Enhancing Prescription Practices and Mitigating Medication Errors through Prescription Audits: A Quality Improvement Initiative at a Tertiary Healthcare Facility

Enhancing Prescription Practices and Mitigating Medication Errors through Prescription Audits: A Quality Improvement Initiative at a Tertiary Healthcare Facility | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Prescription Practices and Mitigating Medication Errors through Prescription Audits: A Quality Improvement Initiative at a Tertiary Healthcare Facility Afra Moideen, Somu G, Vishnu Sunil, Harshavardhan Sai Sadineni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7252469/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Errors in prescribing medications are a considerable preventable harm in healthcare. Handwritten prescriptions are still widely used in resource-limited settings, which further increases the risk of errors in documentation. Such errors can be identified, quantified and corrected using regular prescription audits, thus optimising patient care and medication use safety. Methods: A cross-sectional audit of 3,120 inpatient and 980 outpatient prescriptions was conducted using a mixed-methods approach over six months across six departments in a tertiary healthcare facility in South India. All the prescriptions were assessed using a pre-validated 15-parameter checklist, including prescriber identification, formatting standards, timing, dosage, and drug interaction risks. Variation in error trends over time was visualised by constructing Statistical Process Control (SPC) charts. Results: Among the inpatient prescriptions, departments such as Neurology (92.2%) and Medicine (59%) recorded the highest error rates. The most frequent inpatient errors were omission of prescriber identity (60%), missing timing of medication (28%), and formatting inconsistencies (6%). Outpatient prescriptions had been compared for month-wise compliance variation, with key issues being missing prescriber details (23%), use of non-standard abbreviations (19%), and potential drug interactions (14%). The SPC charts do not merely reflect isolated incidents but also prove persistent, statistically significant deviations. Conclusion: Prescription safety is not entirely a matter of clinical knowledge but also system design. Our study shows that various patterns can be observed distinctly and reflect vulnerabilities specific to the institution and cannot be addressed by individual behaviour vigilance. Prescription audits must also evolve from being retrospective tools to becoming real-time components of quality assessment. Hospital Medicine Public Administration Prescription Audit Medication Errors Patient Safety Statistical Process Control Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Errors in prescribing and dispensing medication in healthcare pose significant concerns worldwide, causing potential consequences to the overall quality of health, particularly patient safety and financial resources. From previous literature, prescription errors contribute to the deaths of 7,000–9,000 individuals annually, in the United States alone, while millions more experience medication-related side effects or other adverse events, which are often underreported. The impact on individuals and the healthcare system can be shown by the economic burden surpassing USD 40 billion annually for treating patients with medication errors.( 1 ) Primarily occurring during the prescribing process, medication errors contain a spectrum of mistakes, ranging from incorrect prescriptions to dosage, route, or frequency errors. An alarming percentage of 80% of medication errors are preventable, according to the World Health Organisation (WHO) report, hence the urgent need for systematic interventions to mitigate these risks. ( 2 ) There is extensive research and literature on medication errors and drug safety; however, the full extent of pharmaceutical errors still needs to be addressed and discussed, as documented cases represent only a fraction of the actual occurrences. The healthcare industry uses various strategies to evaluate and enhance patient care quality, making auditing a vital tool. A prescription audit is a systematic review process to assess and improve prescription practices to optimise patient care outcomes. Central to this process is the prescription, a written medical order issued by a registered practitioner that authorises patient treatment. The components of an ideal prescription include the medication's generic name, dosage, route of administration, and timing, along with the prescriber's signature, date, and medical registration number. ( 3 ) While considering the importance of prescriptions in patient care, errors in writing remain prevalent, especially during prescribing and dispensing in outpatient settings, where many still go unidentified. The errors tend to occur in various stages of the complete process, from medication selection to administration and monitoring, posing significant risks to patient safety. Recognising the potential for technology to reduce and tackle these risks, interventions such as computerised physician order entry (CPOE) have demonstrated considerable efficacy, reducing medication errors by 55–80%. ( 4 ) Prescription errors can occur in different forms, including illegible handwriting, drug interactions, incorrect dosages, and incomplete orders, with the majority of total medication errors being prescribing faults. Therefore, prescription auditing becomes a key step and strategy to promote rational drug use. Audits help identify errors and deficiencies by reviewing healthcare procedures and documentation and evaluating them against current accepted standards. This enables