Systemic Error in Radiology

ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images

BACKGROUND AND OBJECTIVE

Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images.

METHODS

Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing.

RESULTS

The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset.

CONCLUSION

The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.

Australian Dentist's Knowledge and Perceptions of Factors Affecting Radiographic Interpretation

BACKGROUND

Errors of interpretation of radigraphic images, also known as interpretive errors, are a critical concern as they can have profound implications for clinical decision making. Different types of interpretive errors, including errors of omission and misdiagnosis, have been described in the literature. These errors can lead to unnecessary or harmful treat/or prolonged patient care. Understanding the nature and contributing factors of interpretive errors is important in developing solutions to minimise interpretive errors. By exploring the knowledge and perceptions of dental practitioners, this study aimed to shed light on the current understanding of interpretive errors in dentistry.

METHODS

An anonymised online questionnaire was sent to dental practitioners in New South Wales (NSW) between September 2020 and March 2022. A total of 80 valid responses were received and analysed. Descriptive statistics and bivariate analysis were used to analyse the data.

RESULTS

The study found that participants commonly reported interpretive errors as occurring 'occasionally', with errors of omission being the most frequently encountered type. Participants identified several factors that most likely contribute to interpretive errors, including reading a poor-quality image, lack of clinical experience and knowledge, and excessive workload. Additionally, general practitioners and specialists held different views regarding factors affecting interpretive errors.

CONCLUSION

The survey results indicate that dental practitioners are aware of the common factors associated with interpretive errors. Errors of omission were identified as the most common type of error to occur in clinical practice. The findings suggest that interpretive errors result from a mental overload caused by factors associated with image quality, clinician-related, and image interpretation. Managing and identifying solutions to mitigate these factors are crucial for ensuring accurate and timely radiographic diagnoses. The findings of this study can serve as a foundation for future research and the development of targeted interventions to enhance the accuracy of radiographic interpretations in dentistry.

Retrospective cohort study of wrong-patient imaging order errors: how many reach the patient?

Studying near-miss errors is essential to preventing errors from reaching patients. When an error is committed, it may be intercepted (near-miss) or it will reach the patient; estimates of the proportion that reach the patient vary widely. To better understand this relationship, we conducted a retrospective cohort study using two objective measures to identify wrong-patient imaging order errors involving radiation, estimating the proportion of errors that are intercepted and those that reach the patient. This study was conducted at a large integrated healthcare system using data from 1 January to 31 December 2019. The study used two outcome measures of wrong-patient orders: (1) wrong-patient orders that led to misadministration of radiation reported to the New York Patient Occurrence Reporting and Tracking System (NYPORTS) (misadministration events); and (2) wrong-patient orders identified by the Wrong-Patient Retract-and-Reorder (RAR) measure, a measure identifying orders placed for a patient, retracted and rapidly reordered by the same clinician on a different patient (near-miss events). All imaging orders that involved radiation were extracted retrospectively from the healthcare system data warehouse. Among 293 039 total eligible orders, 151 were wrong-patient orders (3 misadministration events, 148 near-miss events), for an overall rate of 51.5 per 100 000 imaging orders involving radiation placed on the wrong patient. Of all wrong-patient imaging order errors, 2% reached the patient, translating to 50 near-miss events for every 1 error that reached the patient. This proportion provides a more accurate and reliable estimate and reinforces the utility of systematic measure of near-miss errors as an outcome for preventative interventions.

A Hybrid 2D Gaussian Filter and Deep Learning Approach with Visualization of Class Activation for Automatic Lung and Colon Cancer Diagnosis

Cancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by designing an automated diagnosis system. The system focuses on early detection through image pre-processing with a 2D Gaussian filter, while maintaining simplicity to minimize computational requirements and runtime. The study employs three Convolutional Neural Network (CNN) models-MobileNet, VGG16, and ResNet50-to diagnose five types of cancer: Colon Adenocarcinoma, Benign Colonic Tissue, Lung Adenocarcinoma, Benign Lung Tissue, and Lung Squamous Cell Carcinoma. A large dataset comprising 25 000 histopathological images is utilized. Additionally, the research addresses the need for safety levels in the model by using Class Activation Mapping (CAM) for explanatory purposes. Experimental results indicate that the proposed system achieves a high diagnostic accuracy of 99.38% for lung and colon cancers. This high performance underscores the effectiveness of the automated system in detecting these types of cancer. The findings from this study support the potential for early diagnosis of lung and colon cancers, which can facilitate timely therapeutic interventions and improve patient outcomes.

