Medication error with methotrexate. / Feildosering av metotreksat.

Background: A woman in her seventies presented to the accident and emergency department (A&E) with shortness of breath that had increased over a period of three weeks. She had a history of COPD, hypertension and polymyalgia rheumatica. A medication error involving methotrexate, used for autoimmune diseases, was discovered during her medical history review. Case presentation: The patient arrived with stable vital signs, including 94 % oxygen saturation and a respiratory rate of 20 breaths/min. She had been taking 2.5 mg of methotrexate daily for the past three weeks instead of the prescribed weekly dose of 15 mg. Other examinations revealed no alarming findings, except for a slightly elevated D-dimer level. Interpretation: Considering her medical history and exclusion of other differential diagnoses, methotrexate toxicity was suspected. The patient was admitted to the hospital and intravenous folinic acid was initiated as an antidote treatment. Five days later, the patient was discharged with an improvement in the shortness of breath. This case underscores the importance of effective communication in health care, particularly in complex cases like this, where understanding dosages and administration is crucial. Medical history, clinical examinations and medication reviews, often involving clinical pharmacists, are vital in the A&E to reveal medication errors.

Competency-Based Medication Administration and Error Reporting.

BACKGROUND: Accuracy is needed with medication administration, a skill that involves rule-based habits and clinical reasoning. This pilot study investigated the use of an evidence-based checklist for accuracy with oral medication administration and error reporting among prelicensure nursing students. Checklist items were anchored in the mnemonic C-MATCH-REASON© (Client, Medication, ADRs, Time, Client History, Route, Expiration date, Amount, Site, Outcomes, Notation). METHOD: Nineteen participants randomly assigned to crossover sequence AB or BA (A: checklist; B: no checklist) practiced simulation scenarios with embedded errors. Nursing faculty used an observation form to track error data. RESULTS: Using the C-MATCH-REASON© checklist compared with not using the checklist supported rule adherence (p = .005), knowledge-based error reduction (p = .011), and total error reduction (p = .010). The null hypothesis was not rejected for errors found (p = .061) nor reported (p = .144), possibly due to sample size. CONCLUSION: C-MATCH-REASON© was effective for error reduction. Study replication with a larger sample is warranted. [J Nurs Educ. 2024;63(5):320-327.].

How Should We Draw on Pharmacists' Expertise to Manage Drug Shortages in Hospitals?

This article argues that drug shortages should be addressed as crises that exacerbate already compromised US health care infrastructure. Clinicians, especially pharmacists, can help limit threats that shortages pose to patients. For example, pharmacists can canvass procurement options, consolidate inventory, and prepare medications to prevent need for some clinical interventions. This article describes how pharmacists' preparation and training equip them to help clinical teams navigate shortages by equitably rationing limited medicines, suggesting appropriate therapeutic alternatives, modifying drug administration routes, or delaying interventions. Pharmacists' roles can be key, since good management of supplies during drug shortages can mitigate risk of worse-than-usual clinical outcomes, mitigate risk of medication errors, and reduce some financial burdens on the overall health care system.

Knowledge and Perception Regarding Medication Error Among Nurses.

BACKGROUND: Patient safety is the major concern in providing quality care. Medication errors have been identified as the most common type of preventable errors. This study aimed to assess the knowledge and perception regarding medication error among nurses. METHODS: A quantitative cross-sectional research design was used. The study was conducted in four different private hospitals in Lalitpur. A total enumerative sampling technique was used to select 302 nurses from these hospitals. Descriptive statistical methods were used to assess socio-demographic variables and inferential statistics methods such as the chi-squared test was used to analyse the association between knowledge, perception, and its socio-demographic characteristics. RESULTS: Most of the respondents 244 (80.8%) agreed the cause of medication error occurs due to unclear handwriting and 217 (71.9%) agreed prescribing the wrong route or dose and time. Mostly respondents 126 (41.7%) had inadequate knowledge, 101 (33.4%) had adequate knowledge and 75 (24.8%) had moderate knowledge on medication error. Mostly respondents 273 (90.4%) had positive perception and 26 (8.6 %) had negative perception. CONCLUSIONS: Most of the nurses had inadequate knowledge but has positive perception on medication error. Appropriate strategies for reducing nurses' workload, barriers to reporting, and sensitization workshops in a regular basis by the administrator should be developed to address medication errors and enhance patient safety in hospital settings.

