Root Cause Of Errors Research Articles

SemLa: A Visual Analysis System for Fine-Grained Text Classification

Fine-grained text classification requires models to distinguish between many fine-grained classes that are hard to tell apart. However, despite the increased risk of models relying on confounding features and predictions being especially difficult to interpret in this context, existing work on the interpretability of fine-grained text classification is severely limited. Therefore, we introduce our visual analysis system, SemLa, which incorporates novel visualization techniques that are tailored to this challenge. Our evaluation based on case studies and expert feedback shows that SemLa can be a powerful tool for identifying model weaknesses, making decisions about data annotation, and understanding the root cause of errors.

Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering

Deep neural networks (DNNs) have demonstrated superior performance over classical machine learning to support many features in safety-critical systems. Although DNNs are now widely used in such systems (e.g., self driving cars), there is limited progress regarding automated support for functional safety analysis in DNN-based systems. For example, the identification of root causes of errors, to enable both risk analysis and DNN retraining, remains an open problem. In this article, we propose SAFE, a black-box approach to automatically characterize the root causes of DNN errors. SAFE relies on a transfer learning model pre-trained on ImageNet to extract the features from error-inducing images. It then applies a density-based clustering algorithm to detect arbitrary shaped clusters of images modeling plausible causes of error. Last, clusters are used to effectively retrain and improve the DNN. The black-box nature of SAFE is motivated by our objective not to require changes or even access to the DNN internals to facilitate adoption. Experimental results show the superior ability of SAFE in identifying different root causes of DNN errors based on case studies in the automotive domain. It also yields significant improvements in DNN accuracy after retraining, while saving significant execution time and memory when compared to alternatives.

Human-centric zero-defect manufacturing: State-of-the-art review, perspectives, and challenges

Zero defect manufacturing (ZDM) aims at eliminating defects throughout the value stream as well as the cost of rework and scrap. The ambitious goal of zero defects requires the extensive utilization of emerging technologies. Amidst the major drive for technological advancement, humans are often kept out of the loop because they are perceived as the root cause of error. The report from the European Commission on Industry 5.0 emphasizes that human-centric is a key pillar in building a more resilient industry and is vital to incorporate the human component into the manufacturing sector. However, we did not find any publications that explain what human-centric ZDM is, nor what the roles of humans are in advancing ZDM. As a contribution to bridging this gap, a systematic literature review is conducted using different databases. We collected 36 publications and categorised them into 3 different human roles which are managers, engineers, and operators. From our search, we found out that managers play a vital role in cultivating ZDM in the entire organization to prevent errors despite the fact they often do not have direct contact with the production line as operators. Operators can help advance ZDM through knowledge capturing with feedback functions to the engineer to better design a corrective action to prevent errors. Assistive technologies such as extended reality are efficient tools used by operators to eliminate human errors in production environments. Human-centric is now a goal in the future manufacturing sector, but it could face barriers such as high technological investments and resistance to changes in their work tasks. This paper can contribute to paving the roadmap of human-centric ZDM to bring defects to zero and reposition the manufacturing sector to become more resilient.

Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering

Deep neural networks (DNNs) have demonstrated superior performance over classical machine learning to support many features in safety-critical systems. Although DNNs are now widely used in such systems (e.g., self driving cars), there is limited progress regarding automated support for functional safety analysis in DNN-based systems. For example, the identification of root causes of errors, to enable both risk analysis and DNN retraining, remains an open problem. In this paper, we propose SAFE, a black-box approach to automatically characterize the root causes of DNN errors. SAFE relies on a transfer learning model pre-trained on ImageNet to extract the features from error-inducing images. It then applies a density-based clustering algorithm to detect arbitrary shaped clusters of images modeling plausible causes of error. Last, clusters are used to effectively retrain and improve the DNN. The black-box nature of SAFE is motivated by our objective not to require changes or even access to the DNN internals to facilitate adoption. Experimental results show the superior ability of SAFE in identifying different root causes of DNN errors based on case studies in the automotive domain. It also yields significant improvements in DNN accuracy after retraining, while saving significant execution time and memory when compared to alternatives.