targeted interventions to enhance prescription quality and ultimately improve patient care. ( 5 ) Through our study, we aim to assess current prescription writing practices using a detailed prescription audit in a tertiary care hospital's inpatient and outpatient settings in South India. It will help determine areas for improvement, propose actionable interventions and ultimately optimise medication use safety. Methodology The study assesses the overall quality of prescription practices at a tertiary care teaching hospital in South India, with the objectives: To analyse the existing current practices in both inpatient and outpatient settings To identify common errors using a standardised assessment tool To formulate strategies for the mitigation of these errors and improving adherence to current prescription standards A mixed-methods approach was used over a six-month period, between February 2021 and June 2021. Both qualitative prospective and retrospective observational analyses were employed to conduct prescription audits in inpatient (IP) and outpatient (OP) settings, respectively. A total of 3,120 inpatient prescriptions across six departments (Medicine, Surgery, Neurology, Psychiatry, Cardiology, and Dermatology) were evaluated using a retrospective review. Simultaneously, 980 outpatient prescriptions were audited prospectively over the same period. Prescriptions were selected through a simple random sampling process. All the prescriptions were assessed using a pre-validated 15-parameter checklist. The important audit elements include the prescriber's name and registration number, medication name in capital letters, dosage, timing, route of administration, use of standard abbreviations, and signature. Trained personnel reviewed each prescription manually and documented findings to maintain the accuracy and consistency of data across reviewers. The sample size calculation for the OP audit was based on the formula: n=(Z^2×p(1 − p))/d^2​ Where Z is the z-score for 95% confidence, p is the estimated proportion of patients visiting the pharmacy, and d is the precision (set at ± 5%). Ultimately, a sample size of 800 prescriptions was calculated and increased to account for data variability across the different months. Quantitative data was analysed using various components of descriptive statistics, including frequencies, percentages, and central tendency measures to determine compliance rates and identify areas for improvement. Variation in error trends over time was visualised by constructing Statistical Process Control (SPC) charts. Ethical approval: The Institutional Review Board (IRB) provided approval prior to data collection. All prescription data were anonymised to ensure patient confidentiality and privacy. We did not record any patient identifiers, and the inpatient audit was retrospective; therefore, the board granted a waiver of informed consent. Results The study was conducted over the six months from February to July 2021, and a total of 3,120 inpatient (IP) and 980 outpatient (OP) prescriptions were audited across six clinical departments. Statistical Process Control (SPC) charts helped visualise and analyse the variations in prescription compliance across departments, parameters, and periods. In the inpatient audit, disparities in inter-departmental error rates were charted (Figure 1). Neurology proved to show the highest rate of non-compliant prescriptions at 92.2%, owing to the complexity of neuropharmacological regimens often involving multiple medications and precise dosing schedules. Medicine followed with a 59% error rate, Psychiatry with 47.2%, Cardiology with 41.6%, and Surgery with 23.3%. These variations suggest that departments dealing with varied medication regimens had higher error rates. In contrast, Dermatology demonstrated the lowest error rate at 5.8%, which shows a more standardised and protocol-driven prescribing pattern. A parameter-wise analysis of these inpatient errors, shown in Figure 2 and summarised in Table 1, identified missing prescriber identification, comprising the doctor's name or registration number, as the most frequent deviation, present in 60% of the prescriptions, which is required for accountability and follow-up. Also 28% of the prescriptions had issues in the timing of medication administration, potentially posing a risk to accurate administration and continuity of care. Moreover, 6% arise from prescriptions not being written in capital letters, leading to misinterpretations, while another 6% lack the doctor's signature necessary for legal validation. Though not dominant in number, inconsistencies in dosage and incomplete administration instructions were also noted during manual analysis. The inpatient SPC chart (Figure 3) helped in reinforcing these trends, where missing identification (n=840), untimed medication orders (n=388), and formatting inconsistencies (n=85) were the statistically significant outliers. In the outpatient setting, 980 prescriptions were evaluated, and an analysis of compliance over the period was charted and noted to have significant fluctuations (Figure 4). For instance, March and May showed higher levels of adherence to audit parameters, while June and August recorded dips, indicating that the application of standard prescription practices is inconsistent. Figure 5 outlines that 23% of errors stem from missing identifiable information of the prescribing doctor, and another area of issues is that of non-standard or ambiguous abbreviations in 19% of prescriptions. Also, potential drug interactions account for 14%, which provides an area for improvement in clinical vigilance and the potential use of embedded decision-support mechanisms. Incorrect dosage entries, lack of timing