Creation of an Open Framework for Point-of-Care Computer-Assisted Reporting and Decision Support Tools for Radiologists

Standardizing imaging findings representation: harnessing Common Data Elements semantics and Fast Healthcare Interoperability Resources structures

OBJECTIVES

Designing a framework representing radiology results in a standards-based data structure using joint Radiological Society of North America/American College of Radiology Common Data Elements (CDEs) as the semantic labels on standard structures. This allows radiologist-created report data to integrate with artificial intelligence-generated results for use throughout downstream systems.

MATERIALS AND METHODS

We developed a framework modeling radiology findings as Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) observations using CDE set/element identifiers as standardized semantic labels. This framework deploys CDE identifiers to specify radiology findings and attributes, providing consistent labels for radiology report concepts-diagnoses, recommendations, tabular/quantitative data-with built-in integration with RadLex, SNOMED CT, LOINC, and other ontologies. Observation structures fit within larger HL7 FHIR DiagnosticReport resources, providing output including both nuanced text and structured data.

RESULTS

Labeling radiology findings as discrete data for interchange between systems requires two components: structure and semantics. CDE definitions provide semantic identifiers for findings and their component values. The FHIR observation resource specifies a structure for associating identifiers with radiology findings in the context of reports, with CDE-encoded observations referring to definitions for CDE identifiers in a central repository. The discussion includes an example of encoding pulmonary nodules on a chest CT as CDE-labeled observations, demonstrating the application of this framework to exchange findings throughout the imaging workflow, making imaging data available to downstream clinical systems.

DISCUSSION

CDE-labeled observations establish a lingua franca for encoding, exchanging, and consuming radiology data at the level of individual findings, facilitating use throughout healthcare systems.

IMPORTANCE

CDE-labeled FHIR observation objects can increase the value of radiology results by facilitating their use throughout patient care.

Patient-level factors influencing adherence to follow-up imaging recommendations

PURPOSE

To determine which, if any, patient-level factors were associated with differences in completion of follow-up imaging recommendations at a tertiary academic medical center.

METHODS

In this IRB-approved, retrospective cohort study, approximately one month of imaging recommendations were reviewed from 2017 at a single academic institution that contained key words recommending follow-up imaging. Age, gender, race/ethnicity, insurance, smoking history, primary language, BMI, and home address were recorded via chart extraction. Home addresses were geocoded to Census Block Groups and assigned to a quintile of neighborhood socioeconomic status. A multivariate logistic regression model was used to evaluate each predictor variable with significance set to p = 0.05.

RESULTS

A total of 13,421 imaging reports that included additional follow-up recommendations were identified. Of the 1013 included reports that recommended follow-up, 350 recommended additional imaging and were analyzed. Three hundred eight (88.00%) had corresponding follow-up imaging present and the insurance payor was known for 266 (86.36%) patients: 146 (47.40%) had commercial insurance, 35 (11.36%) had Medicaid, and 85 (27.60%) had Medicare. Patients with Medicaid had over four times lower odds of completing follow-up imaging compared to patients with commercial insurance (OR 0.24, 95% CI 0.06-0.88, p = 0.032). Age, gender, race/ethnicity, smoking history, primary language, BMI, and neighborhood socioeconomic status were not independently associated with differences in follow-up imaging completion.

CONCLUSION

Patients with Medicaid had decreased odds of completing follow-up imaging recommendations compared to patients with commercial insurance.

Analysis of Radiology Report Recommendation Characteristics and Rate of Recommended Action Performance

IMPORTANCE

Following up on recommendations from radiologic findings is important for patient care, but frequently there are failures to carry out these recommendations. The lack of reliable systems to characterize and track completion of actionable radiology report recommendations poses an important patient safety challenge.

OBJECTIVES

To characterize actionable radiology recommendations and, using this taxonomy, track and understand rates of loop closure for radiology recommendations in a primary care setting.

DESIGN, SETTING, AND PARTICIPANTS

Radiology reports in a primary care clinic at a large academic center were redesigned to include actionable recommendations in a separate dedicated field. Manual review of all reports generated from imaging tests ordered between January 1 and December 31, 2018, by primary care physicians that contained actionable recommendations was performed. For this quality improvement study, a taxonomy system that conceptualized recommendations was developed based on 3 domains: (1) what is recommended (eg, repeat a test or perform a different test, specialty referral), (2) specified time frame in which to perform the recommended action, and (3) contingency language qualifying the recommendation. Using this framework, a 2-stage process was used to review patients' records to classify recommendations and determine loop closure rates and factors associated with failure to complete recommended actions. Data analysis was conducted from April to July 2021.