An Analytical Two-Machine Equivalent Method of DFIG-Based Wind Power Plants Considering Complete FRT Processes

In this paper, an analytical two-machine equivalent method of DFIG-based wind power plants (WPPs) is proposed. Through simulations of a 49.5 MW WPP, the root causes of errors of the single-machine equivalent are identified. Taking a four-machine WPP as an example, analytical expressions of fault-ride through (FRT) behaviors of the WPP model and its single-machine equivalent model are derived, and root causes of errors are revealed in theory including: inconsistent transient power behaviors in the initial FRT stage; unequal quasi-steady-state active power during a fault; and mismatching initial points and ramp rates of active power after the fault clearance. To eliminate equivalent errors, an improved equivalent method is proposed with two steps: 1) Group wind turbines into two clusters by the crowbar actions and represent them as two equivalent machines; 2) Correct their controller reference settings based on the derived FRT behavior expressions of the detailed WPP. Simulation results verify that the proposed method can accurately reproduce FRT behaviors of the WPP under various wind scenarios and voltage dips.

Energy Metering Integrated Circuit Behavior beyond Standards Requirements

During the last few years, the accuracy of static electricity meters (SEM) has been questioned. Significant metering deviations with respect to a reference meter have been observed at customer premises, and laboratory experimental tests results support such findings. The root cause of such errors remains unknown, as there are multiple elements that could affect the accuracy of electricity meters. Furthermore, standard compliant meters exposed to distorted signals may produce negligible, positive or negative relative error depending on the instrument design. Distorted current signals with fast amplitude transitions have produced the highest error in SEMs reported in the literature. In this paper, the accuracy of an energy metering Integrated Circuit (IC) is evaluated beyond the limits of the standards requirements employing a selection of distorted signals from the standards, real-world captured signals and a set of waveforms designed to test the IC under fast changing currents conditions, which are representative of the waveforms resulting from power electronic devices. The experimental results reveal an accuracy boundary imposed by Gibb’s phenomenon for fast changing current signals and a strong relationship between the IC’s measurement error and two key parameters of the measured waveform: signal slope and phase angle. This paper therefore provides a methodology for the comprehensive analysis of SEMs in future power systems which are dominated with power electronic-controlled electrical demand and contributes to the search for the root cause of error in SEMs exposed to distorted waveforms.

Retrospective study on performance of constancy check device in Linac beam monitoring using Statistical Process Control.

To examine the application of Statistical Process Control (SPC) and Ishikawa diagrams for retrospective evaluation of machine Quality Assurance (QA) performance in radiotherapy. SPC is a popular method for supplementing the performance of QA techniques in healthcare. This work investigates the applicability of SPC techniques and Ishikawa charts in machine QA. SPC has been applied to recommend QA limits on the particular beam parameters using the QUICKCHECK webline QA portable constancy check device for 6MV and 10MV flattened photon beams from the Elekta Versa HD linear accelerator (Linac). Four machine QA parameters - beam flatness, beam symmetry along gun target direction and left-right direction, and beam quality factor (BQF) - were selected for retrospective analysis. Shewhart charts, Exponentially Weighted Moving Average (EWMA) charts and Cumulative Sum (CUSUM) charts were obtained for each parameter. The root causes for a failure in machine QA were broken down into an Ishikawa diagram enabling the user to identify the root cause of error and rectify the problem subsequently. Shewhart charts and EWMA charts applied could detect loss in control in one variable in the 6MV beams and in all four variables in 10MV beams. CUSUM charts detected offsets in the readings. The Ishikawa chart exhaustively included the possible errors that lead to loss of control. SPC is proven to be effective for detection of loss in control in machine QA. The Ishikawa chart provides the set of probable root causes of machine error useful while troubleshooting.

Failure in Residency Education: Lessons Learned From Harry Potter, Oprah Winfrey, and the Marigold Hotel.

Failure in Residency Education: Lessons Learned From Harry Potter, Oprah Winfrey, and the Marigold Hotel.