Population Health Management in Radiology: Economic Considerations

There is a growing emphasis on population health management (PHM) in the United States, in part because it has the worst health outcomes indices among high-income countries despite spending by far the most on health care. Successful PHM is expected to lead to a healthier population with reduced health care utilization and cost. The role of radiology in PHM is increasingly being recognized, including efforts in care coordination, secondary prevention, and appropriate imaging utilization, among others. To further discuss economic considerations for PHM, we must understand the evolving health care payer environment, which combines fee-for-service and increasingly, an alternative payment model framework developed by the Health Care Payment Learning and Action Network. In considering the term "value-based care," perceived value needs to accrue to those who ultimately pay for care, which is more commonly employers and the government. This perspective drives the design of alternative payment models and thus should be taken into consideration to ensure sustainable practice models.

Causes and diagnostic utility of musculoskeletal MRI recall examinations

AIM

To determine the causes and diagnostic utility of musculoskeletal (MSK) magnetic resonance imaging (MRI) recall examinations.

MATERIALS AND METHODS

An institutional review board-approved retrospective review was conducted of all MSK MRI examinations performed at a single academic institution over 10 years where radiologists requested the patient return for additional imaging. The reason for the recall was documented. Recalls were reviewed in consensus by two MSK radiologists to determine whether additional sequences resulted in a change in the final report. Recall causes were divided into four categories: (1) radiologist-related: incorrect field of view (FOV) or incorrect protocol; (2) technologist-related: incorrect FOV or incorrect/incomplete protocol performed, or technically poor-quality images; (3) patient-related motion artefact; (4) unexpected lesion discovered. Fisher's exact test was used to assess for statistical significance.

RESULTS

The recall rate was 0.25% (156/62,930). Of the total 129 recalls returning for imaging, 42 (33%) were radiologist-related, 45 (35%) were technologist-related, six (5%) were patient-related, and 36 (28%) had an unexpected lesion requiring additional sequences. For clinical utility, 42% resulted in a change from the initial report. Recalls due to radiologist error, incorrect FOV, or unexpected lesion caused a significant change in the final report; however, recalls due to technologist error, patient motion artefact, or incorrect protocol did not.

CONCLUSION

MRI MSK recalls are uncommon, and the most common reasons are incorrect FOV, incorrect protocol, and unexpected lesion. Radiologist-related errors in protocols and FOV led to a significant change in the final report and should be targeted as areas for improvement to reduce recall examinations.

Object recognition ability predicts category learning with medical images

We investigated the relationship between category learning and domain-general object recognition ability (o). We assessed this relationship in a radiological context, using a category learning test in which participants judged whether white blood cells were cancerous. In study 1, Bayesian evidence negated a relationship between o and category learning. This lack of correlation occurred despite high reliability in all measurements. However, participants only received feedback on the first 10 of 60 trials. In study 2, we assigned participants to one of two conditions: feedback on only the first 10 trials, or on all 60 trials of the category learning test. We found strong Bayesian evidence for a correlation between o and categorisation accuracy in the full-feedback condition, but not when feedback was limited to early trials. Moderate Bayesian evidence supported a difference between these correlations. Without feedback, participants may stick to simple rules they formulate at the start of category learning, when trials are easier. Feedback may encourage participants to abandon less effective rules and switch to exemplar learning. This work provides the first evidence relating o to a specific learning mechanism, suggesting this ability is more dependent upon exemplar learning mechanisms than rule abstraction. Object-recognition ability could complement other sources of individual differences when predicting accuracy of medical image interpretation.

