Beyond the silence: Unveiling the governmental and systemic roots of patient safety barriers - Letter on Fekadu et al.

Enhancing clinical safety in radiation oncology: A data-driven approach to risk management

PurposeThe purpose of this paper is to examine quality improvement (QI) initiatives in acute care hospitals, the factors associated with success, and the impacts on patient care and safety.Design/methodology/approachAn extensive online survey was completed by senior managers responsible for QI. The survey assessed QI project types, QI methods, staff engagement, and barriers and factors in the success of QI initiatives.FindingsThe response rate was 37 percent, 46 surveys were completed from 125 acute care hospitals. QI initiatives had positive impacts on patient safety and care. Staff in all hospitals reported conducting past or present hand-hygiene QI projects and C. difficile and surgical site infection were the next most frequent foci. Hospital staff not having time and problems with staff prioritizing QI with other duties were identified as important QI barriers. All respondents reported hospital leadership support, data utilization and internal champions as important QI facilitators. Multiple regression models identified nurses’ active involvement and medical staff engagement in QI with improved patient care and physicians’ active involvement and medical staff engagement with greater patient safety.Practical implicationsThere is the need to study how best to support and encourage physicians and nurses to become more engaged in QI.Originality/valueQI initiatives were shown to have positive impacts on patient safety and patient care and barriers and facilitating factors were identified. The results indicated patient care and safety would benefit from increased physician and nurse engagement in QI initiatives.

Background: Person- and family-centered care is one of the recommendations to achieve quality of care and patient safety. However, many health professionals associate the family with insecurity in care. Objective: To analyze, based on nurses’ statements, the advantages and disadvantages of the family’s presence in hospitals for the safety of hospitalized patients. Methods: This was a qualitative interpretative study based on James Reason’s risk model, conducted through semi-structured interviews with 10 nurses selected by convenience. A content analysis was performed using Bardin’s methodology and MAXQDA Plus 2022 software. Results: We identified 17 categories grouped according to the representation of the family in patient safety: The family as a Potentiator of Security Failures (7) and Family as a Safety Barrier (10). Conclusions: The higher number of categories identified under Family as a Safety Barrier shows that nurses see strong potential in the family’s involvement in patient safety. By identifying the need to intervene with and for families so that their involvement is safe, we observed an increase in the complexity of nursing care, which suggests the need to improve nursing ratios, according to the participants.

Evaluación de la efectividad de un procedimiento de identificación de pacientes con alergia en urgencias pediátricas

Objective: This study aims to evaluate the patient safety attitudes, skills, knowledge and barriers related to reporting a medical error from a nursing perspective in obstetric departments. Martials and Methods: A cross-sectional and descriptive study was conducted on a sample of 200 nurses and midwives. Patient safety attitudes, skills, and knowledge (PS-ASK) Scale was used to collect data from nurses. Results: Nurses had good knowledge and a positive attitude toward patient safety. However, the participants had higher scores in attitude than knowledge and skills. No significant difference was found between nurses and midwives regarding patient safety knowledge, attitude, and skills (p> 0.05). There are significant positive relationships between nurses' knowledge and a variety of safety attitude and skills (p < 0.05). The top three errors reported were: error during medication preparation and administration, failing to regularly monitoring of fetus's heart rate, and patient falls from beds or while walking without adequate supervision. Conclusion: Patient safety is considered one of the important areas in the health care industry. In the current study, the overall safety knowledge and skills could be described as good but not sufficient. This could be attributed to a lack of sufficient training and education. Reporting errors is still a problem due to fear of consequences.

It is recognized that older adults and people with disabilities are more vulnerable and face significant obstacles in their health care. The panorama of patient safety incidents, the barriers these populations encounter in their health care, and the contexts in which they occur need clarification. This study aimed to identify, in the scientific literature, the types of patient safety incidents, the barriers that are most reported in the health care process for older adults and people with disabilities, and the environments where they are most reported. A scoping literature review was carried out using Scopus and PubMed. Search word categories were patient safety terms, barrier terms, and population terms. Twenty-seven articles focused on safety incidents, 16 reported barriers, and 7 reported on both. Medication incidents were the most common incidents reported in both populations. However, reported barriers differed between populations. These populations face various factors that can affect their health care processes. The information available on patient safety and barriers for older adults and people with disabilities must be deepened and expanded.