documentation, and incomplete usage instructions comprise the other deviations, although they are less frequent. As seen in Figure 6, the SPC analysis of outpatient prescriptions reinforces the omission of doctor details and the use of unsafe abbreviations as consistent errors. This graph did not merely reflect error proportions but also traced their stability over time. Unlike an categorical error breakdown in figure 5, it provides a process-level view of recurring violations. Upon comparing the findings from inpatient and outpatient audits, it was evident that they shared a common vulnerability in missing prescriber identification. However, the nature of the other errors diverged. Timing-related issues were far more prominent in inpatient files, probably due to the operational importance of timestamped medication delivery in wards. On the other hand, the outpatient prescription audit had more risks owing to ambiguous abbreviations and undocumented drug interactions. We tabulated these differences in Table 1. Table 1: Summary of Prescription Errors in Inpatient and Outpatient Audits S.No Error Type Inpatient Prescriptions (n=3120) Outpatient Prescriptions (n=980) 1 Missing doctor identification 60% (n=840) 23% 2 Timing not mentioned 28% (n=388) Included under dosage/timing 3 Non-standard abbreviations Not recorded 19% 4 Drug interactions Not recorded 14% 5 Not in capital letters 6% (n=85) Not significant 6 Missing doctor signature ~6% Included in signature/compliance category 7 Incorrect dosage / incomplete instructions Minor/Unclassified Present but <10% Discussion Comprehensive healthcare audits enhance patient care standards by evaluating current practices against benchmark recommendations and guidelines. (6) In our study, we audited the inpatient and outpatient settings of six clinical departments of a tertiary care teaching hospital in South India, identifying preventable lapses in adherence to prescribing standards. Evaluating over 4,000 prescriptions over six months using a structured checklist and statistical control tools documents systemic errors, interdepartmental variation, and prescribing behaviour. The audit shows that the errors found are not isolated entities but instead rooted systemic issues. The recurrent omission of prescriber identification appeared in 60% of inpatient and 23% of outpatient prescriptions, which is a striking breach of accountability and traceability. While being consistent with prior Indian audits, this was especially evident in high-complexity departments like Neurology and Medicine, suggesting a correlation not only with individual behaviour but also with clinical workload. (7) Unlike traditional audits that offer only prevalence data, SPC chart analysis helps highlight the consistent temporal patterns. (8) The findings that errors like non-standard abbreviations, failure to mention drug strength and time/frequency of administration, particularly in the outpatient setting, give us an overall idea of process level stability. (9) Junior residents, who rotate frequently and contribute to a high proportion of outpatient volume, may lack structured onboarding in prescription standards. (10) Thus, there is also a need for regular periodic prescription audits in training curriculum, feedback cycles, and even clinical appraisal systems. Errors that carry potential clinical implications are of particular concern, potential drug interactions (14%) and non-standard abbreviations (19%), both of which can potentially elevate risk for adverse drug events (ADEs). While these frequencies appear modest compared to those reported in existing literature among electronically audited prescriptions, the key difference lies in the safeguards and different settings. More errors were identified through the real-time pharmacist-driven electronic prescription audit tool integrated with clinical decision support systems (CDSS), which flags interactions and frequency errors at the point of care. Hence, there is a pressing need for standardisation tools, such as structured prescribing templates or integrated drug alert systems.(11) Computerised Physician Order Entry (CPOE) systems used alongside CDSS have been reported to show a 55–80% reduction in medication errors. However, in India, their use remains limited by infrastructural and training barriers. This study further highlights that even partial digitisation, such as auto-filled doctor IDs, mandatory timestamping, and default capitalisation for drugs, can dramatically reduce frequent omissions. Moreover, real-time auditing shows that proactive pharmacist interventions can intercept errors before they reach the patient. (12) Models like the U.S. Prescription Drug Monitoring Program (PDMP) act as a policy blueprint for surveillance internationally, enabling real-time alerts for drug duplication, allergies, and interactions.(13) It has been proven to reduce ADEs significantly in ambulatory care settings. A cost-effective middle ground for India would be a phased adoption strategy with pilot e-prescription tools targeting high-burden departments. The World Health Organisation's "Medication Without Harm" initiative was launched as part of the Global Patient Safety Challenge and promotes three pillars, including medication systems, healthcare professionals, and patients/public. (14) Our study also echoes with the WHO's core prescribing indicators. In the Indian context, from a policy standpoint, we recommend actively embedding WHO-aligned principles into NABH-mandated audits, pharmacovigilance programs, and EMR transitions as strategies that address both technological and behavioural components of prescription safety. We recommend a multi-tiered strategy that includes implementing mandatory