Evaluation of a medication error monitoring system to reduce the incidence of medication errors in a clinical setting

The association between the adverse event reporting system and burnout and job satisfaction of nurses: Workplace violence as a mediator

AIMS

This study aims to explore the association between the implementation of the adverse event reporting system (AERS), burnout, and job satisfaction among psychiatric nurses, with a focus on examining the mediating effect of workplace violence from patients.

BACKGROUND

Many organizational and personal factors contribute to burnout and job satisfaction experienced by nurses. AERS, serving as a key component of organizational-level quality improvement system, impacts the overall workplace wellness of nurses.

METHODS

A national sample of 9,744 psychiatric nurses from 41 psychiatric hospitals across 29 provinces in China participated. Burnout was measured by the Maslach Burnout Inventory. Job satisfaction was measured using the Minnesota Satisfaction Questionnaire. Workplace violence was assessed by nurses' experience of verbal and physical violence. Multilevel linear regression analyses were carried out to examine if AERS impacts burnout and job satisfaction and to identify the mediating role of workplace violence.

RESULTS

AERS was positively associated with job satisfaction, but negatively with burnout and workplace violence. Workplace violence exhibited a positive association with burnout and a negative association with job satisfaction. Mediation analyses indicated that the associations between AERS, burnout, and job satisfaction were mediated by workplace violence.

CONCLUSIONS

The application of AERS is associated with a reduction in workplace violence in hospitals, which contributes to the diminished burnout and heightened job satisfaction among psychiatric nurses.

IMPLICATIONS FOR NURSING PRACTICE AND HEALTH POLICY

The study highlights the importance of organizational efforts and mechanisms in promoting nurses' well-being. It is necessary for hospital management to create a safe workplace through the implementation of AERS.

A refined management system focusing on medication dispensing errors: A 14-year retrospective study of a hospital outpatient pharmacy

Objectives

This study aimed to evaluate the efficiency of a 14-year refined management system for the reduction of dispensing errors in a large-scale hospital outpatient pharmacy and to determine the effects of person-related and environment-related factors on the occurrence of dispensing errors.

Methods

A retrospective study was performed. Data on dispensing errors, inventory and account management from 2008 to 2021 were collected from the electronic system and evaluated using the direct observation method and the Plan-Do-Check-Act (PDCA) cycle.

Results

The consistency of the inventory and accounts increased substantially (from 86.93 % to 99.75 %) with the implementation of the refined management program. From 2008 to 2021, the total number of dispensing errors was reduced by approximately 96.1 %. The number of dispensing errors in quantity and name was reduced by approximately 98.2 % and 95.07 %, respectively. A remarkable reduction in the error rate was achieved (from 0.014 % to 0.00002 %), and the rate of dispensing errors was significantly reduced (0.019 % vs. 0.0003 %, p < 0.001). Across all medication dispensing errors, human-related errors decreased substantially (208 vs. 7, p < 0.05), as did non-human-related errors also (202 vs. 9, p < 0.05). There was a correlation between the occurrence of errors and pharmacists' sex (females generally made fewer errors than males), age (more errors were made by those aged 31-40 years), and working years (more errors were made by those with more than 11 years of work experience) from 2016 to 2021. The technicians improved during this procedure.

Conclusions

Refined management using the PDCA cycle was helpful in preventing dispensing errors and improving medication safety for patients.

Unveiling medication errors in liver transplant patients towards enhancing the imperative patient safety

Background

Medication errors (MEs) are a significant healthcare problem that can harm patients and increase healthcare expenses. Being immunocompromised, liver-transplant patients are at high risk for complications if MEs inflict harmful or damaging effects. The present study reviewed and analyzed all MEs reported in Liver Transplant Patients.

Methods

All MEs in the Liver Transplant Patients admitted between January 2016 to August 2022 were retrieved through the computerized physician order entry system, which two expert pharmacists classified according to the type and severity risk index.

Results

A total of 314 records containing 407 MEs were committed by at least 71 physicians. Most of these errors involved drugs unrelated to managing liver-transplant-related issues. Antibiotic prescriptions had the highest mistake rate (17.0%), whereas immunosuppressants, routinely used in liver transplant patients, rank second with fewer than 14% of the identified MEs. The most often reported MEs (43.2%) are type-C errors, which, despite reaching patients, did not cause patient harm. Subgroup analysis revealed several factors associated with a statistically significant great incidence of MEs among physicians treating liver transplant patients.

Conclusion

Although a substantial number of MEs occurred with liver transplant patients, the majority are not related to liver-transplant medications, which mainly belonged to type-C errors. This could be attributed to polypharmacy of transplant patients or the heavy workload on health care practitioners. Improving patient safety requires adopting regulations and strategies to promptly identify MEs and address potential errors.