Gulf of Mexico Seafood Harvesters: Part 1. Occupational Injury and Fatigue Risk Factors

During 2000–2009, 116 Gulf of Mexico (GoM) fishers were killed (23% of the US total) while working in the shrimp, finfish, oyster, clam, and crab fisheries. The purpose of this literature review is to identify injury-related risk factors to better assess the frequency and severity of injuries experienced by fish harvesters in the GoM. Methods: The method of this study is a comprehensive narrative literature review of findings useful for the prevention of fatal and non-fatal injuries among GoM fish harvesters published since 2005. Search engine terms were used to identify relevant literature that included fatalities, injuries, fatigue, and several other terms in combination (e.g., string search with “fishing”). Results: We reviewed 48 articles; the most common cause of fish harvester deaths in the GoM is falls overboard with scant use of personal flotation devices and vessel disasters in which flooding and collision were the most lethal. The root cause of errors resulting in many disasters may have been operator fatigue, but fatigue is also an adverse health effect resulting from working conditions. Non-fatal injuries arise from multiple sources that include working with gears, slips and trips, struck-by or against objects, machine or line entanglements, and falls. Conclusion: Principal risk factors are a lack of sleep aboard fishing vessels, vessel flooding and collisions, poor weather, slips on deck, contact with gear, not wearing personal flotation devices, poor swimming ability, and fishing alone on a vessel or the deck.

Reduction of Inpatient Hospital Length of Stay in Lumbar Fusion Patients With Implementation of an Evidence-Based Clinical Care Pathway.

Quality improvement with before and after evaluation of the intervention. To improve lumbar spine postoperative care and quality outcomes through a series of Lean quality improvement events designed to address root causes of error and variation. Lumbar spine fusion procedures are common, but highly variable in process of care, outcomes, and cost. We implemented a standardized lumbar spine fusion clinical care pathway through a series of Lean quality improvement events. The pathway included an evidence-based electronic order set; a patient visual tool; and multidisciplinary communication, and was designed to delineate expectations for patients, staff, and providers. To evaluate the effectiveness of the intervention, we performed a quality improvement study with before and after evaluation of consecutive patients from January 2012 to September 2014. Outcomes were hospital length of stay and quality measures before and after the April 1, 2013 intervention. Data were analyzed with chi-square and t tests for before and after comparisons, and were explored graphically for temporal trends with statistical process control charts. Our study population was 458 patients (mean 65 years, 65% women). Length of stay decreased from 3.9 to 3.4 days, a difference of 0.5 days (CI 0.3, 0.8, P < 0.001). Discharge disposition also improved with 75% (183/244) being discharged to home postintervention versus 64% (136/214) preintervention (P = 0.002). Urinary catheter removal also improved (P = 0.003). Patient satisfaction scores were not significantly changed. Applying Lean methods to produce standardized clinical pathways is an effective way of improving quality and reducing waste for lumbar spine fusion patients. We believe that quality improvements of this type are valuable for all spine patients, to provide best care outcomes at lowest cost. 4.

P101 How to do more with less in the molecular HLA laboratory?

Aim Providing molecular HLA services with increased volumes and no additional staff, while maintaining or improving the quality of output. Methods A contracted LEAN consultant created an in-house team to study the molecular HLA lab workflow, consisting of evaluating sample volumes, error rates, and staffing levels. The team then implemented the following: Phase one was to cross train staff to perform all lab functions as cross training was viewed to offer flexibility in all aspects of the workload and process. Phase two was to determine the most efficient workflow. Standard Work, Single-Piece-Flow, along with standard batches versus Right-Size-Batches were studied to evaluate the efficacy of each. Phase three studied the error rates and re-work rates for all processes to determine root cause of errors, to include evaluating staff errors and the need for automation and/or upgrading technology. Phase four consisted of the design of a visual, posted Production Board, to include process timing, task assignments, and daily metrics including sample volumes and individual and collective staff output. Results Even as the volumes continue to increase, the implementation of the changes resulting from the points above have shown a substantial impact, including: Error rate reductions. For example, the decrease in the pre-analytical error rate from 7.9% to 3.2%. Turn-Around-Time (TAT) reductions. For example, the TAT for Chimerism testing has decreased by half, even as the volume has increased by almost 300%. Increased cross training. Allows for greater flow of ideas on efficiency and cooperation in response to higher volumes. Increased team work and staff morale. Better communication via the production board has increased job satisfaction. Improvements in automation and technology. Data gathered helped obtain new equipment and software. Improvements in services offered. Data on the bone marrow program showed that SBT would not offer the resources needed to meet our TAT goals. The decision was made to validate and implement NGS as our method of high resolution typing. Conclusions As the lab continues to grow and adjust, efficiency, staff morale, and client satisfaction continue to improve as a result of the LEAN implementations. Now, areas of concern are considerably easier to define and problem-solve.

