Error Predictor Research Articles

Post-process correction improves the accuracy of satellite PM2.5 retrievals

Abstract. Estimates of PM2.5 levels are crucial for monitoring air quality and studying the epidemiological impact of air quality on the population. Currently, the most precise measurements of PM2.5 are obtained from ground stations, resulting in limited spatial coverage. In this study, we consider satellite-based PM2.5 retrieval, which involves conversion of high-resolution satellite retrieval of aerosol optical depth (AOD) into high-resolution PM2.5 retrieval. To improve the accuracy of the AOD-to-PM2.5 conversion, we employ the machine-learning-based post-process correction to correct the AOD-to-PM conversion ratio derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis model data. The post-process-correction approach utilizes a fusion and downscaling of satellite observation and retrieval data, MERRA-2 reanalysis data, various high-resolution geographical indicators, meteorological data, and ground station observations for learning a predictor for the approximation error in the AOD-to-PM2.5 conversion ratio. The corrected conversion ratio is then applied to estimate PM2.5 levels given the high-resolution satellite AOD retrieval data derived from Sentinel-3 observations. The region of study is central Europe during the year 2019. Our model produces PM2.5 estimates with a spatial resolution of 100 m at satellite overpass times with R2 = 0.55 and RMSE = 6.2 µg m−3. The corresponding metrics for monthly averages are R2 = 0.72 and RMSE = 3.7 µg m−3. Additionally, we have incorporated an ensemble of neural networks to provide error envelopes for machine-learning-related uncertainty in the PM2.5 estimates. The proposed approach can produce accurate high-resolution PM2.5 data that can be very useful for air quality monitoring, emission regulation, and epidemiological studies.

A Multivariate Approach to Quantifying Risk Factors Impacting Stereotactic Robotic-Guided Stereoelectroencephalography.

Stereoelectroencephalography (SEEG) is an important method for invasive monitoring to establish surgical candidacy in approximately half of refractory epilepsy patients. Identifying factors affecting lead placement can mitigate potential surgical risks. This study applies multivariate analyses to identify perioperative factors affecting stereotactic electrode placement. We collected registration and accuracy data for consecutive patients undergoing SEEG implantation between May 2022 and November 2023. Stereotactic robotic guidance, using intraoperative imaging and a novel frame-based fiducial, was used for planning and SEEG implantation. Entry-point (EE), target-point (TE), and angular errors were measured, and statistical univariate and multivariate linear regression analyses were performed. Twenty-seven refractory epilepsy patients (aged 15-57 years) undergoing SEEG were reviewed. Sixteen patients had unilateral implantation (10 left-sided, 6 right-sided); 11 patients underwent bilateral implantation. The mean number of electrodes per patient was 18 (SD = 3) with an average registration mean error of 0.768 mm (SD = 0.108). Overall, 486 electrodes were reviewed. Univariate analysis showed significant correlations of lead error with skull thickness (EE: P = .003; TE: P = .012); entry angle (EE: P < .001; TE: P < .001; angular error: P = .030); lead length (TE: P = .020); and order of electrode implantation (EE: P = .003; TE: P = .001). Three multiple linear regression models were used. All models featured predictors of implantation region (157 temporal, 241 frontal, 79 parietal, 9 occipital); skull thickness (mean = 5.80 mm, SD = 2.97 mm); order (range: 1-23); and entry angle in degrees (mean = 75.47, SD = 11.66). EE and TE error models additionally incorporated lead length (mean = 44.08 mm, SD = 13.90 mm) as a predictor. Implantation region and entry angle were significant predictors of error (P ≤ .05). Our study identified 2 primary predictors of SEEG lead error, region of implantation and entry angle, with nonsignificant contributions from lead length or order of electrode placement. Future considerations for SEEG may consider varying regional approaches and angles for more optimal accuracy in lead placement.

Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy.