digital prescription templates with embedded prescriber identifiers, regular audit and feedback loops, use of banned abbreviation lists, and compulsory continuing medical education (CME) modules on safe prescribing. Visual dashboards would further strengthen compliance by establishing department-wise error tracking. Ultimately, including prescription audits in quality indicators would further strengthen institutional guidelines. Conclusion Prescription safety is not entirely a matter of clinical knowledge but also system design. Our study shows that various patterns can be observed distinctly and reflect vulnerabilities specific to the institution and cannot be addressed by individual behaviour vigilance. A more structured, audit-guided prescription practice would correct errors and further prevent them from occurring. Prescription quality can be improved over time using digital tools, real-time feedback systems, AI integration, and a practice of prescribing that places importance on clarity and accountability. Healthcare systems are evolving, so prescription audits must evolve from being retrospective tools to becoming real-time components of quality assessment. Institutions can then align with standard safety frameworks and promise the delivery of safer, reliable care. Declarations Funding: No external funding was received. Conflicts of Interest: The authors declare that they have no known competing interests. References Wittich CM, Burkle CM, Lanier WL (2014) Medication errors: an overview for clinicians. Mayo Clin Proc. ;89(8):1116–25 Kumar YEP, Rajasekhar GD (2020) A study of prescription auditing in inpatient general medicine in tertiary care government hospital. Int J Res Med Sci 8(11):3979–3982 Kandula PK, Rao SB, Sangeetha K, Reddy YJV, Gudi SK, A STUDY OF PRESCRIPTION, AUDIT IN OUTPATIENT DEPARTMENT OF A TERTIARY CARE TEACHING HOSPITAL IN INDIA (2017) AN OBSERVATIONAL STUDY. J Drug Delivery Ther 7(3):92–97 Shamliyan TA, Duval S, Du J, Kane RL (2008) Just What the Doctor Ordered. Review of the Evidence of the Impact of Computerized Physician Order Entry System on Medication Errors. Health Serv Res 43(1p1):32–53 Velo GP, Minuz P (2009) Medication errors: prescribing faults and prescription errors. Br J Clin Pharmacol 67(6):624–628 Willmington C, Belardi P, Murante AM, Vainieri M (2022) The contribution of benchmarking to quality improvement in healthcare. A systematic literature review. BMC Health Serv Res 22(1):139 Mulkalwar S, Patel A, David S, Pabari K, Math P, Tilak AV (2024) Prescription Audit for WHO Prescribing Indicators and Prescription Errors in a Tertiary Care Teaching Hospital. Med J Dr DY Patil Vidyapeeth 17(2):299 Jr JAS. The Use of Statistical Process Control Charts in Hospital Epidemiology. Infect Control Hosp Epidemiol. (1993) ;14(11):649–656 Evaluation of Medication Errors by Prescription Audit at a Tertiary Care Teaching Hospital - Kaushal, Navadia P, Patel CR, Patel JM Sajal K. Pandya, 2023 [Internet]. [cited 2025 Jul 29]. Available from: https://journals.sagepub.com/doi/full/ 10.1177/0976500X231222689 What is the scale of prescribing errors committed by junior doctors? A systematic review - Ross – 2009 - British Journal of Clinical Pharmacology - Wiley Online Library [Internet]. [cited 2025 Jul 29]. Available from: https://bpspubs.onlinelibrary.wiley.com/doi/full/ 10.1111/j.1365-2125.2008.03330.x Priya K, Thottumkal AV, Warrier AR, Krishna SG, Joseph N Impact of Electronic Prescription Audit Process to Reduce Outpatient Medication Errors. pharmaceutical-sciences [Internet]. 2017 [cited 2025 Jul 29];79(6). Available from: http://www.ijpsonline.com/articles/impact-of-electronic-prescription-audit-process-to-reduce-outpatient-medication-errors-3419.html Kuperman GJ, Gibson RF (2003) Computer Physician Order Entry: Benefits, Costs, and Issues. Ann Intern Med 139(1):31–39 D’Souza RS, Lang M, Eldrige JS Prescription Drug Monitoring Program. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 Jul 29]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK532299/ Donaldson LJ, Kelley ET, Dhingra-Kumar N, Kieny MP, Sheikh A (2017) Medication Without Harm: WHO’s Third Global Patient Safety Challenge. Lancet 389(10080):1680–1681 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7252469","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493156395,"identity":"422cbf78-92d3-43b5-8d63-5b700bb88737","order_by":0,"name":"Afra Moideen","email":"","orcid":"","institution":"Kasturba Medical College, Manipal","correspondingAuthor":false,"prefix":"","firstName":"Afra","middleName":"","lastName":"Moideen","suffix":""},{"id":493156396,"identity":"f1ef54ed-d237-407f-b4a6-7b537033a83c","order_by":1,"name":"Somu G","email":"","orcid":"","institution":"Kasturba Medical College, 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From previous literature, prescription errors contribute to the deaths of 7,000–9,000 individuals annually, in the United States alone, while millions more experience medication-related side effects or other adverse events, which are often underreported. The impact on individuals and the healthcare system can be shown by the economic burden surpassing USD 40\u0026nbsp;billion annually for treating patients with medication errors.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003ePrimarily occurring during the prescribing process, medication errors contain a spectrum of mistakes, ranging from incorrect prescriptions to dosage, route, or frequency errors. An alarming percentage of 80% of medication errors are preventable, according to the World Health Organisation (WHO) report, hence the urgent need for systematic interventions to mitigate these risks. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) There is extensive research and literature on medication errors and drug safety; however, the full extent of pharmaceutical errors still needs to be addressed and discussed, as documented cases represent only a fraction of the actual occurrences.\u003c/p\u003e\u003cp\u003eThe healthcare industry uses various strategies to evaluate and enhance patient care quality, making auditing a vital tool. A prescription audit is a systematic review process to assess and improve prescription practices to optimise patient care outcomes. Central to this process is the prescription, a written medical order issued by a registered practitioner that authorises patient treatment. The components of an ideal prescription include the medication's generic name, dosage, route of administration, and timing, along with the prescriber's signature, date, and medical registration number. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eWhile considering the importance of prescriptions in patient care, errors in writing remain prevalent, especially during prescribing and dispensing in outpatient settings, where many still go unidentified. The errors tend to occur in various stages of the complete process, from medication selection to administration and monitoring, posing significant risks to patient safety. Recognising the potential for technology to reduce and tackle these risks, interventions such as computerised physician order entry (CPOE) have demonstrated considerable efficacy, reducing medication errors by 55–80%. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003ePrescription errors can occur in different forms, including illegible handwriting, drug interactions, incorrect dosages, and incomplete orders, with the majority of total medication errors being prescribing faults. Therefore, prescription auditing becomes a key step and strategy to promote rational drug use. Audits help identify errors and deficiencies by reviewing healthcare procedures and documentation and evaluating them against current accepted standards. This enables targeted interventions to enhance prescription quality and ultimately improve patient care. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThrough our study, we aim to assess current prescription writing practices using a detailed prescription audit in a tertiary care hospital's inpatient and outpatient settings in South India. It will help determine areas for improvement, propose actionable interventions and ultimately optimise medication use safety.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe study assesses the overall quality of prescription practices at a tertiary care teaching hospital in South India, with the objectives:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo analyse the existing current practices in both inpatient and outpatient settings\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo identify common errors using a standardised assessment tool\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo formulate strategies for the mitigation of these errors and improving adherence to current prescription standards\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA mixed-methods approach was used over a six-month period, between February 2021 and June 2021. Both qualitative prospective and retrospective observational analyses were employed to conduct prescription audits in inpatient (IP) and outpatient (OP) settings, respectively.\u003c/p\u003e\u003cp\u003e A total of 3,120 inpatient prescriptions across six departments (Medicine, Surgery, Neurology, Psychiatry, Cardiology, and Dermatology) were evaluated using a retrospective review. Simultaneously, 980 outpatient prescriptions were audited prospectively over the same period. Prescriptions were selected through a simple random sampling process.\u003c/p\u003e\u003cp\u003eAll the prescriptions were assessed using a pre-validated 15-parameter checklist. The important audit elements include the prescriber's name and registration number, medication name in capital letters, dosage, timing, route of administration, use of standard abbreviations, and signature. Trained personnel reviewed each prescription manually and documented findings to maintain the accuracy and consistency of data across reviewers.\u003c/p\u003e\u003cp\u003eThe sample size calculation for the OP audit was based on the formula:\u003c/p\u003e\u003cp\u003en=(Z^2×p(1 − p))/d^2​\u003c/p\u003e\u003cp\u003eWhere Z is the z-score for 95% confidence, p is the estimated proportion of patients visiting the pharmacy, and d is the precision (set at ± 5%). Ultimately, a sample size of 800 prescriptions was calculated and increased to account for data variability across the different months.\u003c/p\u003e\u003cp\u003eQuantitative data was analysed using various components of descriptive statistics, including frequencies, percentages, and central tendency measures to determine compliance rates and identify areas for improvement. Variation in error trends over time was visualised by constructing Statistical Process Control (SPC) charts.\u003c/p\u003e\n\u003cp\u003eEthical approval: The Institutional Review Board (IRB) provided approval prior to data collection. All prescription data were anonymised to ensure patient confidentiality and privacy. We did not record any patient identifiers, and the inpatient audit was retrospective; therefore, the board granted a waiver of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study was conducted over the six months from February to July 2021, and a total of 3,120 inpatient (IP) and 980 outpatient (OP) prescriptions were audited across six clinical departments. Statistical Process Control (SPC) charts helped visualise and analyse the variations in prescription compliance across departments, parameters, and periods.