Reading and interpretation of chest X-ray in adults with community-acquired pneumonia

The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups

Purpose

To externally test four chest radiograph classifiers on a large, diverse, real-world dataset with robust subgroup analysis.

Materials and Methods

In this retrospective study, adult posteroanterior chest radiographs (January 2016-December 2020) and associated radiology reports from Trillium Health Partners in Ontario, Canada, were extracted and de-identified. An open-source natural language processing tool was locally validated and used to generate ground truth labels for the 197 540-image dataset based on the associated radiology report. Four classifiers generated predictions on each chest radiograph. Performance was evaluated using accuracy, positive predictive value, negative predictive value, sensitivity, specificity, F1 score, and Matthews correlation coefficient for the overall dataset and for patient, setting, and pathology subgroups.

Results

Classifiers demonstrated 68%-77% accuracy, 64%-75% sensitivity, and 82%-94% specificity on the external testing dataset. Algorithms showed decreased sensitivity for solitary findings (43%-65%), patients younger than 40 years (27%-39%), and patients in the emergency department (38%-60%) and decreased specificity on normal chest radiographs with support devices (59%-85%). Differences in sex and ancestry represented movements along an algorithm's receiver operating characteristic curve.

Conclusion

Performance of deep learning chest radiograph classifiers was subject to patient, setting, and pathology factors, demonstrating that subgroup analysis is necessary to inform implementation and monitor ongoing performance to ensure optimal quality, safety, and equity.Keywords: Conventional Radiography, Thorax, Ethics, Supervised Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2023See also the commentary by Huisman and Hannink in this issue.

Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

IMPORTANCE

The efficient and accurate interpretation of radiologic images is paramount.

OBJECTIVE

To evaluate whether a deep learning-based artificial intelligence (AI) engine used concurrently can improve reader performance and efficiency in interpreting chest radiograph abnormalities.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter cohort study was conducted from April to November 2021 and involved radiologists, including attending radiologists, thoracic radiology fellows, and residents, who independently participated in 2 observer performance test sessions. The sessions included a reading session with AI and a session without AI, in a randomized crossover manner with a 4-week washout period in between. The AI produced a heat map and the image-level probability of the presence of the referrable lesion. The data used were collected at 2 quaternary academic hospitals in Boston, Massachusetts: Beth Israel Deaconess Medical Center (The Medical Information Mart for Intensive Care Chest X-Ray [MIMIC-CXR]) and Massachusetts General Hospital (MGH).

MAIN OUTCOMES AND MEASURES

The ground truths for the labels were created via consensual reading by 2 thoracic radiologists. Each reader documented their findings in a customized report template, in which the 4 target chest radiograph findings and the reader confidence of the presence of each finding was recorded. The time taken for reporting each chest radiograph was also recorded. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were calculated for each target finding.

RESULTS

A total of 6 radiologists (2 attending radiologists, 2 thoracic radiology fellows, and 2 residents) participated in the study. The study involved a total of 497 frontal chest radiographs-247 from the MIMIC-CXR data set (demographic data for patients were not available) and 250 chest radiographs from MGH (mean [SD] age, 63 [16] years; 133 men [53.2%])-from adult patients with and without 4 target findings (pneumonia, nodule, pneumothorax, and pleural effusion). The target findings were found in 351 of 497 chest radiographs. The AI was associated with higher sensitivity for all findings compared with the readers (nodule, 0.816 [95% CI, 0.732-0.882] vs 0.567 [95% CI, 0.524-0.611]; pneumonia, 0.887 [95% CI, 0.834-0.928] vs 0.673 [95% CI, 0.632-0.714]; pleural effusion, 0.872 [95% CI, 0.808-0.921] vs 0.889 [95% CI, 0.862-0.917]; pneumothorax, 0.988 [95% CI, 0.932-1.000] vs 0.792 [95% CI, 0.756-0.827]). AI-aided interpretation was associated with significantly improved reader sensitivities for all target findings, without negative impacts on the specificity. Overall, the AUROCs of readers improved for all 4 target findings, with significant improvements in detection of pneumothorax and nodule. The reporting time with AI was 10% lower than without AI (40.8 vs 36.9 seconds; difference, 3.9 seconds; 95% CI, 2.9-5.2 seconds; P < .001).

CONCLUSIONS AND RELEVANCE

These findings suggest that AI-aided interpretation was associated with improved reader performance and efficiency for identifying major thoracic findings on a chest radiograph.