We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have demonstrated this method in brain OAR AS on MRI, using internal and external (third-party) AS models. Thirty-two retrospectives, MRI planned, glioma cases were randomly selected from a local clinical cohort for ACo training. A generator was trained adversarialy to produce internal autosegmentations (IAS) with a discriminator to estimate voxel-wise IAS uncertainty, given the input MRI. Confidence maps for each proposed segmentation were produced for operator use in AS editing and were compared with "difference to gold-standard" error maps. Nine cases were used for testing ACo performance on IAS and validation with two external deep learning segmentation model predictions [external model with low-quality AS (EM-LQ) and external model with high-quality AS (EM-HQ)]. Matthew's correlation coefficient (MCC), false-positive rate (FPR), false-negative rate (FNR), and visual assessment were used for evaluation. Edge removal and geometric distance corrections were applied to achieve more useful and clinically relevant confidence maps and performance metrics. ACo showed generally excellent performance on both internal and external segmentations, across all OARs (except lenses). MCC was higher on IAS and low-quality external segmentations (EM-LQ) than high-quality ones (EM-HQ). On IAS and EM-LQ, average MCC (excluding lenses) varied from 0.6 to 0.9, while average FPR and FNR were ≤0.13 and ≤0.21, respectively. For EM-HQ, average MCC varied from 0.4 to 0.8, while average FPR and FNR were ≤0.37 and ≤0.22, respectively. ACo was a reliable predictor of uncertainty and errors on AS generated both internally and externally, demonstrating its potential as an independent, reference-free QA tool, which could help operators deliver robust, efficient autosegmentation in the radiotherapy clinic.

Exploring the relationship between frequent cannabis use, belief updating under uncertainty and psychotic-like symptoms.

Cannabis users present an important group for investigating putative mechanisms underlying psychosis, as cannabis-use is associated with an increased risk of psychosis. Recent work suggests that alterations in belief-updating under uncertainty underlie psychosis. We therefore compared belief updating under uncertainty between cannabis and non-cannabis users. 49 regular cannabis users and 52 controls completed the Space Game, via an online platform used for behavioral testing. In the task, participants were asked to predict the location of the stimulus based on previous information, under different uncertainty conditions. Mixed effects models were used to identify significant predictors of mean score, confidence, performance error and learning rate. Both groups showed decreased confidence in high noise conditions, and increased belief updating in more volatile conditions, suggesting that they could infer the degree and sources of uncertainty. There were no significant effects of group on any of the performance indices. However, within the cannabis group, frequent users showed worse performance than less frequent users. Belief updating under uncertainty is not affected by cannabis use status but could be impaired in those who use cannabis more frequently. This finding could show a similarity between frequent cannabis use and psychosis risk, as predictors for abnormal belief-updating.

Investigating the Professional Commitment and its Correlation with Patient Safety Culture and Patient Identification Errors: Evidence from a Cross-Sectional Study from Nurses' Perspectives

Introduction Nurses' professional commitment is crucial to their qualifications, impacting patient safety. This study aims to explore the relationship between nurses' professional commitment and patient safety culture, specifically focusing on patient identification errors. Methods A cross-sectional study was conducted in 2022 involving 340 nurses working in educational medical hospitals in southern Iran. Data collection instruments included questionnaires on nurses' professional commitment, patient identification errors, and patient safety culture. Descriptive and inferential statistics, such as t-tests, ANOVA, Pearson's correlation coefficient, and multiple linear regression, were performed using SPSS software version 23, with a significance level set at p = 0.05. Results The mean scores for professional commitment, patient safety culture, and errors in patient identification, as perceived by the nurses, were 77.64 ± 14.32 (out of 130), 2.71 ± 0.78 (out of 5), and 16.41 ± 4.58 (out of 32), respectively. There was a statistically significant correlation between professional commitment and errors in patient identification (r = -0.577, p &lt; 0.001) as well as patient safety culture (r = 0.456, p &lt; 0.001). Regression analysis revealed that nursing job satisfaction, understanding of nursing, self-sacrifice for the nursing profession, and engagement with the nursing profession were predictors of patient safety culture and errors in patient identification (p &lt; 0.05). Conclusion Nurses' professional commitment was found to be at a moderate level. Furthermore, the dimensions of professional commitment significantly influenced patient safety culture and errors in patient identification. To enhance nurses' professional commitment, and subsequently improve patient safety culture and reduce identification errors, it is recommended to implement management mechanisms that increase job satisfaction, understanding, engagement, and professional self-sacrifice among nurses.

Delineating patient errors in an intracavernosal injection program.

