Application of the Failure Mode and Effects Analysis (FMEA) to identify vulnerabilities and opportunities for improvement prior to implementing a computerized prescription order entry (CPOE) system in a university hospital oncology clinic

Introduction Prior to implementing a new computerized prescription order entry (CPOE) application, the potential risks associated with this system were assessed and compared to those of paper-based prescriptions. The goal of this study is to identify the vulnerabilities of the CPOE process in order to adapt its design and prevent these potential risks. Methods and materials Failure mode and effects analysis (FMEA) was used as a prospective risk-management technique to evaluate the chemotherapy medication process in a university hospital oncology clinic. A multidisciplinary team assessed the process and compared the critical steps of a newly developed CPOE application versus paper-based prescriptions. The potential severity, occurrence and detectability were assessed prior to the implementation of the CPOE application in the clinical setting. Results The FMEA led to the identification of 24 process steps that could theoretically be vulnerable, therefore called failure modes. These failure modes were grouped into four categories of potential risk factors: prescription writing, patient scheduling, treatment dispensing and patient follow-up. Criticality scores were calculated and compared for both strategies. Three failure modes were prioritized and led to modification of the CPOE design. Overall, the CPOE pathway showed a potential risk reduction of 51% compared to paper-based prescriptions. Conclusion FMEA was found to be a useful approach to identify potential risks in the chemotherapy medication process using either CPOE or paper-based prescriptions. The e-prescription mode was estimated to result in less risk than the traditional paper mode.

The Practice of Automated Drug Dispensing Technology on the Reduction of Medication Errors in the Medication Process

This review is used to describe the automated dispensing systems increasing opportunities for improving the health care system. Safe automated dispensing systems (ADS) were suitable for providing a patient’s medication therapy when pharmacists are caregivers. This review highlights the use of time-saving technologies such as automated dispensing machines, automated dispensing cabinets, and robotic original pack dispensing systems which have been suggested as potential mechanisms for reducing medication errors, improve accuracy, safety, and efficiency of medication dispensing. The implementation of automated dispensing machines improves the quality of the medication distribution process as compared with Manual Dispensing System. This review also emphasizes the impact of new emerging technologies of an automated dispensing system (ADS) on reducing medication errors and ADE. The Automated dispensing system is a key strategy of improving patient safety through increasing interaction between the patient and pharmacist, resulting in a chance for pharmacists to carry out new clinical functions. This review focus on the practices of automated drug dispensing in different terms in order to reduce the medication errors drug medication process.

Quality standards for safe medication in nursing homes: development through a multistep approach including a Delphi consensus study

ObjectivesThe aim of the study was to develop quality standards reflecting minimal requirements for safe medication processes in nursing homes.DesignIn a first step, relevant key topics for safe medication processes were deducted from a systematic search for similar guidelines, prior work and discussions with experts. In a second step, the essential requirements for each key topic were specified and substantiated with a literature-based rationale. Subsequently, the requirements were evaluated with a piloted, two-round Delphi study.SettingNursing homes in Switzerland.ParticipantsInterprofessional panel of 25 experts from science and practice.Primary and secondary outcome measuresEach requirement was rated for its relevance for a safer and resident-oriented medication on a 9-point Likert-Scale based on the RAND/UCLA method. The requirements were considered relevant if, in the second round, the median relevance rating was ≥7 and the proportion of ratings ≥7 was ≥80%.ResultsFive key topics with a total of 87 requirements were elaborated and rated in the Delphi study. After the second round (response rate in both rounds 100%), 85 requirements fulfilled the predefined criteria and were therefore included in the final set of quality standards. The five key topics are: (I) ‘The medication is reviewed regularly and in defined situations’, (II) ‘The medication is reviewed in a structured manner’, (III) ‘The medication is monitored in a structured manner’, (IV) ‘All healthcare professionals are committed to an optimal interprofessional collaboration’ and (V) ‘Residents are actively involved in medication process’.ConclusionsWe developed normative quality standards for a safer and resident-oriented medication in Swiss nursing homes. Altogether, 85 requirements define the medication processes and the behaviour of healthcare professionals. A rigorous implementation may support nursing homes in taking a step towards safer and resident-oriented medication.