\u003c/p\u003e\n\u003cp\u003eIn the inpatient audit, disparities in inter-departmental error rates were charted (Figure 1). Neurology proved to show the highest rate of non-compliant prescriptions at 92.2%, owing to the complexity of neuropharmacological regimens often involving multiple medications and precise dosing schedules. Medicine followed with a 59% error rate, Psychiatry with 47.2%, Cardiology with 41.6%, and Surgery with 23.3%. These variations suggest that departments dealing with varied medication regimens had higher error rates. In contrast, Dermatology demonstrated the lowest error rate at 5.8%, which shows a more standardised and protocol-driven prescribing pattern.\u003c/p\u003e\n\u003cp\u003eA parameter-wise analysis of these inpatient errors, shown in Figure 2 and summarised in Table 1, identified missing prescriber identification, comprising the doctor\u0026apos;s name or registration number, as the most frequent deviation, present in 60% of the prescriptions, which is required for accountability and follow-up. Also 28% of the prescriptions had issues in the timing of medication administration, potentially posing a risk to accurate administration and continuity of care. Moreover, 6% arise from prescriptions not being written in capital letters, leading to misinterpretations, while another 6% lack the doctor\u0026apos;s signature necessary for legal validation. Though not dominant in number, inconsistencies in dosage and incomplete administration instructions were also noted during manual analysis. The inpatient SPC chart (Figure 3) helped in reinforcing these trends, where missing identification (n=840), untimed medication orders (n=388), and formatting inconsistencies (n=85) were the statistically significant outliers.\u003c/p\u003e\n\u003cp\u003eIn the outpatient setting, 980 prescriptions were evaluated, and an analysis of compliance over the period was charted and noted to have significant fluctuations (Figure 4). For instance, March and May showed higher levels of adherence to audit parameters, while June and August recorded dips, indicating that the application of standard prescription practices is inconsistent. Figure 5 outlines that 23% of errors stem from missing identifiable information of the prescribing doctor, and another area of issues is that of non-standard or ambiguous abbreviations in 19% of prescriptions. Also, potential drug interactions account for 14%, which provides an area for improvement in clinical vigilance and the potential use of embedded decision-support mechanisms. Incorrect dosage entries, lack of timing documentation, and incomplete usage instructions comprise the other deviations, although they are less frequent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs seen in Figure 6, the SPC analysis of outpatient prescriptions reinforces the omission of doctor details and the use of unsafe abbreviations as consistent errors. This graph did not merely reflect error proportions but also traced their stability over time. Unlike an categorical error breakdown in figure 5, it provides a process-level view of recurring violations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUpon comparing the findings from inpatient and outpatient audits, it was evident that they shared a common vulnerability in missing prescriber identification. However, the nature of the other errors diverged. Timing-related issues were far more prominent in inpatient files, probably due to the operational importance of timestamped medication delivery in wards. On the other hand, the outpatient prescription audit had more risks owing to ambiguous abbreviations and undocumented drug interactions. We tabulated these differences in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eTable 1: Summary of Prescription Errors in Inpatient and Outpatient Audits\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.04795%;\"\u003e\n \u003cp\u003eS.No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6164%;\"\u003e\n \u003cp\u003eError Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5685%;\"\u003e\n \u003cp\u003eInpatient Prescriptions (n=3120)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7671%;\"\u003e\n \u003cp\u003eOutpatient Prescriptions (n=980)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.04795%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6164%;\"\u003e\n \u003cp\u003eMissing doctor identification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5685%;\"\u003e\n \u003cp\u003e60% (n=840)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7671%;\"\u003e\n \u003cp\u003e23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.04795%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6164%;\"\u003e\n \u003cp\u003eTiming not mentioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5685%;\"\u003e\n \u003cp\u003e28% (n=388)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7671%;\"\u003e\n \u003cp\u003eIncluded under dosage/timing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.04795%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6164%;\"\u003e\n \u003cp\u003eNon-standard abbreviations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5685%;\"\u003e\n \u003cp\u003eNot recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7671%;\"\u003e\n \u003cp\u003e19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.04795%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6164%;\"\u003e\n \u003cp\u003eDrug interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5685%;\"\u003e\n \u003cp\u003eNot recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7671%;\"\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.04795%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6164%;\"\u003e\n \u003cp\u003eNot in capital letters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5685%;\"\u003e\n \u003cp\u003e6% (n=85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7671%;\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.04795%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6164%;\"\u003e\n \u003cp\u003eMissing doctor signature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5685%;\"\u003e\n \u003cp\u003e~6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7671%;\"\u003e\n \u003cp\u003eIncluded in signature/compliance category\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.04795%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6164%;\"\u003e\n \u003cp\u003eIncorrect dosage / incomplete instructions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.5685%;\"\u003e\n \u003cp\u003eMinor/Unclassified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7671%;\"\u003e\n \u003cp\u003ePresent but \u0026lt;10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eComprehensive healthcare audits enhance patient care standards by evaluating current practices against benchmark recommendations and guidelines. (6) In our study, we audited the inpatient and outpatient settings of six clinical departments of a tertiary care teaching hospital in South India, identifying preventable lapses in adherence to prescribing standards. Evaluating over 4,000 prescriptions over six months using a structured checklist and statistical control tools documents systemic errors, interdepartmental variation, and prescribing behaviour.\u003c/p\u003e\n\u003cp\u003eThe audit shows that the errors found are not isolated entities but instead rooted systemic issues. The recurrent omission of prescriber identification appeared in 60% of inpatient and 23% of outpatient prescriptions, which is a striking breach of accountability and traceability. While being consistent with prior Indian audits, this was especially evident in high-complexity departments like Neurology and Medicine, suggesting a correlation not only with individual behaviour but also with clinical workload. (7)\u003c/p\u003e\n\u003cp\u003eUnlike traditional audits that offer only prevalence data, SPC chart analysis helps highlight the consistent temporal patterns. (8) The findings that errors like non-standard abbreviations, failure to mention drug strength and time/frequency of administration, particularly in the outpatient setting, give us an overall idea of process level stability. (9)\u003c/p\u003e\n\u003cp\u003eJunior residents, who rotate frequently and contribute to a high proportion of outpatient volume, may lack structured onboarding in prescription standards. (10) Thus, there is also a need for regular periodic prescription audits in training curriculum, feedback cycles, and even clinical appraisal systems.\u003c/p\u003e\n\u003cp\u003eErrors that carry potential clinical implications are of particular concern, potential drug interactions (14%) and non-standard abbreviations (19%), both of which can potentially elevate risk for adverse drug events (ADEs). While these frequencies appear modest compared to those reported in existing literature among electronically audited prescriptions, the key difference lies in the safeguards and different settings. More errors were identified through the real-time pharmacist-driven electronic prescription audit tool integrated with clinical decision support systems (CDSS), which flags interactions and frequency errors at the point of care. Hence, there is a pressing need for standardisation tools, such as structured prescribing templates or integrated drug alert systems.(11)\u003c/p\u003e\n\u003cp\u003eComputerised Physician Order Entry (CPOE) systems used alongside CDSS have been reported to show a 55–80% reduction in medication errors. However, in India, their use remains limited by infrastructural and training barriers. This study further highlights that even partial digitisation, such as auto-filled doctor IDs, mandatory timestamping, and default capitalisation for drugs, can dramatically reduce frequent omissions. Moreover, real-time auditing shows that proactive pharmacist interventions can intercept errors before they reach the patient. \u0026nbsp;(12)\u003c/p\u003e\n\u003cp\u003eModels like the U.S. Prescription Drug Monitoring Program (PDMP) act as a policy blueprint for surveillance internationally, enabling real-time alerts for drug duplication, allergies, and interactions.(13) It has been proven to reduce ADEs significantly in ambulatory care settings. A cost-effective middle ground for India would be a phased adoption strategy with pilot e-prescription tools targeting high-burden departments.\u003c/p\u003e\n\u003cp\u003eThe World Health Organisation's \"Medication Without Harm\" initiative was launched as part of the Global Patient Safety Challenge and promotes three pillars, including medication systems, healthcare professionals, and patients/public. (14) Our study also echoes with the WHO's core prescribing indicators. In the Indian context, from a policy standpoint, we recommend actively embedding WHO-aligned principles into NABH-mandated audits, pharmacovigilance programs, and EMR transitions as strategies that address both technological and behavioural components of prescription safety.\u003c/p\u003e\n\u003cp\u003eWe recommend a multi-tiered strategy that includes implementing mandatory digital prescription templates with embedded prescriber identifiers, regular audit and feedback loops, use of banned abbreviation lists, and compulsory continuing medical education (CME) modules on safe prescribing. Visual dashboards would further strengthen compliance by establishing department-wise error tracking. Ultimately, including prescription audits in quality indicators would further strengthen institutional guidelines.