Quality Frameworks for Virtual Care: Expert Panel Recommendations

Given the significant advance of virtual care in the past year and half, it seems timely to focus on quality frameworks and how they have evolved collaboratively across health care organizations. Massachusetts General Hospital's (MGH) Center for TeleHealth and Mass General Brigham's (MGB) Virtual Care Program are committed to hosting annual symposia on key topics related to virtual care. Subject matter experts across the country, health care organizations, and academic medical centers are invited to participate. The inaugural MGH/MGB Virtual Care Symposium, which focused on rethinking curriculum, competency, and culture in the virtual care era, was held on September 2, 2020. The second MGH/MGB Virtual Care Symposium was held on November 2, 2021, and focused on virtual care quality frameworks. Resultant topics were (1) guiding principles necessary for the future of virtual care measurement; (2) best practices deployed to measure quality of virtual care and how they compare and align with in-person frameworks; (3) evolution of quality frameworks over time; (4) how increased adoption of virtual care has impacted patient access and experience and how it has been measured; (5) the pitfalls and barriers which have been encountered by organizations in developing virtual care quality frameworks; and (6) examples of how quality frameworks have been applied in various use cases. Common elements of a quality framework for virtual care programs among symposium participants included improving the patient and provider experience, a focus on achieving health equity, monitoring success rates and uptime of the technical elements of virtual care, financial stewardship, and clinical outcomes. Virtual care represents an evolution in the access to care paradigm that helps keep health care aligned with other modern industries in digital technology and systems adoption. With advances in health care delivery models, it is vitally important that the quality measurement systems be adapted to include virtual care encounters. New methods may be necessary for asynchronous transactions, but synchronous virtual visits and consults can likely be accommodated in traditional quality frameworks with minimal adjustments. Ultimately, quality frameworks for health care will adapt to hybrid in-person and virtual care practices.

Improving Radiology Trainees' Perception Using Where's Waldo?

RATIONALE AND OBJECTIVES

Perception is an essential skill leading to expertise in diagnostic radiology. We determined if practicing "Where's Waldo?" images improves accuracy and speed with which first and second year radiology residents detect abnormalities on chest radiographs (CXRs).

MATERIALS AND METHODS

Residents at three institutions were pretested using 50 CXRs, identifying locations of potential abnormalities. They were then split into trained (examining 7 "Where's Waldo?" images over three weeks) and control groups (no "Where's Waldo?"). They were then re-tested on the 50 CXRs. At one site, visual search parameters were acquired. Data were analyzed with repeated measures ANOVAs.

RESULTS

There was no significant difference in performance for trained vs control (F = 0.622, p = 0.436), with both improving significantly on post-test (F = 4.72, p = 0.037). Session time decreased significantly for both groups from pre to post-test (F = 81.47, p < 0.0001) and the decrease was significantly more (F = 31.59, p < 0.0001) for the trained group than the control group as well as for PGY with PGY3 having a larger average decrease in session time than PGY2. Eye-tracking data also showed significant increases in per image search efficiency with training.

CONCLUSION

Practicing "Where's Waldo?" or similar nonradiology search tasks may facilitate the acquisition of radiology image search but not detection skills, impacting reading efficiency more than detection accuracy.

Radiation Safety and Accidental Radiation Exposures in Nuclear Medicine

Medical radiation accidents and unintended events may lead to accidental or unintended medical exposure of patients and exposure of staff or the public. Most unintended exposures in nuclear medicine will lead to a small increase in risk; nevertheless, these require investigation and a clinical and dosimetric assessment. Nuclear medicine staff are exposed to radiation emitted directly by radiopharmaceuticals and by patients after administration of radiopharmaceuticals. This is particularly relevant in PET, due to the penetrating 511 keV γ-rays. Dose constraints should be set for planning the exposure of individuals. Staff body doses of 1-25 µSv/GBq are reported for PET imaging, the largest component being from the injection. The preparation and administration of radiopharmaceuticals can lead to high doses to the hands, challenging dose limits for radionuclides such as ⁹⁰Y and even ¹⁸F. The risks of contamination can be minimized by basic precautions, such as carrying out manipulations in purpose-built facilities, wearing protective clothing, especially gloves, and removing contaminated gloves or any skin contamination as quickly as possible. Airborne contamination is a potential problem when handling radioisotopes of iodine or administering radioaerosols. Manipulating radiopharmaceuticals in laminar air flow cabinets, and appropriate premises ventilation are necessary to improve safety levels. Ensuring patient safety and minimizing the risk of incidents require efficient overall quality management. Critical aspects include: the booking process, particularly if qualified medical supervision is not present; administration of radiopharmaceuticals to patients, with the risk of misadministration or extravasation; management of patients' data and images by information technology systems, considering the possibility of misalignment between patient personal data and clinical information. Prevention of possible mistakes in patient identification or in the management of patients with similar names requires particular attention. Appropriate management of pregnant or breast-feeding patients is another important aspect of radiation safety. In radiopharmacy activities, strict quality assurance should be implemented at all operational levels, in addition to adherence to national and international regulations and guidelines. This includes not only administrative aspects, like checking the request/prescription, patient's data and the details of the requested procedure, but also quantitative tests according to national/international pharmacopoeias, and measuring the dispensed activity with a calibrated activity meter prior to administration. In therapy with radionuclides, skin tissue reactions can occur following extravasation, which can result in localized doses of tens of Grays. Other relevant incidents include confusion of products for patients administered at the same time or malfunction of administration devices. Furthermore, errors in internal radiation dosimetry calculations for treatment planning may lead to under or over-treatment. According to literature, proper instructions are fundamental to keep effective dose to caregivers and family members after patient discharge below the Dose constraints. The IAEA Basic Safety Standards require measures to minimize the likelihood of any unintended or accidental medical exposures and reporting any radiation incident. The relative complexity of nuclear medicine practice presents many possibilities for errors. It is therefore important that all activities are performed according to well established procedures, and that all actions are supported by regular quality assurance/QC procedures.