Assessing Knowledge, Attitude, and Practices towards Ivermectin Pre-exposure Prophylaxis for COVID-19 among Health Care Workers

Background: Health care workers (HCWs) are more likely to be at high risk of SARS-CoV-2 infection due to their direct and/or indirect participation in treatment facilities. Here, we aimed to evaluate the knowledge, attitude, and practices of ivermectin pre-exposure prophylaxis (PEP) in HCWs. Materials and Methods: In this observational study HCWs who were directly/indirectly involved in the medication of COVID-19 patients were selected. The study questionnaire included demographic data; knowledge, attitudes, practices, and associated adverse drug reactions (ADR) after using ivermectin as pre-exposure prophylaxis in COVID-19. Results: The mean age of the selected 306 participants was 34.41 {±standard deviation 4.08}. 66.66% of the participants were men. 15.69% of individuals had co-morbidities. HCWs were about COVID-19, and 94.12% of people know the role of ivermectin as PEP. Additionally, 180 participants (58.82%) realized that ivermectin can cause adverse drug reactions (ADRs). 70.59% of the study-participants admitted that ivermectin has a protective effect on COVID-19, while 62.75% of the participants believed that the PEP benefits of using ivermectin outweigh the risks. 57.37% (n = 210) of HCW used ivermectin and completed the recommended medication process. Eighteen participants (8.57%) experienced adverse reactions and reported to the ADR monitoring center. Conclusion: 70.59% of the studied participants agreed that ivermectin has a protective effect on COVID-19, and 57.37% of the participants have taken ivermectin as PEP. However, 8.57% of the individuals reported ADR, but none of the participants were severe enough to discontinue the drug.

Use of the failure mode and effect analysis tool in the clinical medication process in an intensive care unit

Objective: To describe failure modes and establish contingency measures related to the clinical medication process using medical prescriptions of patients admitted to an Intensive Respiratory Therapy Unit (UTIR), using the Failure Mode and Effects Analysis (FMEA) tool. Methods: This is a descriptive and cross-sectional study carried out in an Intensive Care Unit of a public hospital in Fortaleza, Brazil, from November/2015 to March/2016. Study population included adults aging ≥ 18 years in intensive care at the UTIR. The study included the medical prescriptions released on Mondays, Wednesdays, and Fridays. The study was divided in five phases: situational diagnosis, formation of a multiprofessional team, assessment of failure modes (FM), monitoring of FM and calculation of the priority coefficient (PC). In the FM assessment, scoring of the three indicators of the FMEA was used within a range of 1-10, whereas a score of 10 characterized the most concerning situation. Therefore, the indicators gravity (G), prevalence (P) and detection (D) were analyzed. The study was carried out with an active interaction between the subjects of the group and several in-person and virtual sessions were performed. Drugs used in the study were categorized for therapeutic class, according to the Anatomical Therapeutic Chemical Classification System. Data analysis was performed using Microsoft Office Excel® 2013 software. Results: 301 prescriptions were analyzed, with the identification of 452 FMs, which related mostly to systemic antibacterials (21.6%, n = 8), psycholeptics (13.5%, n = 5) and antithrombotic agents (10.8%, n = 4). FMs were divided in eleven categories, from which “drug interaction” (36.8%; n = 14), “dose adjustment” (21.1%, n = 8) and “food-drug interaction” (7.9%, n = 3) were the most frequent. The PC of the detected FMs varied between 28 and 294, and 42.1% (n = 16) of them presented PC above 100. Median of the indicators G (6 – min: 3; max: 9), D (7 – min: 3; max 7) and priority coefficient (72 – min: 28; max: 294) indicate that FM had generally moderate gravity, low prevalence and low detection. For the majority of FMs (72.7%, n = 28), the chosen conduct was ‘not to accept’ and the established contingency measure included a sentinel event notification. Conclusion: The use of FMEA enabled the identification, classification, and prioritization of risks of the clinical medication process in the UTIR. This study indicates the need to implement measures that increase safety in the clinical practice of the study Intensive Care Unit.

An Investigation of Human Errors in Medication Adverse Event Improvement Priority Using a Hybrid Approach

The aim of this study was to analyze and provide an in-depth improvement priority for medication adverse events. Thus, the Human Factor Analysis and Classification System with subfactors was used in this study to analyze the adverse events. Subsequently, the improvement priority for the subfactors was determined using the hybrid approach in terms of the Analytical Hierarchy Process and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution. In Of the 157 medical adverse events selected from the Taiwan Patient-safety Reporting system, 25 cases were identified as medication adverse events. The Human Factor Analysis and Classification System and root cause analysis were used to analyze the error factors and subfactors that existed in the medication adverse events. Following the analysis, the Analytical Hierarchy Process and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution were used to determine the improvement priority for subfactors. The results showed that the decision errors, crew resource management, inadequate supervision, and organizational climate contained more types of subfactors than other error factors in each category. In the current study, 16 improvement priorities were identified. According to the results, the improvement priorities can assist medical staff, researchers, and decisionmakers in improving medication process deficiencies efficiently.

Prediction of adverse drug reactions based on knowledge graph embedding

Background Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data. Method Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs. Result First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863. Conclusion In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.