The Accuracy of Lung Ultrasonography Diagnosis of Community-Acquired Pneumonia, in an Adult Cohort

Objective:

Community-acquired pneumonia (CAP) is a common respiratory infection, and diagnosis is frequently performed using a chest radiography (CXR). Sonography is an available method with less radiation exposure, but has not been confirmed for diagnosis of CAP. The objective was to compare the diagnostic accuracy of sonography.

Materials and Methods:

In this cross-sectional study, 90 adult patients (aged >18 years) were admitted to the emergency department of two university-affiliated hospitals in Southwest Iran, from July to December 2019, with a confirmed diagnosis of CAP. The patient symptoms and CXR results were included as part of this study. Within 24 hours after obtaining a CXR, a lung ultrasonogram (LUS) was performed. The diagnostic accuracy of semiquantitative LUS (SQLUS) was compared with CXR results using the Pearson chi-square test and Fisher’s exact test.

Results:

The mean age of participants was 52.98 ± 16.77 years. 51 were men (56.7%). 28 patients (31.1%), who had abnormal SQLUS results, were not associated with CXR findings ( P = .296). SQLUS showed poor diagnostic accuracy for LUS (31.11%).

Conclusion:

This study results could not confirm LUS as an accurate method for diagnosing CAP in adult patients; although due to the convenient sample of adults and clinical-based diagnosis of CAP, any generalization of the results should be made with caution.

COVID-19 pneumonia chest radiographic severity score: variability assessment among experienced and in-training radiologists and creation of a multireader composite score database for artificial intelligence algorithm development

OBJECTIVE

The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on chest radiograph (CXR), and to create a multireader database suitable for AI development.

METHODS

In this study, CXRs from polymerase chain reaction positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intraobserver variability. Severity classification was assessed using a 4-class system: normal (0), mild (1), moderate (2), and severe (3). A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multireader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability.

RESULTS

A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ± 1), 189 men). Interobserver variability between the radiologists ranges between 0.67 and 0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower interobserver agreement of 0.66 with each other and between 0.69 and 0.71 with experienced radiologists. Intraobserver agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, 2 or 3 class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference.

CONCLUSION

Experienced and in-training radiologists demonstrate good inter- and intraobserver agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms.

ADVANCES IN KNOWLEDGE

Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.

What is pneumonia?

The diagnosis of pneumonia is both simple and complex. Recent research is challenging our concept of pneumonia and radiological gold standards that have underpinned research for decades. In particular, the accuracy of chest radiographs in diagnosing pneumonia is now highly questionable when compared with computed tomography scans. Depending on the question being asked, pneumonia can be defined in clinical, pathological, radiological, or microbiological contexts, or frequently a combination of all of these. However, while the field is changing, until we have new studies defining pneumonia in new ways, clinicians can be reassured that existing guidelines based on “old” standards remain as valid as they have always been.

Recent research has challenged our concept of pneumonia. New studies will define pneumonia in new ways, but clinicians can be reassured that existing guidelines based on “old” standards remain valid.https://bit.ly/3kJiV2N

[Management of Adult Community-Acquired Pneumonia and Prevention - Update 2021 - Guideline of the German Respiratory Society (DGP), the Paul-Ehrlich-Society for Chemotherapy (PEG), the German Society for Infectious Diseases (DGI), the German Society of Medical Intensive Care and Emergency Medicine (DGIIN), the German Viological Society (DGV), the Competence Network CAPNETZ, the German College of General Practitioneers and Family Physicians (DEGAM), the German Society for Geriatric Medicine (DGG), the German Palliative Society (DGP), the Austrian Society of Pneumology Society (ÖGP), the Austrian Society for Infectious and Tropical Diseases (ÖGIT), the Swiss Respiratory Society (SGP) and the Swiss Society for Infectious Diseases Society (SSI)]

The present guideline provides a new and updated concept of the management of adult patients with community-acquired pneumonia. It replaces the previous guideline dating from 2016.The guideline was worked out and agreed on following the standards of methodology of a S3-guideline. This includes a systematic literature search and grading, a structured discussion of recommendations supported by the literature as well as the declaration and assessment of potential conflicts of interests.The guideline has a focus on specific clinical circumstances, an update on severity assessment, and includes recommendations for an individualized selection of antimicrobial treatment.The recommendations aim at the same time at a structured assessment of risk for adverse outcome as well as an early determination of treatment goals in order to reduce mortality in patients with curative treatment goal and to provide palliation for patients with treatment restrictions.