Intracavernosal injection therapy (ICI) is a well-established therapeutic strategy for men with erectile dysfunction. Complications are often related to patient error when performong ICI. The objective of this study was to examine patient errors in an established patient training program for performing ICI and identify factors that could predict major errors. Patients enrolled in our ICI program are trained on technical aspects, and dose titration is begun. Patients are given explicit instructions during training, both verbally and in written form. Records were reviewed for men using ICI for ≥6months. Multivariable analysis was used to define predictors of major errors. Errors were listed as minor (zero-response injection, penile bruising, expired medication) and major (errors potentially leading to priapism: dose self-titration, double injecting). Overall, 1368 patients met the inclusion criteria and were included in the analysis. The mean patient age was 66 ± 22 (range 29-91) years. Regarding education, 41% of patients had graduate-level education, 48% had college education, and 11% high school education. Mean follow-up was 3.2 ± 7.6 (range 0.5-12) years. The agents used were trimix (62%), bimix (35%), papaverine (2%), and prostaglandin E1 monotherapy (1%). At least 1 error occurred during self-administration in 42% of patients during their time in the program. Errors included zero response to medication due to technical error (8% of patients), penile bruising (34%), use of an expired bottle (18%), self-titration (5%), and double injecting (4% of patients); 12% of men committed ≥1 error during their time in the program. On multivariable analysis, independent predictors of the occurrence of a major error included: young age, graduate-level education, and <12months of injection use. To the best of our knowledge, this is the first reported study to investigate ICI errors and risk factors. The identification of factors predictive of major errors allows for more tailored and intensive training in this subset of patients. Strengths of this study include a large patient population (1386 men) with a considerable follow-up time. Additionally, the rigorous training, education, and monitoring of the participants, as well as the use of formal definitions, enhances the accuracy and reliability of the results. Despite the strengths of the study, recall bias may be a limitation concern. The majority of patients were error free, and the majority of the errors were minor in nature. Major errors occurred in <10% of patients. Younger age, graduate-level education, and less experience with ICI were independent predictors of major errors.

Understanding the operators’ cloud change errors based on cognitive abilities and personality traits: An investigation integrated with quantitative and qualitative methods

While cloud services make industrial data convenient, they also expose it to cloud incidents. Operators' error in cloud change activities is a leading factor for cloud incidents, which have received relatively less attention in cloud security research. This study conducted a two-stage research process using an integrated approach to explore the stable individual factors related to cloud change errors. First, in the qualitative research, content analysis based on interviews and historical documents was conducted to extract the operator's cognitive abilities and personality traits and develop hypotheses. Five cognitive abilities and six personality traits were extracted. Second, quantitative research based on an experiment was conducted to test relationships between operators' different types of cloud change errors and 1) cognitive ability and 2) personality traits, respectively. Results of error type comparisons suggested that operators generated more uncorrected errors than corrected errors and more operational errors than omission errors in cloud change activities. The multivariate Poisson regression analysis suggested that cognitive abilities of sustained attention, divided attention, and long-term memory negatively predicted the number of operators' total errors, uncorrected errors, and operational errors. Regarding personality traits, with the increase in resilience capacity and carefulness and the decrease in self-esteem, the number of different types of errors reduced, except for omission errors. Working memory and risk-taking propensity were also significant predictors of the number of uncorrected errors with negative and positive coefficients, respectively. Logical reasoning, emotional stability, and sense of responsibility were not observed as predictors of cloud change errors. The present findings have several implications for the industry and cloud providers to enhance industrial cloud data security regarding human cognitive abilities and personality traits.

Prevalence of medication errors and its related factors in Iranian nurses: an updated systematic review and meta-analysis

