Error Scenarios Research Articles

TU‐C‐108‐09: What VMAT Delivery Errors Can Be Detected with Commercial Dosimetric Systems? ‐ A Comparison of Three Dosimetric Systems

Purpose: To study the sensitivity of three commercial dosimetric systems MULTIcube (MatriXX Evolution), ArcCHECK and Octavius‐4D, in detecting errors during the delivery of Volumetric Modulated Arc Therapy (VMAT). Methods: A simple VMAT arc with 111 Control‐Points (CP) was created in Pinnacle using a 6MV photon beam model for an Elekta‐Synergy accelerator. The aperture size for each CP was set to 5 × 10 cm2 and equal weights were assigned to all CPs. Three error scenarios considering Multi Leaf Collimator (MLC) position and dose delivery were simulated: Type1: Systematic shift in MLC position (from 1mm to 5mm) to the whole arc and systematic variation in output (from 1% to 10%) to the whole arc, Type2: Sag in MLC position as a function of gantry angle (amplitude ranging from 1mm to 5mm) and output variation as a function of gantry angle (amplitude ranging from 1% to 10%), Type3: MLC and output errors (ranging from 1mm to 10mm and 1% to 100% respectively) introduced to only two of the CPs. The difference in gamma (γ) pass rate between the error‐free and error‐simulated measurements with 2%/2mm tolerance was calculated to assess the sensitivity of dosimetric systems in detecting errors. Results: All three dosimetric systems detected type1 error. The γ pass rate reduced gradually as the error value increased beyond 2%/2mm tolerance. The MULTIcube system failed to detect the type2 errors whereas the γ pass rate in ArcCHECK and Octavius measurements decreased as the simulated error value increased. For type3 errors MULTIcube and Octavius measurements showed no change in pass rate whereas Arccheck measurements showed 5.9% and 2.6% decrease in pass rate at 10mm MLC error and 100% dose error respectively. Conclusion: ArcCHECK was shown to be sensitive to all types of simulated error. MULTIcube showed the least sensitivity, detecting only systematic errors in whole arc

A Generalized Fellegi–Sunter Framework for Multiple Record Linkage With Application to Homicide Record Systems

We present a probabilistic method for linking multiple datafiles. This task is not trivial in the absence of unique identifiers for the individuals recorded. This is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation, and in general when multiple record systems need to be integrated for posterior analysis. Our method generalizes the Fellegi–Sunter theory for linking records from two datafiles and its modern implementations. The goal of multiple record linkage is to classify the record K-tuples coming from K datafiles according to the different matching patterns. Our method incorporates the transitivity of agreement in the computation of the data used to model matching probabilities. We use a mixture model to fit matching probabilities via maximum likelihood using the Expectation–Maximization algorithm. We present a method to decide the record K-tuples membership to the subsets of matching patterns and we prove its optimality. We apply our method to the integration of the three Colombian homicide record systems and perform a simulation study to explore the performance of the method under measurement error and different scenarios. The proposed method works well and opens new directions for future research.

An audit of prescribing errors in neonates and paediatrics

AimsAn online system known as ‘Datix’ is utilised locally for the voluntary reporting of prescribing errors. The actual rate of prescribing errors in neonates and children is unknown. This re-audit...

Growth Models for Fisheries: The Effect of Unbalanced Sampling Error On Model Selection, Parameter Estimation, and Biological Predictions