Exploration of the barriers of reporting nursing errors in intensive care units: A qualitative study.

The aim of this study was to explore the barriers to reporting nursing errors in intensive care units in Iranian hospitals. A descriptive qualitative analysis design was used. The data were collected through in-depth semi-structured interviews with a purposive sample of 16 nurses working in four general intensive care units in Kurdistan province, Iran. Interviews were transcribed and finally analysed through conventional content analysis. There are four major barriers to the reporting of errors by nurses working in Iranian critical care units: (a) saving professional reputation and preventing stigma; (b) fear of consequences - punishment, legal problems and organisational misconduct; (c) feelings of insecurity - pointing a finger at nurses and lack of managerial support and (d) not investigating the root cause of error. The findings revealed the need to support and provide security to nurses and to consider and find the cause of error occurrence. Managers must provide the required personal, professional and legal support for nurses to encourage them to effectively report errors, discover the root cause of errors and take measures to prevent them.

Tracking And Positioning Of Mobile Systems In Telecommunication Networks

Mobile phone tracking refers to the attaining of the current position of a mobile phone, stationary or moving. With the growing needs technology has also modulated itself to meet the demands of the people. Navigation is one among the trending technology in which people are more concerned about their current position. Some of the location based technologies that need a great attention are locating stolen mobiles, emergency calls, and different billing services. All of these location based services need a more reliable methods for implementing them in real world. The major problem is to create technologies that result in yielding good performance is when path between transmitter and receiver is blocked and electrical pulses whose frequency similar to transmitted frequency often causes interference. This is the Non-Line-Of-Sight (NLOS) problem, and it is the root cause of error that results in showing up the position of mobile far away from actual point.

Medical Errors: Why Now and What's Next?

It is said in many cultures that err is human. This was strongly reflected in Prophet Mohammed’s (peace be upon him) hadith which said: All children of Adam can make mistakes, and the most gracious of them are the ones who repent from their mistakes. This hadith reflects a basic physical, mental, and emotional concept that faces human beings: error. Such a concept is also applicable to healthcare services, given the inherent limitations of human performance in terms of power, ability to deal effectively with large amounts of information and memory, and the complicated nature of healthcare systems. Interest in medical errors by all parties involved in healthcare services delivery has only gained momentum the last 15–20 years. This has led to changes in dealing with medical errors from a see nothing, do nothing approach to having an open discussion and investigation in order to identify the root cause of errors and search for preventive mechanisms.1 Such changes are attributed to various reasons. The first reason is the improved educational level of patients who have an increased awareness of their rights and responsibilities within the healthcare system, as well as increased expectations of receiving good quality care with secure safety. As a result, many healthcare systems consider patients a valuable source of information as they bring new ideas to the consultation room.2 Some systems have gone even further by including patients’ views in their planning.3 This in mind, the Directorate General of Health and Consumer Protection in Europe launched a Eurobarometer survey on the perception of medical errors in the 25 Member States and in the pre-accession and candidate countries and the Turkish Cypriot Community in 2005.4 The study found that almost four in five (78%) European Union citizens classify medical errors as an important problem in their country. The study also found out that 38% and 40% of respondents rank the issue as very and fairly important, respectively. Similarly, a study assessing community perception of medical errors in Oman showed that 79% of the respondents believed they knew what was meant by medical errors.5 Of these, 35% were related to wrong prescribing of medication while 29% were related to wrong diagnosis. The second reason for such high attention is the poor quality of care with high incidences of medical errors as shown by many studies.6,7 The third reason is the change in disease trends that has been witnessed by many healthcare systems from dealing with acute, mostly infectious diseases, which usually require few visits to the healthcare facility, to chronic diseases that require regular and frequent visits. The chronic nature of these illnesses exposes patients to multiple investigations and medications that ultimately carry the risk of exposure to errors or at least side effects or complications.8 The fourth reason is the hazardous and complicated nature of the healthcare system, compared to other industrial systems.9 For example, delivering a single dose of medication is the end-result of a complicated process that involves 10–15 steps10; each step carries its own chance of error by the staff carrying out that step with higher chances of committing an error as the number of steps increases, thus placing patients at more risk.11 Such nature has prompted many professional bodies to create solid sets of activities that assure patients safety.12 The fifth reason is the role of media in bringing medical errors to light and spreading stories of adverse events to community members.13 However, community members may get misled by some of the stories they read or hear. Furthermore, people may leave the consultation without being made aware of either their diagnosis or their management plan. These may lead community members to misinterpret expected side effects or complications as medical errors.5 Eventually, the reasons outlined play a major role in bringing patient safety to the frontline of discussions by all those involved in healthcare services delivery in Oman.14 However, there remains areas that needs to be considered in order to have an error-proof system, such as developing capacity building programs for healthcare professionals on patients safety as well as developing a supportive patient safety culture within our organizations.15

