written in capital letters, 64.5% (6607) mentioned dosages, 88.7% (9088) had frequency, and 89.5% (9167) had duration mentioned. Route of administration was mostly mentioned and accounted for 10,169 (99.3%) of the total drugs prescribed.
Maximum medications prescribed belonged to the therapeutic category of antiulcer followed by analgesic and nonsteroidal anti-inflammatory drugs (NSAIDs), vitamin supplements, antibacterial, and others as shown in Figure 1 (The therapeutic categories are mentioned as per the NLEM 2022).
Figure 1.
Distribution of drugs based on their therapeutic drug category (expressed as percentage)
Discussion
Prescription analysis according to the WHO core prescribing indicators has been widely conducted globally, including in India; however, managing large datasets has been challenging. This study utilized a digital prescription analytical platform VaidyaRx, which automates data collection, collation, and analysis. The platform provides efficient, accurate, and scalable processing of prescriptions, enabling comprehensive assessment of prescribing practices. It offers flexibility to analyze specific parameters, such as antimicrobial use, and provides real-time insights for hospital administrators to monitor drug consumption trends, plan procurement, control inventory, and achieve substantial cost savings. The use of digital platforms offers better analytical accuracy and scalability.[5]
Globally, more than half of medications are inappropriately prescribed, delivered, or consumed, and 30%–40% of patients fail to receive clinically appropriate treatment.[8] Misuse, overuse, or underuse of drugs may pose serious health hazards, such as increased ADR, drug interactions, and antibiotic resistance resulting in higher morbidity and mortality.
The Government of India, National Medical Commission, from time-to-time issued advisories to prescribe drugs in generic form. Generic prescribing in this study was found to be in only 37.5% of prescription. A similar low generic prescribing of 6.4% was also reported in another Indian study conducted by Shanmugapriya et al.[9] This requires behavioral changes among Indian prescribers.
Percentage encounter with injection was 11% which is much lower than 68.1% reported in a recent Indian study from a tertiary care hospital.[10] Prescribing oral medication where injections do not have any specific advantage is a rational approach. Higher injection use in tertiary care hospitals is likely because of complicated and referred patients.
The average number of drugs prescribed per encounter was 3.2, which is somewhat similar to another Indian study reported by Chenchula et al. (3.5), against the WHO reference range 1.6–1.8.[11] Restricting the prescribing to only necessary medications will help to reduce avoidable drug interactions and ADRs and improve compliance and adherence, especially in the elderly. The polypharmacy, however, sometimes may be required for the management of multiple diseases/symptoms although the elderly population is vulnerable because of compromised renal and hepatic systems.
It was observed that 21.2% of prescriptions had 4 drugs, 25.2% had 3 drugs, and 20.8% had 2 drugs. The prevalence of high polypharmacy and the need for appropriate preventive intervention have been advocated earlier also.[9] One of the important causes of medication error, non-compliance, more ADRs and drug interaction, and even AMR are due to prescriptions being incomplete or not clearly understood by patients. In this study, 89.5% specified the duration, and 88.7% included frequency, whereas only 64.5% mentioned the dose of medication. Mentioning dose may be critical for drugs with narrow therapeutic windows. Pharmacists play a crucial role in preventing errors in such cases.
Illegible handwriting is also a significant cause of medication errors. This could be because of lack of time and load of patients. A senior author of this paper chaired an inquiry where a patient admitted with renal failure wrongly consumed Folitrax® (methotrexate) for 6 months in place of Folvite® (folic acid) because of confusion due to illegible handwriting. Such medication errors can be prevented if the medicines are written in capital letters. Only a small number of hospitals in India are switching to digital prescriptions. In this study, 65.3% of medicines were written in capital letters, which is a satisfying practice.
The evidence-based medicine warrants prescribing drugs only with specific indications. In the present study, antiulcer (19.5%) medications were highly prescribed, pantoprazole being the most common (37.3%) followed by analgesics, NSAID, and vitamins and minerals. Our experts had difference of opinion in their rationality.
A study conducted in the UK found that 75% of prescribed antibiotics may be unnecessary, with only 25% being justifiably indicated.[12] In this study, 24.9% of prescriptions had antibiotics, of which the maximum being prescribed in general surgery followed by orthopedics and general medicine. In this OPD setup, all antibiotics were prescribed empirically. Incorrect dosage, failure to specify treatment duration, and inadequate guidance may increase the risk of AMR. Though empirical antibiotics cannot be fully avoided, it is important that it should also be evidence based for which local antibiogram is an important supporting tool. The continued information on the use pattern of antibiotics and the antibiogram in a hospital setup are important for making evidence-based antibiotic policy and stewardship programs. The prescribing of unwarranted antibiotics has been an important cause of AMR which is an immediate health concern globally, for which an antimicrobial stewardship needs to be strengthened.
