Patient Safety Risks Research Articles

Ethical Dilemmas of Using Artificial Intelligence in Medicine.

Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field. The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses. A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015-2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma. The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings. AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.

Development and Validation of the Hospital-to-Home-Health Transition Quality (H3TQ) Index: A Novel Measure to Engage Patients and Home Health Providers in Evaluating Hospital-to-Home Care Transition Quality: A Novel Measure to Engage Patients and Home Health Providers in Evaluating Hospital-to-Home Care Transition Quality.

Patients requiring skilled home health care (HH) after hospitalization are at high risk of adverse events. Human factors engineering (HFE) approaches can be useful for measure development to optimize hospital-to-home transitions. To describe the development, initial psychometric validation, and feasibility of the Hospital-to-Home-Health-Transition Quality (H3TQ) Index to identify patient safety risks. Development : A multisite, mixed-methods study at 5 HH agencies in rural and urban sites across the United States. Testing : Prospective H3TQ implementation on older adults' hospital-to-HH transitions. Populations Studied : Older adults and caregivers receiving HH services after hospital discharge, and their HH providers (nurses and rehabilitation therapists). The H3TQ is a 12-item count of hospital-to-HH transitions best practices for safety that we developed through more than 180 hours of observations and more than 80 hours of interviews. The H3TQ demonstrated feasibility of use, stability, construct validity, and concurrent validity when tested on 75 transitions. The vast majority (70%) of hospital-to-HH transitions had at least one safety issue, and HH providers identified more patient safety threats than did patients/caregivers. The most frequently identified issues were unsafe home environments (32%), medication issues (29%), incomplete information (27%), and patients' lack of general understanding of care plans (27%). The H3TQ is a novel measure to assess the quality of hospital-to-HH transitions and proactively identify transitions issues. Patients, caregivers, and HH providers offered valuable perspectives and should be included in safety reporting. Study findings can guide the design of interventions to optimize quality during the high-risk hospital-to-HH transition.

A Review of IoMT Security and Privacy related Frameworks

The Internet of Medical Things (IoMT) integrates smart connectivity with healthcare, improving services but imposing cybersecurity and privacy concerns. Frameworks (such as EMRI, SaYo-Pillow, HL7, FHIR, HIMSS) and regulations (such as EU MDR) are in existence but need regular reviewing for enhancement. A crucial requirement to ensure assuring the security and privacy of the IoMT ecosystem is acceptable, standardized frameworks with effective mechanisms, regulations, and policies. The paper reviews recent IoMT frameworks, architectures, standards, and regulations, analysing deployed and proposed systems with the aim of identifying research areas to improve the realm of IoMT security and privacy. The paper assesses security and data privacy in healthcare through case studies by comparing attributes and discussing benefits and limitations. The analysis extends to geographic scopes that adopt these frameworks. Furthermore, it explores emerging technologies (such as using blockchain) in securing IoMT within specific frameworks. IoMT ecosystems faces significant security and privacy challenges due to inherent complexities, leading to evolving cyber threats. The vulnerabilities expose medical devices and patient data, risking patient safety and scrutinizing reputations of healthcare institutions. Despite promising technologies, the lack of tailored security measures, guidelines, and policies, coupled with adoption barriers, contributes to these concerns. There is a crucial need to develop specific research, standards, and policies for ensuring cybersecurity and data privacy in the IoMT. Collaboration among healthcare, government, and technology stakeholders is essential to establish effective regulations and best practices. The vulnerability of the healthcare sector, attributed to legacy devices and disjointed data systems, emphasizes the necessity for robust security risk assessment models to address challenges imposed within the rapidly evolving IoMT.

