The RSNA ImageShare - Enabling Secure, Transparent, and Easy Image Exchange.

In the early 2000s, the radiology community was awakened to the limitations of electronic media (CDs, DVDs) for exchanging imaging exams. Clinicians frustrated by the time-consuming task of opening discs, while Internet-based exchange of music, photos, and videos were becoming more widespread. The RSNA, which had extensive experience working on interoperability issues in medical imaging, began to look for opportunities to address the issue. In 2007, in the wake of the financial crisis, the National Institute of Biomedical Imaging and Bioengineering (NIBIB) issued an RFP to address Internet-based exchange of medical images. The RFP defined requirements for the network, including that it needed to be patient controlled and standards based. The RSNA was awarded funding for what came to be known as RSNA ImageShare. Over the next 8 years, the RSNA worked in partnership with several vendors and academic institutions to create a network for sharing image-enabled personal health records (PHR). The foundation of interoperability standards used in ImageShare was provided by Integrating the Healthcare Enterprise (IHE), a standards-development organization with which RSNA has had a long association. In 2018 and 2019, the RSNA looked at what had been accomplished and asked if we could take that next step at a national level and promote a solution by which any standards-compliant party could exchange imaging exams through an HIE mechanism.

An Interview with the Carequality Imaging Use Case Implementers.

Imagine you had a cell phone plan that only allowed you to call other customers within the same carrier network. That is the situation most healthcare providers experience when joining a data sharing network. Carequality is a network-to-network trust framework that brings together the entire healthcare industry to overcome this challenge by providing a national-level, consensus built, common interoperability framework to enable health information exchange between and among health data sharing networks. The RSNA partnered with Carequality in 2019 to develop an implementation guide to enable the Imaging Exchange Use Case. The implementation guide was published in December 2019 for early adopters to sign up as implementers to the Carequality framework. Exchange standards must be clearly laid out so that all implementers can easily follow and be held accountable to enable interoperability of medical imaging. The guide was reviewed and tested by implementers and approved for production use in March 2021. Since the launch of the implementation guide, five Carequality Implementers have participated in Carequality's Image Exchange Use Case: Ambra Health, Hyland, Life Image, Nuance, and Philips. These implementers recognized a gap in image interoperability and the need for change and collaboration. Carequality has asked each of the implementers to share their thoughts on issues pertinent to becoming an implementer and imaging interoperability with the hope that the reader will gain insight as to the evolution of network-based image exchange.

Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup.

Sharing radiologic image annotations among multiple institutions is important in many clinical scenarios; however, interoperability is prevented because different vendors' PACS store annotations in non-standardized formats that lack semantic interoperability. Our goal was to develop software to automate the conversion of image annotations in a commercial PACS to the Annotation and Image Markup (AIM) standardized format and demonstrate the utility of this conversion for automated matching of lesion measurements across time points for cancer lesion tracking. We created a software module in Java to parse the DICOM presentation state (DICOM-PS) objects (that contain the image annotations) for imaging studies exported from a commercial PACS (GE Centricity v3.x). Our software identifies line annotations encoded within the DICOM-PS objects and exports the annotations in the AIM format. A separate Python script processes the AIM annotation files to match line measurements (on lesions) across time points by tracking the 3D coordinates of annotated lesions. To validate the interoperability of our approach, we exported annotations from Centricity PACS into ePAD (http://epad.stanford.edu) (Rubin et al., Transl Oncol 7(1):23-35, 2014), a freely available AIM-compliant workstation, and the lesion measurement annotations were correctly linked by ePAD across sequential imaging studies. As quantitative imaging becomes more prevalent in radiology, interoperability of image annotations gains increasing importance. Our work demonstrates that image annotations in a vendor system lacking standard semantics can be automatically converted to a standardized metadata format such as AIM, enabling interoperability and potentially facilitating large-scale analysis of image annotations and the generation of high-quality labels for deep learning initiatives. This effort could be extended for use with other vendors' PACS.

Interoperability and Considerations for Standards-Based Exchange of Medical Images: HIMSS-SIIM Collaborative White Paper.

This white paper explores the considerations of standards-based interoperability of medical images between organizations, patients, and providers. In this paper, we will look at three different standards-based image exchange implementations that have been deployed to facilitate exchange of images between provider organizations. The paper will describe how each implementation uses applicable technology and standards; the image types that are included; and the governance policies that define participation, access, and trust. Limitations of the solution or non-standard approaches to solve challenges will also be identified. Much can be learned from successes elsewhere, and those learnings will point to recommendations of best practices to facilitate the adoption of image exchange.

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.

A first-in-human phase 1a study of the bispecific anti-DLL4/anti-VEGF antibody navicixizumab (OMP-305B83) in patients with previously treated solid tumors.