MAIN OUTCOMES AND MEASURES

Radiology recommendations, time frames, and contingencies. Rates of carrying out vs not closing the loop on these recommendations in the recommended time frame were assessed.

RESULTS

A total of 598 radiology reports were identified with structured recommendations: 462 for additional or future radiologic studies and 196 for nonradiologic actions (119 specialty referrals, 47 invasive procedures, and 43 other actions). The overall rate of completed actions (loop closure) within the recommended time frame was 87.4%, with 31 open loop cases rated by quality expert reviewers to pose substantial clinical risks. Factors associated with successful loop closure included (1) absence of accompanying contingency language, (2) shorter recommended time frames, and (3) evidence of direct radiologist communication with the ordering primary care physicians. A clinically significant lack of loop closure was found in approximately 5% of cases.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that creating structured radiology reports featuring a dedicated recommendations field permits the development of taxonomy to classify such recommendations and determine whether they were carried out. The lack of loop closure suggests the need for more reliable systems.

Radiological Structured Report Integrated with Quantitative Imaging Biomarkers and Qualitative Scoring Systems

The benefits of structured reporting (SR) in radiology are well-known and have been widely described. However, there are limitations that must be overcome. Radiologists may be reluctant to change the conventional way of reporting. Error rates could potentially increase if SR is used improperly. Interruption of the visual search pattern by keeping the eyes focused on the report rather than the images may increase reporting time. Templates that include unnecessary or irrelevant information may undermine the consistency of the report. Last, the lack of support for multiple languages may hamper the adaptation of the report to the target audience. This work aims to mitigate these limitations with a web-based structured reporting system based on templates. By including field validators and logical rules, the system avoids reporting mistakes and allows to automatically calculate values and radiological qualitative scores. The system can manage quantitative information from imaging biomarkers, combining this with qualitative radiological information usually present in the structured report. It manages SR templates as plugins (IHE MRRT compliant and compatible with RSNA's Radreport templates), ensures a seamless integration with PACS/RIS systems, and adapts the report to the target audience by means of natural language extracts generated in multiple languages. We describe a use case of SR template for prostate cancer including PI-RADS 2.1 scoring system and imaging biomarkers. For the time being, the system comprises 24 SR templates and provides service in 37 hospitals and healthcare institutions, endorsing the success of this contribution to mitigate some of the limitations of the SR.

Talking Points: Enhancing Communication Between Radiologists and Patients

Radiologists communicate along multiple pathways, using written, verbal, and non-verbal means. Radiology trainees must gain skills in all forms of communication, with attention to developing effective professional communication in all forms. This manuscript reviews evidence-based strategies for enhancing effective communication between radiologists and patients through direct communication, written means and enhanced reporting. We highlight patient-centered communication efforts, available evidence, and opportunities to engage learners and enhance training and simulation efforts that improve communication with patients at all levels of clinical care.

Validation pipeline for machine learning algorithm assessment for multiple vendors

A standardized objective evaluation method is needed to compare machine learning (ML) algorithms as these tools become available for clinical use. Therefore, we designed, built, and tested an evaluation pipeline with the goal of normalizing performance measurement of independently developed algorithms, using a common test dataset of our clinical imaging. Three vendor applications for detecting solid, part-solid, and groundglass lung nodules in chest CT examinations were assessed in this retrospective study using our data-preprocessing and algorithm assessment chain. The pipeline included tools for image cohort creation and de-identification; report and image annotation for ground-truth labeling; server partitioning to receive vendor “black box” algorithms and to enable model testing on our internal clinical data (100 chest CTs with 243 nodules) from within our security firewall; model validation and result visualization; and performance assessment calculating algorithm recall, precision, and receiver operating characteristic curves (ROC). Algorithm true positives, false positives, false negatives, recall, and precision for detecting lung nodules were as follows: Vendor-1 (194, 23, 49, 0.80, 0.89); Vendor-2 (182, 270, 61, 0.75, 0.40); Vendor-3 (75, 120, 168, 0.32, 0.39). The AUCs for detection of solid (0.61–0.74), groundglass (0.66–0.86) and part-solid (0.52–0.86) nodules varied between the three vendors. Our ML model validation pipeline enabled testing of multi-vendor algorithms within the institutional firewall. Wide variations in algorithm performance for detection as well as classification of lung nodules justifies the premise for a standardized objective ML algorithm evaluation process.