Root Causes of Errors in a Simulated Prehospital Pediatric Emergency

Systematic evaluation of prehospital provider performance during actual resuscitations is difficult. Although prior studies reported pediatric drug-dosing mistakes and other types of management errors, the underlying causes of those errors were not investigated. The objective of this study was to identify causes of errors during a simulated, prehospital pediatric emergency. Two-person emergency medical services (EMS) crews from five geographically diverse agencies participated in a validated simulation of an infant with altered mental status, seizures, and respiratory arrest using their own equipment and drugs. A scoring protocol was used to identify errors. A debriefing conducted by a trained facilitator immediately after the simulated event elicited root causes of active and latent errors, which were analyzed by thematic qualitative assessment methods. Forty-five crews completed the study. Clinically important themes that emerged from the data included oxygen delivery, equipment organization and use, glucose measurement, drug administration, and inappropriate cardiopulmonary resuscitation. Delay in delivery of supplemental oxygen resulted from two different automaticity errors and a 54% failure rate in using an oropharyngeal airway (OPA). Most crews struggled to locate essential pediatric equipment. Three found broken or inoperable bag/valve/masks (BVMs), resulting in delayed ventilation. Some mistrusted their intraosseous (IO) injection gun device; others used it incorrectly. Only 51% of crews measured blood glucose; some discovered that glucometers were not stored in their sealed pediatric bags. The error rate for diazepam dosing was 47%; for midazolam, it was 60%. Underlying causes of dosing errors were found in four domains (cognitive, procedural, affective, and teamwork), and they included incorrect estimates of weight, incorrect use of the Broselow pediatric emergency tape, faulty recollection of doses, difficulty with calculations under stress, mg/kg to mg to mL conversion errors, inaccurate measurement of volumes, use of the wrong end of prefilled syringes, and failure to crosscheck doses with partners. Simulation, followed immediately by facilitated debriefing, uncovered underlying causes of active cognitive, procedural, affective, and teamwork errors, latent errors, and error-producing conditions in EMS pediatric care.

Human Factors Engineering in the Department of Veterans Affairs: Operations and Research Initiatives Related to Healthcare Information Technology and Medical Devices

The Department of Veterans Affairs (VA) health care system currently includes 152 medical centers, with at least one in each state, Puerto Rico, and the District of Columbia. VA operates more than 1,400 sites of care, including 909 ambulatory care and community-based outpatient clinics, 135 community living centers (nursing homes), 47 residential rehabilitation treatment programs, 232 Veterans Centers and 108 comprehensive home-care programs. In 2010, the system supported 75.6 million outpatient visits and 679,000 inpatient admissions.1, 2In 1999, the VA National Center for Patient Safety (NCPS) was established to lead the VA’s patient safety efforts and to develop a culture of healthcare safety throughout the Veterans Health Administration. The NCPS program promotes the use of human factors engineering methods that focus on how users interact with technology. Within the Department of Veterans Affairs various organizations have expanded the use of human factors engineering methods as a key element in addressing patient safety from a systems-based perspective. These entities include a range of groups that work in operational and research domains to identify and mitigate root causes of error with traditional medical devices and healthcare information technology to reduce the likelihood of patient harm while continuing to enhance and advance the design of healthcare tools and environments.The expertise of the panel members includes human factors and biomedical engineering, cognitive psychology, information science, healthcare information technology and informatics, and clinical knowledge of medical technology and nursing. Each panelist will briefly introduce the organization they work in, provide an overview of their human factors activities, and briefly describe example(s) of specific projects, with emphasis on the benefit or lessons learned via these activities. Attendees will learn strategies to apply human factors engineering in healthcare and deepen their understanding of human performance challenges in this domain.