Use of Pulmonary Computed Tomography for Evaluating Suspected Stroke-Associated Pneumonia

OBJECTIVES

Accurate and timely diagnosis of pneumonia complicating stroke remains challenging and the diagnostic accuracy of chest X-ray (CXR) in the setting of stroke-associated pneumonia (SAP) is uncertain. The overall objective of this study was to evaluate the use of pulmonary computed tomography (CT) in diagnosis of suspected SAP.

MATERIALS AND METHODS

Patients with acute ischemic stroke (IS) or intracerebral hemorrhage (ICH) were recruited within 24h of clinically suspected SAP and underwent non-contrast pulmonary CT within 48h of antibiotic initiation. CXR and pulmonary CT were reported by two radiologists. Pulmonary CT was used as the reference standard for final diagnosis of SAP. Sensitivity, specificity, positive and negative predictive values (PPV and NPV), and diagnostic odds ratio (OR) for CXR were calculated.

RESULTS

40 patients (36 IS, 4 ICH) with a median age of 78y (range 44y-90y) and a median National Institute of Health Stroke Scale score of 13 (range 3-31) were included. All patients had at least one CXR and 35/40 patients (88%) underwent pulmonary CT. Changes consistent with pneumonia were present in 15/40 CXRs (38%) and 12/35 pulmonary CTs (34%). 9/35 pulmonary CTs (26%) were reported normal. CXR had a sensitivity of 58.3%, specificity of 73.9%, PPV of 53.8 %, NPV of 77.2 %, diagnostic OR of 3.7 (95% CI 0.7 - 22) and an accuracy of 68.5% (95% CI 50.7% -83.1%).

DISCUSSION

CXR has limited diagnostic accuracy in SAP. The majority of patients started on antibiotics had no evidence of pneumonia on pulmonary CT with potential implications for antibiotic stewardship.

CONCLUSIONS

Pulmonary CT could be applied as a reference standard for evaluation of clinical and biomarker diagnostic SAP algorithms in multi-center studies.

Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm

AIM

To evaluate the suitability of a deep-learning (DL) algorithm for identifying normality as a rule-out test for fully automated diagnosis in frontal adult chest radiographs (CXR) in an active clinical pathway.

MATERIALS AND METHODS

This multicentre study included 3,887 CXRs from four distinct NHS institutions. A convolutional neural network (CNN) was developed and trained prior to this study and was used to classify a subset of examinations with the lowest abnormality scores as high confidence normal (HCN). For each radiograph, the ground truth (GT) was established using two independent reviewers and an arbitrator in case of discrepancy.

RESULTS

The DL algorithm was able to classify 15% of all examinations as HCN, with a corresponding precision of 97.7%. There were 0.33% of examinations classified incorrectly as HCN, with 84.6% of these examinations identified as borderline cases by the radiologist GT process.

CONCLUSION

A DL algorithm can achieve a high level of precision as a fully automated diagnostic tool for reporting a subset of CXRs as normal. The removal of 15% of all CXRs has the potential to significantly reduce workload and focus radiology resources on more complex examinations. To optimise performance, site-specific deployment of algorithms should occur with robust feedback mechanisms for incorrect classifications.

COVID-19 Screening Using a Lightweight Convolutional Neural Network with Generative Adversarial Network Data Augmentation

COVID-19 is a disease that can be spread easily with minimal physical contact. Currently, the World Health Organization (WHO) has endorsed the reverse transcription-polymerase chain reaction swab test as a diagnostic tool to confirm COVID-19 cases. This test requires at least a day for the results to come out depending on the available facilities. Many countries have adopted a targeted approach in screening potential patients due to the cost. However, there is a need for a fast and accurate screening test to complement this targeted approach, so that the potential virus carriers can be quarantined as early as possible. The X-ray is a good screening modality; it is quick at capturing, cheap, and widely available, even in third world countries. Therefore, a deep learning approach has been proposed to automate the screening process by introducing LightCovidNet, a lightweight deep learning model that is suitable for the mobile platform. It is important to have a lightweight model so that it can be used all over the world even on a standard mobile phone. The model has been trained with additional synthetic data that were generated from the conditional deep convolutional generative adversarial network. LightCovidNet consists of three components, which are entry, middle, and exit flows. The middle flow comprises five units of feed-forward convolutional neural networks that are built using separable convolution operators. The exit flow is designed to improve the multi-scale capability of the network through a simplified spatial pyramid pooling module. It is a symmetrical architecture with three parallel pooling branches that enable the network to learn multi-scale features, which is suitable for cases wherein the X-ray images were captured from all over the world independently. Besides, the usage of separable convolution has managed to reduce the memory usage without affecting the classification accuracy. The proposed method managed to get the best mean accuracy of 0.9697 with a low memory requirement of just 841,771 parameters. Moreover, the symmetrical spatial pyramid pooling module is the most crucial component; the absence of this module will reduce the screening accuracy to just 0.9237. Hence, the developed model is suitable to be implemented for mass COVID-19 screening.