Clinical Care of Hyperthyroidism Using Wearable Medical Devices in a Medical IoT Scenario

This paper presents an in-depth study and analysis of clinical care of patients with hyperthyroidism using wearable medical devices in the context of medical IoT scenarios. According to the use scenario of the gateway and the connectivity of the equipment, the hardware architecture, hardware interfaces, functionality, and performance of the gateway were briefly designed, so as to monitor patients with hyperthyroidism more comprehensively and save labor costs. The gateway can provide access to different devices and adaptation functions to different hardware interfaces and provide hardware support for the subsequent deployment of the proposed new medical communication protocols and related information systems. A medical data convergence information system based on multidevice management and multiprotocol parsing was designed and implemented. The system enables the management and configuration of different medical devices and access to data through the targeted parsing of the underlying medical device communication protocols. The system also provides the automatic adaptation of multiple types of underlying medical device communication protocols and automatic parsing of multiple versions and can provide multiple devices to process fused data streams or device information and data from a single device. The use of event-driven asynchronous communication eliminates the tight dependency on service invocation in the synchronous communication approach. The use of a metadata-based data model structure enables model extensions to accommodate the impact of iterative business requirements on the database structure. Real-time patient physiological data transmission for intraoperative monitoring based on the MQTT protocol and video transmission for intraoperative patient monitoring based on the RTMP protocol were implemented. The development of the intelligent medical monitoring service system was completed, and the system was tested, optimized, and deployed. The functionality and performance of the system were tested, the performance issue of slow query speed was optimized, and the deployment of the project using Docker containers was automated.

Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas

OBJECTIVE

To assess the impact of using a computer-assisted reporting and decision support (CAR/DS) tool at the radiologist point-of-care on ordering provider compliance with recommendations for adrenal incidentaloma workup.

METHOD

Abdominal CT reports describing adrenal incidentalomas (2014 - 2016) were retrospectively extracted from the radiology database. Exclusion criteria were history of cancer, suspected functioning adrenal tumor, dominant nodule size < 1 cm or ≥ 4 cm, myelolipomas, cysts, and hematomas. Multivariable logistic regression models were employed to predict follow-up imaging (FUI) and hormonal screening orders as a function of patient age and sex, nodule size, and CAR/DS use. CAR/DS reports were compared to conventional reports regarding ordering provider compliance with, frequency, and completeness of, guideline-warranted recommendations for FUI and hormonal screening of adrenal incidentalomas using Chi-square test.

RESULT

Of 174 patients (mean age 62.4; 51.1% women) with adrenal incidentalomas, 62% (108/174) received CAR/DS-based recommendations versus 38% (66/174) unassisted recommendations. CAR/DS use was an independent predictor of provider compliance both with FUI (Odds Ratio [OR]=2.47, p = 0.02) and hormonal screening (OR=2.38, p = 0.04). CAR/DS reports recommended FUI (97.2%,105/108) and hormonal screening (87.0%,94/108) more often than conventional reports (respectively, 69.7% [46/66], 3.0% [2/66], both p <0.0001). CAR/DS recommendations more frequently included instructions for FUI time, protocol, and modality than conventional reports (all p <0.001).

CONCLUSION

Ordering providers were at least twice as likely to comply with report recommendations for FUI and hormonal evaluation of adrenal incidentalomas generated using CAR/DS versus unassisted reporting. CAR/DS-directed recommendations were more adherent to guidelines than those generated without.

A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems

Objective

Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs.

Materials and Methods

We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features.

Results

Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria.

Conclusion

Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.

Artificial Intelligence Enabling Radiology Reporting

The radiology reporting process is beginning to incorporate structured, semantically labeled data. Tools based on artificial intelligence technologies using a structured reporting context can assist with internal report consistency and longitudinal tracking. To-do lists of relevant issues could be assembled by artificial intelligence tools, incorporating components of the patient's history. Radiologists will review and select artificial intelligence-generated and other data to be transmitted to the electronic health record and generate feedback for ongoing improvement of artificial intelligence tools. These technologies should make reports more valuable by making reports more accessible and better able to integrate into care pathways.