BackgroundNurses may make medication errors during the implementation of therapeutic interventions, which initially threaten the patient’s health and safety and prolong their hospital stay. These errors have always been a challenge for healthcare systems. Given that factors such as the timing, type, and causes of medication errors can serve as suitable predictors for their occurrence, we have decided to conduct a review study aiming to investigate the prevalence of medication errors and the associated factors among Iranian nurses.MethodsIn this systematic review and meta-analysis, studies were searched on PubMed, Web of Science, Scopus, Google Scholar, IranMedex, Magiran, and SID databases using a combination of keywords and Boolean functions. The study that reported the prevalence of medication errors among nurses in Iran without time limitation up to May 2023 was included in this study.ResultsA total of 36 studies were included in the analysis. The analysis indicates that 54% (95% CI: 43, 65; I2 = 99.3%) of Iranian nurses experienced medication errors. The most common types of medication errors by nurses were wrong timing 27.3% (95% CI: 19, 36; I2 = 95.8%), and wrong dosage 26.4% (95% CI: 20, 33; I2 = 91%). Additionally, the main causes of medication errors among nurses were workload 43%, fatigue 42.7%, and nursing shortage 38.8%. In this study, just 39% (95% CI: 27, 50; I2 = 97.1%) of nurses with medication errors did report their errors. Moreover, the prevalence of medication errors was more in the night shift at 41.1%. The results of the meta-regression showed that publication year and the female-to-male ratio are good predictors of medical errors, but they are not statistically significant(p > 0.05).ConclusionsTo reduce medication errors, nurses need to work in a calm environment that allows for proper nursing interventions and prevents overcrowding in departments. Additionally, considering the low reporting of medication errors to managers, support should be provided to nurses who report medication errors, in order to promote a culture of reporting these errors among Iranian nurses and ensure patient safety is not compromised.

Examining the Relationship Between Nurse Fatigue, Alertness, and Medication Errors.

Working for extended hours in a physically and mentally demanding profession has subjected nurses to occupational fatigue. Limited evidence exists about nurse fatigue and alertness changes throughout shift work and their relationship with medication errors and near misses. The purposes of this study were to: (1) assess the relationship between nurses' fatigue and alertness, (2) evaluate nurses' fatigue and alertness changes throughout their shift, and (3) examine the relationship between nurses' fatigue, alertness, and medication errors and near misses. This prospective study is part of a larger mixed-method study. Fatigue and alertness data from 14 work and non-workdays were collected from a convenience sample of 90 nurses. A wearable actigraph (ReadibandTM) was used to measure alertness, while ecological momentary assessment (EMA) using text messaging was used to measure nurses' fatigue. A 1-unit increase in fatigue was associated with a 1.06-unit reduction in nurses' alertness score (β = -1.06, 95% CI: [-1.33, -0.78], p < .01). Night-shift nurses experienced a 31-point reduction in alertness from the start to the end of the work shift. Nurses' fatigue, but not alertness, was associated with medication errors and near misses (OR = 1.26, 95% CI [1.07, 1.48], p = .01). Initiating fatigue mitigation measures during mid-shift, especially for night-shift nurses, may be a viable option to mitigate fatigue and alertness deterioration among nurses and to maintain patient safety. The multifaceted nature of fatigue, as captured by EMA, is a stronger predictor of medication errors and near misses than device-measured alertness.

Performance of an offline systematic error correction strategy in pediatric patients receiving adjuvant conformal radiotherapy for Wilm's tumor.

Radiotherapy plays a key role as an adjuvant treatment in pediatric Wilm's tumor, improving both survival and quality of life. The success of radiotherapy depends on the precise delivery of radiation dose to the tumor while sparing radiosensitive structures in the vicinity of the tumor. Pediatric patients pose unique challenges in achieving accurate radiotherapy delivery due to their inability to understand instructions and the high radiosensitivity of their tissues. Thus, it is important to determine the optimum geometric verification strategy that will ensure accurate delivery of the prescribed target as specified in the patient's treatment plan. To evaluate the performance of an offline geometric correction strategy in ensuring accuracy and reproducibility during radiotherapy delivery in Wilm's tumor patients. The extended no-action level offline correction strategy was applied in the radiotherapy delivery of 45 Wilm's tumor patients. Gross errors from the first three fractions were used to calculate the mean errors which were then applied as offline correction factors. Mean errors among different groups were compared using a two-way analysis of variance (ANOVA) and Dunnett's pairwise comparisons. All statistical analyses and data visualization were performed using GraphPad Prism version 7 (Insight Partners, GraphPad Holdings, LLC). A total of 45 patients were included in the study. In all three orthogonal directions, the recorded gross errors were significantly lower after the application of the systematic error corrections. Random errors were significantly larger in the longitudinal direction compared to lateral (mean difference = 0.28, p = 0.036) and vertical directions (mean difference = 0.37 cm, p = 0.003). Patients' age was a significant predictor of random errors whereby the magnitude of random error decreased with increasing age. This study shows that the offline correction strategy used is effective in ensuring the accuracy of radiotherapy delivery in pediatric Wilm's tumor patients.