From Counterexamples to Incremental Interactive Tracing of Errors

Abstract This article summarizes the goals and the first results of the project CITE — from Counterexamples to Interactive Incremental Tracing of Errors — at the National Institute of Informatics (NII) in Tokyo, Japan. CITE aims at developing the fundamental methods for generating both comprehensive and comprehensible error reports based on model checking results. Model checking is a powerful and efficient method for finding flaws in hardware designs, object-oriented software, business processes, and hypermedia applications. One remaining major obstacle to a broader application of model checking is its limited usability for non-experts. It requires much effort and insight to determine the root cause of errors from counterexamples generated by model checkers in the case of a specification violation. The CITE project addresses this problem by proposing tree structured error reports that can be refined successively to the desired level of detail according to the user´s interest. First evaluations demonstrate that the proposed approach reveals more errors and explains the cause of errors more accurately than the counterexamples of existing model checkers.

Reducing errors in the development, maintenance and utilisation of ontologies

Ontologies and ontology-based information systems are becoming more commonplace in knowledge management. For engineering applications such as product design, ontologies can be utilised for knowledge capture/reuse and frameworks that allow for the integration and collaboration of a wide variety of tools and methods as well as participants in design (marketing/sales, engineers, customers, suppliers, distributors, manufacturing, etc.) who may be distributed globally across time, location, and culture. With this growth in the use of ontologies, it is critical to recognise and address errors that may occur in their representation, maintenance and utilisation. Passing undetected and unresolved errors downstream can cause error avalanche and could diminish the acceptance, further development and promise of significant impact that ontologies hold for product design, manufacturing, or any knowledge management environment within an organisation. This paper categorises errors and their causal factors, summarises possible solutions in ontology and ontology-based utilisation, and puts forward an ontology-based Root Cause Analysis (RCA) method to help find the root cause of errors. Error identification and collection methods are described first, followed by an error taxonomy with associated causal factors. Finally, an error ontology and associated SWRL (Semantic Web Rule Language) rules are built to facilitate the error taxonomy, the root cause analysis and solution analysis for these errors. Ultimately, this work should reduce errors in the development, maintenance and utilisation of ontologies and facilitate further development and use of ontologies in knowledge management.

An Evaluation of the Relationship Between the Implementation of a Newly Designed Prescription Drug Label at Target Pharmacies and Health Outcomes

Medication errors represent a major public health concern, and inadequate prescription drug labels have been identified as a root cause of errors. A new prescription medication labeling system was implemented by Target pharmacies in May 2005 and aimed to improve health outcomes. To evaluate whether the new Target label influenced patient health services utilization. Derived from 2 large health plans. RESEARCH DESIGN AND MEASURES: Using administrative claims, we identified patients with 1 of 9 chronic diseases who filled prescriptions at Target pharmacies and a matched sample who filled prescriptions at other community pharmacies. We stratified our cohort into new and prevalent medication users and evaluated the impact of the Target label on outpatient, emergency department and inpatient health services use. We used linear regression and segmented linear regression to evaluate the new-user and prevalent-user analyses, respectively. Our sample included 23,745 Target pharmacy users and 162,369 matched non-Target pharmacy users. In the new-user analysis, we found no significant change in rates of both outpatient (event rate ratio: 0.53; 95% CI: 0.15-1.86) and inpatient and emergency department (Event rate ratio: 0.88; 95% CI: 0.62-1.24) health services utilization in Target users after implementation when compared with non-Target users. Similarly, in the prevalent user analysis, we found no change in the level or slope of outpatient or emergency/inpatient services in Target users after implementation of the new label when compared with non-Target users. We found no statistically significant change in health services use attributable to the implementation of the new prescription drug label at Target pharmacies. These findings highlight the challenge of influencing health outcomes with interventions to improve health literacy.

Medication label improvement: An issue at the intersection of health literacy and patient safety

Medication errors are the most common form of mistakes that lead to patient injury, hospitalisation and death. A large proportion of outpatient medication errors occur as a result of patients not administering a medicine as intended. Poor patient understanding of unclear and inconsistent medication labelling is a likely root cause of error. Improving the readability and understanding of instructions and supplementary information for prescription drugs is necessary to stimulate appropriate and safe medication use among patients. Standardised, evidence-based medication labelling is needed. Health literacy and language concordance should be considered in designing an enhanced medication label. Efforts must be taken to improve medication labelling as an important step towards ensuring equity in patient access to clear, concise and consistent prescription medication information.