Imaging Informatics Fellowship Curriculum: Building Consensus on the Most Critical Topics and the Future of the Informatics Fellowship.

The existing fellowship imaging informatics curriculum, established in 2004, has not undergone formal revision since its inception and inaccurately reflects present-day radiology infrastructure. It insufficiently equips trainees for today's informatics challenges as current practices require an understanding of advanced informatics processes and more complex system integration. We sought to address this issue by surveying imaging informatics fellowship program directors across the country to determine the components and cutline for essential topics in a standardized imaging informatics curriculum, the consensus on essential versus supplementary knowledge, and the factors individual programs may use to determine if a newly developed topic is an essential topic. We further identified typical program structural elements and sought fellowship director consensus on offering official graduate trainee certification to imaging informatics fellows. Here, we aim to provide an imaging informatics fellowship director consensus on topics considered essential while still providing a framework for informatics fellowship programs to customize their individual curricula.

Patient Trajectories Among Persons Hospitalized for COVID-19 : A Cohort Study.

BACKGROUND: Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts. OBJECTIVE: To determine the factors on hospital admission that are predictive of severe disease or death from COVID-19. DESIGN: Retrospective cohort analysis. SETTING: Five hospitals in the Maryland and Washington, DC, area. PATIENTS: 832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020. MEASUREMENTS: Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death. RESULTS: Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 [63%] had mild to moderate disease and 171 [20%] had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively. LIMITATION: The study was done in a single health care system. CONCLUSION: A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Correction to: Collaborative and Reproducible Research: Goals, Challenges, and Strategies.

The paper below had been published originally without open access, but has been republished with open access.

Radiology Resident Assessment and Feedback Dashboard.

Assessment of residents is optimally performed through processes and platforms that provide daily feedback, which can be immediately acted on. Given the documentation required by the Accreditation Council for Graduate Medical Education (ACGME), effective data management, integration, and presentation are crucial to ease the burden of manual documentation and increase the timeliness of actionable information. To this end, the authors modeled the learning activities of residents using the Experience Application Programming Interface (xAPI) framework, which is a standard framework for the learning community. On the basis of the xAPI framework and using open-source software to extend their existing infrastructure, the authors developed a Web-based dashboard that provides residents with a more holistic view of their educational experience. The dashboard was designed around the ACGME radiology milestones and provides real-time feedback to residents using various assessment metrics derived from multiple data sources. The purpose of this article is to describe the dashboard's architecture and components, the design and technical considerations, and the lessons learned in implementing the dashboard. ©RSNA, 2018.

Patient-Centered Radiology with FHIR: an Introduction to the Use of FHIR to Offer Radiology a Clinically Integrated Platform.

Fast Healthcare Interoperability Resources (FHIR) is an open interoperability standard that allows external software to quickly search for and access clinical information from the electronic medical record (EMR) in a method that is developer-friendly, using current internet technology standards. In this article, we highlight the new FHIR standard and illustrate how FHIR can be used to offer the field of radiology a more clinically integrated and patient-centered system, opening the EMR to external radiology software in ways unfeasible with traditional standards. We explain how to construct FHIR queries relevant to medical imaging using the Society for Imaging Informatics in Medicine (SIIM) Hackathon application programming interface (API), provide sample queries for use, and suggest solutions to offer a patient-centered, rather than an image-centered, workflow that remains clinically relevant.

Collaborative and Reproducible Research: Goals, Challenges, and Strategies.

Combining imaging biomarkers with genomic and clinical phenotype data is the foundation of precision medicine research efforts. Yet, biomedical imaging research requires unique infrastructure compared with principally text-driven clinical electronic medical record (EMR) data. The issues are related to the binary nature of the file format and transport mechanism for medical images as well as the post-processing image segmentation and registration needed to combine anatomical and physiological imaging data sources. The SiiM Machine Learning Committee was formed to analyze the gaps and challenges surrounding research into machine learning in medical imaging and to find ways to mitigate these issues. At the 2017 annual meeting, a whiteboard session was held to rank the most pressing issues and develop strategies to meet them. The results, and further reflections, are summarized in this paper.

Hello World Deep Learning in Medical Imaging.

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.

The National Mammography Database: Preliminary Data.