Predictors of procedural errors in class II resin composite restorations using bitewing radiographs

ObjectiveTo identify the potential factors that induce procedural errors during posterior proximal resin composite restorations placed by dental students. Materials and MethodsThis retrospective study evaluated 803 bitewing radiographs of posterior proximal resin composite restorations placed by dental students at Imam Abdulrahman bin Faisal University. Atypical radiographic signs of failure were screened, and different patient-, operator-, and clinical-related factors were recorded. Chi-square test was used to examine the relationship between procedural errors and recorded factors. Stepwise adjusted logistic regression model was performed to identify predictors of procedural errors. ResultsThe most observed errors were internal gaps at the bonding interface and internal voids. Molars had 0.39 the risk of internal voids (odds ratio [OR] = 0.39; confidence interval [CI] = 0.25–0.60; P = <0.0001), 0.41 the risk of sharp angle (OR = 0.41; CI = 0.24–0.68; P = <0.001), and 0.57 the risk of open contact (OR = 0.57; CI = 0.34–0.97; P = 0.04) compared to premolars. Those who were >40 years of age had 1.79 the risk of overhang compared to younger patients (OR = 1.79; CI = 1.04–3.11; P = <0.04). First molars and premolars had 0.64 the risk of overhang compared to second molars and premolars (OR = 0.64; CI = 0.41–1.00; P = 0.04). Junior students had 1.97 the risk of internal gap compared to their senior counterparts (OR = 1.97; CI = 1.20–3.21; P = 0.008). Mesial restorations had 0.38 the risk of external gap compared to mesio-occluso-distal (MOD) restorations (OR = 0.38; CI = 0.19–0.78; P = 0.003). Restorations with a margin coronal to the cemento-enamel junction (CEJ) had 0.44 the risk of external gap compared to those restorations with a margin apical to the CEJ (OR = 0.44; CI = 0.29–0.66; P = <0.0001). ConclusionOur findings suggested a higher incidence of procedural errors in restoring premolars and MOD cavity preparations. Therefore, it is crucial to enhance the comprehensiveness of laboratory training and expose students to diverse clinical scenarios and various techniques.

“Multivariate analysis of the impact of sleep and working hours on medical errors: a MICE approach"

BackgroundThe main objective of this study was to describe the relationship between working conditions, sleep and psycho-affective variables and medical errors.MethodsThis was an observational, analytical and cross-sectional study in which 661 medical residents answered questionnaires about working conditions, sleep and psycho-affective variables. Actigraphic sleep parameters and peripheral temperature circadian rhythm were measured in a subgroup of 38 subjects. Bivariate and multivariate predictors of medical errors were assessed.ResultsMedical residents reported working 66.2 ± 21.9 weekly hours. The longest continuous shift was of 28.4 ± 10.9 h. They reported sleeping 6.1 ± 1.6 h per day, with a sleep debt of 94 ± 129 min in workdays. A high percentage of them reported symptoms related to psycho-affective disorders. The longest continuous shift duration (OR = 1.03 [95% CI, 1.00–1.05], p = 0.01), working more than six monthly on-call shifts (OR = 1.87 [95% CI, 1.16–3.02], p = 0.01) and sleeping less than six hours per working day (OR = 1.66 [95% CI, 1.10–2.51], p = 0.02) were independently associated with self-reported medical errors. The report of medical errors was associated with an increase in the percentage of diurnal sleep (2.2% [95% CI, 0.1–4.3] vs 14.5% [95% CI, 5.9–23.0]; p = 0.01) in the actigraphic recording.ConclusionsMedical residents have a high working hour load that affect their sleep opportunities, circadian rhythms and psycho-affective health, which are also related to the report of medical errors. These results highlight the importance of implementing multidimensional strategies to improve medical trainees’ sleep and wellbeing, increasing in turn their own and patients’ safety.