How much time do emergency department physicians spend on medication-related tasks? A time- and-motion study.

BACKGROUND: Medication-related problems are an important cause of emergency department (ED) visits, and medication errors are reported in up to 60% of ED patients. Procedures such as medication reconciliation and medication review can identify and prevent medication-related problems and medication errors. However, this work is often time-consuming. In EDs without pharmacists, medication reconciliation is the physician's responsibility, in addition to the primary assignments of examining and diagnosing the patient. The aim of this study was to identify how much time ED physicians spend on medication-related tasks when no pharmacists are present in the EDs. METHODS: An observational time-and-motion study of physicians in three EDs in Northern Norway was conducted using Work Observation Method by Activity Timing (WOMBAT) to collect and time-stamp data. Observations were conducted in predefined two-hour observation sessions with a 1:1 relationship between observer and participant, during Monday to Friday between 8 am and 8 pm, from November 2020 to October 2021. RESULTS: In total, 386 h of observations were collected during 225 observation sessions. A total of 8.7% of the physicians' work time was spent on medication-related tasks, of which most time was spent on oral communication about medications with other physicians (3.0%) and medication-related documentation (3.2%). Physicians spent 2.2 min per hour on medication reconciliation tasks, which includes retrieving medication-related information directly from the patient, reading/retrieving written medication-related information, and medication-related documentation. Physicians spent 85.6% of the observed time on non-medication-related clinical or administrative tasks, and the remaining time was spent standby or moving between tasks. CONCLUSION: In three Norwegian EDs, physicians spent 8.7% of their work time on medication-related tasks, and 85.6% on other clinical or administrative tasks. Physicians spent 2.2 min per hour on tasks related to medication reconciliation. We worry that patient safety related tasks in the EDs receive little attention. Allocating dedicated resources like pharmacists to contribute with medication-related tasks could benefit both physicians and patients.

An observational study of root-cause analysis of medication errors in elderly with methotrexate toxicity.

INTRODUCTION: Medication errors are inherent in a healthcare system. This results in both time and cost burdens for both the patient and the health system. The aim of this study was to conduct a root-cause analysis of medication errors in elderly patients with methotrexate toxicity, analyze associated factors, and propose solutions. METHODS: This single-center prospective study was designed to identify medication errors in cases of methotrexate toxicity between November 2022 to May 2023. Categorical data and free-text data are used to describe incidents. Harm assessment, factors related to medication errors, and preventability were evaluated for each case. Possible strategies to prevent similar occurrences are discussed. RESULTS: Out of a total of 15 patients who presented during the study period, nine suffered from methotrexate toxicity due to medication errors. Most medication errors occurred during prescribing or dispensing (seven cases). Inadequate knowledge about medication and dosage, inadequate communication was identified as a contributing factor for all medication errors. Patients on long-term methotrexate treatment are at high risk of methotrexate toxicity. CONCLUSION: This study highlights the challenges of health literacy and lacking communication between healthcare providers and patients that can be met through community pharmacy programs for the elderly in lower-middle-income countries.

The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review.

OBJECTIVE: Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures. MATERIALS AND METHODS: We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software. RESULTS: Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation. DISCUSSION AND CONCLUSION: AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models' development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.

Development and validation of a clinical decision support system to prevent anticoagulant duplications.

BACKGROUND AND OBJECTIVE: Unintended duplicate prescriptions of anticoagulants increase the risk of serious adverse events. Clinical Decision Support Systems (CDSSs) can help prevent such medication errors; however, sophisticated algorithms are needed to avoid alert fatigue. This article describes the steps taken in our hospital to develop a CDSS to prevent anticoagulant duplication (AD). METHODS: The project was composed of three phases. In phase I, the status quo was established. In phase II, a clinical pharmacist developed an algorithm to detect ADs using daily data exports. In phase III, the algorithm was integrated into the hospital's electronic health record system. Alerts were reviewed by clinical pharmacists before being sent to the prescribing physician. We conducted a retrospective analysis of all three phases to assess the impact of the interventions on the occurrence and duration of ADs. Phase III was analyzed in more detail regarding the acceptance rate, sensitivity, and specificity of the alerts. RESULTS: We identified 91 ADs in 1581 patients receiving two or more anticoagulants during phase I, 70 ADs in 1692 patients in phase II, and 57 ADs in 1575 patients in phase III. Mean durations of ADs were 1.8, 1.4, and 1.1 calendar days during phases I, II, and III, respectively. In comparison to the baseline in phase I, the relative risk reduction of AD in patients treated with at least two different anticoagulants during phase III was 42% (RR: 0.58, CI: 0.42-0.81). A total of 429 alerts were generated during phase III, many of which were self-limiting, and 186 alerts were sent to the respective prescribing physician. The acceptance rate was high at 97%. We calculated a sensitivity of 87.4% and a specificity of 87.9%. CONCLUSION: The stepwise development of a CDSS for the detection of AD markedly reduced the frequency and duration of medication errors in our hospital, thereby improving patient safety.