Field studies that attempt to estimate the mean growth rates of individuals in a population usually yield unbalanced data and various forms of sampling error. If these data are a poor representation of a population, then the estimates of growth rates may also be unrepresentative of the population. We present an assessment of the performance and uncertainty in 4 growth models—the von Bertalanffy, Gompertz, inverse logistic, and Schnute models—fitted to data with various scenarios of sampling error. The performance of each model was determined by comparing highest likelihoods outcomes to known case outcomes. A Monte Carlo simulation framework was used to generate data consisting of 8 typical scenarios of sampling error common in tag-recapture data. Each growth model was evaluated according to 2 metrics: the error rate (i.e., a metric for model uncertainty) and the prediction error (i.e., the accuracy of biological predictions such as age). Results indicate that an inadequate size range in the data (i.e., a lack of juvenile size classes) would often lead to a high error rate. When negative growth increment data are included, the K parameter of the von Bertalanffy model increased, and the L∞ decreased. The inverse logistic model sometimes produced absurd parameter estimates but nevertheless generated the lowest prediction errors. A high prediction error can potentially have far more serious implications to fishery stock assessments than is currently appreciated. Given widespread use of the von Bertalanffy and Gompertz models, selected solely on the basis of model selection criteria, it is clear that greater care and scrutiny are warranted in the selection of growth models in the presence of sampling error.

On higher ground: ethical reasoning and its relationship with error disclosure

BackgroundThere is broad consensus that disclosure of harmful medical errors is vital to improve safety and is ethically required. Although most physicians-in-training are taught ethics, there have been no empirical...

Network RTK 환경에서 위성에 의한 이상 검출 기법

A positioning technique using the augmentation system has been researched to improve the accuracy. The network RTK is the precise positioning technique using carrier phase correction data from reference stations and is constantly being researched. The study for the system accuracy has been performed but system integrity research has not been done as much as system accuracy. In this paper, we presented the anomaly detection algorithm by satellite system and the diagnosis algorithm to a basic research in the integrity on network RTK. And the presented algorithms are verified on the DL-V3 dual-frequency receiver and the simulated error scenario using the GSS7700.

Advantages and limitations of the ‘worst case scenario’ approach in IMPT treatment planning

The ‘worst case scenario’ (also known as the minimax approach in optimization terms) is a common approach to model the effect of delivery uncertainties in proton treatment planning. Using the ‘dose-error-bar distribution’ previously reported by our group as an example, we have investigated in more detail one of the underlying assumptions of this method. That is, the dose distributions calculated for a limited number of worst case patient positioning scenarios (i.e. limited number of shifts sampled on a spherical surface) represent the worst dose distributions that can occur during the patient treatment under setup uncertainties. By uniformly sampling patient shifts from anywhere within a spherical error-space, a number of treatment scenarios have been simulated and dose deviations from the nominal dose distribution have been computed. The dose errors from these simulations (comprehensive approach) have then been compared to the dose-error-bar approach previously reported (surface approximation) using both point-by-point and dose– and error–volume–histogram analysis (DVH/EVHs). This comparison has been performed for two different clinical cases treated using intensity modulated proton therapy (IMPT): a skull-base and a spinal-axis tumor. Point-by-point evaluation shows that the surface approximation leads to a correct estimation (95% accuracy) of the potential dose errors for the 96% and 85% of the irradiated voxels, for the two investigated cases respectively. We also found that the voxels for which the surface approximation fails are generally localized close to sharp soft tissue–bone interfaces and air cavities. Moreover, analysis of EVHs and DVHs for the two cases shows that the percentage of voxels of a given volume of interest potentially affected by a certain maximum dose error is correctly estimated using the surface approximation and that this approach also accurately predicts the upper and lower bounds of the DVH curves that can occur under positioning uncertainties. In conclusion, the assumption that the larger the patient shift the worse the dose error does not always hold on a point-by-point basis. Nevertheless, when performing a volumetric analysis, a limited set of worst case error scenarios correctly represents the worst quality of the plan in presence of setup errors. As a consequence of these results, we believe that the worst case scenario approach can be used in the IMPT planning procedure for estimating plan robustness provided that the possible limitations of this approach are known.