Nursing students' perceptions of patient safety culture and barriers to reporting medication errors: A cross-sectional study.

Introduction: Health care system structure is prone to human error. Medical errors are one of the major challenges that health systems in all countries are grappling with to minimize and reduce the damage caused by them. The aim of this study was to assess the Patient Safety Attitudes, Skills, Knowledge and Barriers Related to Reporting Medical Errors by Nursing Students in Ilam, Iran. Methods: A cross-sectional mixed method was conducted to this study. Sampling was conducted by census of all students entering nursing criteria in Ilam in 2016. A number of 140 students participated in this study. The tool used in this study was created by Schnall et al. that measures knowledge, attitudes and skills related to medical errors reporting. Data were analyzed with t test, regression and correlation coefficients and descriptive statistical methods. Results: The results showed that nursing students had a positive attitude with respect to the reporting of medical errors (p = 0.01). They also have the low knowledge to medical errors and reporting them. There were significant differences in all groups and subgroups of knowledge, attitude, and skills (except creating of safety culture subgroup) between the two group’s students. Moreover, the main reason for not reporting was the lack of knowledge and fear of punishment. Conclusions: The results of this study help those who involve in the health care system to improve patient safety and improve the process of reporting medical errors by nursing students’ participation in the process of reporting error, while improving knowledge and attitudes through nursing education with the effective educational models. As a result, there is a need to educate students on reporting systems.

Medicine label design plays an important role in improving patient safety. This study aimed at identifying facilitators and barriers in a medicine label system to prevent medication errors in clinical use by health care professionals. The study design is qualitative and exploratory, with a convenience sample of 10 nurses and 10 physicians from different acute care specialties working in hospitals in the Capital Region of Denmark. In 2 patient simulation scenarios and a sorting task, the participants selected the medicines from a range of ampules, vials, and infusion bags. After each scenario and in the end of the study, the participants were interviewed. Notes were validated with the participants, and content was analyzed. The label design benefited from the standardized construction of the labels, the clear layout and font, and some warning signs. The complexity of the system and some inconsistencies (different meaning of colors) posed challenges, when considered with the actual application context, in which there is little time to get familiar with the design features. For optimizing medicine labels and obtaining the full benefit of label design features on patient safety, it is necessary to consider the context in which they are used.

Research into the distribution of medication errors usually focuses on isolated stages within the medication use process. Our study aimed to provide a novel process-oriented approach to medication incident analysis focusing on medication error chains. Our study was conducted across a 900-bed teaching hospital in Switzerland. All reported 1,591 medication errors 2009-2012 were categorized using the Medication Error Index NCC MERP and the WHO Classification for Patient Safety Methodology. In order to identify medication error chains, each reported medication incident was allocated to the relevant stage of the hospital medication use process. Only 25.8% of the reported medication errors were detected before they propagated through the medication use process. The majority of medication errors (74.2%) formed an error chain encompassing two or more stages. The most frequent error chain comprised preparation up to and including medication administration (45.2%). "Non-consideration of documentation/prescribing" during the drug preparation was the most frequent contributor for "wrong dose" during the administration of medication. Medication error chains provide important insights for detecting and stopping medication errors before they reach the patient. Existing and new safety barriers need to be extended to interrupt error chains and to improve patient safety.