An integrative risk assessment approach to enhancing patient safety in Continuous Renal Replacement Therapy (CRRT)

Continuous Renal Replacement Therapy (CRRT) serves as an intervention strategy for the management of acute kidney injury (AKI) in critically ill patients. However, owing to its complex nature and the potential for complications, the implementation of CRRT demands continuous monitoring to prevent patient safety risks. This study aims to identify and validate prevalent risks linked to CRRT within a real-world clinical setting, intending to propose preventive measures grounded in expert insights. To systematically categorize and visually depict the risks, their consequences, preventive measures, and recovery controls, our study employed the Bowtie method in conjunction with the Systems Engineering Initiative for Patient Safety (SEIPS) model. In addition to considering patient-related factors that exhibit variability among critically ill individuals, our key findings showed that the most influential risks impacting the effective delivery of CRRT are incidents of clotted filters, bleeding risks arising from the necessity of anticoagulation for filter efficacy, vascular catheter-related bloodstream infections, variations in proficiency levels among healthcare professionals regarding CRRT modalities, especially in operating the CRRT machines, high nursing workload, frequent nursing turnover, occurrences of hypophosphatemia, variability in CRRT prescribing patterns, and issues related to communication among stakeholders. This research sheds light on the primary risks associated with CRRT and provides practical and viable strategies for effective management. Furthermore, the Bowtie diagram developed as part of this study serves as a valuable tool for visually representing the healthcare system and facilitating the identification of system-related risks within healthcare settings.

Medication Error Prevention Using Healthcare Failure Mode And Effect Analysis At Clinical Pharmacy Installation

Medication error is the second most common patient safety incident worldwide. Medication errors can be defined as unintentional failures in medication services that have the potential to cause harm to patients. Maintaining safe health services is highly dependent on the ability of service providers to proactively conduct patient safety risk analysis, one of which is using the Healthcare Failure Mode and Effect Analysis (HFMEA) method. This study was aimed at obtaining the HFMEA design as an effort to prevent medication errors at Company X Clinical Pharmacy Installation. The research method used is qualitative research with a specific type of Operations Research. Data collected by in-depth interviews, observation, secondary data analysis, and focus group discussion. The results of this study found factors that cause medication errors to occur from organizational factors and staff factors. These results then analyzed for the HFMEA design which obtained 11 risks that required attention and then 14 action plans are made to overcome them. This study successfully developed HFMEA design to prevent medication error in Company X Clinical Pharmacy Installation.

Determining Risk Management Priorities and Suggesting Strategies in the Management of Family Health Centres

Aim: The purpose of this study is to identify the risks faced by decision-makers in the management of Family Health Center (FHC) and to propose strategies according to their relative importance. Materials and Methods: Eight different risks were identified as a result of the literature review. In order to evaluate the identified risks, four different physicians with at least ten years of managerial experience in FHCs and two health managers with at least ten years of experience in their field were included in the study. SWARA (Stepwise Weight Assessment Ratio Analysis) method was used to evaluate and analyze the opinions. SWARA method is a weight calculation method based on expert opinions developed for the finalization of decision problems. Results: According to the results of the analysis, experts ranked financial risks (%27), clinical and patient safety risks (%21), and operational risks (%15) as the most important risks, respectively. Hazard risks (%4) were assessed as the least risky in relative terms. For FHCs to carry out their activities effectively, financial risks should be minimized first. Conclusions: Additional measures should be taken by the public for the use and rental of FHCs. Diversification of other sources of income can be ensured by expanding the health services provided beyond the resource transfer linked to the public budget. Regional investment and incentives can be developed by municipalities. Strengthening data protection policies to assess the compliance of medical practices with legal standards and to ensure the security of personal patient data can also reduce legal risks. In FHCs, general safety issues, especially patient safety, are also priority issues that should be investigated and legislation should be developed. In addition, management courses/training can be added to the family medicine specialty curriculum to improve the managerial skills of physicians.

Implementation of a health information technology safety classification system in the Veterans Health Administration's Informatics Patient Safety Office.