Self-administration medication errors at home and its predictors among illiterate and low-literate community-dwelling older adults with polypharmacy: A negative binomial hierarchical regression.

BACKGROUND: Older adults with polypharmacy are more prone to medication errors. People with low educational attainment have more difficulties in taking their medications. OBJECTIVES: This study aimed to identify the extent of medication self-administration errors (MSEs) and the contributing factors among illiterate and low-literate community-dwelling older adults with polypharmacy. METHOD: The present cross-sectional study was conducted among people aged 60 and above. The data were collected using the sociodemographic, clinical, and Belief about Medicines Questionnaires (BMQ). To determine the extent of MSE, a medication error checklist was used. The negative binomial hierarchical regression model in the five blocks was performed. RESULTS: The final sample size was 276 people. The frequency of MSEs in the last 6 months was 69.2%. Sixteen percent of participants had made four or more mistakes. The most common MSEs were forgetting, improper taking of medications with food, improper timing, incorrect dosage (lower dose), and forgetting the doctor's instructions. Near 18% of participants reported adverse events following their mistakes. The significant predictors of MSEs were being completely illiterate (p = 0.021), the higher number of doctor visits per year (p = 0.014), irregularly seeing doctors (p < .001), the higher number of medications (p < .001), and having poor medication beliefs (p < .001). CONCLUSION: Despite the high prevalence of MSEs among older patients, practical strategies to deal with them at their homes have not been established among health systems. MSE as a multifactorial event can be caused by a collection of internal and external factors. Further studies to identify the role of patients, clinicians, procedures, and systems in developing MSEs as interconnected components are needed.

Calculating the cost of medication errors: A systematic review of approaches and cost variables.

INTRODUCTION: Medication errors are an unnecessary cost to a healthcare system and patients of a country. This review aimed to systematically identify published cost variables used to calculate the cost of medication errors and to explore any updates on findings already known on calculating the cost of medication errors during the past 10 years. METHODS: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases, PubMed, Scopus, Emerald and JSTOR were searched, using keywords "medication error" AND "cost" and predetermined inclusion criteria. Duplicate articles were removed. Quality check was done using 10 criteria. Cost variables used in calculating the cost of medication errors were extracted from each article. RESULTS: Among 3088 articles, 33 articles were selected for review. Most studies were conducted in Western countries. Cost variables used (types and number) by different studies varied widely. Most studies (N=29) had used direct costs only. A few studies (N=4) had used both direct and indirect costs for the purpose. Perspectives considered when calculating cost of medication errors also varied widely. A total of 35 variables used to calculate medication error costs were extracted from selected articles. CONCLUSION: Variables used to calculate the cost of medication errors were not uniform across studies. Almost a decade after systematic reviews previously reporting on this area, a validated methodology to calculate the cost of medication errors has still not been reported to date and highlights the still pending necessity of a standard method to be established.

Designing a tool ensuring older patients the right medication at the right time after discharge from hospital- the first step in a participatory design process.