In 1994, American Association of Physicists in Medicine (AAPM) task group (TG) report 40 established that plan check and chart review is part of medical physics major responsibilities.1 As the treatment technique complexity increases, patients' plan check and chart review becomes more critical to treatment accuracy and patient safety, yet more cumbersome as the checking items increase dramatically. AAPM published two scientific reports in 2020 specifically to address the efficiency strategies and minimum requirements.2, 3 Both reports discussed the benefits of computer automation in reducing human labor and improving process efficiency, whereas they also emphasized the difficulty and limitation of implementing computer-aided programs for various clinical practices. There poses a dilemma that computer-automation can save clinical physicists' time, while implementing a computer-aided chart check program requires high standardization in nomenclature and continuous maintenance to accommodate ever-changing technology and various clinical workflows. This article debates on the proposition "Computer automation for physics chart check should be adopted in clinic to replace manual chart checking for radiotherapy." Herein, we have Mr. Edward Clouser argues for the proposition whereas Dr. Quan Chen argues against the proposition. Mr. Clouser received M.S. in Physics in 2003 from Cleveland State University. He received his clinical training as a medical physicist at the Cleveland Clinic and stayed on faculty post-graduation. He has worked at the Mayo Clinic in Arizona for 14 yr, among which he has been serving as the program director of the Medical Physics Residency for 7 yr. His current interests include developing tools to automate clinical work including chart review and weekly checks for dosimetrists and physicists. He holds the rank of Assistant Professor of Radiation Oncology in the Mayo School of Medicine and is board certified by the American Board of Radiology. Dr. Chen received his PhD in Medical Physics from the University of Wisconsin-Madison in 2004. He started his career in industry as a senior research physicist at Tomotherapy before joining University of Virginia in 2011. Currently he is an Associate Professor at University of Kentucky. His research interests cover a wide range of topics include dose calculation algorithms, motion management, adaptive therapy, kV dosimetry, innovative quality assurance method, as well as Artificial Intelligence (AI). He has cofounded a company (Carina Medical LLC) to develop AI-based applications for Radiation Oncology. Dr. Chen has also developed many clinical tools that was in use at different centers to improve the safety and efficiency of clinical services. Dr. Chen is an Associate Editor of journal of applied clinical medical physics (JACMP) and serves on several committees at AAPM. Chart checking has long been a primary task of clinical medical physicists in the process of ensuring treatment planning integrity. Historically, we would look through a paper chart and maybe a few printed pages from the treatment planning system to verify adherence to general planning rules and finding transcription errors. The concepts of a chart check are held in the individual physicist's head and the effectiveness in identifying errors are mostly based on individual physicist's experience and attention to details. As the radiation oncology treatment planning and delivery technologies evolve to a rather high level of complexity, chart checking requires a far more complicated and organized venture. Thanks to digital imaging and communications in medicine (DICOM) file standardization, record and verify systems, and other software advances, patients' detailed treatment data can be created, transferred, and delivered in a rather secure and integrated manner. Manual transcription errors for plan and machine settings should be nearly extinct. In a single vendor environment for oncology information system (OIS), treatment planning system (TPS), and treatment delivery system, any sort of errors pertaining files transfer are eliminated. Meanwhile, Medical Physics as an industry has moved away from "in my head" QA steps and is promoting more advanced techniques such as using checklists and other industry-born systems like Failure Mode Effects Analysis (FMEA) and process control. AAPM TG-100 and Medical Physics Practice Guidelines (MPPG) 4a point the direction the field is heading to.4, 5 Automation also clearly fits in "MedPhys 3.0" under the second initiative "Smart Tools." The AAPM TG 275 described importance of the chart check in physics QA process.2 The task group included a review of publications related to automation and automation tools, and listed "Develop automated tools to assist with physics plan and chart review tasks" in their "Key Recommendations" to software vendors section and also recommended "automating checks where possible" in the conclusion. TG-275 supplement 1 included a total of 171 potential QA items for initial chart check with 109 of them as partially or fully being automated. Ending manual transcription is listed as a "Key Recommendation" in TG-275. My proposition herein is that automation in chart checking benefits all clinical physicists and fall under the umbrella of safety. The following paragraphs will spell out how and why each benefit leads us to safety, and will also address efficiency, human error and effects of fatigue, and improvements in workflow. The efficiency through automation is obvious: a computer can do certain tasks much faster than humans. However, there are far less obvious gains in efficiency when automating a chart check. Historically, a dosimetrist or physicist creates a treatment plan, a physician reviews it, a dosimetrist finalizes the plan, and then a physicist performs the chart check. This manual workflow is fine, so long as the physicist doesn't find any problems. If the plan needs adjustment, there is inefficiency in the process. There may also be some awkwardness of telling someone you don't agree with their work. In addition, introducing human to human communication with potentially emotional or subjective interactions in a workflow might add to unpredictable problems. By automating some of the plan checks and shifting the automation to occur during the planning process, the time sink of the iterative process of passing the plan between planner and checker could be avoided. Smooth, well-defined workflows are safer workflows. This concept of automating and moving the QA to before the chart check is supported in TG-275 as best practice. Automation eliminates the natural burnout for human beings. If charts come in to be checked at an even pace with a predictable distribution of errors, physicists may be able to handle them with full attention. The reality is that urgent charts come in unexpectedly and sometimes multiple come in together. Clinical physicists must all have experienced the chaos that urgent patient starts in 45 min and requires immediate chart checking, while some might come in late on a Friday afternoon after a whole day of high-intensity procedures, i.e. brachytherapy. Human nature dictates the fact that we cannot always perform at our best. On the contrary, a computer doesn't get tired, need to eat a meal, or care if it is a Friday night. Errors can slip past our best intentions; they are far less likely to slip past a well-written algorithm. Not letting errors get past our safety barriers is clearly a safer condition. Gopan et al. concluded in their 2016 article regarding errors not being caught that "Suggestions for improvement include the automation of