Implement the 5-type health information technology (HIT) patient safety concern classification system for HIT patient safety issues reported to the Veterans Health Administration's Informatics Patient Safety Office. A team of informatics safety analysts retrospectively classified 1year of HIT patient safety issues by type of HIT patient safety concern using consensus discussions. The processes established during retrospective classification were then applied to incoming HIT safety issues moving forward. Of 140 issues retrospectively reviewed, 124 met the classification criteria. The majority were HIT failures (eg, software defects) (33.1%) or configuration and implementation problems (29.8%). Unmet user needs and external system interactions accounted for 20.2% and 10.5%, respectively. Absence of HIT safety features accounted for 2.4% of issues, and 4% did not have enough information to classify. The 5-type HIT safety concern classification framework generated actionable categories helping organizations effectively respond to HIT patient safety risks.

Controlling the hospital aquatic reservoir of multidrug-resistant organisms: a cross-sectional study followed by a nested randomized trial of sink decontamination

ObjectivesThe hospital water environment is an important reservoir of multidrug-resistant organisms (MDROs) and presents a risk for patient safety. We assessed the effectiveness of thermal and chemical interventions on sinks contaminated with MDRO in the hospital setting. MethodsWe conducted a cross-sectional assessment of MDRO contamination of sinks and toilets in 26 clinical wards of a tertiary care hospital. MDRO-contaminated sink traps were then replaced and randomized (1:1:1) to receive chemical (sodium hypochlorite), thermal disinfection (steam), or no intervention. Interventions were repeated weekly for 4 weeks. Sinks were resampled 7 days after the last intervention. The primary outcome was the proportion of decontaminated sinks. MDROs of interest were extended spectrum beta-lactamase (ESBL) producing and carbapenemase-producing Enterobacterales, and non-fermentative Gram-negative bacilli. ResultsIn the cross-sectional assessment, at least one MDRO was identified in 258 (36%) of the 748 samples and in 91 (47%) of the 192 water sources. In total, 57 (42%) of the 137 sinks and 34 (62%) of the 55 toilets were contaminated with 137 different MDROs. The most common MDRO were ESBL Enterobacterales (69%, 95/137), followed by Verona Integron-Borne Metallo-β-Lactamase (VIM) carbapenemase producing Pseudomonas aeruginosa (9%, 12/137) and Citrobacter spp. (6%, 5/137). In the nested randomized trial, five of the 16 sinks (31%) in the chemical disinfection group were decontaminated, compared with 8 of 18 (44%) in the control group (OR 0.58; 95% CI, 0.14–2.32) and 9 of 17 (53%) in the thermal disinfection group (OR 1.40; 95% CI, 0.37–5.32). DiscussionOur study failed to demonstrate an added benefit of repeated chemical or thermal disinfection, beyond changing sink traps, in the MDRO decontamination of sinks. Routine chlorine-based disinfection of sinks may need to be reconsidered.

Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The “ADVENTURE” study

BackgroundReporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of novel machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives. MethodsWe used machine learning to analyze 9559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020 . We validated the labeling of 135,000 unique de-identified AE reports and determined the associations between different system's root causes and patient consequences. The model was validated by independent expert anesthesiologists. ResultsThe machine learning (ML) and Artificial Intelligence (AI) model trained on 9559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were “difficult orotracheal intubation” (16.9% of AE reports), “medication error” (10.5%), and “post-induction hypotension” (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for “difficult intubation”, 43.2% sensitivity, and 98.9% specificity for “medication error.” ConclusionsThis unsupervised ML method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex risk patterns and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning and Natural language processing (NLP) models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards and used to better inform and enhance decision-making for improved risk management and patient safety. Trial RegistrationThe study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

Quality of care transition, patient safety incidents, and patients’ health status: a structural equation model on the complexity of the discharge process