OBJECTIVE: The purposes of our study were to analyze screening mammography data submitted to the National Mammography Database (NMD) since its inception to confirm data collection feasibility, to draw parallels to data from the Breast Cancer Surveillance Consortium (BCSC), and to examine trends over time. We also retrospectively evaluated practice-level variation in terms of practice type, practice setting, census region, and annual volume. MATERIALS AND METHODS: Data from 90 mammography facilities in the NMD registry were analyzed. The registry receives mammography data collected as part of standard clinical practice, including self-reported demographic information, clinical findings, screening mammography interpretation, and biopsy results. Outcome metrics calculated were cancer detection rate, recall rate, and positive predictive values for biopsy recommended (PPV2) and biopsy performed (PPV3). RESULTS: The NMD successfully collected and analyzed data for 3,181,437 screening mammograms performed between January 2008 and December 2012. Mean values for outcomes were cancer detection rate of 3.43 per 1000 (95% CI, 3.2-3.7), recall rate of 10% (95% CI, 9.3-10.7%), PPV2 of 18.5% (95% CI, 16.7-20.2%), and PPV3 of 29.2% (95% CI, 26.2-32.3%). No statistically significant difference was seen in performance measurements on the basis of practice type, practice setting, census region, or annual volume. NMD performance measurements parallel those reported by the BCSC. CONCLUSION: The NMD has become the fastest growing mammography registry in the United States, providing nationwide performance metrics and permitting comparison with published benchmarks. Our study shows the feasibility of using the NMD to audit mammography facilities and to provide current, ongoing benchmark data.

The Impact of Imaging Informatics Fellowships.

Imaging informatics (II) is an area within clinical informatics that is particularly important in the field of radiology. Provider groups have begun employing dedicated radiologist-informaticists to bridge medical, information technology and administrative functions, and academic institutions are meeting this demand through formal II fellowships. However, little is known about how these programs influence graduates' careers and perceptions about professional development. We electronically surveyed 26 graduates from US II fellowships and consensus leaders in the II community-many of whom were subspecialty diagnostic radiologists (68%) employed within academic institutions (48%)-about the perceived impact of II fellowships on career development and advancement. All graduates felt that II fellowship made them more valuable to employers, with the majority of reporting ongoing II roles (78%) and continued used of competencies (61%) and skills (56%) gained during fellowship in their current jobs. Other key benefits included access to mentors, protected time for academic work, networking opportunities, and positive impacts of annual compensation. Of respondents without II fellowship training, all would recommend fellowships to current trainees given the ability to gain a "still rare" but "essential skill set" that is "critical for future leaders in radiology" and "better job opportunities." While some respondents felt that II fellowships needed further formalization and standardization, most (85%) disagreed with requiring a 2-year II fellowship in order to qualify for board certification in clinical informatics. Instead, most believed that fellowships should be integrated with clinical residency or fellowship training while preserving formal didactics and unstructured project time. More work is needed to understand existing variations in II fellowship training structure and identify the optimal format for programs targeted at radiologists.

Guide to effective quality improvement reporting in radiology.

Substantial societal investments in biomedical research are contributing to an explosion in knowledge that the health delivery system is struggling to effectively implement. Managing this complexity requires ingenuity, research and development, and dedicated resources. Many innovative solutions can be found in quality improvement (QI) activities, defined as the "systematic, data-guided activities designed to bring about immediate, positive changes in the delivery of healthcare in particular settings." QI shares many similarities with biomedical research, but also differs in several important ways. Inclusion of QI in the peer-reviewed literature is needed to foster its advancement through the dissemination, testing, and refinement of theories, methods, and applications. QI methods and reporting standards are less mature in health care than those of biomedical research. A lack of widespread understanding and consensus regarding the purpose of publishing QI-related material also exists. In this document, guidance is provided in evaluating quality of QI-related material and in determining priority of submitted material for publication.

Changes to stage 1 meaningful use in 2014: impact on radiologists.