specific physics checks performed during the pretreatment physics plan review and the standardization of the review process."6 Automation saves time in the overall workflow, thus allowing more time be allocated to those more important checks, or steps that might be scored the highest risk in making errors in FMEA. Manual steps tend to be bottlenecks in a clinical process. The efficiency argument I started with has benefits beyond the actual chart check. Fully or partially automating any step in a process allows the workflow to move along to the next step in the process faster by removing barriers from human delays. Automation also lends itself to meaningful data collection. If the results of every chart check are reliably collected, they can then be reviewed and analyzed. Data can be collected manually as well, I won't deny that, but it becomes time consuming and prone to errors if it is not automated. In an institution that has multiple staff members involved in planning and chart checking, this data can be valuable in establishing patterns in practice and potentially leading to targeted practice improvement projects. All physicists can understand the power of data, and tackling any problem is much easier with data. Plan/Chart checking is a key step to ensure the quality and safety of radiation therapy treatment. A large-scale study on 4407 incidents reported at 2 academic radiation oncology clinics revealed that physics initial chart review and physics weekly chart review are the two most effective quality control (QC) processes for detecting those reported high severity incidents.7 Chart checking is specified by AAPM1 and ACR-ASTRO8 as an important duty for medical physicists. The recently published AAPM TG-275 has also made recommendations for physics initial plan and weekly chart review to strengthen the effectiveness of these activities in ensuring the safety and quality of care for patients receiving radiation treatments.2 The advancement in technologies has tremendously increased the complexity of radiation therapy treatment. This has increased the burden for physicists to perform a thorough chart check. There have been many efforts to develop automated chart checking tools to reduce human efforts and errors. Researchers at University of Iowa have developed an electronic radiation therapy plan quality assurance (QA) system (EQS)9 which later becomes CATERS (Computer Aided Treatment Event Recognition System).10 This system checks the consistency of the plan parameters designed in the TPS compared to those in the OIS, to ensure plan transfer integrity. In addition, various logic consistency checks are implemented to alert inconsistent findings or possible errors such as target dose deviation from physician's prescription, inappropriate parameters that are known to cause interlocks, etc. A similar system has been developed at Washington University in St. Louis before 2012.11, 12 It was subsequently expanded to include more functions such as the verification of treatment delivery through the EPID,13 adaptive radiotherapy,14 proton therapy,15 and MR guided radiotherapy.16 Researchers at University of Michigan (UM) developed a Plan-Checker Tool (PCT) to automate part of the chart checking tasks.17 Commercial vendors have also released a few solutions to facilitate plan/chart checking tasks, including ClearCheck/ChartCheck from Radformation Inc., Mobius3D/MobiusFX from Varian Medical Systems, and PlanCheck/PlanIQ from Sun Nuclear Corp. The plan/chart checking functions provided by these vendors are similar to those in-house developed at academic centers. Although automated chart checking tools, both in-house and commercially developed, are available, none of them are even close to fully replacing manual checks. The automated checking functions offered are only a very small subset of the actual checks performed by physicists. For example, the PCT system which was developed fairly recently (2016) only automated 19 of 33 checklist items identified at their institution. Note that the recently published AAPM TG-2752 Table S1.A.ii listed over 170 physics check items for photon/electron EBRT initial plan/chart review and Table S1.A.iii showed that 87 of them have failure modes of RPN > 100. So far, none of the software claimed to be able to fully replace manual checks or reviews. There are many obstacles preventing the implementation of an automated system that can replace physicists in plan/chart checking. The automated chart checking functions implemented so far mostly rely on the entry and existence of structured data. A number appeared in one data field will be compared with a number appeared in the other data field or a box checked somewhere. However, the data in the patient chart are not always structured. There can be key information entered as a free text in the form of a note. Often, it can simply exist in the patient chart as a scanned document (i.e. patient's prior treatment record is often faxed from a different clinic). While it is easy for human to understand the information carried in those texts, computer apprehension requires optical character recognition (OCR) and natural language processing (NLP) that confound computer scientist for over 50 yr. While only recently, the success of IBM Watson in Jeopardy! showed promise in this area, the subsequent failures of IBM's attempt to adopt it in the medical field showed discouraging obstacles.18 Similarly, an important aspect during plan/chart checks involves image review, that is, to evaluate contours accuracy or appropriate image fusion. While there are research attempts to perform contour quality assurance with computer algorithms,19, 20 no literature has shown the automation of image review for plan/chart checks. While it is foreseeable that the advancement of computer technologies, especially the artificial intelligence technologies, might allow us to implement computer automation in every chart checking task, a remaining obstacle for creating an automated chart checking software is to handle the ever-evolving technology development and ever-changing patient's individual scenarios in radiation oncology practice. Currently on the market exists numerous combinations of treatment modalities, treatment planning systems, OISs, etc. To be able to handle all systems requires tremendous knowledge and efforts. All in-house developed solutions only focus on specific configuration for the developer's institution. Even for commercial software, the support of different systems can be limited. For example, the ClearCheck/ChartCheck from Radformation Inc. only supports Eclipse (Varian Medical System). Furthermore, the clinical practice also varies between institutions and between physicians in the same institution. There can also be changes to clinical practices as new recommendations on treatment emerge, which further limits the general utilization of an automated chart checking system developed for one particular institution and creates maintenance issues when changes occur in clinical practice, that is, roster changes or new technology adoption. For example, some of the automation of chart checking tasks require certain naming convention,17 a different clinic adopting this automation would involve either changing their naming convention, or modifications in the automation software. Therefore, the high maintenance of such software in a highly variable and rapidly changing environment might not necessarily lead to a labor or time saving. As with any software program, automated chart check can have "bugs". Aside from programmer's mistakes, the most common "bugs" in the program often originate from the design