Strengthening gradients in the tropical west Pacific connect to European summer temperatures on sub-seasonal timescales

Abstract. Recent work has shown that (sub-)seasonal variability in tropical Pacific convection, closely linked to the El Niño–Southern Oscillation (ENSO), relates to summertime circulation over the Euro-Atlantic. The teleconnection is non-stationary, probably due to long-term changes in both the tropical Pacific and extra-tropical Atlantic. It also appears imperfectly captured by numerical models. A dipole in west Pacific sea surface temperatures (SSTs) was found to be the best predictor of errors in numerical sub-seasonal forecasts of European temperature. In this diagnostic study we use reanalysis data to further investigate the teleconnection pathway and the processes behind its non-stationarity. We show that SST gradients associated with the dipole represent a combination of ENSO variability and west Pacific warming, and have become stronger since 1980. Associated patterns of suppressed and enhanced tropical heating are followed by quasi-stationary waves that linger for multiple weeks. Situations with La Niña-like gradients are followed by high-pressure centres over eastern Europe and Russia, three to six weeks later. Inverted situations are followed by high pressure over western Europe, three to six weeks later. The latter situation is conditional on a strong meridional tripole in north Atlantic SSTs and a co-located jet stream. Overall, the sub-seasonal pathway diagnosed in this study connects to patterns detected on seasonal scales, and confirms earlier findings that the summertime connectivity between the Pacific and Europe has shifted in recent decades.

Determinants of Refractive Errors on School-going Children Attending Ophthalmic Clinic of AlMoosa Hospital in Saudi Arabia

Objective: The risk factors for vision problems in youngsters are relatively unknown in Saudi Arabia. The purpose of this study was to examine the determinants of refractive errors among school-going children attending the ophthalmic clinic of AlMoosa Hospital in Saudi Arabia. Methodology: A sample of 161 school-going children were recruited voluntarily to complete a set of measures examining the determinants of refractive errors. The binary logistic regression model was applied to determine the relationship between independent and dependent variables. Results: Refractive errors were common among Saudi school-going children, with myopia being the most common type of refractive error. About 96 (59.6%) samples had myopia, 28 (17.4%) children had hyperopia and 27 (16.8%) children did not have any refractive error. Genetic traits and machine dependence were found to be significant predictors of refractive error. Conclusion: The preschool eye test and routine vision investigation should be performed on children to detect refractive errors early.

Evaluation of medication errors in patients with kidney diseases in Quetta, Pakistan.

Medication errors represent a significant challenge in healthcare, as they can lead to enduring harm for patients and impose substantial financial burdens on the healthcare system. To effectively mitigate medication errors, it is imperative to gain a comprehensive understanding of their frequency and the contributing variables. Thus, the primary objective of this study was to evaluate the occurrence of medication errors among patients with kidney diseases in Quetta, Pakistan. The objective of this study was to assess medication errors in patients diagnosed with kidney diseases in Quetta, Pakistan. The research was conducted at the Balochistan Institute of Nephro-Urology Quetta (BINUQ) Hospital, which serves as a tertiary care center specializing in the treatment of kidney diseases. A cross-sectional descriptive study design was employed over a period of six months. The study population consisted of patients admitted to the Nephro-urology wards at BINUQ Hospital during the specified duration. Data collection encompassed various methodologies, including checklist-guided observation, review of prescription order forms, documentation of drug administration, and comprehensive analysis of patient medical records. Descriptive and analytical analyses were conducted using SPSS version 23. Univariate analysis was employed to identify independent variables associated with medication errors, employing a significance level of p<0.01. The multivariate logistic regression analysis incorporated variables that exhibited a significant association with medication errors during the univariate analysis. Only those variables demonstrating a p-value of less than 0.05 at a 95% confidence level were considered significant predictors of medication administration errors within the final multivariate model. Among the 274 medication errors identified in the study, documentation errors accounted for 118 cases (12.06%), administration errors for 97 cases (9.91%), prescribing errors for 34 cases (3.47%), and dispensing errors for 25 cases (2.55%). Statistical analysis revealed significant associations (p<0.05) between forgetfulness and duty shift, and medication errors in the documentation process. Similarly, inattention was significantly associated (p<0.05) with both prescribing and dispensing errors. Furthermore, the number of medications received emerged as the most influential factor associated with medication errors. Patients receiving 4-6 medications exhibited an odds ratio of 9.08 (p<0.001) compared to patients receiving 1-3 medications, while patients receiving more than 6 medications had an odds ratio of 4.23 (p<0.001) in relation to patients receiving 1-3 medications. In conclusion, this study determined that documentation errors were the most prevalent medication errors observed in patients with kidney disease in Quetta, Pakistan. Forgetfulness and duty shift were associated with documentation errors, whereas inattention was linked to prescribing and dispensing errors. The significant risk factor for medication errors was found to be a high number of prescribed medications. Therefore, strategies aimed at reducing medication errors should prioritize enhancements in documentation practices, alleviating medication burden, and increasing awareness among healthcare providers.