BackgroundThe transition of patients between care contexts poses patient safety risks. Discharges to home from inpatient care can be associated with adverse patient outcomes. Quality in discharge processes is essential in ensuring safe transitions for patients. Current evidence relies on bivariate analyses and neglects contextual factors such as treatment and patient characteristics and the interactions of potential outcomes. This study aimed to investigate the associations between the quality and safety of the discharge process, patient safety incidents, and health-related outcomes after discharge, considering the treatments’ and patients’ contextual factors in one comprehensive model.MethodsPatients at least 18 years old and discharged home after at least three days of inpatient treatment received a self-report questionnaire. A total of N = 825 patients participated. The assessment contained items to assess the quality and safety of the discharge process from the patient’s perspective with the care transitions measure (CTM), a self-report on the incidence of unplanned readmissions and medication complications, health status, and sociodemographic and treatment-related characteristics. Statistical analyses included structural equation modeling (SEM) and additional analyses using logistic regressions.ResultsHigher quality of care transition was related to a lower incidence of medication complications (B = -0.35, p < 0.01) and better health status (B = 0.74, p < 0.001), but not with lower incidence of readmissions (B = -0.01, p = 0.39). These effects were controlled for the influences of various sociodemographic and treatment-related characteristics in SEM. Additional analyses showed that these associations were only constant when all subscales of the CTM were included.ConclusionsQuality and safety in the discharge process are critical to safe patient transitions to home care. This study contributes to a better understanding of the complex discharge process by applying a model in which various contextual factors and interactions were considered. The findings revealed that high quality discharge processes are associated with a lower likelihood of patient safety incidents and better health status at home even, when sociodemographic and treatment-related characteristics are taken into account. This study supports the call for developing individualized, patient-centered discharge processes to strengthen patient safety in care transitions.

An improved implementation of metrological traceability concepts is needed to benefit from standardization of laboratory results.

Non-harmonization of laboratory results represents a concrete risk for patient safety. To avoid harms, it is agreed that measurements by in vitro diagnostic medical devices (IVD-MD) on clinical samples should be traceable to higher-order references and adjusted to give the same result. However, metrological traceability is not a formal claim and has to be correctly implemented, which in practice does not happen for a non-negligible number of measurands. Stakeholders, such as higher-order reference providers, IVD manufacturers, and External Quality Assessment organizers, have major responsibilities and should improve their contribution by unambiguously and rigorously applying what is described in the International Organization for Standardization 17511:2020 standard and other documents provided by the international scientific bodies, such as Joint Committee on Traceability in Laboratory Medicine and IFCC. For their part, laboratory professionals should take responsibility to abandon non-selective methods and move to IVD-MDs displaying proper selectivity, which is one of the indispensable prerequisites for the correct implementation of metrological traceability. The practicality of metrological traceability concepts is not impossible but relevant education and appropriate training of all involved stakeholders are essential to obtain the expected benefits in terms of standardization.

Large language models for preventing medication direction errors in online pharmacies

Errors in pharmacy medication directions, such as incorrect instructions for dosage or frequency, can increase patient safety risk substantially by raising the chances of adverse drug events. This study explores how integrating domain knowledge with large language models (LLMs)—capable of sophisticated text interpretation and generation—can reduce these errors. We introduce MEDIC (medication direction copilot), a system that emulates the reasoning of pharmacists by prioritizing precise communication of core clinical components of a prescription, such as dosage and frequency. It fine-tunes a first-generation LLM using 1,000 expert-annotated and augmented directions from Amazon Pharmacy to extract the core components and assembles them into complete directions using pharmacy logic and safety guardrails. We compared MEDIC against two LLM-based benchmarks: one leveraging 1.5 million medication directions and the other using state-of-the-art LLMs. On 1,200 expert-reviewed prescriptions, the two benchmarks respectively recorded 1.51 (confidence interval (CI) 1.03, 2.31) and 4.38 (CI 3.13, 6.64) times more near-miss events—errors caught and corrected before reaching the patient—than MEDIC. Additionally, we tested MEDIC by deploying within the production system of an online pharmacy, and during this experimental period, it reduced near-miss events by 33% (CI 26%, 40%). This study shows that LLMs, with domain expertise and safeguards, improve the accuracy and efficiency of pharmacy operations.

Co-worker unprofessional behaviour and patient safety risks: an analysis of co-worker reports across eight Australian hospitals.