Computer-Assisted Reporting and Decision Support in Standardized Radiology Reporting for Cancer Imaging

PURPOSE

Recent advances in structured reporting are providing an opportunity to enhance cancer imaging assessment to drive value-based care and improve patient safety.

METHODS

The computer-assisted reporting and decision support (CAR/DS) framework has been developed to enable systematic ingestion of guidelines as clinical decision structured reporting tools embedded within the radiologist's workflow.

RESULTS

CAR/DS tools can reduce the radiology reporting variability and increase compliance with clinical guidelines. The lung cancer use-case is used to describe various scenarios of a cancer imaging structured reporting pathway, including incidental findings, screening, staging, and restaging or continued care. Various aspects of these tools are also described using cancer-related examples for different imaging modalities and applications such as calculators. Such systems can leverage artificial intelligence (AI) algorithms to assist with the generation of structured reports and there are opportunities for new AI applications to be created using the structured data associated with CAR/DS tools.

CONCLUSION

These AI-enabled systems are starting to allow information from multiple sources to be integrated and inserted into structured reports to drive improvements in clinical decision support and patient care.

Structured Data Capture for Oncology

Lack of interoperability is one of the greatest challenges facing healthcare informatics. Recent interoperability efforts have focused primarily on data transmission and generally ignore data capture standardization. Structured Data Capture (SDC) is an open-source technical framework that enables the capture and exchange of standardized and structured data in interoperable data entry forms (DEFs) at the point of care. Some of SDC’s primary use cases concern complex oncology data such as anatomic pathology, biomarkers, and clinical oncology data collection and reporting. Its interoperability goals are the preservation of semantic, contextual, and structural integrity of the captured data throughout the data’s lifespan. SDC documents are written in eXtensible Markup Language (XML) and are therefore computer readable, yet technology agnostic—SDC can be implemented by any EHR vendor or registry. Any SDC-capable system can render an SDC XML file into a DEF, receive and parse an SDC transmission, and regenerate the original SDC form as a DEF or synoptic report with the response data intact. SDC is therefore able to facilitate interoperable data capture and exchange for patient care, clinical trials, cancer surveillance and public health needs, clinical research, and computable care guidelines. The usability of SDC-captured oncology data is enhanced when the SDC data elements are mapped to standard terminologies. For example, an SDC map to Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) enables aggregation of SDC data with other related data sets and permits advanced queries and groupings on the basis of SNOMED CT concept attributes and description logic. SDC supports terminology maps using separate map files or as terminology codes embedded in an SDC document.

How to Use LI-RADS to Report Liver CT and MRI Observations

Primary liver cancer is the fourth leading cause of cancer-related deaths worldwide, with hepatocellular carcinoma (HCC) comprising the vast majority of primary liver malignancies. Imaging plays a central role in HCC diagnosis and management. As a result, the content and structure of radiology reports are of utmost importance in guiding clinical management. The Liver Imaging Reporting and Data System (LI-RADS) provides guidance for standardized reporting of liver observations in patients who are at risk for HCC. LI-RADS standardized reporting intends to inform patient treatment and facilitate multidisciplinary communication and decisions, taking into consideration individual clinical factors. Depending on the context, observations may be reported individually, in aggregate, or as a combination of both. LI-RADS provides two templates for reporting liver observations: in a single continuous paragraph or in a structured format with keywords and imaging findings. The authors clarify terminology that is pertinent to reporting, highlight the benefits of structured reports, discuss the applicability of LI-RADS for liver CT and MRI, review the elements of a standardized LI-RADS report, provide guidance on the description of LI-RADS observations exemplified with two case-based reporting templates, illustrate relevant imaging findings and components to be included when reporting specific clinical scenarios, and discuss future directions. An invited commentary by Yano is available online. Online supplemental material is available for this article. Work of the U.S. Government published under an exclusive license with the RSNA.