Lung ultrasound in children with pneumonia: interoperator agreement on specific thoracic regions

The objective of this study was to evaluate the interoperator agreement of lung ultrasonography (LUS) on specific thoracic regions in children diagnosed with pneumonia and to compare the findings of the LUS with the chest X-ray. Participants admitted to the ward or PICU underwent LUS examinations performed by an expert and a novice operator. A total of 261 thoracic regions in 23 patients were evaluated. Median age and weight of participants were 30 months and 11.6 kg, respectively. A substantial overall agreement between operators was found for normal lung tissue (κ = 0.615, 95% confidence interval (95% CI) = 0.516-0.715) and for consolidations (κ = 0.635, 95% CI = 0.532-0.738). For B-lines, a moderate agreement was observed (κ = 0.573, 95% CI = 0.475-0.671). An almost perfect agreement was found for pleural effusion (κ = 0.868, 95% CI = 0.754-0.982). The diagnosis of consolidations by LUS showed a high sensitivity (93% for both operators) but a low specificity (14% for expert and 25% for novice operator). While intubated patients presented significantly more consolidations, nonintubated patients presented more normal ultrasound patterns.Conclusion: Even when performed by operators with very distinct degrees of experience, LUS had a good interoperator reliability for detecting sonographic patterns on specific thoracic regions. What is Known: • Lung ultrasound is feasible, safe, and highly accurate for the diagnosis of pneumonia in children; however, it does not allow global visualization of the thorax in a single moment as in chest X-rays, and, similar to the stethoscope, partial thorax assessments must be performed sequentially. What is New: • This is the first study evaluating the agreement of LUS on specific thoracic regions between operators with distinct degrees of experience performing the sonograms. • There is a good agreement between an expert operator and a novice operator who underwent a brief theoretical-practical training program on LUS.

Acute scoliosis as an unusual presentation of pneumonia: A case report

We describe the unique case of a child with pneumonia presenting with acute scoliosis and abdominal pain, without any typical features of the disease.A 10-year-old girl presented to the emergency department on 3 consecutive days with right-sided abdominal pain. There were no associated features, in particular, no fevers or respiratory symptoms. On the first 2 presentations, observation,examination, and blood test findings were unremarkable. Chest x-ray and abdominal ultrasound were also normal. On the third presentation a marked scoliosis was noted and abdominal examination revealed right-sided tenderness with rebound. The patient was admitted and a computed tomographic scan of the abdomen arranged. Unexpectedly, this revealed a right lower lobe pneumonia and associated pleural effusion. Despite treatment, the parapneumonic effusion enlarged rapidly and she developed respiratory distress, necessitating transfer to a tertiary centre.The diagnosis of pneumonia can be challenging because of a lack of respiratory signs, the masking of systemic features by antipyretic effects of first-line analgesics, and a high rate of false-negative chest radiographs. The development of acute scoliosis should lead the clinician to strongly consider pneumonia in such circumstances.

Determining the clinical significance of errors in pediatric radiograph interpretation between emergency physicians and radiologists

OBJECTIVES

Emergency physicians (EPs) interpret plain radiographs for management and disposition of patients. Radiologists subsequently conduct their own interpretations, which may differ. The purposes of this study were to review the rate and nature of discrepancies between radiographs interpreted by EPs and those of radiologists in the pediatric emergency department, and to determine their clinical significance.

METHODS

We conducted a retrospective review of discrepant radiology reports from a single-site pediatric emergency department from October 2012 to December 2014. All radiographs were interpreted first by the staff EP, then by a radiologist. The report was identified as a "discrepancy" if these reports differed. Radiographs were categorized by body part and discrepancies classified as false positive, false negative, or not a discrepancy. Clinically significant errors that required a change in management were tracked.