Impact of diagnostic errors on adverse outcomes: learning from emergency department revisits with repeat CT or MRI

Background

To investigate diagnostic errors and their association with adverse outcomes (AOs) during patient revisits with repeat imaging (RVRIs) in the emergency department (ED).

Results

Diagnostic errors stemming from index imaging studies and AOs within 30 days in 1054 RVRIs (≤ 7 days) from 2005 to 2015 were retrospectively analyzed according to revisit timing (early [≤ 72 h] or late [> 72 h to 7 days] RVRIs). Risk factors for AOs were assessed using multivariable logistic analysis. The AO rate in the diagnostic error group was significantly higher than that in the non-error group (33.3% [77 of 231] vs. 14.8% [122 of 823], p < .001). The AO rate was the highest in early revisits within 72 h if diagnostic errors occurred (36.2%, 54 of 149). The most common diseases associated with diagnostic errors were digestive diseases in the radiologic misdiagnosis category (47.5%, 28 of 59) and neurologic diseases in the delayed radiology reporting time (46.8%, 29 of 62) and clinician error (27.3%, 30 of 110) categories. In the matched set of the AO and non-AO groups, multivariable logistic regression analysis revealed that the following diagnostic errors contributed to AO occurrence: radiologic error (odds ratio [OR] 3.56; p < .001) in total RVRIs, radiologic error (OR 3.70; p = .001) and clinician error (OR 4.82; p = .03) in early RVRIs, and radiologic error (OR 3.36; p = .02) in late RVRIs.

Conclusion

Diagnostic errors in index imaging studies are strongly associated with high AO rates in RVRIs in the ED.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01108-0.

Biopsy of the same organ within 30 days: a potential radiology performance measure

PURPOSE

To assess the potential value of repeat image-guided biopsy within 30 days as a radiology performance metric.

METHODS

This was a HIPAA-compliant IRB-approved retrospective cohort study of all consecutive ultrasound- and CT-guided core biopsies of the chest, abdomen, and pelvis performed at one institution November 2016 to June 2020. The inclusion criterion was repeat biopsy of the same organ within 30 days of the initial biopsy. Details of both biopsies were recorded, including indication, organ, post-biopsy histology, performing service, performing provider. Histologic concordance between initial and repeat biopsies was calculated. Proportions and 95% confidence intervals were calculated.

RESULTS

Repeat biopsy was performed after 1.9% (95% CI 1.5-2.4% [N = 89]) of 4637 initial biopsies. For structures with ≥ 100 biopsies performed, the repeat biopsy proportion ranged from 1.3% (5/378, US-guided renal biopsy) to 2.7% (11/413, CT-guided retroperitoneal biopsy). The most common indication for initial biopsy was possible malignancy (66% [59/89]). The most common indication for repeat biopsy was radiology-histology discrepancy (36% [32/89]). Repeat biopsies were more likely to show malignant cells and to have diagnostic tissue (Repeat: 48.3% malignant; 20.2% benign; 1.1% nondiagnostic; Initial: 25.8% malignant; 23.6% benign; 14.6% nondiagnostic). The most common histology difference after repeat biopsy was a change in malignant diagnosis (38.2% [34/89]).