BACKGROUND: On average, older patients use five or more medications daily, increasing the risk of adverse drug reactions, interactions, or medication errors. Healthcare sector transitions increase the risk of information loss, misunderstandings, unclear treatment responsibilities, and medication errors. Therefore, it is crucial to identify possible solutions to decrease these risks. Patients, relatives, and healthcare professionals were asked to design the solution they need. METHODS: We conducted a participatory design approach to collect information from patients, relatives, and healthcare professionals. The informants were asked to design their take on a tool ensuring that patients received the correct medication after discharge from the hospital. We included two patients using five or more medications daily, one relative, three general practitioners, four nurses from different healthcare sectors, two hospital physicians, and three pharmacists. RESULTS: The patients' solution was a physical location providing a medication overview, including side effects and interactions. Healthcare professionals suggested different solutions, including targeted and timely information that provided an overview of the patient's diagnoses, treatment and medication. The common themes identified across all sub-groups were: (1) Overview of medications, side effects, and diagnoses, (2) Sharing knowledge among healthcare professionals, (3) Timely discharge letters, (4) Does the shared medication record and existing communication platforms provide relevant information to the patient or healthcare professional? CONCLUSION: All study participants describe the need for a more concise, relevant overview of information. This study describes elements for further elaboration in future participatory design processes aimed at creating a tool to ensure older patients receive the correct medication at the correct time.

Medication errors in veterinary anesthesia: a literature review.

OBJECTIVE: To provide an overview of medication errors (MEs) in veterinary medicine, with a focus on the perianesthetic period; to compare MEs in veterinary medicine with human anesthesia practice, and to describe factors contributing to the risk of MEs and strategies for error reduction. DATABASES USED: PubMed and CAB abstracts; search terms: [("patient safety" or "medication error∗") AND veterin∗]. CONCLUSIONS: Human anesthesia is recognized as having a relatively high risk of MEs. In veterinary medicine, MEs were among the most commonly reported medical error. Predisposing factors for MEs in human and veterinary anesthesia include general (e.g. distraction, fatigue, workload, supervision) and specific factors (e.g. requirement for dose calculations when dosing for body mass, using several medications within a short time period and preparing syringes ahead of time). Data on MEs are most commonly collected in self-reporting systems, which very likely underestimate the true incidence, a problem acknowledged in human medicine. Case reports have described a variety of MEs in the perianesthetic period, including prescription, preparation and administration errors. Dogs and cats were the most frequently reported species, with MEs in cats more commonly associated with harmful outcomes compared with dogs. In addition to education and raising awareness, other strategies described for reducing the risk of MEs include behavioral, communication, identification, organizational, engineering and cognitive aids.

Filgrastim Use in the Treatment of Azathioprine-Induced Myelosuppression Toxicity After Prescription Error in the Feline.

Only one report on the successful use of filgrastim (granulocyte colony-stimulating factor) in cats for severe neutropenia following azathioprine toxicity exists. Here, we report on a case in which a cat was prescribed methimazole but the medication was filled incorrectly with azathioprine tablets and the prescription label indicated a methimazole dosing regimen that was administered for three days before recognition of the error. On presentation, the cat's physical examinations were consistent with previous examinations before ingestion of azathioprine. A complete blood cell count revealed neutropenia and leukopenia. The cat later developed hyporexia, dehydration, and vomiting. Treatment included antinausea and appetite stimulant medications, filgrastim, and antibiotics. Filgrastim given as subcutaneous injections over the course of treatment increased neutrophil cell counts after suppression. The cat made a full recovery after responding to the treatment protocol. Based on the perceived response to filgrastim in this single feline case report, its use can be considered for the treatment of azathioprine-induced neutropenia in cats.

Intelligent supervision of PIVAS drug dispensing based on image recognition technology.

Pharmacy Intravenous Admixture Services (PIVAS) are places dedicated to the centralized dispensing of intravenous drugs, usually managed and operated by professional pharmacists and pharmacy technicians, and are an integral part of modern healthcare. However, the workflow of PIVAS has some problems, such as low efficiency and error-prone. This study aims to improve the efficiency of drug dispensing, reduce the rate of manual misjudgment, and minimize drug errors by conducting an in-depth study of the entire workflow of PIVAS and applying image recognition technology to the drug checking and dispensing process. Firstly, through experimental comparison, a target detection model suitable for drug category recognition is selected in the drug-checking process of PIVAS, and it is improved to improve the recognition accuracy and speed of intravenous drug categories. Secondly, a corner detection model for drug dosage recognition was studied in the drug dispensing stage to further increase drug dispensing accuracy. Then the PIVAS drug category recognition system and PIVAS drug dosage recognition system were designed and implemented.

Improving Accuracy of Medication Reconciliation for Hospitalized Children: A Quality Project.