Application of the HEART method to enhance patient safety in the intensive care unit

BACKGROUND: The intensive care unit (ICU) is a complex, dynamic, high stress and time-sensitive place. While a variety of rules and regulations provided to reduce medication errors in recent years, many studies have emphasized that medication errors still happen. OBJECTIVE: The purpose of this investigation is to predict, reveal and assess medication errors among surgical intensive care unit (SICU) nurses. METHODS: This study was performed in one of the public hospitals in Shiraz, namely Shahid Faghihi hospital. The human error assessment and reduction technique (HEART) method was adopted to measure and assess medication errors in the ICU. RESULTS: Findings indicate that ICU nurses perform 27 main tasks and 125 sub-tasks. The results also showed that setting and using DC shock task has the highest human error probability value, and assessment of patients by a nutritionist has the lowest human error probability value. CONCLUSION: Medical errors are key challenges in the ICU. Therefore, alternative solutions to mitigate medication errors and enhance patient safety in the ICU are necessary. Although the technique can be used in healthcare; there is a need to localize the coefficients and definitions to achieve more accurate results and take appropriate controls. Employing experienced people and providing conditions that reduce the possibility of errors in nurses, increasing the number of staff, and developing specialized and simulated training were identified as the most important control strategies to reduce errors in nurses.

Medication errors in Najran, Saudi Arabia: reporting, responsibility, and characteristics: a cross-sectional study

Background

Medication error is a preventable adverse effect of medical care, whether or not it is evident or harmful to the patient. Disclosure of medication errors and improvement of patient safety are inexorably related, and they provide one of the strongest reasons to report and disclose errors, including near misses in which no harm comes to the patient. This study aimed to identify medication errors at the southern province of Saudi Arabia.

Methods

A cross-sectional retrospective study was conducted by reviewing all medical records in the King Khaled Hospital in Najran, Saudi Arabia. Medication errors related information were extracted from the electronic medical system for the duration between 2018 and 2020.

Results

During the study period of 2018 to 2020, a total of 4860 medication errors were identified. More than half of the reported medication errors (66.9%) were linked to ordering, prescribing, or transcribing medications. The most commonly reported medication errors connected to ordering/prescribing/transcribing were inappropriate dosage, dosage units, and therapeutic duplication of medication. The most commonly reported medication errors linked to administration were missing documentation during administration, not performing independent double-checks during the administration of high alert medications, and the administration of look-alike sound-alike (LASA) medications. The intensive care unit (ICU), female medical ward, and male medical ward were the most commonly reported locations for medication errors. Pharmacists detected more than half of the reported medication errors. Physicians were found to be responsible for 66.0% of reported medication errors, followed by nurses.

Conclusion

Medication errors are common in hospital settings in Saudi Arabia's southern provinces. Efforts should be made to improve drug ordering, prescribing, and transcription in hospital settings. To guarantee optimum practices, the entire medical team should take responsibility for the patient's optimal medication administration.

Assessing the impact of a mixed intervention model on the reduction of medication administration errors in an Australian hospital

BACKGROUND

Medication errors remain one of the most common types of incidents reported in Australian hospitals. Studies have reported that for every 10 medication administrations, a medication administration error is likely to occur and reach the patient, potentially contributing to a preventable patient harm.

OBJECTIVE

To assess the impact of a mixed intervention model on medication administration errors in an Australian hospital.

METHODS

Two types of intervention model (human and system orientated) were implemented through collaboration with key stakeholders (nurses, educators, and policy makers) to reduce medication administration errors across this 650-bed multisite Australian hospital from August 2018 to June 2019. To assess the impact of the mixed intervention model, the total number of reported medication errors and the number of medication administration errors were retrieved from the hospital electronic medication management system for 12 months before (from June 2017 to July 2018) and after (from July 2019 to June 2020) implementation of all interventions.

RESULTS

Implementation of a mixed intervention model through collaboration with stakeholders resulted in significant reduction in the number of medication administration errors, and those with harm (from 68 to 55%, P < 0.0001 and from 12 to 8%, P = 0.0001 respectively). Additionally, the severity of medication administration errors was also reduced (HR 0.562, 95% CI (0.298-1.062)) in the post-intervention phase.

CONCLUSION

Introducing a mixed intervention model reduces medication administration errors across health settings and has the potential to drive excellence in healthcare.

Association Between Physicians' Workload and Prescribing Quality in One Tertiary Hospital in China

BACKGROUND

Alarming increasing trends in physician workload have attracted much attention in recent years. Heavy workload may compromise the quality of medication use. Previous studies have identified a series of factors contributing to inappropriate prescribing; however, there is no demonstrated evidence supporting an association between workload and the appropriateness of physicians' prescriptions in China. This study aimed to investigate the relationship between physician workload and prescription quality in a tertiary hospital in Beijing, China.