Quality risk analysis in a cGMP environment: multiple models for comprehensive failure mode identification during the computer system lifecycle

Pharmaceutical quality systems use various inputs to ensure product quality and prevent failures that might have patient consequences. These inputs are generally data from failures that have already occurred, for example process deviations or customer complaints. Risk analysis techniques are well-established in certain other industries and have become of interest to pharmaceutical manufacturers because they allow potential quality failures to be predicted and mitigating action taken in advance of their occurring. Failure mode and effects analysis (FMEA) is one such technique, and in this study it was applied to implement a computerized manufacturing execution system in a pharmaceutical manufacturing environment. After introduction, the system was monitored to detect failures that did occur and these were analyzed to determine why the risk analysis method failed to predict them. Application of FMEA in other industries has identified weaknesses in predicting certain error types, specifically its dependence on other techniques to model risk situations and its poor analysis of non-hardware risks, such as human error, and this was confirmed in this study. Hierarchical holographic modeling (HHM), a technique for identifying risk scenarios in wide-scope analyses, was applied subsequently and identified additional potential failure modes. The technique for human error rate prediction (THERP) has previously been used for the quantitative analysis of human error risk and the event tree from this technique was adapted and identified further human error scenarios. These were input to the FMEA for prioritization and mitigation, thereby strengthening the risk analysis in terms of failure modes considered.

Approximate and Pseudo-Likelihood Analysis for Logistic Regression Using External Validation Data to Model Log Exposure

A common goal in environmental epidemiologic studies is to undertake logistic regression modeling to associate a continuous measure of exposure with binary disease status, adjusting for covariates. A frequent complication is that exposure may only be measurable indirectly, through a collection of subject-specific variables assumed associated with it. Motivated by a specific study to investigate the association between lung function and exposure to metal working fluids, we focus on a multiplicative-lognormal structural measurement error scenario and approaches to address it when external validation data are available. Conceptually, we emphasize the case in which true untransformed exposure is of interest in modeling disease status, but measurement error is additive on the log scale and thus multiplicative on the raw scale. Methodologically, we favor a pseudo-likelihood (PL) approach that exhibits fewer computational problems than direct full maximum likelihood (ML) yet maintains consistency under the assumed models without necessitating small exposure effects and/or small measurement error assumptions. Such assumptions are required by computationally convenient alternative methods like regression calibration (RC) and ML based on probit approximations. We summarize simulations demonstrating considerable potential for bias in the latter two approaches, while supporting the use of PL across a variety of scenarios. We also provide accessible strategies for obtaining adjusted standard errors to accompany RC and PL estimates.

Prevention of a wrong‐location misadministration through the use of an intradepartmental incident learning system

A series of examples are presented in which potential errors in the delivery of radiation therapy were prevented through use of incident learning. These examples underscore the value of reporting near miss incidents. Using a departmental incident learning system, eight incidents were noted over a two-year period in which fields were treated "out-of-sequence," that is, fields from a boost phase were treated, while the patient was still in the initial phase of treatment. As a result, an error-prevention policy was instituted in which radiation treatment fields are "hidden" within the oncology information system (OIS) when they are not in current use. In this way, fields are only available to be treated in the intended sequence and, importantly, old fields cannot be activated at the linear accelerator control console. No out-of-sequence treatments have been reported in more than two years since the policy change. Furthermore, at least three near-miss incidents were detected and corrected as a result of the policy change. In the first two, the policy operated as intended to directly prevent an error in field scheduling. In the third near-miss, the policy operated "off target" to prevent a type of error scenario that it was not directly intended to prevent. In this incident, an incorrect digitally reconstructed radiograph (DRR) was scheduled in the OIS for a patient receiving lung cancer treatment. The incorrect DRR had an isocenter which was misplaced by approximately two centimeters. The error was a result of a field from an old plan being scheduled instead of the intended new plan. As a result of the policy described above, the DRR field could not be activated for treatment however and the error was discovered and corrected. Other quality control barriers in place would have been unlikely to have detected this error. In these examples, a policy was adopted based on incident learning, which prevented several errors, at least one of which was potentially severe. These examples underscore the need for a rigorous, systematic incident learning process within each clinic. The experiences reported in this technical note demonstrate the value of near-miss incident reporting to improve patient safety.