Lung-RADS Version 1.1: Challenges and a Look Ahead, From the AJR Special Series on Radiology Reporting and Data Systems

In 2014, the American College of Radiology (ACR) created Lung-RADS 1.0. The system was updated to Lung-RADS 1.1 in 2019, and further updates are anticipated as additional data become available. Lung-RADS provides a common lexicon and standardized nodule follow-up management paradigm for use when reporting lung cancer screening (LCS) low-dose CT (LDCT) chest examinations and serves as a quality assurance and outcome monitoring tool. The use of Lung-RADS is intended to improve LCS performance and lead to better patient outcomes. To date, the ACR's Lung Cancer Screening Registry is the only LCS registry approved by the Centers for Medicare & Medicaid Services and requires the use of Lung-RADS categories for reimbursement. Numerous challenges have emerged regarding the use of Lung-RADS in clinical practice, including the timing of return to LCS after planned follow-up diagnostic evaluation; potential substitution of interval diagnostic CT for future LDCT; role of volumetric analysis in assessing nodule size; assessment of nodule growth; assessment of cavitary, subpleural, and category 4X nodules; and variability in reporting of the S modifier. This article highlights the major updates between versions 1.0 and 1.1 of Lung-RADS, describes the system's ongoing challenges, and summarizes current evidence and recommendations.

Utilization of Structured Reporting to Monitor Outcomes of Doppler Ultrasound Performed for Deep Vein Thrombosis

Determining the clinical impact of imaging exams at the enterprise level is problematic, as radiology reports historically have been created with the content meant primarily for the referring provider. Structured reporting can establish the foundation for enterprise monitoring of imaging outcomes without manual review providing the framework for assessment of utilization and quality. Ultrasound (US) for deep vein thrombosis evaluation (DVT) is an ideal testbed for assessing this functionality. The system standard template for Doppler US for extremity venous evaluation for DVT was updated with a discrete fixed picklist of impression options and implemented system wide. Template utilization and interpretive outcomes were actively monitored and use reinforced as part of standard clinical practice. From January 1, 2017 to December 31, 2017, 9111 US exams for DVT were performed with 8997 utilizing structured reporting (98.75%). Of those in the structured reporting group, 1074 (11.79%) were positive for any type of DVT with 732 (8.03%) reported as Acute/New above the knee. Positive rates for any type of DVT were 10.29% emergency department, 14.17% inpatient, and 13.20% outpatient. While being the lowest positive rate, the emergency department had the highest overall volume of exams. Structured reporting for DVT US assessment outcomes can be implemented with a very high rate of radiologist adoption and adherence providing accurate determination of positive rates, month by month, in differing patient locations. Structured elements can be used to automatically trigger downstream processes; in our institution, this will alert providers in the EHR if the patient does not receive anticoagulation within 2 h of a positive test. This lays the foundation for effective enterprise assessment of imaging outcomes forming the basis of future quality and safety initiatives on optimizing health system resource utilization.

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve patient safety and drive value-based imaging. We discuss the advantages and potential applications that may be realized with the synergy between AI and CDS systems. From the perspective of both radiologist and ordering provider, CDS could be significantly empowered using AI. CDS enhanced by AI could reduce friction in radiology workflows and can aid AI developers to identify relevant imaging features their tools should be seeking to extract from images. Furthermore, these systems can generate structured data to be used as input to develop machine learning algorithms, which can drive downstream care pathways. For referring providers, an AI-enabled CDS solution could enable an evolution from existing imaging-centric CDS toward decision support that takes into account a holistic patient perspective. More intelligent CDS could suggest imaging examinations in highly complex clinical scenarios, assist on the identification of appropriate imaging opportunities at the health system level, suggest appropriate individualized screening, or aid health care providers to ensure continuity of care. AI has the potential to enable the next generation of CDS, improving patient care and enhancing providers' and radiologists' experience.

Adding Value in Radiology Reporting

The major goal of the radiology report is to deliver timely, accurate, and actionable information to the patient care team and the patient. Structured reporting offers multiple advantages over traditional free-text reporting, including reduction in diagnostic error, comprehensiveness, adherence to national consensus guidelines, revenue capture, data collection, and research. Various technological innovations enhance integration of structured reporting into everyday clinical practice. This review discusses the benefits of innovations in radiology reporting to the clinical decision process, the patient experience, the cost of imaging, and the overall contributions to the health of the population. Future directions, including the use of artificial intelligence, are reviewed.