Each country prepares its essential list of medicines with important objective of optimizing the resources and ensuring access of effective, safe, and quality medicines to its citizens. In India, the medicines listed in NLEM are deemed to be scheduled drugs for price control. The last edition of NLEM of India, 2022, is publicly available and doctors are encouraged to prescribe from this list. Analysis of prescribing from NLEM is useful for assessing adherence to rational prescribing practices. Of the 10,242 drugs prescribed in 3158 prescriptions, 36.7% were from the NLEM. Department-specific interventions will further improve prescribing from NLEM. The trend of prescribing from non-NLEM largely varies from doctor to doctor. It may not be the true reflection of prescribing in a specific discipline. Pulmonary medicine and general surgery accounted for the highest FDC usage, raising concerns about the rationality of certain prescriptions. The top 5 commonly prescribed FDCs which are not listed in NLEM were aceclofenac + paracetamol, sucralfate + oxetacaine, ofloxacin + ornidazole, esomeprazole + domperidone, and tramadol + acetaminophen. Recently, several FDCs have been banned in India because of irrationality.[13] The use of FDC, unless fully justified, should be discouraged.
The limitations of this study are: (a) it is a single-center study involving 5 major medicines prescribing departments and (b) the duration was 4 months. Although more than 3000 prescriptions were assessed .
Though this study is at a single center, it has limitation of not capturing the regional variations, However, encouraged by this, similar prescription analytical study has been initiated in 3 other medical institutions in the country.
Conclusions
The study demonstrates that the flexible and scalable digital analytical platform is useful for faster and accurate assessment of prescriptions, taking corrective measures, useful for reducing medication error, and making appropriate antimicrobial stewardship strategy and policy decision for optimizing health resources. In India, health is a state subject, and there is a greater need for improving the prescribing practices involving contemporary digital technologies which will be effective in promoting rational therapeutics.
Conflicts of interest
There are no conflicts of interest.
Acknowledgment
The authors acknowledge the support of doctors in validating their digitalized prescriptions.
Funding Statement
Nil.
References
- 1.Irving G, Neves AL, Dambha-Miller H, Oishi A, Tagashira H, Verho A, et al. International variations in primary care physician consultation time: A systematic review of 67 countries. BMJ Open. 2017;7:e017902. doi: 10.1136/bmjopen-2017-017902. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Padula WV, Steinberg I. What current and missing data can teach us about medication errors. BMJ Qual Saf. 2021;30:89–91. doi: 10.1136/bmjqs-2019-010555. [DOI] [PubMed] [Google Scholar]
- 3.Manikandan S. The National List of Essential Medicines of India 2022 (NLEM 2022): Tommy, toe the line. Lancet Reg Health Southeast Asia. 2023;13:100202. doi: 10.1016/j.lansea.2023.100202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kariuki S. Global burden of antimicrobial resistance and forecasts to 2050. Lancet. 2024;404:1172–3. doi: 10.1016/S0140-6736(24)01885-3. [DOI] [PubMed] [Google Scholar]
- 5.Gupta M, Soeny K. Algorithms for rapid digitalization of prescriptions. Vis Inform. 2021;5:54–69. [Google Scholar]
- 6.Alsoweih HA, Fageehi AA, Hadadi JH, Sharahili IM, Alsubhi FA, Aljabry IS. The impact of digital health technologies on pharmacy services and patient care. Int J Community Med Public Health. 2024;11:2059–64. [Google Scholar]
- 7.Shrestha R, Prajapati S. Assessment of prescription pattern and prescription error in outpatient department at tertiary care district hospital, Central Nepal. J Pharm Policy Pract. 2019;12:16. doi: 10.1186/s40545-019-0177-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Goruntla N, Ssesanga J, Bommireddy BR, Thammisetty DP, Kasturi Vishwanathasetty V, Ezeonwumelu JO, et al. Evaluation of rational drug use based on WHO/INRUD core drug use indicators in a secondary care hospital: A cross-sectional study in Western Uganda. Drug Healthc Patient Saf. 2023;15:125–35. doi: 10.2147/DHPS.S424050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Shanmugapriya S, Saravanan T, Rajee SS, Venkatrajan R, Thomas PM. Drug prescription pattern of outpatients in a tertiary care teaching hospital in Tamil Nadu. Perspect Clin Res. 2018;9:133–8. doi: 10.4103/picr.PICR_86_17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sahoo AK, Gupta D, Dhaneria S, Thangaraju P, Singh A. Retrospective analysis of drug prescription statistics in a tertiary care center in India: Recommendations for promoting prudent utilization of drugs. J Res Pharm Pract. 2020;9:146–50. doi: 10.4103/jrpp.JRPP_20_29. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Chenchula S, Gupta R, Gupta SK, Padmavathi R, Pathan S. Assessment of WHO core drug use indicators at a tertiary care institute of national importance in India. Bioinformation. 2022;18:888–93. doi: 10.6026/97320630018888. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Murray CJ, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. Lancet. 2022;399:629–55. doi: 10.1016/S0140-6736(21)02724-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Gnk G, Manoharan V. Fixed dose combinations banned in India -a review. Int J Pharm Sci Res. 2020;12:987–91. [Google Scholar]