An Exploration of Nurses' Attitudes and Beliefs on Reporting Medication Errors.

BACKGROUND: Medication errors in health care are prevalent. Nurses play an important role in reporting; however errors remain underreported in incident reporting systems. Understanding the perspective of nurses will inform strategies to improve reporting and build systems to reduce errors. PURPOSE: The purpose of this study was to explore nurses' perceptions and attitudes of medication error reporting practices. METHODS: This qualitative study used direct content analysis to analyze interview sessions with 21 total nurses. RESULTS: Participant's description of medication error reporting practices fell into 2 themes. Internal factors described circumstances within nurses themselves that affect reporting. External factors described outside influences from processes or places. CONCLUSIONS: Medication error reporting is a multidimensional phenomenon with internal and external factors impacting nurses' attitudes and willingness to report errors. Nurses need support from leadership to understand that reporting medication errors can improve practice and impact patient outcomes.

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.

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.

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.

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.

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.

Enhanced Free-Text Search for Aggregated Medication Error Report Analysis and Risk Detection.

OBJECTIVES: Detecting medication errors (MEs) and learning from them are the key elements of medication safety management in health care. While the aggregation of the data and learning across the ME reports could help detect and manage organizational risks, the inconsistent and partly missing structural data complicate the analysis. The objective of this study was to examine whether an analysis of free-text data of aggregated ME reports could contribute to the detection of organizational risks. METHODS: A retrospective, cross-sectional analysis of ME reports from a patient safety incident reporting system in a tertiary hospital 2017-2021. Clustering of characteristics and variables of ME reports with an enhanced free-text search of the 10 most frequent active substances (TOP10) related to ME reports using Microsoft Excel. Validity analysis of the four most frequent active substances of the search results (TOP4). Evaluation of the possible impact of the enhanced free-text search method on ME report analysis and risk detection. RESULTS: The enhanced free-text search increased significantly the number of relevant ME reports of TOP10 active substances from 698 reports to 1578 reports. The validity of the enhanced free-text search results in TOP4 active substances was more than 74%. The enhanced free-text search revealed also new ME findings. CONCLUSIONS: Enhanced free-text search can contribute to the aggregate analysis of clustered ME reports and to the improvement of ME risk detection. The enhanced free-text search method enables more comprehensive analysis of the free-text data with commonly available software and provides new insights into medication safety improvement.

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.

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.

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.

Retraining transplant pharmacy staff to reduce Medicare Part B prescription billing errors post-transplant.

BACKGROUND: Medicare Part B (MedB) imposes penalties for certain errors in prescription billing of post-transplant medications, which can greatly affect pharmacy revenue. To prevent MedB billing fines, pharmacy staff must be cognizant of specific MedB requirements. OBJECTIVE: This quality improvement project aimed to retrain certified pharmacy technicians (CPhTs) on common billing errors and evaluate changes in error rates and potential fines after retraining. We aimed to determine whether retraining CPhTs minimizes MedB prescription billing errors and reduces potential fines owed by the Vanderbilt Transplant Pharmacy (VTP) to the Centers for Medicare and Medicaid Services (CMS). METHODS: This was a single-center, quality improvement study including post-transplant patients with at least one MedB prescription billing error who filled prescriptions through VTP. All CPhTs involved in MedB prescription billing received retraining focused on the top 3 errors in MedB billing identified at VTP: early refills, missing relationship of caller to patient and residence of patient on order documentation, or no day supply remaining recorded on the order file. Retraining consisted of developing a training checklist, testing current knowledge levels, individualized nonpunitive coaching based on technician specific errors, and retesting for knowledge retention. Outcomes included the number of prescriptions with at least one MedB prescription billing error and the projected amount of dollars fined owing to errors recorded during the 3 months before and 3 months after retraining. RESULTS: Fourteen CPhTs received retraining. Average refill too soon errors decreased by 37.5% (10.7% vs. 6.7%), average missing relationship by 21.7% (7.7% vs. 6%), and day supply errors by 39.7% (1.7% vs. 1%). Error reductions equaled a 28.2% decrease (approximately $12,700) in potential fines. CONCLUSION: Retraining focused on MedB billing error successfully reduced error frequency and fines from CMS. MedB billing error fines can be costly for pharmacies dispensing high-cost medications; therefore, identifying common errors and training staff can be useful and financially prudent.