The goal of this work is to provide radiologists an update regarding changes to stage 1 of meaningful use in 2014. These changes were promulgated in the final rulemaking released by the Centers for Medicare and Medicaid Services and the Office of the National Coordinator for Health Information Technology in September 2012. Under the new rules, radiologists are exempt from meaningful use penalties provided that they are listed as radiologists under the Provider Enrollment, Chain and Ownership System (PECOS). A major caveat is that this exemption can be removed at any time. Additional concerns are discussed in the main text. Additional changes discussed include software editions independent of meaningful use stage (i.e., 2011 edition versus 2014 edition), changes to the definition of certified electronic health record technology (CEHRT), and changes to specific measures and exemptions to those measures. The new changes regarding stage 1 add complexity to an already complex program, but overall make achieving meaningful use a win-win situation for radiologists. There are no penalties for failure and incentive payments for success. The cost of upgrading to CEHRT may be much less than the incentive payments, adding a potential new source of revenue. Additional benefits may be realized if the radiology department can build upon a modern electronic health record to improve their practice and billing patterns. Meaningful use and electronic health records represent an important evolutionary step in US healthcare, and it is imperative that radiologists are active participants in the process.

Building blocks for a clinical imaging informatics environment.

Over the past 20 years, imaging informatics has been driven by the widespread adoption of radiology information and picture archiving and communication and speech recognition systems. These three clinical information systems are commonplace and are intuitive to most radiologists as they replicate familiar paper and film workflow. So what is next? There is a surge of innovation in imaging informatics around advanced workflow, search, electronic medical record aggregation, dashboarding, and analytics tools for quality measures (Nance et al., AJR Am J Roentgenol 200:1064-1070, 2013). The challenge lies in not having to rebuild the technological wheel for each of these new applications but instead attempt to share common components through open standards and modern development techniques. The next generation of applications will be built with moving parts that work together to satisfy advanced use cases without replicating databases and without requiring fragile, intense synchronization from clinical systems. The purpose of this paper is to identify building blocks that can position a practice to be able to quickly innovate when addressing clinical, educational, and research-related problems. This paper is the result of identifying common components in the construction of over two dozen clinical informatics projects developed at the University of Maryland Radiology Informatics Research Laboratory. The systems outlined are intended as a mere foundation rather than an exhaustive list of possible extensions.

Improving the Identification of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods.

Purpose: To compare the performance of 3 phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions. Design: Three phenotyping methods were used to identify clinical conditions including unspecified DR, nonproliferative DR (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative DR, diabetic macular edema (DME), vitreous hemorrhage, retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (ICD-10 Lookup System). The next 2 methods used a Bidirectional Encoder Representations from Transformers with a dense Multilayer Perceptron output layer natural language processing (NLP) framework. The NLP framework was applied either to free-text of provider notes (Text-Only NLP System) or both free-text and ICD-10 diagnosis codes (Text-and-International Classification of Diseases [ICD] NLP System). Subjects: Adults ≥18 years with diabetes mellitus seen at the Wilmer Eye Institute. Methods: We compared the performance of the 3 phenotyping methods in identifying the DR related conditions with gold standard chart review. We also compared the estimated disease prevalence using each method. Main Outcome Measures: Performance of each method was reported as the macro F1 score. The agreement between the methods was calculated using the kappa statistic. Prevalence estimates were also calculated for each method. Results: A total of 91 097 patients and 692 486 office visits were included in the study. Compared with the gold standard, the Text-and-ICD NLP System had the highest F1 score for most clinical conditions (range 0.39-0.64). The agreement between the ICD-10 Lookup System and Text-Only NLP System varied (kappa of 0.21-0.81). The prevalence of DR and related conditions ranged from 1.1% for NVG to 17.9% for DME (using the Text-and-ICD NLP System). Conclusions: The prevalence of DR and related conditions varied significantly depending on the methodology of identifying cases. The best performing phenotyping method was the Text-and-ICD NLP System that used information in both diagnosis codes as well as free-text notes. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.

ABSTRACT: Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data.Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance.The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.

A framework for understanding an open scientific community using automated harvesting of public artifacts.