Impact of Radiology Report Wording on Care of Patients With Acute Epiploic Appendagitis

OBJECTIVE. The purpose of this study was to evaluate the association between the diagnostic certainty expressed by the wording of CT report impressions and subsequent use of standard treatment with analgesics versus nonstandard antibiotic administration in patients with acute epiploic appendagitis (EA). MATERIALS AND METHODS. Demographic, clinical, and radiologic data from a 10-year cohort of patients with acute EA were retrospectively analyzed and correlated with standard treatment with analgesics versus nonstandard treatment with antibiotics. A level of certainty was assigned to the CT report language based on the wording of the impression statements by two radiologists; their interreader agreement was assessed with kappa statistics. Bivariate analyses were performed to correlate all variables with antibiotic administration and to assess for collinearity. Multivariate logistic regression was performed to identify independent predictors of antibiotic use in patients with acute EA. RESULTS. Of 124 patients with CT-diagnosed acute EA, 22% (27/124) received antibiotic treatment. After the CT report impressions were evaluated, 27% (34/124) were categorized as low certainty and 73% (90/124) as high certainty (κ = 0.958, p < 0.001). Multivariate regression was significant (p < 0.001, Nagelkerke R² = 0.249) and found CT report impressions' level of certainty (odds ratio [OR] = 6.1, p < 0.001) and evaluation in an outpatient clinic rather than an emergency department (ED) (OR = 4.4, p = 0.003) to be independent predictors of antibiotic administration for patients with acute EA. Outpatient presentation was also correlated with age, abdominal pain duration, and left-colonic involvement in the bivariate analysis (all p ≤ 0.01). CONCLUSION. The diagnostic certainty conveyed by the wording of CT report impressions correlated with antibiotic treatment decisions for patients with acute EA. Patients whose report impressions expressed low rather than high certainty were six times more likely to receive antibiotic therapy; patients evaluated at outpatient clinics rather than EDs were four times more likely.

The state of structured reporting: the nuance of standardized language

Radiology reports are the principal form of communication with the referring provider. Unfortunately, they can be a form of communication riddled with errors and inscrutable statements burying the intended meaning, failing to achieve the main task for which it was made: communicating key imaging findings as they pertain to the clinical question being posed. Structured reporting is a multifaceted and modular solution to problematic reports, with variable iterations and benefits. Structured reports have been adapted across departments and even national societies, with standardized format, content and language. Newer developments include contextual reporting and common data elements. Herein, we discuss the various forms and levels of structured reporting and the latest advancements, as well as the general acceptance within radiology. We also discuss some areas for improvement as the practice of structured reporting matures.

The ASNR-ACR-RSNA Common Data Elements Project: What Will It Do for the House of Neuroradiology?

The American Society of Neuroradiology has teamed up with the American College of Radiology and the Radiological Society of North America to create a catalog of neuroradiology common data elements that addresses specific clinical use cases. Fundamentally, a common data element is a question, concept, measurement, or feature with a set of controlled responses. This could be a measurement, subjective assessment, or ordinal value. Common data elements can be both machine- and human-generated. Rather than redesigning neuroradiology reporting, the goal is to establish the minimum number of "essential" concepts that should be in a report to address a clinical question. As medicine shifts toward value-based service compensation methodologies, there will be an even greater need to benchmark quality care and allow peer-to-peer comparisons in all specialties. Many government programs are now focusing on these measures, the most recent being the Merit-Based Incentive Payment System and the Medicare Access Children's Health Insurance Program Reauthorization Act of 2015. Standardized or structured reporting is advocated as one method of assessing radiology report quality, and common data elements are a means for expressing these concepts. Incorporating common data elements into clinical practice fosters a number of very useful downstream processes including establishing benchmarks for quality-assurance programs, ensuring more accurate billing, improving communication to providers and patients, participating in public health initiatives, creating comparative effectiveness research, and providing classifiers for machine learning. Generalized adoption of the recommended common data elements in clinical practice will provide the means to collect and compare imaging report data from multiple institutions locally, regionally, and even nationally, to establish quality benchmarks.

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.

Structured reporting adds clinical value in primary CT staging of diffuse large B-cell lymphoma

OBJECTIVES

To evaluate whether template-based structured reports (SRs) add clinical value to primary CT staging in patients with diffuse large B-cell lymphoma (DLBCL) compared to free-text reports (FTRs).