A key component of professional accountability programmes is online reporting tools that allow hospital staff to report co-worker unprofessional behaviour. Few studies have analysed data from these systems to further understand the nature or impact of unprofessional behaviour amongst staff. Ethos is a whole-of-hospital professional accountability programme that includes an online messaging system. Ethos has now been implemented across multiple Australian hospitals. This study examined reported unprofessional behaviour that staff indicated created a risk to patient safety. This study included 1310 Ethos submissions reporting co-worker unprofessional behaviour between 2017 and 2020 across eight Australian hospitals. Submissions that indicated the behaviour increased the risk to patient safety were identified. Descriptive summary statistics were presented for reporters and subjects of submissions about unprofessional behaviour. Logistic regression was applied to examine the association between each unprofessional behaviour (of the six most frequently reported in the Ethos submissions) and patient safety risk reported in the submissions. The descriptions in the reports were reviewed and the patient safety risks were coded using a framework aligned with the World Health Organization's International Classification for Patient Safety. Of 1310 submissions about unprofessional behaviour, 395 (30.2%) indicated that there was a risk to patient safety. Nurses made the highest number of submissions that included a patient safety risk [3.47 submissions per 100 nursing staff, 95% confidence interval (CI): 3.09-3.9] compared to other professional groups. Medical professionals had the highest rate as subjects of submissions for unprofessional behaviour with a patient safety risk (5.19 submissions per 100 medical staff, 95% CI: 4.44-6.05). 'Opinions being ignored' (odds ratio: 1.68; 95% CI: 1.23-2.22; P < .001) and 'someone withholding information which affects work performance' were behaviours strongly associated with patient safety risk in the submissions (odds ratio: 2.50; 95% CI: 1.73-3.62; P < .001) compared to submissions without a patient safety risk. The two main types of risks to patient safety described were related to clinical process/procedure and clinical administration. Commonly reported events included staff not following policy or protocol; doctors refusing to review a patient; and interruptions and inadequate information during handover. Our findings indicate that unprofessional behaviour was associated with risks to patient safety. Co-worker reports about unprofessional behaviour have significant value as they can be used by organizations to better understand how unprofessional behaviour can disrupt work practices and lead to risks to patient safety.

An Analysis of Incident Reports Related to Electronic Medication Management: How They Change Over Time.

Electronic medication management (EMM) systems have been shown to introduce new patient safety risks that were not possible, or unlikely to occur, with the use of paper charts. Our aim was to examine the factors that contribute to EMM-related incidents and how these incidents change over time with ongoing EMM use. Incidents reported at 3 hospitals between January 1, 2010, and December 31, 2019, were extracted using a keyword search and then screened to identify EMM-related reports. Data contained in EMM-related incident reports were then classified as unsafe acts made by users and the latent conditions contributing to each incident. In our sample, 444 incident reports were determined to be EMM related. Commission errors were the most frequent unsafe act reported by users (n = 298), whereas workarounds were reported in only 13 reports. User latent conditions (n = 207) were described in the highest number of incident reports, followed by conditions related to the organization (n = 200) and EMM design (n = 184). Over time, user unfamiliarity with the system remained a key contributor to reported incidents. Although fewer articles to electronic transfer errors were reported over time, incident reports related to the transfer of information between different computerized systems increased as hospitals adopted more clinical information systems. Electronic medication management-related incidents continue to occur years after EMM implementation and are driven by design, user, and organizational conditions. Although factors contribute to reported incidents in varying degrees over time, some factors are persistent and highlight the importance of continuously improving the EMM system and its use.

Assessment of Claimant, Clinical, and Financial Characteristics of Teleradiology Medical Malpractice Cases.

