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

Contributing Factors to Adverse Events in Long-Term Care Facilities in Ireland, a Content Analysis

OBJECTIVES

There is substantial research on contributing factors to adverse events (AEs) in acute settings. Little is known about AEs in long-term care facilities (LTCFs). Our aim was to identify contributing factors to AEs from LTCFs for older persons and people with disability to inform quality improvement.

DESIGN

Content analysis of statutory notifications of AEs from LTCFs using a modified version of the Human Factors Analysis and Classifications System (HFACS) applied to health care.

SETTING

A sample of high risk-rated notifications of AEs received by the regulator in 2018 and 2019 was drawn from the Database of Statutory Notifications from Social Care in Ireland (n = 156).

METHODS

Two researchers independently analyzed notifications to identify contributing factors using our modified HFACS. The number of factors identified in each level, subcategory, and nanocode of the HFACS was calculated along with percentage representation of factors within notifications and percentage contribution of individual factors to the total number of factors identified. The number and percentage contributions of factors were also calculated, disaggregated by notification type.

RESULTS

Contributing factors from all levels of the HFACS were identified. The most common contributing factor was "Resident factors" followed by "Unsafe acts." No contributing factors were attributed to 68 notifications (43.6%). Multiple contributing factors were attributed to 45 notifications (28.8%). The largest percentage of factors was identified in notifications of serious injuries (27.4%).

CONCLUSION AND IMPLICATIONS

It is the responsibility of LTCFs to account for "Resident factors" in system design, risk management, and care plans. Developing systems and processes that identify where residents have higher risk of harm may reduce the occurrence of high-risk events and thus improve resident safety. The large proportion of notifications where no contributing factors were identified suggests the need for improved reflection and reporting from LTCFs and for more specific questions on notification forms.

Human Error Analysis and Modeling of Medication-Related Adverse Events in Taiwan Using the Human Factors Analysis and Classification System and Logistic Regression

Medical institutions worldwide strive to avoid adverse medical events, including adverse medication-related events. However, studies on the comprehensive analysis of medication-related adverse events are limited. Therefore, we aimed to identify the error factors contributing to medication-related adverse events using the Human Factors Analysis and Classification System (HFACS) and to develop error models through logistic regression. These models calculate the probability of a medication-related adverse event when a healthcare system defect occurs. Seven experts with at least 12 years of work experience (four nurses and three pharmacists) were recruited to analyze thirty-seven medication-related adverse events. The findings indicate that decision errors, physical/mental limitations, failure to correct problems, and organizational processes were the four factors that most frequently contributed to errors at the four levels of the HFACS. Seven error models of two types (error occurrence and error analysis pathways) were established using logistic regression models, and the relative probabilities of failure factor occurrences were calculated. Based on our results, medical staff can use the error models as a new analytical approach to improve and prevent adverse medication events, thereby improving patient safety.

A computer architecture based on disruptive information technologies for drug management in hospitals

The drug management currently carried out in hospitals is inadequate due to several factors, such as processes carried out manually, the lack of visibility of the hospital supply chain, the lack of standardized identification of medicines, inefficient stock management, an inability to follow the traceability of medicines, and poor data exploitation. Disruptive information technologies could be used to develop and implement a drug management system in hospitals that is innovative in all its phases and allows these problems to be overcome. However, there are no examples in the literature that show how these technologies can be used and combined for efficient drug management in hospitals. To help solve this research gap in the literature, this article proposes a computer architecture for the whole drug management process in hospitals that uses and combines different disruptive computer technologies such as blockchain, radio frequency identification (RFID), quick response code (QR), Internet of Things (IoT), artificial intelligence and big data, for data capture, data storage and data exploitation throughout the whole drug management process, from the moment the drug enters the hospital until it is dispensed and eliminated.

A cross-sectional study of the knowledge, skills, and 6 rights on medication administration by nurses at emergency department

Introduction: Medication errors occur when a patient is given the wrong drug or receives incorrect pharmacological therapy. Incorrect drug administration can cause fatal errors resulting in the patient’s death. Approximately 44,000-98,000 patients die each year due to medication errors and this condition is found often in the Emergency Room (ER) due to the complexity. Therefore, this study aims to analyze the relationship between nurses’ knowledge, skills, and 6 rights on medication at Emergency Department. Design and Methods: This is an analytical observational study involving 70 nurses randomly selected using consecutive sampling and working at the ER department of Saiful Anwar Hospital Malang. Data were collected through a questionnaire and analyzed descriptively to determine the knowledge and skills of nurses and the implementation of the correct principles of medication administration. Results: The results showed that most of the emergency room nurses had good knowledge and skills in applying the correct principles of medicine. However, the majority could not calculate the drug dose accurately. The spearman rank results showed that there was a relationship between knowledge and the 6 correct drug principles (p<0.001, α= 0,05; r = 0.491) with a percentage of 44%. This indicates that a higher knowledge results in the correct implementation of the drug. Similarly, there was a significant positive correlation between skills and proper medicine (p<0.001, α= 0,05; r = 0.378). Conclusions: It can be inferred that a higher nurse’s knowledge and skill results in a better administration of medicine.

A hybrid model integrating FMEA and HFACS to assess the risk of inter-city bus accidents

The incidence of inter-city bus accidents receives a lot of attention from the public because they often cause heavy casualties. The Human Factors Analysis and Classification System (HFACS) is the prevailing tool used for traffic accident risk assessment. However, it has several shortcomings, for example: (1) it can only identify the potential failure modes, but lacks the capability for quantitative risk assessment; (2) it neglects the severity, occurrence and detection of different failure modes; (3) it is unable to identify the degree of risk and priorities of the failure modes. This study proposes a novel hybrid model to overcome these problems. First, the HFACS is applied to enumerate the failure modes of inter-city bus operation. Second, the Z-number-based best–worst method is used to determine the weights of the risk factors based on the failure mode and effects analysis results. Then, a Z-number-based weighted aggregated sum product Assessment is utilized to calculate the degree of risk of the failure modes and the priorities for improvement. The results of this study determine the top three ranking failure modes, which are personal readiness from pre-conditions for unsafe behavior, human resources from organizational influence, and driver decision-making error from unsafe behavior. Finally, data for inter-city buses in Taiwan in a case study to illustrate the usefulness and effectiveness of the proposed model. In addition, some management implications are provided.