Background: The Observational Health Data Sciences and Informatics (OHDSI) community has emerged as a leader in observational research on real-world clinical data for promoting evidence for healthcare and decision-making. The community has seen rapid growth in publications, citations, and the number of authors. Components of its successful uptake have been attributed to an open science and collaborative culture for research and development. Investigating the adoption of OHDSI as a field of study provides an opportunity to understand how communities embrace new ideas, onboard new members, and enhance their impact. Objective: To track, study, and evaluate an open scientific community's growth and impact. Method: We present a modern architecture leveraging open application programming interfaces to capture publicly available data (PubMed, YouTube, and EHDEN) on open science activities (publication, teaching, and engagement). Results: Three interactive dashboard were implemented for each publicly available artifact (PubMed, YouTube, and EHDEN). Each dashboard provides longitudinal summary analysis and has a searchable table, which differs in the available features related to each public artifact. Conclusion: We discuss the insights enabled by our approach to monitor the growth and impact of the OHDSI community by capturing artifacts of learning, teaching, and creation. We share the implications for different users based on their functional needs. As other scientific networks adopt open-source frameworks, our framework serves as a model for tracking the growth of their community, driving the perception of their development, engaging their members, and attaining higher impact.

Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension.

The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.

Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study.

PURPOSE: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DESIGN: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. SUBJECTS: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion). METHODS: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate. MAIN OUTCOME MEASURES: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. RESULTS: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60). CONCLUSIONS: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.

Background: SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods: Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings: Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation: In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding: National Institutes of Health, United States Department of Veterans Affairs.

Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM.

BACKGROUND: Sodium-glucose cotransporter 2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) reduce the risk of major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head clinical trials. OBJECTIVES: The aim of this study was to compare the cardiovascular effectiveness of SGLT2is, GLP-1 RAs, dipeptidyl peptidase-4 inhibitors (DPP4is), and clinical sulfonylureas (SUs) as second-line antihyperglycemic agents in T2DM. METHODS: Across the LEGEND-T2DM (Large-Scale Evidence Generation and Evaluation Across a Network of Databases for Type 2 Diabetes Mellitus) network, 10 federated international data sources were included, spanning 1992 to 2021. In total, 1,492,855 patients with T2DM and cardiovascular disease (CVD) on metformin monotherapy were identified who initiated 1 of 4 second-line agents (SGLT2is, GLP-1 RAs, DPP4is, or SUs). Large-scale propensity score models were used to conduct an active-comparator target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, on-treatment Cox proportional hazards models were fit for 3-point MACE (myocardial infarction, stroke, and death) and 4-point MACE (3-point MACE plus heart failure hospitalization) risk and HR estimates were combined using random-effects meta-analysis. RESULTS: Over 5.2 million patient-years of follow-up and 489 million patient-days of time at risk, patients experienced 25,982 3-point MACE and 41,447 4-point MACE. SGLT2is and GLP-1 RAs were associated with lower 3-point MACE risk than DPP4is (HR: 0.89 [95% CI: 0.79-1.00] and 0.83 [95% CI: 0.70-0.98]) and SUs (HR: 0.76 [95% CI: 0.65-0.89] and 0.72 [95% CI: 0.58-0.88]). DPP4is were associated with lower 3-point MACE risk than SUs (HR: 0.87; 95% CI: 0.79-0.95). The pattern for 3-point MACE was also observed for the 4-point MACE outcome. There were no significant differences between SGLT2is and GLP-1 RAs for 3-point or 4-point MACE (HR: 1.06 [95% CI: 0.96-1.17] and 1.05 [95% CI: 0.97-1.13]). CONCLUSIONS: In patients with T2DM and CVD, comparable cardiovascular risk reduction was found with SGLT2is and GLP-1 RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of SGLT2is and GLP-1 RAs should be prioritized as second-line agents in those with established CVD.

The future of the radiology information system.

OBJECTIVE: Today in the hospital setting, several functions of the radiology information system (RIS), including order entry, patient registration, report repository, and the physician directory, have moved to enterprise electronic medical records. Some observers might conclude that the RIS is going away. In this article, we contend that because of the maturity of the RIS market compared with other areas of the health care enterprise, radiology has a unique opportunity to innovate. CONCLUSION: While most of the hospital enterprise spends the next several years going through the digital transformation converting from paper to a digital format, radiology can leap ahead in its use of analytics and information technology. This article presents a summary of new RIS functions still maturing and open to innovation in the RIS market.