Ease of Use ratings and real-world child restraint system errors from Safe Kids Illinois seat checks, 2015–2019

Background Child restraint systems (CRSs) significantly reduce risk of crash-related injury, however installation and use errors undermine their benefits. The National Highway Traffic Safety Administration (NHTSA) created the Ease of Use (EOU) rating system to help guide consumers and incentivize manufacturers to improve their products. The EOU rating system assigns one to five stars to four CRS features and overall. Our study assessed the relationship between EOU ratings and CRS installation and use errors documented in seat checks conducted by child passenger safety technicians (CPSTs). Methods We performed a secondary analysis of data from Safe Kids Illinois seat check records from 2015 through 2019 and EOU ratings from 2008 to 2020. Five types of errors were documented by CPSTs. Study authors (JYL and MLM) used a tiered system to match seat check model numbers to EOU ratings. We calculated chi-square statistics and performed logistic regression analyses to assess for EOU as a predictor of relevant CRS errors (e.g., tether errors for forward-facing CRSs). Results Our analyses included 2132 seat check observations, of which 217 (10.2%) were exact, 244 (10.5%) were probable, and 1671 (78.4%) were near matches via sorting and web search. Errors were most common for seat belts (70.7%) and least common for recline angle (36.9%). Star ratings for instructions, vehicle installation, and labels were associated with recline angle and seat belt errors. Star ratings for instructions, labels, and securing child were associated with harness errors. CRSs with 4-star and 5-star ratings had lower odds of errors for recline angle (Odds Ratio (OR) 0.62; 95% Confidence Interval (CI): 0.43, 0.89 and OR 0.31; 95% CI: 0.17, 0.56) lower anchors (OR 0.59; 95% CI 0.40, 0.89 and OR 0.38; 95% CI: 0.21, 0.68), and harness (OR 0.56; 95% CI: 0.40, 0.76 and OR 0.19; 95% CI: 0.10, 0.35) when compared with 1 and 2-star CRSs. Conclusions This study provides evidence in support of NHTSA’s EOU ratings as predictors of some CRS installation and use errors among caregivers who obtain seat checks. A higher star rating may be helpful for caregivers when choosing a CRS that will yield lower installation errors.

Using recovered radio transmitters to estimate positioning error and a generalized Monte Carlo simulation to incorporate error into animal telemetry analysis

BackgroundMobile radio tracking is an important tool in fisheries research and management. Yet, the accuracy of location estimates can be highly variable across studies and within a given dataset. While some methods are available to deal with error, they generally assume a static value for error across all detections. We provide a novel method for making detection-specific error estimates using detections of recovered transmitters (i.e., mortalities or tag expulsion). These data are used to establish the relationship between received signal strength (RSS) and positional error, which can then be used to predict positional error of detections for fish at large. We then show how detection-specific estimates can be integrated into a Monte Carlo framework to analyze movement in ways robust to spatial uncertainty.ResultsIn a telemetry study in a large river (~ 90 m), we recovered 22 transmitters to estimate and model positional error. Error averaged 94 m (range = 1–727 m) for transmitters tracked by researchers on foot using a Yagi antenna, and 200 m (range = 1–1141 m) for transmitters tracked from vehicles using an omnidirectional whip antenna. Transmitters located near roads were tracked more accurately with both methods. Received signal strength was a strong predictor of positional error (r2 = 0.86, ground tracking; 0.65, tracking from truck) and was thus used to make detection-specific estimates of error for detections of fish at large. Monte Carlo analysis for a binary movement classification revealed that only 18% of location estimates could be confidently assigned to movement (p < 0.05); the remainder were associated with stasis or movement that was within the range of positional error. Ignoring positional error led to positive bias of up to 1300% in individual movement estimates and varied seasonally—it was highest when fish were inactive and lowest when fish were most active.ConclusionUsing recovered transmitters and RSS models to estimate telemetry error is a viable alternative to staged ‘dummy transmitter’ trials and assuming error is a constant. Our proposed approaches to incorporate detection-specific error estimates into analysis are broadly applicable and can ‘make the most’ out of highly accurate detections while also cautiously extracting spatial information from less-accurate detections.