of the chart checking program. Usually, the chart check logics (checklists) used in manual chart check is implemented. Known errors captured with manual chart checks in the past can be used to test the program. There is a major logical flaw in this design, that is, you cannot catch an error that you did not foresee. Rarely occurred errors may not be considered during the implantation of automated chart checking programs. However, rarely occurred errors can still cause severe outcomes. There have been reports on errors missed by the automatic chart checking program.9 Although "patches" are normally developed to address these errors, they cannot address other unforeseeable errors, which might require endless program patches, thus exhausts implementing physicists or IT technicians. Therefore, completely relying on the automated QA can be impractical or even dangerous. Finally, automated chart checking programs can only analyze information documented in charts. However, if the error occurs at the documentation step, it may not be caught by analyzing the chart itself. Often, these errors might come with high severity. For example, the "Miscommunication about prior dose, pacemaker, or pregnancy" has the 2nd highest RPN score among photon/electron EBRT high-risk failure modes according to TG-275.2 If the prior treatment checkbox in the patient chart was accidently left unchecked (although the medical resident in charge of this patient knows about the prior treatment and requested the prior treatment dose), the chart checking program will still believe that the patient has no prior treatment and performs routine chart check accordingly. However, a physicist checking this case may capture the prior treatment information of the patient from various venues, i.e. chart rounds, dosimetry huddle, emails communications, or additional external dicom files for this patient. Human wisdom, experience, and communication abilities can never be replaced by rule-following robots. I would like to start my rebuttal by saying I agree with nearly everything my opponent has laid out. I don't think we can replace people with automation, today. I do think that we can and should find as many things as possible to automate with full automation as a goal, not an ultimatum. We should look at chart checking automation as a spectrum, not a Boolean. Most technologies evolve, and most are very "ho-hum" or even dangerous when they're new. I can get on a plane from my home in Phoenix and be in London, 5300 miles away, in less than half a day. If we took the plane the Wright brothers flew and determined it was dangerous and therefore not worth pursuing, that journey would take weeks, not hours. Even today, planes are not 100% safe, but we all accept a small amount of risk for the massive rewards. I would never trivialize the loss of life or minimize the importance of what we do as Medical Physicists. In fact, I'm trying to make the opposite argument, that the human can't be trusted to achievement improvement on their own for the very important quality assurance duties we perform. We need to commit to automation in order to aid the evolution and to keep it as safe as possible. Just like human flight, the end result will be worth the potential problems along the way. Most arguments to avoid automated chart checking fall in a classic human emotional bias known as "status quo bias." The current state of affairs is viewed as a reference point and any move from it, (regardless of direction!) is perceived as a loss. This was well described in the results of experiments by Samuelson and Zeckhauser in their 1988 article in the Journal of Risk and Uncertainty.21 In summary, when given a choice, humans will more likely pick what they have, rather than something else, even if the alternative has clear benefits. Minor examples in our everyday lives might be keeping our current insurance company or mobile phone carrier, even though switching could save us money. Everyone's bias level is different, but we tend to keep what we have. My opponent's last argument for human vs. automated chart checking is that a human might have better information in making a decision; perhaps because they attended Chart Rounds or read something outside of the Record and Verify system. I agree that a state with more data is a better state than less. That just means that data needs to get to the automation, not an abandonment of the data. Human's miss errors all the time and we collectively learn from those errors. The entire purpose of programs like AAPM/ASTRO's ROILS (Radiation Oncology Incident Learning System) is to learn from mistakes. Adding or altering code is no different than learning about an incident and adjusting your practice to prevent that mistake at your institution. The biggest difference being the code won't forget over time, you and I might. "Awkwardness of telling someone you don't agree with their work," "human to human communication … might add to unpredictable problems." My opponent considered human to human communication negative, which should be avoided if possible. However, I believe in-person communication is the major advantage of having human touches vs. using machine/automated tools. Communication includes two vital aspects: express yourself and understand others. As mentioned in my opening statement, clinic is a complex and dynamic environment. Errors can happen due to various reasons. In addition, false-positives could be generated from a chart checking routine that did not fully consider some of the peculiar or rare cases. Human to human communication renders quick and comprehensive understanding of the circumstances, possible of errors or and solutions that reduce or even prevent errors. All the can lead to of our chart check in order to better of errors in rare should not be or of out on the of patient safety. There is no that certain chart checking tasks could and should be automated. The of data between TPS and the is such an However, as detailed in my opening statement, the complex nature of our practice environment will lead to complex rules in the chart checking algorithm. As the complexity of the system so the of errors and the difficulty to fully In addition, there are many data, and information outside of the chart that is to be by the automated chart checking The most dangerous aspect of the automated chart checking is that may not fully understand the rules and There can be or of a chart checking that it can catch certain error the fact that it might only check one error in the workflow among many that could lead to a specific A full automated that can cover all of chart checking, even if it can be will only cover the clinical scenarios treatment clinical report As clinical practice the may to cover all the It is then to the human physicist to ensure the safety of the which includes a thorough chart checking, before new "patches" can be However, it is very likely that the physicists may have been on chart checking as they have been relying on the automated chart checking for We believe that while the automation of chart checking is it will not and should not fully replace manual chart checking. The focus of the should not be on the development of a system that can automate chart checking under any clinical environment and able to capture all possible errors. the should be on the development of a of tools that can perform well chart checking Physicists should have a full understanding of the and of these tools. However, it should still be human physicists will the information provided by these automated tools, as well as other information and outside of the to a treatment can be