Background The increasing use of teleradiology has been accompanied by concerns relating to risk management and patient safety. Purpose To compare characteristics of teleradiology and nonteleradiology radiology malpractice cases and identify contributing factors underlying these cases. Materials and Methods In this retrospective analysis, a national database of medical malpractice cases was queried to identify cases involving telemedicine that closed between January 2010 and March 2022. Teleradiology malpractice cases were identified based on manual review of cases in which telemedicine was coded as one of the contributing factors. These cases were compared with nonteleradiology cases that closed during the same time period in which radiology had been determined to be the primary responsible clinical service. Claimant, clinical, and financial characteristics of the cases were recorded, and continuous or categorical data were compared using the Wilcoxon rank-sum test or Fisher exact test, respectively. Results This study included 135 teleradiology and 3474 radiology malpractices cases. The death of a patient occurred more frequently in teleradiology cases (48 of 135 [35.6%]) than in radiology cases (685 of 3474 [19.7%]; P < .001). Cerebrovascular disease was a more common final diagnosis in the teleradiology cases (13 of 135 [9.6%]) compared with the radiology cases (124 of 3474 [3.6%]; P = .002). Problems with communication among providers was a more frequent contributing factor in the teleradiology cases (35 of 135 [25.9%]) than in the radiology cases (439 of 3474 [12.6%]; P < .001). Teleradiology cases were more likely to close with indemnity payment (79 of 135 [58.5%]) than the radiology cases (1416 of 3474 [40.8%]; P < .001) and had a higher median indemnity payment than the radiology cases ($339 230 [IQR, $120 790-$731 615] vs $214 063 [IQR, $66 620-$585 424]; P = .01). Conclusion Compared with radiology cases, teleradiology cases had higher clinical and financial severity and were more likely to involve issues with communication. © RSNA, 2024 See also the editorial by Mezrich in this issue.

Roots Cause of the Drug Shortage and Expired Drug Problem at Hospital X

Good drug management in the Hospital Pharmacy Installation is the key to the smooth running of pharmaceutical services. The availability of the necessary drugs at the right time and place affects patient safety and satisfaction. Empty drugs and expired drugs are problems in drug management, mainly Hospital X. Empty drugs result in the replacement of drugs in prescriptions, thus increasing the burden on pharmaceutical services and creating risks for patient safety. Expired drugs cause hospital losses. This study aims to describe the root cause of drug shortages and expired drugs in Hospital X. The research used a qualitative approach to dig deeper into these complex problems—elicitation of data using semi-structured interviews supported by secondary data from the hospital. Data were analyzed using a Fishbone diagram to get a systematic overview of the root causes of problems based on specific domains. The root causes of expired drug problems and empty drug substitution in Hospital X are materials/inputs, methods, people, machinery/equipment, measurement (process evaluation), and the pharmacy facility environment domains.

ENHANCING MEDICATION QUALITY CONTROL IN NIGERIA: A COMPREHENSIVE ANALYSIS OF REGULATORY CHALLENGES AND SOLUTIONS

Medication quality control in Nigeria faces multifaceted challenges rooted in regulatory inefficiencies, infrastructural limitations, and systemic inadequacies. This study undertakes a comprehensive analysis of these challenges while proposing pragmatic solutions to bolster the quality control framework in the Nigerian pharmaceutical sector. Drawing upon extensive literature review and expert insights, the research identifies key regulatory challenges impeding effective medication quality control in Nigeria. These challenges encompass inadequate regulatory capacity, inconsistent enforcement mechanisms, rampant substandard and counterfeit medications, and limited access to essential medicines. Additionally, infrastructural deficiencies such as inadequate laboratory facilities and insufficient human resources further exacerbate the problem, leading to compromised patient safety and public health risks. In response to these challenges, the study proposes a multifaceted approach to enhance medication quality control in Nigeria. This includes strengthening regulatory frameworks through legislative reforms aimed at bolstering enforcement capabilities and harmonizing standards with international best practices. Moreover, improving regulatory capacity through training programs and investment in state-of-the-art laboratory infrastructure is essential to enhance surveillance and detection of substandard medications. Furthermore, fostering collaboration between regulatory agencies, healthcare providers, and pharmaceutical manufacturers is imperative to streamline supply chains and ensure the integrity of medications from production to distribution. Additionally, public awareness campaigns and community engagement initiatives play a pivotal role in empowering consumers to make informed decisions and report instances of substandard medications. By addressing these regulatory challenges and implementing comprehensive solutions, Nigeria can significantly enhance medication quality control, safeguard patient health, and foster a conducive environment for pharmaceutical innovation and growth.&#x0D; Keywords: Medical, Medication, Drugs, Quality Control, Nigeria, Regulations, Review.