METHODS

In this two-centre study SRs and FTRs were acquired for 16 CT examinations. Thirty-two reports were independently scored by four haematologists using a questionnaire addressing completeness of information, structure, guidance for patient management and overall quality. The questionnaire included yes-no, 10-point Likert scale and 5-point scale questions. Altogether 128 completed questionnaires were evaluated. Non-parametric Wilcoxon signed-rank test and McNemar's test were used for statistical analysis.

RESULTS

SRs contained information on affected organs more often than FTRs (95 % vs. 66 %). More SRs commented on extranodal involvement (91 % vs. 62 %). Sufficient information for Ann-Arbor classification was included in more SRs (89 % vs. 64 %). Information extraction was quicker from SRs (median rating on 10-point Likert scale=9 vs. 6; 7-10 vs. 4-8 interquartile range). SRs had better comprehensibility (9 vs. 7; 8-10 vs. 5-8). Contribution of SRs to clinical decision-making was higher (9 vs. 6; 6-10 vs. 3-8). SRs were of higher quality (p < 0.001). All haematologists preferred SRs over FTRs.

CONCLUSIONS

Structured reporting of CT examinations for primary staging in patients with DLBCL adds clinical value compared to FTRs by increasing completeness of reports, facilitating information extraction and improving patient management.

KEY POINTS

• Structured reporting in CT helps clinicians to assess patients with lymphoma. • This two-centre study showed that structured reporting improves information content and extraction. • Patient management may be improved by structured reporting. • Clinicians preferred structured reports over free-text reports.

Missed Lung Cancer

The chest radiograph is one of the most commonly used imaging studies and is the modality of choice for initial evaluation of many common clinical scenarios. Over the last two decades, chest computed tomography has been increasingly used for a wide variety of indications, including respiratory illnesses, trauma, oncologic staging, and more recently lung cancer screening. Diagnostic radiologists should be familiar with the common causes of missed lung cancers on imaging studies in order to avoid detection and interpretation errors. Failure to detect these lesions can potentially have serious implications for both patients as well as the interpreting radiologist.

ESR paper on structured reporting in radiology

Structured reporting is emerging as a key element of optimising radiology's contribution to patient outcomes and ensuring the value of radiologists' work. It is being developed and supported by many national and international radiology societies, based on the recognised need to use uniform language and structure to accurately describe radiology findings. Standardisation of report structures ensures that all relevant areas are addressed. Standardisation of terminology prevents ambiguity in reports and facilitates comparability of reports. The use of key data elements and quantified parameters in structured reports ("radiomics") permits automatic functions (e.g. TNM staging), potential integration with other clinical parameters (e.g. laboratory results), data sharing (e.g. registries, biobanks) and data mining for research, teaching and other purposes. This article outlines the requirements for a successful structured reporting strategy (definition of content and structure, standard terminologies, tools and protocols). A potential implementation strategy is outlined. Moving from conventional prose reports to structured reporting is endorsed as a positive development, and must be an international effort, with international design and adoption of structured reporting templates that can be translated and adapted in local environments as needed. Industry involvement is key to success, based on international data standards and guidelines.

KEY POINTS

• Standardisation of radiology report structure ensures completeness and comparability of reports. • Use of standardised language in reports minimises ambiguity. • Structured reporting facilitates automatic functions, integration with other clinical parameters and data sharing. • International and inter-society cooperation is key to developing successful structured report templates. • Integration with industry providers of radiology-reporting software is also crucial.

Structured Reporting in Radiology

Radiology reports are vital for patient care as referring physicians depend upon them for deciding appropriate patient management. Traditional narrative reports are associated with excessive variability in the language, length, and style, which can minimize report clarity and make it difficult for referring clinicians to identify key information needed for patient care. Structured reporting has been advocated as a potential solution for improving the quality of radiology reports. The Association of University Radiologists-Radiology Research Alliance Structured Reporting Task Force convened to explore the current and future role of structured reporting in radiology and summarized its finding in this article. We review the advantages and disadvantages of structured radiology reports and discuss the current prevailing sentiments among radiologists regarding structured reports. We also discuss the obstacles to the use of structured reports and highlight ways to overcome some of those challenges. We also discuss the future directions in radiology reporting in the era of personalized medicine.