RESULTS

There were 25,304 plain radiographs completed during the study period, of which 252 (1.00%) were identified as discrepant. The most common were chest radiographs (41.7%) due to missed pneumonia, followed by upper and lower extremities (26.2% and 17.5%, respectively) due to missed fractures. Of the 252 discrepancies, 207 (82.1%) were false negatives and 45 (17.9%) were false positives. In total, 105 (0.41% of all radiographs) were clinically significant.

CONCLUSION

There is a low rate of discrepancy in the interpretation of pediatric emergency radiographs between emergency department physicians and radiologists. The majority of errors occur with radiographs of the chest and upper extremities. The low rate of clinically significant discrepancy allows safe management based on EP interpretation.

[Influenza and community acquired pneumonia in German primary care]

Influenza and community-acquired pneumonia (CAP) impose a considerable annual burden on the German primary care system. Yet there is a lack of epidemiological data from the country's outpatient sector on groups at risk as well as on the complications of these diseases.The Robert Koch Institute (RKI) initiated the study to identify population groups at increased risk for influenza or CAP as well as related comorbidities and sequelae. We present the methodology of the study and the descriptive analysis of the patients.ICD-10-based data was collected in 89 primary health care practices between January 2012 and April 2015 using a data extraction tool developed on behalf of the RKI. Case-based anonymized information was recorded for all patients in whom influenza, CAP or other acute respiratory infections (ARI) were diagnosed. For each patient information on all diagnoses including the date were retrospectively and prospectively collected (each for six months) as well as age, sex and influenza vaccination.Data on 156,803 patients with ARI was collected, of them 7909 patients with influenza (within influenza waves) and 8528 patients with CAP diagnoses. Influenza diagnoses showed a strong seasonal pattern and captured annual influenza waves in Germany. Of the influenza cases 1.6 % had a following diagnosis of CAP within 30 days. Age-specific prevalence of chronic diseases such as asthma and diabetes was significantly higher in the study population as compared to the German population.The developed tool delivers in a standardized fashion ICD-10-coded epidemiological data on population-based burden of influenza and CAP in Germany. As the descriptive analysis showed, the collected dataset is a reliable and solid basis for the further investigations of the study questions.

A chest radiograph scoring system in patients with severe acute respiratory infection: a validation study

BACKGROUND

The term severe acute respiratory infection (SARI) encompasses a heterogeneous group of respiratory illnesses. Grading the severity of SARI is currently reliant on indirect disease severity measures such as respiratory and heart rate, and the need for oxygen or intensive care. With the lungs being the primary organ system involved in SARI, chest radiographs (CXRs) are potentially useful for describing disease severity. Our objective was to develop and validate a SARI CXR severity scoring system.

METHODS

We completed validation within an active SARI surveillance project, with SARI defined using the World Health Organization case definition of an acute respiratory infection with a history of fever, or measured fever of ≥ 38 °C; and cough; and with onset within the last 10 days; and requiring hospital admission. We randomly selected 250 SARI cases. Admission CXR findings were categorized as: 1 = normal; 2 = patchy atelectasis and/or hyperinflation and/or bronchial wall thickening; 3 = focal consolidation; 4 = multifocal consolidation; and 5 = diffuse alveolar changes. Initially, four radiologists scored CXRs independently. Subsequently, a pediatrician, physician, two residents, two medical students, and a research nurse independently scored CXR reports. Inter-observer reliability was determined using a weighted Kappa (κ) for comparisons between radiologists; radiologists and clinicians; and clinicians. Agreement was defined as moderate (κ > 0.4-0.6), good (κ > 0.6-0.8) and very good (κ > 0.8-1.0).

RESULTS

Agreement between the two pediatric radiologists was very good (κ = 0.83, 95% CI 0.65-1.00) and between the two adult radiologists was good (κ = 0.75, 95% CI 0.57-0. 93). Agreement of the clinicians with the radiologists was moderate-to-good (pediatrician:κ = 0.65; pediatric resident:κ = 0.69; physician:κ = 0.68; resident:κ = 0.67; research nurse:κ = 0.49, medical students: κ = 0.53 and κ = 0.56). Agreement between clinicians was good-to-very good (pediatrician vs. physician:κ = 0.85; vs. pediatric resident:κ = 0.81; vs. medicine resident:κ = 0.76; vs. research nurse:κ = 0.75; vs. medical students:κ = 0.63 and 0.66). Following review of discrepant CXR report scores by clinician pairs, κ values for radiologist-clinician agreement ranged from 0.59 to 0.70 and for clinician-clinician agreement from 0.97 to 0.99.