CONCLUSION

Repeat percutaneous biopsy within 30 days of the same organ is uncommon (~ 2%), but when indicated, it commonly changes diagnosis and improves diagnostic yield. Repeat biopsy within 30 days is a potential performance measure for radiology procedure services.

Investigating errors in medical imaging: medical malpractice cases in Finland

Objective

The objectives of the study were to survey patient injury claims concerning medical imaging in Finland in 1991–2017, and to investigate the nature of the incidents, the number of claims, the reasons for the claims, and the decisions made concerning the claims.

Materials and methods

The research material consisted of patient claims concerning imaging, sent to the Finnish Patient Insurance Centre (PVK). The data contained information on injury dates, the examination code, the decision code, the description of the injury, and the medical grounds for decisions.

Results

The number of claims included in the study was 1054, and the average number per year was 87. The most common cause was delayed diagnosis (404 claims, 38.3%). Most of the claims concerned mammography (314, 29.8%), radiography (170, 16.1%), and MRI (162, 15.4%). According to the decisions made by the PVK, there were no delays in 54.6% of the examinations for which claims were made. About 30% of all patient claims received compensation, the most typical reason being medical malpractice (27.7%), followed by excessive injuries and injuries caused by infections, accidents and equipment (2.7%).

Conclusion

Patient injury in imaging examinations and interventions cannot be completely prevented. However, injury data are an important source of information for health care. By analysing claims, we can prevent harm, increase the quality of care, and improve patient safety in medical imaging.

Narrowing the Gap: Imaging Disparities in Radiology

It may seem unlikely that the field of radiology perpetuates disparities in health care, as most radiologists never interact directly with patients, and racial bias is not an obvious factor when interpreting images. However, a closer look reveals that imaging plays an important role in the propagation of disparities. For example, many advanced and resource-intensive imaging modalities, such as MRI and PET/CT, are generally less available in the hospitals frequented by people of color, and when they are available, access is impeded due to longer travel and wait times. Furthermore, their images may be of lower quality, and their interpretations may be more error prone. The aggregate effect of these imaging acquisition and interpretation disparities in conjunction with social factors is insufficiently recognized as part of the wide variation in disease outcomes seen between races in America. Understanding the nature of disparities in radiology is important to effectively deploy the resources and expertise necessary to mitigate disparities through diversity and inclusion efforts, research, and advocacy. In this article, the authors discuss disparities in access to imaging, examine their causes, and propose solutions aimed at addressing these disparities.

The use of imaging to identify immunocompromised children requiring biopsy for invasive fungal rhinosinusitis

BACKGROUND AND PURPOSE

Children with severe immunocompromise due to cancer therapy or hematopoietic cell transplant are at risk both for potentially lethal invasive fungal rhinosinusitis (IFRS), and for complications associated with gold-standard biopsy diagnosis. We investigated whether early imaging could reliably identify or exclude IFRS in this population, thereby reducing unnecessary biopsy.

METHODS

We reviewed clinical/laboratory data and cross-sectional imaging from 31 pediatric patients evaluated for suspicion of IFRS, 19 without (age 11.8 ± 5.4 years) and 12 with proven IFRS (age 11.9 ± 4.6 years). Imaging examinations were graded for mucosal thickening (Lund score), for fungal-specific signs (FSS) of bone destruction, extra-sinus inflammation, and nasal mucosal ulceration. Loss of contrast enhancement (LoCE) was assessed separately where possible. Clinical and imaging findings were compared with parametric or nonparametric tests as appropriate. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) analysis. Positive (+LR) and negative likelihood ratios (-LR) and probabilities were calculated.

RESULTS

Ten of 12 patients with IFRS and one of 19 without IFRS had at least one FSS on early imaging (83% sensitive, 95% specific, +LR = 15.83, -LR = 0.18; P < .001). Absolute neutrophil count (ANC) ≤ 200/mm³ was 100% sensitive and 58% specific for IFRS (+LR = 2.38, -LR = 0; P = .001). Facial pain was the only discriminating symptom of IFRS (P < .001). In a symptomatic child with ANC ≤ 200/m³ , the presence of at least one FSS indicated high (79%) probability of IFRS; absence of FSS suggested low (<4%) probability.