Patient-perceived understanding of home-going medication with transitions of care services at a pediatric institution.

BACKGROUND: Transitions of care (TOC) is the coordination and continuity of health care as a patient transfers between different settings. This can include a wide range of services, such as medication reconciliation, patient counseling, bedside delivery of medications, and others that meet individual patient needs. In the pediatric population, patients are at increased risk of potential medication errors and subsequent harm owing to reduced patient and caregiver health literacy, limited dosage form availability, and errors in medication administration. The use of TOC services at the time of hospital discharge in this population has the potential to make a positive impact on patient safety and the treatment of medical conditions. OBJECTIVES: The primary objective of this study was to determine whether patient-perceived understanding of home-going medications was greater in patients and/or caregivers who received medication bedside delivery and education from a pharmacy-led TOC service at a large pediatric academic medical center. The secondary objective was to determine whether the primary practice area of the pharmacist providing medication education led to changes in understanding of home-going medication(s). METHODS: Using institution-wide, patient satisfaction surveys from January 1, 2021, to December 31, 2021, patient and caregiver responses were queried for 2 questions about home-going medications, relating to the understanding of administration and the potential adverse effects. Patients were divided into 2 groups depending on TOC services received, as documented in the electronic medical record (EMR). Survey responses for each of the 2 questions were categorized as top-box percentage by study group. Hypothesis testing between study groups for the primary and secondary outcomes were conducted using chi-squared tests at an alpha of 0.05. Statistical analyses were conducted using SAS version 9.4. RESULTS: Of the 1159 patients included in the study, 441 received TOC services, deemed the intervention group, and 718 did not receive TOC services, deemed the control group. When the intervention and control group were asked about understanding of medication administration, 96.37% versus 93.18% of patients (P = 0.007) gave the most favorable response of "yes, definitely," respectively. Furthermore, 78.51% versus 77.44% of patients (P = 0.053) gave the most favorable response when asked about understanding potential medication adverse effects, respectively. CONCLUSION: Patients receiving TOC services by a member of the pharmacy team had a greater score for understanding of both medication administration and adverse effects. Furthermore, this greater score was consistent among the education provided by the inpatient and outpatient pharmacist.

Study to assess the impact of analytics software on operating room controlled substance management and drug diversion.

PURPOSE: This study investigated the impact of an advanced analytics software solution in the operating room (OR) on tracking and evaluating controlled substance discrepancies. The authors hypothesized that the software would increase identification of these discrepancies and improve the efficiency of the preexisting manual process. METHODS: In this evaluation comparing data from before to after implementation of the software, data were collected using the preexisting manual process for 50 days before implementation, followed by a 25-day period for acclimation to the new software, and ending with a 49-day postimplementation review period. Data collected included the total number of medication discrepancies, time required for discrepancy review and reconciliation by an OR analyst, types of discrepancies, and number of discrepancies leading to provider audits. RESULTS: Before implementation of the analytics software, there were 7,635 OR cases with a total of 674 charting discrepancies (8.83 discrepancies per 100 total OR cases) discovered across 439 OR cases. After implementation, there were 7,454 OR cases with a total of 930 charting discrepancies (12.48 discrepancies per 100 total OR cases; P < 0.0001) discovered across 680 OR cases. While discrepancies increased by 38%, the median review time for the OR analyst per case decreased (P < 0.0001) and the percentage of incidents resolved by the OR analyst increased by 14% while the number of cases requiring additional documentation by the provider decreased by 10%. CONCLUSION: Implementation of advanced analytics software in the OR significantly increased the number of controlled substance charting discrepancies identified compared to the preimplementation review process while increasing the efficiency of the OR analyst.

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.