Risk analysis using fuzzy set theory of the accidental exposure of medical staff during brachytherapy procedures

Estimating the impact of label design on reducing the risk of medication errors by applying HEART in drug administration

Medication errors are one of the biggest problems in healthcare. The medicines' poor labelling design (i.e. look-alike labels) is a well-recognised risk for potential confusion, wrong administration, and patient damage. Human factors and ergonomics (HFE) encourages the human-centred design of system elements, which might reduce medication errors and improve people's well-being and system performance.

OBJECTIVE

The aim of the present study is twofold: (i) to use a human reliability analysis technique to evaluate a medication administration task within a simulated scenario of a neonatal intensive care unit (NICU) and (ii) to estimate the impact of a human-centred design (HCD) label in medication administration compared to a look-alike (LA) label.

METHOD

This paper used a modified version of the human error assessment and reduction technique (HEART) to analyse a medication administration task in a simulated NICU scenario. The modified technique involved expert nurses quantifying the likelihood of unreliability of a task and rating the conditions, including medicine labels, which most affect the successful completion of the task.

RESULTS

Findings suggest that error producing conditions (EPCs), such as a shortage of time available for error detection and correction, no independent checking of output, and distractions, might increase human error probability (HEP) in administering medications. Results also showed that the assessed HEP and the relative percentage of contribution to unreliability reduced by more than 40% when the HCD label was evaluated compared to the LA label.

CONCLUSION

Including labelling design based on HFE might help increase human reliability when administering medications under critical conditions.

Artificial intelligence applications in brachytherapy: A literature review

PURPOSE

Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field.

METHODS AND MATERIALS

We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy.

RESULTS

A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed.

CONCLUSIONS

AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures.

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.

Computation of epistemic uncertainty due to limited data samples in small field dosimetry using Fuzzy Set Theory

OBJECTIVE

To estimate the epistemic (or fuzzy) uncertainty, arising due to limited data samples in the measurement of the output factors (OFs) of the small fields using Fuzzy Set Theory (FST).

METHODS

EBT3 film samples of size 50 × 50 mm² were used for the measurement of the OF of stereotactic radiosurgery (SRS) cones of size 4, 6, 7.5, 10, 12.5 and 15 mm diameter, normalized with respect to the output of 100 × 100 mm² open field size. Three measurements were done per cone/field size. Red color channel was chosen for the dosimetry purpose, net optical density (NOD) was converted to the dose using non-linear relation. To estimate the epistemic uncertainty associated with the measured OFs due to limited number of data samples, a triangular fuzzy number (TFN) was assumed as the fuzziness in the dose delivered by the individual SRS cone/field. Uncertainty in the OF was estimated by applying the Fuzzy Vertex Method (FVM). The membership functions of the OF were constructed for each cone size and the nature of the uncertainty in the OF of the cones was expressed in the terms of its fuzziness. For the sake of completeness of the study, the statistical uncertainty involved in the procedure has also been calculated.

RESULTS

The statistical and fuzzy uncertainties in the measurement of OF of cones range from 3.28 to 6.25% and 2.58 to 5.44% respectively. The smallest cone of 4 mm has the largest values of statistical and fuzzy uncertainties. The membership functions of the OF for the studied cones were triangular in nature.

CONCLUSION

The epistemic uncertainty arising due to limited number of data samples holds a significant fraction of the prescribed dose, and therefore, should not be ignored in the total uncertainty estimation.

ADVANCES IN KNOWLEDGE

This study highlights the significance of epistemic component of measurement uncertainty arising out due to the insufficient/limited number of measurements of a quantity.

A Review of Healthcare Failure Mode and Effects Analysis (HFMEA) in Radiotherapy

This paper presents a review of risk analyses in radiotherapy (RT) processes carried out by using Healthcare Failure Mode Effect Analysis (HFMEA) methodology, a qualitative method that proactively identifies risks to patients and corrects medical errors before they occur. This literature review was performed to provide an overview of how to approach the development of HFMEA applications in modern RT procedures, comparing recently published research conducted to support proactive programs to identify risks. On the basis of the reviewed literature, the paper suggests HFMEA shortcomings that need to be addressed.

Risk assessment of component failure modes and human errors using a new FMECA approach: application in the safety analysis of HDR brachytherapy

Failure mode, effects and criticality analysis (FMECA) is a safety technique extensively used in many different industrial fields to identify and prevent potential failures. In the application of traditional FMECA, the risk priority number (RPN) is determined to rank the failure modes; however, the method has been criticised for having several weaknesses. Moreover, it is unable to adequately deal with human errors or negligence. In this paper, a new versatile fuzzy rule-based assessment model is proposed to evaluate the RPN index to rank both component failure and human error. The proposed methodology is applied to potential radiological over-exposure of patients during high-dose-rate brachytherapy treatments. The critical analysis of the results can provide recommendations and suggestions regarding safety provisions for the equipment and procedures required to reduce the occurrence of accidental events.

Estimation of distance error by fuzzy set theory required for strength determination of HDR (192)Ir brachytherapy sources

Verification of the strength of high dose rate (HDR) (192)Ir brachytherapy sources on receipt from the vendor is an important component of institutional quality assurance program. Either reference air-kerma rate (RAKR) or air-kerma strength (AKS) is the recommended quantity to specify the strength of gamma-emitting brachytherapy sources. The use of Farmer-type cylindrical ionization chamber of sensitive volume 0.6 cm(3) is one of the recommended methods for measuring RAKR of HDR (192)Ir brachytherapy sources. While using the cylindrical chamber method, it is required to determine the positioning error of the ionization chamber with respect to the source which is called the distance error. An attempt has been made to apply the fuzzy set theory to estimate the subjective uncertainty associated with the distance error. A simplified approach of applying this fuzzy set theory has been proposed in the quantification of uncertainty associated with the distance error. In order to express the uncertainty in the framework of fuzzy sets, the uncertainty index was estimated and was found to be within 2.5%, which further indicates that the possibility of error in measuring such distance may be of this order. It is observed that the relative distance li estimated by analytical method and fuzzy set theoretic approach are consistent with each other. The crisp values of li estimated using analytical method lie within the bounds computed using fuzzy set theory. This indicates that li values estimated using analytical methods are within 2.5% uncertainty. This value of uncertainty in distance measurement should be incorporated in the uncertainty budget, while estimating the expanded uncertainty in HDR (192)Ir source strength measurement.