CONCLUSIONS

This five-point CXR scoring tool, suitable for use in poorly- and well-resourced settings and by clinicians of varying experience levels, reliably describes SARI severity. The resulting numerical data enables epidemiological comparisons of SARI severity between different countries and settings.

Speed and accuracy of mobile BlackBerry Messenger to transmit chest radiography images from a small community emergency department to a geographically remote referral center

BACKGROUND

Small emergency departments (EDs) may rely on radiologists at remote centers for interpretations of chest radiographs (CXRs). We investigated systematic transmission of CXR images from a small ED to a geographically remote referral center using the mobile BlackBerry Messenger (BBM) application.

METHODS

Investigators obtained de-identified CXR images of consecutive ED patients via mobile phone camera. Each CXR image, along with a brief clinical history, was sent via BBM to an emergency physician located at a remote referral site, and the receiving physician replied via BBM to confirm reception. All communications, image generation, and image analysis was conducted on mobile phones. The primary outcome was the proportion of BBMs received within two minutes of sending; the secondary outcome was the proportion of BBM replies to the sending physician within five minutes. Image accuracy-comparing the radiologist's interpretation with the receiving emergency physician's interpretation-was estimated using predefined criteria.

RESULTS

Of 1281 consecutive ED patients, 231 (18.0 %) had CXRs obtained, 320 CXRs were analyzed and 611 BBMs sent. All BBMs (100.0%, 95% confidence interval (CI) 99.4-00.0) arrived within two minutes; 595 BBMs (97.4%, 95% CI 95.8-98.4) were replied to within five minutes. Of the 58 CXRs with abnormalities requiring intervention, there were 55 concordances (overall agreement 94.2%, 95% CI 85.9-98.3; kappa 0.95, 95% CI 0.89-1.0) CONCLUSION: Systematic transmission of CXR images from a small ED to a remote large center using mobile phones may be a safe and effective strategy to rapidly communicate important patient information.

Cardiopulmonary physical therapy practice in the paediatric intensive care unit

PURPOSE

Physical therapists play an important role in the pediatric intensive care setting. The purpose of this study was to describe current cardiopulmonary physical therapy (CPT) practices in a pediatric cardiac critical care unit (CCCU) and a pediatric intensive care unit (PICU), as well as to determine the feasibility of obtaining clinically relevant outcome measures in this setting.

METHODS

We obtained reasons for admission, CPT treatment patterns, and availability of chest X-rays interpretation via a retrospective chart review of children who received CPT while in the PICU and CCCU (n=111).

RESULTS

Congenital cardiac conditions (34.2%) and primary respiratory deterioration (27.9%) were the most common reasons for admission; 50% of the children had associated diagnoses (e.g., developmental delay). Manual hyperinflation with expiratory vibration was the most common CPT treatment. Chest X-ray interpretation was available in 72% of the charts.

CONCLUSIONS

Manual hyperinflation with expiratory vibration was used across diagnostic groups in the CCCU and PICU; its effectiveness therefore requires further study. Chest X-ray is an important clinical outcome and therefore needs to be recorded in a standardized manner to be useful for future clinical research studies.

Clinical communication in diagnostic imaging studies: mixed-method study of pre- and post-implementation of a hospital information system

OBJECTIVE

To examine how and why the quality of clinical communication between radiologists and referring physicians was changed in the inpatient imaging process after implementation of a hospital information system (HIS).

METHODS

A mixed-method study of the chest X-ray (CXR) requests and reports, and their involved processes within a pre- and post-HIS implementation setting.

RESULTS

Documentation of patient age, patient ward, and name and signature of requesting physician decreased significantly in post-HIS CXR requests (P<0.05). However, documentation of requested position and technique increased significantly (P<0.05). In post-HIS CXR reports, documentation of patient age, patient chart number, urgent/normal status of requisition, position and technique of CXR, name of referring physician, and date of request were increased significantly (P<0.05). However, documentation of discussion for important findings was decreased significantly (P<0.05). The mean number of words in the body text of post-HIS reports was increased significantly (18.65 vs. 16.3, P = 0.00).Our qualitative findings highlighted that involving nursing and radiology staff in the communication loop between physicians and radiologists after the implementation resulted in extra steps in the workflow and more workload for them. To cope with the new workload, they adopted different workarounds that could explain the results seen in the quantitative study.

CONCLUSION

The HIS improved communication of administrative and identification information but did not improve communication of clinically relevant information. The reason was traced to the complications that the inappropriate implementation of the system brought to clinical workflow and communication loop.