The relative importance of rainfall, temperature and yield data for a regional-scale crop model

When projecting future crop production, the skill of regional scale (>100km resolution) crop models is limited by the spatial and temporal accuracy of the calibration and weather data used. The skill of climate models in reproducing surface properties such as mean temperature and rainfall patterns is of critical importance for the simulation of crop yield. However, the impact of input data errors on the skill of regional scale crop models has not been systematically quantified. We evaluate the impact of specific data error scenarios on the skill of regional scale hindcasts of groundnut yield in the Gujarat region of India, using observed input data with the GLAM crop model. Two methods were employed to introduce error into rainfall, temperature and crop yield inputs at seasonal and climatological timescales: (1) random temporal resequencing, and (2) biasing values.We find that, because the study region is rainfall limited, errors in rainfall data have the most significant impact on model skill overall. More generally, we find that errors in inter-annual variability of seasonal temperature and precipitation cause the greatest crop model error. Errors in the crop yield data used for calibration increased root mean square error by up to 143%. Given that cropping systems are subject both to a changing climate and to ongoing efforts to reduce the yield gap, both potential and actual crop productivity at the regional scale need to be measured.We identify three key endeavours that can improve the ability to assess future crop productivity at the regional scale: (i) increasingly accurate representation of inter-annual climate variability in climate models; (ii) similar studies with other crop models to identify their relative strengths in dealing with different types of climate model error; (iii) the development of techniques to assess potential and actual yields, with associated confidence ranges, at the regional scale.

Resilient data encoding for fault-prone signal transmission in parallelized signed-digit based arithmetic

When arithmetic components are parallelized, fault-prone interconnections can tamper results significantly. Constantly progressing technology scaling leads to a steady increase of errors caused by faulty transmission. Resilient data encoding schemes can be used to offset these negative effects. Focusing on parallel signed-digit based arithmetic frequently used in highspeed systems, we propose suitable data encodings that reduce error rates by 25%. Data encoding should be driven by the occurrence probabilities of digits. We develop a methodology to obtain these probabilities, show an example fault-tolerant encoding, and discuss its impact on communicating parallel arithmetic circuits in an example error scenario.

1009 Clinical Uncertainty and the Paediatric Trainee

Background and AimsUncertainty is at the heart of clinical practice and is encountered by trainees at all levels. This study explored paediatrics trainees’ perceptions of uncertainty.MethodsBasic specialist trainees in Paediatrics...

Impact of radar‐rainfall error structure on estimated flood magnitude across scales: An investigation based on a parsimonious distributed hydrological model

The goal of this study is to diagnose the manner in which radar‐rainfall input affects peak flow simulation uncertainties across scales. We used the distributed physically based hydrological model CUENCAS with parameters that are estimated from available data and without fitting the model output to discharge observations. We evaluated the model's performance using (1) observed streamflow at the outlet of nested basins ranging in scale from 20 to 16,000 km2 and (2) streamflow simulated by a well‐established and extensively calibrated hydrological model used by the US National Weather Service (SAC‐SMA). To mimic radar‐rainfall uncertainty, we applied a recently proposed statistical model of radar‐rainfall error to produce rainfall ensembles based on different expected error scenarios. We used the generated ensembles as input for the hydrological model and summarized the effects on flow sensitivities using a relative measure of the ensemble peak flow dispersion for every link in the river network. Results show that peak flow simulation uncertainty is strongly dependent on the catchment scale. Uncertainty decreases with increasing catchment drainage area due to the aggregation effect of the river network that filters out small‐scale uncertainties. The rate at which uncertainty changes depends on the error structure of the input rainfall fields. We found that random errors that are uncorrelated in space produce high peak flow variability for small scale basins, but uncertainties decrease rapidly as scale increases. In contrast, spatially correlated errors produce less scatter in peak flows for small scales, but uncertainty decreases slowly with increasing catchment size. This study demonstrates the large impact of scale on uncertainty in hydrological simulations and demonstrates the need for a more robust characterization of the uncertainty structure in radar‐rainfall. Our results are diagnostic and illustrate the benefits of using the calibration‐free, multiscale framework to investigate uncertainty propagation with hydrological models.