Patient safety is very important in the health field, and for this, parameters such as having quality in the service provided and guaranteeing primary care in an effective manner must be considered. The objective of this article is to know the aspects of the patient safety culture and the safety climate perceived by the nursing staff who work in the participating hospital. The methodology used is a descriptive, cross-sectional observational type; It will be observational because it is a specific type of study that is defined by having a statistical or demographic nature, where 46 health professionals were surveyed. In this investigation, it was identified that there is disagreement with the actions that are implemented, lack of supervision, lack of monitoring, strategies, reports, and high frequency of communication between professionals where the purpose is to provide patient safety and demonstrate all the notifications of adverse events and incidents.

BackgroundRaising concerns in clinical settings, also known as whistleblowing, is vital for safeguarding patient safety and improving the quality of care. Despite research on whistleblowing in medical and nursing fields, there is limited evidence on this topic within dental education. This study aims to assess the self-reported confidence of undergraduate dental students in raising concerns and identify any barriers.MethodsThis cross-sectional study utilized an online close-ended questionnaire distributed via Google Forms to senior undergraduate dental students from Jordan University of Science and Technology and the University of Jordan, Jordan. Data collection was voluntary, with subsequent analysis performed using RStudio (version 2023.06.2) incorporating R version 4.0.5. T-tests and Analysis of Variance (ANOVA) were used to assess significant variations between results by gender and stage of study.ResultsA total of 382 participants were included in the study yielding a response rate of 30.80%. Of these, 257 were female (67.28%) and 125 were male (32.72%). Overall, 169 (44.24%) participants reported that their institutions had a policy document on raising concerns, while only 71 (18.58%) participants reported receiving formal training in raising concerns at their institution. Approximately 45% of participants reported experiencing situations which warranted raising concerns in clinical settings. The overall mean score for all items was 0.13 (95% CI -0.18 to 0.43). The findings revealed that students were marginally confident in raising concerns related to patient safety. However, their confidence was lower when addressing issues related to the conduct of clinical staff or peers. Common barriers reported included fear of causing trouble, lack of support, and fear of being ignored. ANOVA revealed significant variation by gender and year of study, with female students and final-year students reporting greater self-confidence in raising concerns (p < 0.001).ConclusionsNotwithstanding the limitations of the current study, the results show that participants were marginally confident in raising concerns related to patient safety and several barriers to raising concerns were also identified. These findings underscore the need for dental schools to focus on enhancing students’ confidence and empowering them to report concerns when warranted. A transparent and supportive culture can contribute to improvements in patient safety and enhancing professionalism of dental students.

