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Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are rendered useless if the data quality is bad. This systematic review aims to provide an introduction and guide for researchers who are interested in quality-related issues of physical sensor data. The process and results of the systematic review are presented which aims to answer the following research questions: what are the different types of physical sensor data errors, how to quantify or detect those errors, how to correct them and what domains are the solutions in. Out of 6970 literatures obtained from three databases (ACM Digital Library, IEEE Xplore and ScienceDirect) using the search string refined via topic modelling, 57 publications were selected and examined. Results show that the different types of sensor data errors addressed by those papers are mostly missing data and faults e.g. outliers, bias and drift. The most common solutions for error detection are based on principal component analysis (PCA) and artificial neural network (ANN) which accounts for about 40% of all error detection papers found in the study. Similarly, for fault correction, PCA and ANN are among the most common, along with Bayesian Networks. Missing values on the other hand, are mostly imputed using Association Rule Mining. Other techniques include hybrid solutions that combine several data science methods to detect and correct the errors. Through this systematic review, it is found that the methods proposed to solve physical sensor data errors cannot be directly compared due to the non-uniform evaluation process and the high use of non-publicly available datasets. Bayesian data analysis done on the 57 selected publications also suggests that publications using publicly available datasets for method evaluation have higher citation rates.

Contact isolation of infected or colonised hospitalised patients is instrumental to interrupting multidrug-resistant organism (MDRO) cross-transmission. Many studies suggest an increased rate of adverse events associated with isolation. We aimed to compare isolated to non-isolated patients in intensive care units (ICUs) for the occurrence of adverse events and medical errors. Methods We used the large database of the Iatroref III study that included consecutive patients from three ICUs to compare the occurrence of pre-defined medical errors and adverse events among isolated vs. non-isolated patients. A subdistribution hazard regression model with careful adjustment on confounding factors was used to assess the effect of patient isolation on the occurrence of medical errors and adverse events. Results Two centres of the Iatroref III study were eligible, an 18-bed and a 10-bed ICU (nurse-to-bed ratio 2.8 and 2.5, respectively), with a total of 1,221 patients. After exclusion of the neutropenic and graft transplant patients, a total of 170 isolated patients were compared to 980 non-isolated patients. Errors in insulin administration and anticoagulant prescription were more frequent in isolated patients. Adverse events such as hypo- or hyperglycaemia, thromboembolic events, haemorrhage, and MDRO ventilator-associated pneumonia (VAP) were also more frequent with isolation. After careful adjustment of confounders, errors in anticoagulant prescription [subdistribution hazard ratio (sHR) = 1.7, p  = 0.04], hypoglycaemia (sHR = 1.5, p  = 0.01), hyperglycaemia (sHR = 1.5, p  = 0.004), and MDRO VAP (sHR = 2.1, p  = 0.001) remain more frequent in isolated patients. Conclusion Contact isolation of ICU patients is associated with an increased rate of some medical errors and adverse events, including non-infectious ones.

Addressing physician suicide requires understanding its association with possible risk factors such as burnout and depression. To assess the association between burnout and suicidal ideation after adjusting for depression and the association of burnout and depression with self-reported medical errors. This cross-sectional study was conducted from November 12, 2018, to February 15, 2019. Attending and postgraduate trainee physicians randomly sampled from the American Medical Association Physician Masterfile were emailed invitations to complete an online survey in waves until a convenience sample of more than 1200 practicing physicians agreed to participate. The primary outcome was the association of burnout with suicidal ideation after adjustment for depression. The secondary outcome was the association of burnout and depression with self-reported medical errors. Burnout, depression, suicidal ideation, and medical errors were measured using subscales of the Stanford Professional Fulfillment Index, Maslach Burnout Inventory-Human Services Survey for Medical Personnel, and Mini-Z burnout survey and the Patient-Reported Outcomes Measurement Information System depression Short Form. Associations were evaluated using multivariable regression models. Of the 1354 respondents, 893 (66.0%) were White, 1268 (93.6%) were non-Hispanic, 762 (56.3%) were men, 912 (67.4%) were non-primary care physicians, 934 (69.0%) were attending physicians, and 824 (60.9%) were younger than 45 years. Each SD-unit increase in burnout was associated with 85% increased odds of suicidal ideation (odds ratio [OR], 1.85; 95% CI, 1.47-2.31). After adjusting for depression, there was no longer an association (OR, 0.85; 95% CI, 0.63-1.17). In the adjusted model, each SD-unit increase in depression was associated with 202% increased odds of suicidal ideation (OR, 3.02; 95% CI, 2.30-3.95). In the adjusted model for self-reported medical errors, each SD-unit increase in burnout was associated with an increase in self-reported medical errors (OR, 1.48; 95% CI, 1.28-1.71), whereas depression was not associated with self-reported medical errors (OR, 1.01; 95% CI, 0.88-1.16). The results of this cross-sectional study suggest that depression but not physician burnout is directly associated with suicidal ideation. Burnout was associated with self-reported medical errors. Future investigation might examine whether burnout represents an upstream intervention target to prevent suicidal ideation by preventing depression.

Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable and still have reasonable data volume. The latter may force one to work with representative rather than individual characterization data. Many errors derive from approximations and simplifications used in real-world retrieval schemes, which are reviewed in this paper, along with related error estimation schemes. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which should help to unify retrieval error reporting.