CONCLUSION

In symptomatic, severely immunocompromised children, the presence or absence of fungal-specific imaging findings may effectively rule in or rule out early IFRS, potentially sparing some patients the risks associated with biopsy.

Preliminary Radiology Report Discordances and Patient Outcomes

RATIONALE AND OBJECTIVES

At our institution, resident and fellow radiologists issue preliminary reports for off-hours imaging studies, which are overread by attending radiologists the next day using structured discrepancy templates. In this study, we examined the impact on patient management and outcome of studies with major discordance.

MATERIALS AND METHODS

For our retrospective observational study, preliminary reports between March and June 2017 that received major discordance were identified through report text search. Electronic medical records were reviewed for patient management change and patient outcome.

RESULTS

Of the 199 cases, 52 cases (26%) had management change and 119 cases (60%) did not have management change. In 25 cases (13%), the preliminary report was proven correct on subsequent management. Three cases (2%) were lost to follow-up. In only one case was adverse outcome directly related to the discordant finding. In cases with patient management change, there was higher proportion of perceptual error compared with those without management change (73% versus 59%). In 47 cases (24%), the discordant finding or diagnosis was known to the clinical team, and better history could have avoided the major change.

CONCLUSION

Adverse outcome from the discordant imaging finding was low (0.5%). Major change in preliminary report could be reduced with better clinical history. Patient management change was more frequently seen with perceptual errors, placing greater emphasis on strategies to reduce them.

Towards better metainterpretation: improving the clinician's interpretation of the radiology report

How the clinician interprets the radiology report has a major impact on the patient's care. It is a crucial cognitive task, and can also be a significant source of error. Because the clinician must secondarily interpret the radiologist's interpretation of the images, this step can be referred to as a "metainterpretation". Some considerations for that task are offered from the perspective of a radiologist. A revival of the tradition of discussing cases with the radiologist is encouraged.

Integrity of clinical information in computerized order requisitions for diagnostic imaging

Objective

Assess information integrity (concordance and completeness of documented exam indications from the electronic health record [EHR] imaging order requisition, compared to EHR provider notes), and assess potential impact of indication inaccuracies on exam planning and interpretation.

Methods

This retrospective study, approved by the Institutional Review Board, was conducted at a tertiary academic medical center. There were 139 MRI lumbar spine (LS-MRI) and 176 CT abdomen/pelvis orders performed 4/1/2016-5/31/2016 randomly selected and reviewed by 4 radiologists for concordance and completeness of relevant exam indications in order requisitions compared to provider notes, and potential impact of indication inaccuracies on exam planning and interpretation. Forty each LS-MRI and CT abdomen/pelvis were re-reviewed to assess kappa agreement.

Results

Requisition indications were more likely to be incomplete (256/315, 81%) than discordant (133/315, 42%) compared to provider notes (p < 0.0001). Potential impact of discrepancy between clinical information in requisitions and provider notes was higher for radiologist's interpretation than for exam planning (135/315, 43%, vs 25/315, 8%, p < 0.0001). Agreement among radiologists for concordance, completeness, and potential impact was moderate to strong (Kappa 0.66-0.89). Indications in EHR order requisitions are frequently incomplete or discordant compared to physician notes, potentially impacting imaging exam planning, interpretation and accurate diagnosis. Such inaccuracies could also diminish the relevance of clinical decision support alerts if based on information in order requisitions.

Conclusions

Improved availability of relevant documented clinical information within EHR imaging requisition is necessary for optimal exam planning and interpretation.

Review of learning opportunity rates: correlation with radiologist assignment, patient type and exam priority

BACKGROUND

Common cause analysis of learning opportunities identified in a peer collaborative improvement process can gauge the potential risk to patients and opportunities to improve.

OBJECTIVE

To study rates of learning opportunities based on radiologist assignment, patient type and exam priority at an academic children's hospital with 24/7 in-house attending coverage.

MATERIALS AND METHODS

Actively submitted peer collaborative improvement learning opportunities from July 2, 2016, to July 31, 2018, were identified. Learning opportunity rates (number of learning opportunities divided by number of exams in each category) were calculated based on the following variables: radiologist assignment at the time of dictation (daytime weekday, daytime weekend and holiday, evening, and night) patient type (inpatient, outpatient or emergency center) and exam priority (stat, urgent or routine). A statistical analysis of rate differences was performed using a chi-square test. Pairwise comparisons were made with Bonferroni method adjusted P-values.