A Kalman filter based synchronization scheme for telescope array receivers in deep-space optical communication links

In this paper, the impact of random synchronization errors on the performance of ground-based telescope array receivers for an inter-planetary optical deep-space communication (ODSC) link is investigated. An adaptive method based on Kalman filters is developed for the synchronization and combination of different telescope signals in the array. An end-to-end simulation platform for ODSC link between Earth and planet Mars is implemented that incorporates pulse-position modulation (PPM), direct-detection array receivers, and photon-counting detectors. The effects of atmospheric turbulence and background noise are also modeled. The performance of array receivers is evaluated in terms of probability of symbol error and achievable data rates. The simulation results show that the Kalman filter-based synchronization scheme keeps the synchronization induced power losses to less than 1dB. The analysis also shows that in the worst-case operational scenario and presence of random synchronization errors, an array consisting of hundred, 1m telescopes performs almost similar to a single 10m telescope. Hence, the degradation in the combined signal due to synchronization errors places a minor limitation on the number of telescopes in a telescope array receiver consisting of up to 100 telescope elements.

Nurses’ disclosure of error scenarios in nursing homes

BackgroundLittle work has explored the disclosure of errors in nursing homes (NHs). PurposeThis paper reports how nurses would disclose hypothetical errors that occur in NH settings. MethodA cross-sectional survey was given to a randomly selected sample of registered nurses (RNs) and registered practical nurses (RPNs) working in Ontario, Canada NHs. ResultsOf 1,180 respondents, only half might provide full details and the cause of the error and provide steps in how the error would be prevented if they were in situations described by the hypothetical scenarios. Scenarios that were less serious had an almost 3 times higher likelihood of an explicit apology (OR 2.97; 95% CI 1.36-6.51; P = 0.007). Nurses who were RNs, had more education, had a prior history of disclosing a serious error, and agreed with full disclosure were more likely to respond to disclosing more information about the error. Nurses also reported numerous barriers to effective disclosure in their workplace. ConclusionImprovements in NH safety culture are necessary to enhance the error disclosure process.

TH‐A‐213AB‐08: Robust Multi‐Criteria IMPT Optimization

Purpose: We present a method to include robustness in a multi‐criteria optimization (MCO) framework for intensity‐modulated proton therapy (IMPT). The approach allows one to simultaneously explore the trade‐off between different objectives including robustness and nominal plan quality. Methods: A database of plans each emphasizing different treatment planning objectives, is pre‐computed to approximate the Pareto surface. An IMPT treatment plan that strikes the best balance between the different objectives can be selected by navigating on the Pareto surface. We integrate robustness into MCO by adding robustified objectives and constraints. Uncertainties are modeled by pre‐calculated dose‐influence matrices for a nominal scenario and a number of pre‐defined error scenarios (shifted patient positions, proton beam undershoot and overshoot). A robustified objective represents the worst objective function value that can be realized for any of the error scenarios and thus provides a measure of plan robustness. The optimization method uses a linear projection solver and is capable of handling large problem sizes resulting from a fine dose grid resolution, many scenarios, and a large number of proton pencil beams. Results: A base‐of‐skull case is used to demonstrate that the robust optimization method reduces the sensitivity of the treatment plan to setup and range errors to a degree that is not achieved by a safety margin approach. A chordoma case is analyzed in more detail to demonstrate the involved trade‐offs between target underdose and brainstem sparing as well as robustness and nominal plan quality. The robust optimization for each Pareto optimal plan takes less than 5 min on a standard computer. Conclusions: The uncertainty pertinent to the IMPT procedure can be reduced during treatment planning by optimizing a database of plans that emphasize different treatment objectives, including robustness. The planner can then interactively explore all convex combinations of database plans to decide on the most‐preferred trade‐off.

SU-E-T-636: Comparison of RapidArc-Based Radiosurgery with Cone-Based Cyberknife Treatment for Multiple Intracranial Tumors.