Background Cardiothoracic surgery demands precision and innovation. Advancements in artificial intelligence (AI) and robotic technologies have transformed this field by reducing complication rates, enhancing surgical accuracy, and improving patient recovery. These technologies have optimized outcomes in the UK, minimizing human error (Etienne et al., 2020). This review examines the impact of AI and robotics in cardiothoracic surgery, focusing on clinical benefits in the UK and adoption challenges, particularly in low- and middle-income countries (LMICs). Method A systematic literature review from 2018 to 2023 was conducted using PubMed, MedLine, and The Lancet. Search terms included “AI in surgery” and “robotic cardiothoracic surgery.” Studies were selected based on relevance to AI, robotics, patient safety, cost-effectiveness, and adoption barriers. Results Robotic-assisted surgery has enhanced precision in procedures like mitral valve repair and CABG, with the da Vinci Surgical System reducing operative times by 25%. St Bartholomew’s Hospital reported a 30% reduction in complication rates (Chitwood Jr., 2022). AI algorithms in NHS hospitals reduced readmissions by 20-30%, improving outcomes and cost efficiency (Bellini et al., 2021). Adoption in LMICs remains limited due to high costs, underscoring the need for scalable solutions (Gumbs et al., 2021). Conclusions AI and robotics have revolutionized precision and safety in cardiothoracic surgery, especially in the UK. However, LMICs face adoption barriers requiring cost-effective strategies and partnerships. Ethical concerns like data privacy and algorithmic bias must be addressed to ensure equitable outcomes. Collaboration among surgeons, engineers, and data scientists is crucial to advance these technologies globally.

Artificial intelligence (AI) medical scribes (AI scribes), which ambiently record and transcribe patient-clinician interactions into structured documentation, aim to ameliorate documentation burdens, but their suitability for allied health remains unclear. AI scribes are often designed for doctors, raising concerns about accuracy, workflow integration, and applicability to allied health's diverse documentation needs. While potential benefits include improved efficiency and patient engagement, evidence is lacking for their effectiveness in allied health settings. Risks such as AI bias, patient safety, and integration barriers may also require consideration. This paper argues that further research is needed before widespread allied health adoption, emphasising the need for discipline-specific evaluations to assess AI scribes' viability in allied health practice.