BACKGROUND AND OBJECTIVES: Medication reconciliation is a complex, but necessary, process to prevent patient harm from medication discrepancies. Locally, the steps of medication reconciliation are completed consistently; however, medication errors still occur, which suggest process inaccuracies. We focused on removal of unnecessary medications as a proxy for accuracy. The primary aim was to increase the percentage of patients admitted to the pediatric hospital medicine service with at least 1 medication removed from the home medication list by 10% during the hospital stay by June of 2022. METHODS: Using the Model for Improvement, a multidisciplinary team was formed at a children's hospital, a survey was completed, and multiple Plan-Do-Study-Act cycles were done focusing on: 1. simplifying electronic health record processes by making it easier to remove medications; 2. continuous resident education about the electronic health record processes to improve efficiency and address knowledge gaps; and 3. auditing charts and real-time feedback. Data were monitored with statistical process control charts. RESULTS: The project exceeded the goal, improving from 35% to 48% of patients having at least 1 medication removed from their home medication list. Improvement has sustained for 12 months. CONCLUSIONS: The combination of interventions including simplifying workflow, improving education, and enhancing accountability resulted in more patients with medications removed from their home medication list.

Optimising interruptive clinical decision support alerts for antithrombotic duplicate prescribing in hospital.

INTRODUCTION: Duplicate prescribing clinical decision support alerts can prevent important prescribing errors but are frequently the cause of much alert fatigue. Stat dose prescriptions are a known reason for overriding these alerts. This study aimed to evaluate the effect of excluding stat dose prescriptions from duplicate prescribing alerts for antithrombotic medicines on alert burden, prescriber adherence, and prescribing. MATERIALS AND METHODS: A before (January 1st, 2017 to August 31st, 2022) and after (October 5th, 2022 to September 30th, 2023) study was undertaken of antithrombotic duplicate prescribing alerts and prescribing following a change in alert settings. Alert and prescribing data for antithrombotic medicines were joined, processed, and analysed to compare alert rates, adherence, and prescribing. Alert burden was assessed as alerts per 100 prescriptions. Adherence was measured at the point of the alert as whether the prescriber accepted the alert and following the alert as whether a relevant prescription was ceased within an hour. Co-prescribing of antithrombotic stat dose prescriptions was assessed pre- and post-alert reconfiguration. RESULTS: Reconfiguration of the alerts reduced the alert rate by 29 % (p < 0.001). The proportion of alerts associated with cessation of antithrombotic duplication significantly increased (32.8 % to 44.5 %, p < 0.001). Adherence at the point of the alert increased 1.2 % (4.8 % to 6.0 %, p = 0.012) and 11.5 % (29.4 % to 40.9 %, p < 0.001) within one hour of the alert. When ceased after the alert over 80 % of duplicate prescriptions were ceased within 2 min of overriding. Antithrombotic stat dose co-prescribing was unchanged for 4 out of 5 antithrombotic duplication alert rules. CONCLUSION: By reconfiguring our antithrombotic duplicate prescribing alerts, we reduced alert burden and increased alert adherence. Many prescribers ceased duplicate prescribing within 2 min of alert override highlighting the importance of incorporating post-alert measures in accurately determining prescriber alert adherence.

Developing a Diabetes Discharge Order Set for Patients With Diabetes on Insulin.

BACKGROUND: Insulin, a high-risk medication, is prone to prescribing errors. Patients with diabetes experience higher hospitalization rates and extended hospital stays. Prescription errors, such as missing orders, inappropriate insulin type, missing instructions, and lack of appropriate intensification of insulin regimens are common issues. This project explored the use of system-based interventions and educational tools to minimize errors and improve the quality of insulin discharge regimens. METHODS: A needs assessment and baseline chart review were conducted before adapting a diabetes order set obtained from the University of California, San Diego. Subsequent beta testing and broader implementation were followed by repeat chart reviews to assess the impact. RESULTS: Providers strongly desired an insulin discharge order set, with 98% of those surveyed expressing this preference. Those who were high utilizers of the order set showed increased rates of ordering all supplies (55%), compared with pre-intervention rates (27%). However, no change was observed in the practice of intensifying insulin regimens in patients with uncontrolled diabetes upon discharge. DISCUSSION: Insulin prescribing is prone to error. A diabetes discharge order set may improve the percentage of patients who receive necessary insulin supplies at discharge and provide educational resources to encourage appropriate insulin regimens at hospital discharge.