Chestnut Honey Is Effective against Mixed Biofilms at Different Stages of Maturity.

The irresponsible overuse of antibiotics has increased the occurrence of resistant bacterial strains, which represents one of the biggest patient safety risks today. Due to antibiotic resistance and biofilm formation in bacteria, it is becoming increasingly difficult to suppress the bacterial strains responsible for various chronic infections. Honey was proven to inhibit bacterial growth and biofilm development, offering an alternative solution in the treatment of resistant infections and chronic wounds. Our studies included chestnut honey, valued for its high antibacterial activity, and the bacteria Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, and S. epidermidis, known to form multi-species biofilm communities. Minimum inhibitory concentrations (MIC) of chestnut honey were determined for each bacterial strain. Afterwards, the mixed bacterial biofilms were treated with chestnut honey at different stages of maturity (incubation times: 2, 4, 6, 12, 24 h). The extent of biofilm inhibition was measured with a crystal violet assay and demonstrated by scanning electron microscopy (SEM). As the incubation time increased and the biofilm became more mature, inhibition rates decreased gradually. The most sensitive biofilm was the combination MRSA-S. epidermidis, with a 93.5% inhibition rate after 2 h of incubation. Our results revealed that chestnut honey is suitable for suppressing the initial and moderately mature stages of mixed biofilms.

Taring the scales: Weight-of-evidence framework for biocompatibility evaluations

ISO 10993-1:2018 describes evaluating the biocompatibility profile of a medical device from a risk-based approach. This standard details the battery of information that should be considered within the assessment of a device, including raw material composition data, manufacturing processes, and endpoint testing. The ISO 10993/18562 series requires worst-case assumptions and exposure scenarios to be used in the evaluation, which may result in an over-estimation of patient safety risk. Currently, biocompatibility assessments evaluate each data set independently, and the consequence of this individualized assessment of exaggerated inputs is potential false alarms regarding patient safety. To evaluate these safety concerns, the ISO standards indicate that professional judgement should be used to estimate patient risk but does not provide guidance on incorporating a holistic review of the data into the risk assessment. Recalibrating these worst-case data to evaluate them in a weight-of-evidence (WoE) approach may provide a more realistic data set to determine actual patient risk. This proposed WoE framework combines understanding data applicability with a method for gauging the strength of data that can provide additional support for the final safety conclusion. Using a WoE framework will allow risk assessors to contextualize the data and utilize it to comprehensively estimate patient safety.

Applying Human Factors and Systems Simulation Methods to Inform a Multimillion-Dollar Healthcare Decision.

This article describes a case study of a collaborative human factors (HF) and systems-focused simulation (SFS) project to evaluate potential patient and staff safety risks associated with a multimillion-dollar design and construction decision. The combined integration of HF and SFS methods in healthcare related to testing and informing the design of new environments and processes is underutilized. Few realize the effectiveness of this integration in healthcare to reduce risk and improve decision-making, safety, design, efficiency, patient experience, and outcomes. This project showcases how the combined use of HF and SFS methods can provide objective evidence to help inform decisions. The project was initiated by a healthcare executive team looking for an objective, user informed analysis of a current connector passageway between two existing buildings. The goal was to understand the implications of keeping the current route for simultaneous use for public and patients service flow versus building and financing a new passageway for separate flow and transport. An interprofessional team of intensive care unit professionals participated in two simulations designed to test the current connector. A failure mode and effects analysis and qualitative debrief feedback was used to evaluate risks and potential failures. The evaluation resulted in data that enabled informed executive decision making for the most effective, efficient, and safest option for public, staff, and patient transport between two buildings. This evaluation resulted in the decision to go forward with building a multimillion-dollar new connector passageway to improve integrated care and transport.