METHODS

Our study was a single-center, retrospective study, with all outpatient electronic health records extracted from hospital information system of a tertiary hospital in Beijing from July 1 to November 30, 2015. We used outpatient volume in each 5-hour shift as the measure of physician workload. The evaluation of prescribing quality was based on the Rational Drug Use System. Generalized linear models with a γ distribution and a log link were used to explore factors associated with inappropriate prescribing, and we undertook a series of robustness tests with respect to different exclusion criteria.

RESULTS

A total of 457,784 prescriptions from 502 physicians were included in the study. Physicians had an average workload of 34.3 (±19.8) patients per shift, and the mean rate of inappropriate prescribing per shift was 14.1% (±14.6%). Higher rates of inappropriate prescribing were associated with heavier workloads (P < 0.001). Physicians who worked in the afternoon, chief physicians, those working in surgical department, males, and those with more than 20-year experience had higher rates of inappropriate prescribing with increasing workload.

CONCLUSIONS

Heavier workload was associated with higher risk of prescribing inappropriately. It requires great efforts to determine optimal physician workloads and mitigate the potential adverse effects on the prescription quality.

Theory-Based Failure Modes and Effect Analysis for Medication Errors

Medication Errors (MEs) are still significant challenges, especially in nonautomated health systems. Qualitative studies are mostly used to identify the parameters involved in MEs. Failing to provide accurate information in expert-based decisions can provoke unrealistic results and inappropriate corrective actions eventually. However, mostly, some levels of uncertainty accompany the decisions in real practice. This study tries to present a hybrid decision-making approach to assigning different weights to risk factors and considering the uncertainty in the ranking process in the Failure Modes and Effect Analysis (FMEA) technique. Initially, significant MEs are identified by three groups of qualified experts (doctors, nurses, and pharmacists). Afterward, for assigning weights to the risk factors, Z-number couples with the Stepwise Weight Assessment Ratio Analysis (SWARA) method, named Z-SWARA, to add reliability concept in the decision-making process. Finally, the identified MEs are ranked through the developed Weighted Aggregated Sum Product Assessment (WASPAS) method, namely, Z-WASPAS. To demonstrate the applicability of the proposed approach, the ranking results compare with typical methods, such as fuzzy-WASPAS and FMEA. The findings of the present study highlight improper medication administration as the main failure mode, which can result in a fatality or patient injury. Moreover, the utilization of multiple-criteria decision-making methods in combination with Z-number can be a useful tool in the healthcare management field since it can address the problems by considering reliability and uncertainty simultaneously.

Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study

BACKGROUND

Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan's local databases (TLD) to address this issue. However, the international transferability of this model is unclear.

OBJECTIVE

This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model.

METHODS

The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance.

RESULTS

The interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model.

CONCLUSIONS

Our ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.

The Impact of Physicians' Working Hours on Inappropriate Use of Outpatient Medicine in a Tertiary Hospital in China

BACKGROUND

Inappropriate prescribing is an important health system problem in China. Several studies have identified critical factors influencing prescription quality, but the impact of physicians' working hours remains unknown. In China, tertiary hospitals face ever-increasing outpatient volumes. Physicians are asked to work long hours and the impact of shift duration on prescription quality is unknown.

OBJECTIVE

We aimed to investigate the association between consecutive working hours and the quality of physicians' prescriptions in a Chinese tertiary hospital.

METHODS

We obtained all outpatient electronic health records from the hospital information system (HIS) of a tertiary hospital in Beijing, China from 1 July to 30 November 2015. Prescriptions made during two periods were analyzed: a morning shift from 7:30 to 12:30, and an afternoon shift from 13:30 to 18:30. The time when a physician issued the first prescription was considered the beginning of the work shift and prescriptions within the next 4 consecutive hours were included. Potentially inappropriate prescriptions were based on the Rational Drug Use (RDU) system that was developed and validated for this study. We used multivariable logistic regression to examine the impact of shift duration and other clinical and physician factors on potentially inappropriate prescribing.

RESULTS

Of the total 560,529 prescriptions, 15.3% were classified as inappropriate by the RDU system. Physicians' inappropriate prescribing increased in the last hour in each work shift (odds ratio (OR) for the fourth hour compared to the first = 1.12 (95% CI, 1.09-1.15)). We also found that physicians who worked all day had a higher rate of inappropriate prescribing than those who only worked half a day (OR = 1.05 (95% CI, 1.04-1.07)).

CONCLUSIONS

Longer working hours are a risk factor for inappropriate prescribing. Relevant interventions are urgently needed to establish working hour limits in China to reduce the likelihood of inappropriate prescribing by physicians.