Effect of Lawsuits on Professional Well-Being and Medical Error Rates Among Orthopaedic Surgeons.

Orthopaedic surgery is one of the most cited specialties among legal claims. Malpractice lawsuits are financially burdensome, increase defensive practices, and incur a notable emotional toll on defendants. We sought to determine the effect of malpractice lawsuits on professional well-being and self-reported medical errors among orthopaedic surgeons. We surveyed 305 members of the American Orthopaedic Association to collect information on experiences with medical malpractice lawsuits, demographic and practice characteristics, professional well-being on the Professional Fulfillment Index, and self-reported medical errors. Multivariable logistic regression identified predictors of malpractice lawsuits, professional well-being, and self-reported medical errors. Seventy-three percent (224 of 305) respondents had been involved in a medical malpractice lawsuit. The odds of experiencing a malpractice lawsuit increased by seven percent with each year in practice (OR=1.07, 95% CI: 1.04 to 1.10, P < 0.001) and with specialization in spine surgery. Respondents with a lawsuit in 2 years before the survey reported comparable professional well-being and medical error rates with those without a lawsuit. However, compared with respondents without a lawsuit, respondents with a lawsuit more than 2 years in the past were less likely to report burnout (OR=0.43, 95% CI: 0.20 to 0.90, P = 0.03) and more likely to report a medical error in the past year, which resulted in patient harm (OR=3.51, 95% CI: 1.39 to 8.91, P = 0.008). While malpractice lawsuits negatively affect professional well-being, this effect resolves with time. The effect on medical errors may be more permanent; orthopaedic surgeons who have experienced a lawsuit reported greater rates of medical errors even after these legal issues have been settled. Among orthopaedic surgeons dealing with lawsuits, supportive interventions to protect professional well-being and mitigate the factors which lead to greater medical errors are needed. Prognostic Level III.

Frequency of Medication Errors at the University Hospital Center Of Point-G, Mali

Background: Medical errors are considered as a major threat to patient safety. The objective of our study was to describe essential steps in drug circuitry from prescription to administration at the University Hospital Center of Point-G. Methods: We conducted a prospective cross-sectional study design at the University hospital center of Point G from January 30, 2021 to June 30, 2021. We collected data using a Case Report Form (CRF) and reviewed medication order sheets, medication administration records, and patient charts. To identify the independent predictors of medication errors, we analyzed the collected data using Stata version 12. Results: The study included 41 patients with 96 prescriptions with 35 medication errors. The highest error rate was detected in prescription step (15.6%) followed by administration drug step (12.5%) and drug dispensation step (8.3%). The most frequent type of errors was the prescription of wrong drug combination (65.85%) followed by wrong indication (36.58%), wrong treatment duration (12.2%), wrong medication dispensing (15.5%), drug erroneously omitted during dispensation (4.4%), wrong drug delivery during dispensation (64%), wrong route for drug administration (20%) and wrong frequency of administration (12%). These errors did not compromise the health of the patients. Conclusion: Medication errors were common at the University Hospital of Point G involving mainly errors in drug prescription, administration and dispensation.

A Model Randomization Approach to Statistical Parameter Privacy

In this article, we study a privacy filter design problem for a sequence of sensor measurements whose joint probability density function (p.d.f.) depends on a private parameter. To ensure parameter privacy, we propose a filter design framework which consists of two components: a randomizer and a nonlinear transformation. The randomizer takes the private parameter as input and randomly generates a pseudo parameter. The nonlinear mapping transforms the measurements such that the joint p.d.f. of the filter's output depends on the pseudo parameter rather than the private parameter. It also ensures that the joint p.d.f. of the filter's output belongs to the same family of distributions as that of the measurements. The design of the randomizer is formulated as an optimization problem subject to a privacy constraint, in terms of mutual information, and it is shown that the optimal randomizer is the solution of a convex optimization problem. Using information-theoretic inequalities, we show that the performance of any estimator of the private parameter, based on the output of the privacy filter, is limited by the privacy constraint. The structure of the nonlinear transformation is studied in the special cases of independent and identically distributed, Markovian, and Gauss-Markov measurements. Our results show that the privacy filter in the Gauss-Markov case can be implemented as two one-step ahead Kalman predictors and a set of minimum mean square error predictors. A numerical example on occupancy privacy in a building automation system illustrates the approach.