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePrescription safety is not entirely a matter of clinical knowledge but also system design. Our study shows that various patterns can be observed distinctly and reflect vulnerabilities specific to the institution and cannot be addressed by individual behaviour vigilance. A more structured, audit-guided prescription practice would correct errors and further prevent them from occurring.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrescription quality can be improved over time using digital tools, real-time feedback systems, AI integration, and a practice of prescribing that places importance on clarity and accountability. Healthcare systems are evolving, so prescription audits must evolve from being retrospective tools to becoming real-time components of quality assessment. Institutions can then align with standard safety frameworks and promise the delivery of safer, reliable care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWittich CM, Burkle CM, Lanier WL (2014) Medication errors: an overview for clinicians. Mayo Clin Proc. ;89(8):1116\u0026ndash;25\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar YEP, Rajasekhar GD (2020) A study of prescription auditing in inpatient general medicine in tertiary care government hospital. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ijpsonline.com/articles/impact-of-electronic-prescription-audit-process-to-reduce-outpatient-medication-errors-3419.html\u003c/span\u003e\u003cspan address=\"http://www.ijpsonline.com/articles/impact-of-electronic-prescription-audit-process-to-reduce-outpatient-medication-errors-3419.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuperman GJ, Gibson RF (2003) Computer Physician Order Entry: Benefits, Costs, and Issues. Ann Intern Med 139(1):31\u0026ndash;39\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD\u0026rsquo;Souza RS, Lang M, Eldrige JS Prescription Drug Monitoring Program. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 Jul 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/books/NBK532299/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/books/NBK532299/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonaldson LJ, Kelley ET, Dhingra-Kumar N, Kieny MP, Sheikh A (2017) Medication Without Harm: WHO\u0026rsquo;s Third Global Patient Safety Challenge. Lancet 389(10080):1680\u0026ndash;1681\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Kasturba Medical College","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prescription Audit, Medication Errors, Patient Safety, Statistical Process Control","lastPublishedDoi":"10.21203/rs.3.rs-7252469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7252469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Errors in prescribing medications are a considerable preventable harm in healthcare. Handwritten prescriptions are still widely used in resource-limited settings, which further increases the risk of errors in documentation. Such errors can be identified, quantified and corrected using regular prescription audits, thus optimising patient care and medication use safety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional audit of 3,120 inpatient and 980 outpatient prescriptions was conducted using a mixed-methods approach over six months across six departments in a tertiary healthcare facility in South India. All the prescriptions were assessed using a pre-validated 15-parameter checklist, including prescriber identification, formatting standards, timing, dosage, and drug interaction risks. Variation in error trends over time was visualised by constructing Statistical Process Control (SPC) charts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among the inpatient prescriptions, departments such as Neurology (92.2%) and Medicine (59%) recorded the highest error rates. The most frequent inpatient errors were omission of prescriber identity (60%), missing timing of medication (28%), and formatting inconsistencies (6%). Outpatient prescriptions had been compared for month-wise compliance variation, with key issues being missing prescriber details (23%), use of non-standard abbreviations (19%), and potential drug interactions (14%). The SPC charts do not merely reflect isolated incidents but also prove persistent, statistically significant deviations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Prescription safety is not entirely a matter of clinical knowledge but also system design. Our study shows that various patterns can be observed distinctly and reflect vulnerabilities specific to the institution and cannot be addressed by individual behaviour vigilance. Prescription audits must also evolve from being retrospective tools to becoming real-time components of quality assessment.\u003c/p\u003e","manuscriptTitle":"Enhancing Prescription Practices and Mitigating Medication Errors through Prescription Audits: A Quality Improvement Initiative at a Tertiary Healthcare Facility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 16:29:29","doi":"10.21203/rs.3.rs-7252469/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"de09c4da-ba73-4482-8287-52285c526300","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52366867,"name":"Hospital Medicine"},{"id":52366868,"name":"Public Administration"}],"tags":[],"updatedAt":"2025-07-31T16:29:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-31 16:29:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7252469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7252469","identity":"rs-7252469","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}