In a cluster-randomized trial involving resident physicians working in pediatric ICUs, resident physicians were randomly assigned to schedules that included shifts of 24 hours or more or to schedules with shifts of 16 hours or less. Contrary to the authors’ hypothesis, resident physicians made fewer serious medical errors when they followed the extended schedule.

BackgroundPatient safety measurement remains a global challenge. Patients are an important but neglected source of learning; however, little is known about what patients can add to our understanding of safety. We sought to understand the incidence and nature of patient-reported safety concerns in hospital.MethodsFeedback about the experience of safety within hospital was gathered from 2471 inpatients as part of a multicentre, waitlist cluster randomised controlled trial of an intervention, undertaken within 33 wards across three English NHS Trusts, between May 2013 and September 2014. Patient volunteers, supported by researchers, developed a classification framework of patient-reported safety concerns from a random sample of 231 reports. All reports were then classified using the patient-developed categories. Following this, all patient-reported safety concerns underwent a two-stage clinical review process for identification of patient safety incidents.ResultsOf the 2471 inpatients recruited, 579 provided 1155 patient-reported incident reports. 14 categories were developed for classification of reports, with communication the most frequently occurring (22%), followed by staffing issues (13%) and problems with the care environment (12%). 406 of the total 1155 patient incident reports (35%) were classified by clinicians as a patient safety incident according to the standard definition. 1 in 10 patients (264 patients) identified a patient safety incident, with medication errors the most frequently reported incident.ConclusionsOur findings suggest that patients can provide insight about safety that complements existing patient safety measurement, with a frequency of reported patient safety incidents that is similar to those obtained via case note review. However, patients provide a unique perspective about hospital safety which differs from and adds to current definitions of patient safety incidents.Trial registration numberISRCTN07689702; pre-results.

BackgroundMedication errors are the leading cause of preventable harm in healthcare. Despite proliferation of medication-related clinical decision support systems (CDSS), current systems have limitations. We therefore developed an indication-based prescribing tool. This performs dose calculations using an underlying formulary and provides patient-specific dosing recommendations. Objectives were to compare the incidence and types of erroneous medication orders, time to prescribe (TTP) and perceived workload using the NASA Task Load Index (TLX), in simulated prescribing tasks with and without this intervention. We also sought to identify the workflow steps most vulnerable to error and to gain participant feedback.MethodsA simulated, randomised, cross-over exploratory study was conducted at a London NHS Trust. Participants completed five simulated prescribing tasks with, and five without, the intervention. Data collection methods comprised direct observation of prescribing tasks, self-reported task load and semistructured interviews. A concurrent triangulation design combined quantitative and qualitative data.Results24 participants completed a total of 240 medication orders. The intervention was associated with fewer prescribing errors (6.6% of 120 orders) compared with standard practice (28.3% of 120 orders; odds ratio 0.18, p<0.01), a shorter TTP and lower overall NASA-TLX scores (p<0.01). Control arm workflow vulnerabilities included failures in identifying correct doses, applying maximum dose limits and calculating patient-specific dosages. Intervention arm errors primarily stemmed from misidentifying patient-specific information from the medication scenario. Thematic analysis of participant interviews identified six themes: navigating trust and familiarity, addressing challenges and suggestions for improvement, integration of local guidelines and existing CDSS, intervention endorsement, ‘search by indication’ and targeting specific patient and staff groups.ConclusionThe intervention represents a promising advancement in medication safety, with implications for enhancing patient safety and efficiency. Further real-world evaluation and development of the system to meet the needs of more diverse patient groups, users and healthcare settings is now required.Trial registration numberNCT05493072.

Time spent fishing is the effort metric often studied in fisheries but it may under-represent the effort actually expended by fishers. Entire fishing trips, from the time vessels leave port until they return, may prove more useful for examining trends in fleet dynamics, fisher behavior, and fishing costs. However, such trip information is often difficult to resolve. We identified ~30,000 trips made by vessels that targeted walleye pollock (Gadus chalcogrammus) in the Eastern Bering Sea from 2008-2014 by using vessel monitoring system (VMS) and landings data. We compared estimated trip durations to observer data, which were available for approximately half of trips. Total days at sea were estimated with < 1.5% error and 96.4% of trip durations were either estimated with < 5% error or they were within expected measurement error. With 99% accuracy, we classified trips as fishing for pollock, for another target species, or not fishing. This accuracy lends strong support to the use of our method with unobserved trips across North Pacific fisheries. With individual trips resolved, we examined potential errors in datasets which are often viewed as \"the truth.\" Despite having > 5 million VMS records (timestamps and vessel locations), this study was as much about understanding and managing data errors as it was about characterizing trips. Missing VMS records were pervasive and they strongly influenced our approach. To understand implications of missing data on inference, we simulated removal of VMS records from trips. Removal of records straightened (i.e., shortened) vessel trajectories, and travel distances were underestimated, on average, by 1.5-13.4% per trip. Despite this bias, VMS proved robust for trip characterization and for improved quality control of human-recorded data. Our scrutiny of human-reported and VMS data advanced our understanding of the potential utility and challenges facing VMS users globally.