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.

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.

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.

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.

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.

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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

A comprehensive study of prescribing, administering and drug handling medication errors in ten wards of a university hospital after implementation of electronic prescribing, clinical pharmacists or medication reconciliation.

Background and aim: Medication errors lead to preventable risks. Preventing strategies such as e-prescribing, clinical pharmacists and medication reconciliation have been implemented in recent years. However, information on long-term medication error rates in routine procedures is missing. Investigations: We aimed to identify predefined medication errors in ten wards of a university hospital where e-prescribing, clinical pharmacists and medication reconciliation have been partially implemented. Patient files were reviewed and routine processes were monitored for drug prescription errors (missing, unclear, outdated information), administration errors (wrong dispensed drugs) and drug handling errors (no light-, moisture-protection, wrong splitting, no separation of drugs, which ought to be taken by an empty stomach). Results: We analyzed 959 prescriptions with 933 solid peroral drugs for 182 patients (98 female, median age 66.5 years [Q25-Q75: 56-78 years]; the median number of drugs was 5 [Q25-Q75: 3-7]). The most frequent prescription error was a not specified drug form (91.1%). The most common administration error was a not adequately provided release dose formulation (72.7%). The lack of light protection for observed photosensitive drugs was the most frequent drug handling error (100%). We found a significantly higher amount of complete drug prescriptions with one of the implemented measurements e-prescribing, medication reconciliation and clinical pharmacists (Fisher's exact test two tailed, each p<0.001; CI 95%). Drug administration errors and drug handling errors were not significantly improved. Among the most frequently involved drug were drugs for acid-related disorders, immunosuppressant, and antineoplastic drugs. Conclusions: In the nearly 1,000 prescriptions and drugs analyzed, medication errors were still common. Various preventive strategies had been implemented in recent years, positively influencing the predefined errors rates.

Decreasing Prescribing Errors in Antimicrobial Stewardship Program-Restricted Medications.

OBJECTIVES: Antimicrobial stewardship programs (ASPs) restrict prescribing practices to regulate antimicrobial use, increasing the risk of prescribing errors. This quality improvement project aimed to decrease the proportion of prescribing errors in ASP-restricted medications by standardizing workflow. METHODS: The study took place on all inpatient units at a tertiary care children's hospital between January 2020 and February 2022. Patients <22 years old with an order for an ASP-restricted medication course were included. An interprofessional team used the Model for Improvement to design interventions targeted at reducing ASP-restricted medication prescribing errors. Plan-Do-Study-Act cycles included standardizing communication and medication review, implementing protocols, and developing electronic health record safety nets. The primary outcome was the proportion of ASP-restricted medication orders with a prescribing error. The secondary outcome was time between prescribing errors. Outcomes were plotted on control charts and analyzed for special cause variation. Outcomes were monitored for a 3-month sustainability period. RESULTS: Nine-hundred ASP-restricted medication orders were included in the baseline period (January 2020-December 2020) and 1035 orders were included in the intervention period (January 2021-February 2022). The proportion of prescribing errors decreased from 10.9% to 4.6%, and special cause variation was observed in Feb 2021. Mean time between prescribing errors increased from 2.9 days to 8.5 days. These outcomes were sustained. CONCLUSIONS: Quality improvement methods can be used to achieve a sustained reduction in the proportion of ASP-restricted medication orders with a prescribing error throughout an entire children's hospital.

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.

Look-alike sound-alike (LASA) medication errors in Thai hospitals / Errores de medicación por sonido similar (LASA) en hospitales tailandeses

Look-alike sound-alike (LASA) drugs cause a high proportion of medication errors in hospitals. Drug lists available in hospitals are diverse and complicated. Presently, each hospital has its own LASA drug list and unique management strategies to minimize and prevent LASA errors. Objective: This study aimed to explore the prevalence of LASA drug lists, types of LASA drugs, and categories of medication errors in hospitals in Thailand. Methods: For this crosssectional study, questionnaires were developed and distributed along with a letter to 500 government hospitals (selected from a total of 1,309 hospitals) in Thailand via mail from April to June 2021. Data were analyzed using descriptive statistics (frequencies and percentages). Results: A total of 128 hospitals participated in this study (response rate: 25.60%), including 12 tertiary hospitals (9.38%), 33 secondary hospitals (25.78%), 24 large primary hospitals (18.75%), 51 small primary hospitals (39.84%), and eight private hospitals (6.25%). A total of 2,510 pairs of LASA drugs were identified, which included 1,674 (66.69%) tablets/capsules (Simvastatin 10-Simvastatin 20 pair had the highest frequency), 427 injections (17.01%) (Ceftriaxone-Ceftazidime pair had the highest frequency), 85 liquid dosage forms (3.39%) (Milk of magnesia-alum milk pair had the highest frequency), 74 special techniques in medicine (2.95%) (Seretide evohaler®-Seretide accuhaler® pair had the highest frequency), 49 external used drugs (1.95%) (Clotrimazole cream-Clobetasol cream pair had the highest frequency), and 28 powder dosage forms (1.12%) (ORS for pediatrics-ORS for adult pair had the highest frequency). Conclusion: Despite relevant awareness among healthcare professionals, LASA medication errors occur in hospitals. The most frequent similarities among LASA drugs were detected in their names/pronunciations, and the most common errors belonged to Category B.(AU)

Pharmaceutical Decision Support System Using Machine Learning to Analyze and Limit Drug-Related Problems in Hospitals.

The health product circuit corresponds to the chain of steps that a medicine goes through in hospital, from prescription to administration. The safety and regulation of all the stages of this circuit are major issues to ensure the safety and protect the well-being of hospitalized patients. In this paper we present an automatic system for analyzing prescriptions using Artificial Intelligence (AI) and Machine Learning (ML), with the aim of ensuring patient safety by limiting the risk of prescription errors or drug iatrogeny. Our study is made in collaboration with Lille University Hospital (LUH). We exploited the MIMIC-III (Medical Information Mart for Intensive Care) a large, single-center database containing information corresponding to patients admitted to critical care units at a large tertiary care hospital.