To investigate the feasibility of RapidArc technique on intracranial radiosurgery for multiple lesions. Six patients who were previously treated using cone-based technique, Cyberknife were included in this study. These patients have multiple lesions (6-9, mostly metastasis). In our current clinical practice, each lesion was planned and treated individually. The prescription was 15-21 Gy at 80% with single fraction. These cases were replanned with RapidArc on the platform of Varian Truebeam STx equipped with high resolution MLC leaves of 2.5mm at center. The maximum dose rate is 1400 MU/min at 6 MV for flattering filter free mode. Because of long span of multiple lesions, the targets were divided into two groups with two isocenters. Each plan with one isocenter contains 4 non-coplanar arcs, and dose optimization was performed with the two plans combined. Critical organs, such as eyes, brainstem and brain were constrained. The individual Cyberknife plans were summed to compare with the RapidArc plan. Scenarios of setup error were simulated during RapidArc treatment. RapidArc plans can achieve comparable target coverage and normal tissue avoidance to Cyberknife plans. The brain dose volume histogram (DVH) curves of the two techniques are similar in spite of different appearance of their 3D dose distributions. MU is much higher for summed Cyberknife plan. Because RapidArc can treat several lesions together, the complete treatment time for all lesions is significantly reduced. However RapidArc treatment is susceptible to setup error, which may cause increase in normal tissue dose and decrease in target dose coverage. The level of discrepancy depends on the magnitude of setup error, location and dose distribution of the target. Multiple brain lesions treatment with RapidArc radiosurgery is clinically feasible with setup error fully accounted. It can provide dose performance comparable to cone-based Cyberknife treatment.

A vignette study to examine health care professionals' attitudes towards patient involvement in error prevention

Various authorities recommend the participation of patients in promoting patient safety, but little is known about health care professionals' (HCPs') attitudes towards patients' involvement in safety-related behaviours. To investigate how HCPs evaluate patients' behaviours and HCP responses to patient involvement in the behaviour, relative to different aspects of the patient, the involved HCP and the potential error. Cross-sectional fractional factorial survey with seven factors embedded in two error scenarios (missed hand hygiene, medication error). Each survey included two randomized vignettes that described the potential error, a patient's reaction to that error and the HCP response to the patient. Twelve hospitals in Switzerland. A total of 1141 HCPs (response rate 45%). Approval of patients' behaviour, HCP response to the patient, anticipated effects on the patient-HCP relationship, HCPs' support for being asked the question, affective response to the vignettes. Outcomes were measured on 7-point scales. Approval of patients' safety-related interventions was generally high and largely affected by patients' behaviour and correct identification of error. Anticipated effects on the patient-HCP relationship were much less positive, little correlated with approval of patients' behaviour and were mainly determined by the HCP response to intervening patients. HCPs expressed more favourable attitudes towards patients intervening about a medication error than about hand sanitation. This study provides the first insights into predictors of HCPs' attitudes towards patient engagement in safety. Future research is however required to assess the generalizability of the findings into practice before training can be designed to address critical issues.

Measurement error in network data: A re-classification

Research on measurement error in network data has typically focused on missing data. We embed missing data, which we term false negative nodes and edges, in a broader classification of error scenarios. This includes false positive nodes and edges and falsely aggregated and disaggregated nodes. We simulate these six measurement errors using an online social network and a publication citation network, reporting their effects on four node-level measures – degree centrality, clustering coefficient, network constraint, and eigenvector centrality. Our results suggest that in networks with more positively-skewed degree distributions and higher average clustering, these measures tend to be less resistant to most forms of measurement error. In addition, we argue that the sensitivity of a given measure to an error scenario depends on the idiosyncracies of the measure's calculation, thus revising the general claim from past research that the more ‘global’ a measure, the less resistant it is to measurement error. Finally, we anchor our discussion to commonly-used networks in past research that suffer from these different forms of measurement error and make recommendations for correction strategies.