RESULTS

There were 1,370 learning opportunities submitted on 559,584 studies (overall rate: 0.25%). The differences in rates by assignment were statistically significant (P<0.0001), with the highest rates on exams dictated in the evenings (0.31%) and lowest on those on nights (0.19%). Weekend and holiday daytime (0.26%) and weekday daytime (0.24%) rates fell in between. There were significantly higher rates on inpatients (0.33%) than on outpatients (0.22%, P<0.0001) or emergency center patients (0.16%, P<0.0001). There were no significant differences based on exam priority (stat 0.24%, urgent 0.26% and routine 0.24%, P=0.55).

CONCLUSION

In this study, the highest learning opportunity rates occurred on the evening rotation and inpatient studies, which could indicate an increased risk for patient harm and potential opportunities for improvement.

Perspectives from the other side of the screen: how clinicians and radiologists communicate about diagnostic errors

Background

Miscommunication amongst providers is a major factor contributing to diagnostic errors. There is a need to explore the current state of communications between clinicians and diagnostic radiologists. We compare and contrast the perceptions, experiences, and other factors that influence communication behaviors about diagnostic errors between clinicians and radiologists.

Methods

A survey with questions addressing (1) communication around diagnostic error, (2) types of feedback observed, (3) the manner by which the feedback is reported, and (4) length of time between the discovery of the diagnostic error and disclosing it was created and distributed through two large academic health centers and through listservs of professional societies of radiologists and clinicians.

Results

A total of 240 individuals responded, of whom 58% were clinicians and 42% diagnostic radiologists. Both groups of providers frequently discover diagnostic errors, although radiologists encounter them more frequently. From the qualitative analysis, feedback around diagnostic error included (1) timeliness of error, (2) specificity in description or terminology, (3) collegial in delivery, and (4) of educational value through means such as quality improvement.

Conclusions

Clinicians and radiologists discover diagnostic errors surrounding the interpretation of radiology images, although radiologists discover them more frequently. There is significant opportunity for improvement in education and practice regarding how radiologists and clinicians communicate as a team and, importantly, how feedback is given when an error is discovered. Educators and clinical leaders should consider designing, implementing, and evaluating strategies for improvement.

Analysis of Perceptual Expertise in Radiology - Current Knowledge and a New Perspective

Radiologists rely principally on visual inspection to detect, describe, and classify findings in medical images. As most interpretive errors in radiology are perceptual in nature, understanding the path to radiologic expertise during image analysis is essential to educate future generations of radiologists. We review the perceptual tasks and challenges in radiologic diagnosis, discuss models of radiologic image perception, consider the application of perceptual learning methods in medical training, and suggest a new approach to understanding perceptional expertise. Specific principled enhancements to educational practices in radiology promise to deepen perceptual expertise among radiologists with the goal of improving training and reducing medical error.

Increasing display luminance as a means to enhance interpretation accuracy and efficiency when reducing full-field digital mammography dose

Reducing dose increases noise impacting image quality but can be offset by increasing display luminance. Two contrast detail mammography images were obtained at 26 kV and the same distance between detectors, at 45 and 50 mAs resulting in entrance surface doses of 7.09 and 7.88 mGy, respectively. They were processed to make average gray level of the background independent of the dose level while maintaining original SNR. Eight radiologists viewed the images at 420, 1000  cd/m2 , and SpotView™ a tool that resulted in an average display luminance of 3138.8  cd/m2 . Percent correct (PC) for all three luminances was higher for high versus low dose. Performance was always higher with high dose no matter what the luminance. For low dose, PC was highest with SpotView™, and SpotView™ and 1000  cd/m2 were significantly higher than 420  cd/m2 . At high dose, SpotView™ PC was significantly higher than both lower luminances. Average time per image was lower in high dose, and, at both doses, time decreased as luminance increased, with SpotView™ having significantly shorter times. Increasing luminance from 420 to 1000  cd/m2 significantly increases target detection by ∼3.0% and with SpotView™ by ∼6.2% . Increasing display luminance with SpotView™ significantly decreases reading time by 16.0%.