Purpose Navicixizumab (OMP-305B83) is a bispecific antibody that inhibits delta-like ligand 4 and vascular endothelial growth factor. This Phase 1a trial assessed escalating doses of navicixizumab in refractory solid tumors patients. Design A 3 + 3 dose escalation design was used followed by the treatment of additional patients in an expansion cohort. Study objectives were determination of the maximum tolerated dose, safety, pharmacokinetics, pharmacodynamics, immunogenicity and efficacy. Results Sixty-six patients were treated once every 3 weeks in 8 dose-escalation cohorts (0.5, 1, 2.5, 3.5, 5, 7.5, 10, and 12.5 mg/kg) and an expansion cohort (7.5 mg/kg). The median age was 60 years and 68% of the patients were female. The most commonly enrolled tumor types were ovarian (12), colorectal (11) and breast, pancreatic, uterine and endometrial (4 each) cancers. As only 1 dose limiting toxicity occurred, the maximum tolerated dose was not reached, but 7.5 mg/kg was chosen as the dose for the expansion cohort. The treatment related adverse events (≥15% of patients) were hypertension (57.6%), headache (28.8%), fatigue (25.8%), and pulmonary hypertension (18.2%). Pulmonary hypertension was mostly asymptomatic at doses ≤5 mg/kg (6 Gr1, 1 Gr2), but was more severe at higher doses (4 Gr2, 1 Gr3). Navicixizumab's half-life was 11.4 days and there was a moderate (29%) incidence of anti-drug antibody formation. Four patients (3 ovarian cancer, 1 uterine carcinosarcoma) had a partial response and 17 patients had stable disease. Nineteen patients had a reduction in the size of their target lesions including 7/11 patients with ovarian cancer. Four patients remained on study for >300 days and 2 of these patients were on study for >500 days. Conclusions Navicixizumab can be safely administered with manageable toxicities and these data showed preliminary signs of antitumor activity in multiple tumor types, but was most promising in ovarian cancer. As a result these data justify its continued development in combination Phase 1b clinical trials.

Effect of Clinical Decision Support on Appropriateness of Advanced Imaging Use Among Physicians-in-Training.

OBJECTIVE: Clinical decision support (CDS) tools have been shown to reduce inappropriate imaging orders. We hypothesized that CDS may be especially effective for house staff physicians who are prone to overuse of resources. MATERIALS AND METHODS: Our hospital implemented CDS for CT and MRI orders in the emergency department with scores based on the American College of Radiology's Appropriateness Criteria (range, 1-9; higher scores represent more-appropriate orders). Data on CT and MRI orders from April 2013 through June 2016 were categorized as pre-CDS or baseline, post-CDS period 1 (i.e., intervention with active feedback for scores of ≤ 4), and post-CDS period 2 (i.e., intervention with active feedback for scores of ≤ 6). Segmented regression analysis with interrupted time series data estimated changes in scores stratified by house staff and non-house staff. Generalized linear models further estimated the modifying effect of the house staff variable. RESULTS: Mean scores were 6.2, 6.2, and 6.7 in the pre-CDS, post-CDS 1, and post-CDS 2 periods, respectively (p < 0.05). In the segmented regression analysis, mean scores significantly (p < 0.05) increased when comparing pre-CDS versus post-CDS 2 periods for both house staff (baseline increase, 0.41; 95% CI, 0.17-0.64) and non-house staff (baseline increase, 0.58; 95% CI, 0.34-0.81), showing no differences in effect between the cohorts. The generalized linear model showed significantly higher scores, particularly in the post-CDS 2 period compared with the pre-CDS period (0.44 increase in scores; p < 0.05). The house staff variable did not significantly change estimates in the post-CDS 2 period. CONCLUSION: Implementation of active CDS increased overall scores of CT and MRI orders. However, there was no significant difference in effect on scores between house staff and non-house staff.

A first-in-human dose-escalation study of the oral proteasome inhibitor oprozomib in patients with advanced solid tumors.

PURPOSE: To determine the dose-limiting toxicities (DLTs), maximum tolerated dose (MTD), safety, and pharmacokinetic and pharmacodynamic profiles of the tripeptide epoxyketone proteasome inhibitor oprozomib in patients with advanced refractory or recurrent solid tumors. METHODS: Patients received escalating once daily (QD) or split doses of oprozomib on days 1-5 of 14-day cycles (C). The split-dose arm was implemented and compared in fasted (C1) and fed (C2) states. Pharmacokinetic samples were collected during C1 and C2. Proteasome inhibition was evaluated in red blood cells and peripheral blood mononuclear cells. RESULTS: Forty-four patients (QD, n = 25; split dose, n = 19) were enrolled. The most common primary tumor types were non-small cell lung cancer (18%) and colorectal cancer (16%). In the 180-mg QD cohort, two patients experienced DLTs: grade 3 vomiting and dehydration; grade 3 hypophosphatemia (n = 1 each). In the split-dose group, three DLTs were observed (180-mg cohort: grade 3 hypophosphatemia; 210-mg cohort: grade 5 gastrointestinal hemorrhage and grade 3 hallucinations (n = 1 each). In the QD and split-dose groups, the MTD was 150 and 180 mg, respectively. Common adverse events (all grades) included nausea (91%), vomiting (86%), and diarrhea (61%). Peak concentrations and total exposure of oprozomib generally increased with the increasing dose. Oprozomib induced dose-dependent proteasome inhibition. Best response was stable disease. CONCLUSIONS: While generally low-grade, clinically relevant gastrointestinal toxicities occurred frequently with this oprozomib formulation. Despite dose-dependent increases in pharmacokinetics and pharmacodynamics, single-agent oprozomib had minimal antitumor activity in this patient population with advanced solid tumors.

Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining.

OBJECTIVE: The purpose of this article is to describe structured reporting and the development of large databases for use in data mining in breast imaging. CONCLUSION: The results of millions of breast imaging examinations are reported with structured tools based on the BI-RADS lexicon. Much of these data are stored in accessible media. Robust computing power creates great opportunity for data scientists and breast imagers to collaborate to improve breast cancer detection and optimize screening algorithms. Data mining can create knowledge, but the questions asked and their complexity require extremely powerful and agile databases. New data technologies can facilitate outcomes research and precision medicine.

Patient Perceptions of Participating in the RSNA Image Share Project: a Preliminary Study.

The purpose of this study was to gauge patient perceptions of the RSNA Image Share Project (ISP), a pilot program that provides patients access to their imaging studies online via secure Personal Health Record (PHR) accounts. Two separate Institutional Review Board exempted surveys were distributed to patients depending on whether they decided to enroll or opt out of enrollment in the ISP. For patients that enrolled, a survey gauged baseline computer usage, perceptions of online access to images through the ISP, effect of patient access to images on patient-physician relationships, and interest in alternative use of images. The other survey documented the age and reasons for declining participation for those that opted out of enrolling in the ISP. Out of 564 patients, 470 enrolled in the ISP (83 % participation rate) and 456 of these 470 individuals completed the survey for a survey participation rate of 97 %. Patients who enrolled overwhelmingly perceived access to online images as beneficial and felt it bolstered their patient-physician relationship. Out of 564 patients, 94 declined enrollment in the ISP and all 94 individuals completed the survey for a survey participation rate of 100 %. Patients who declined to participate in the ISP cited unreliable access to Internet and existing availability of non-web-based intra-network images to their physicians. Patients who participated in the ISP found having a measure of control over their images to be beneficial and felt that patient-physician relationships could be negatively affected by challenges related to image accessibility.

Pharmacodynamics and pharmacokinetics of oral topotecan in patients with advanced solid tumours and impaired renal function.

AIMS: The aim of the study was to determine the effect of renal impairment and prior platinum-based chemotherapy on the toxicity and pharmacokinetics of oral topotecan and to identify recommended doses for patients with renal impairment or prior platinum-based (PB) chemotherapy. METHODS: A multicentre phase I toxicity and pharmacokinetic study of oral topotecan was conducted in patients with advanced solid tumours. Patients were grouped by normal renal function with limited or prior PB chemotherapy or impaired renal function (mild [creatinine clearance (CLcr) = 50-79 ml min(-1) ], moderate [CLcr = 30-49 ml min(-1) ], severe [CLcr <30 ml min(-1) ]). RESULTS: Fifty-nine patients were evaluable. Topotecan lactone and total topotecan area under the concentration-time curve (AUC) was significantly increased in patients with moderate and severe renal impairment (109% and 174%, respectively, topotecan lactone and 148% and 298%, respectively, total topotecan). Asian patients (23 in total) had higher AUCs than non-Asian patients with the same degree of renal impairment. Thirteen dose-limiting toxicities (DLTs) were observed, which were mostly haematological. The maximum tolerated dose (MTD) was 2.3 mg m(-2) day(-1) , given on days 1 to 5 in a 21 day cycle, for patients with prior PB chemotherapy or mild renal impairment, and 1.2 mg m(-2) day(-1) for patients with moderate renal impairment (suggested dose 1.9 mg m(-2) day(-1) for non-Asians). Due to incomplete enrolment of patients with severe renal impairment, the MTD was determined as ≥ 0.6 mg m(-2) day(-1) in this cohort. CONCLUSIONS: Oral topotecan dose adjustments are not required in patients with prior PB chemotherapy or mildly impaired renal function, but reduced doses are required for patients with moderate or severe renal impairment.

The Impact of an Artificial Intelligence Certificate Program on Radiology Resident Education.

RATIONALE AND OBJECTIVES: The objective of this study was to evaluate the effectiveness of a pilot artificial intelligence (AI) certificate program in aiding radiology trainees to develop an understanding of the evolving role and application of artificial intelligence in radiology. A secondary objective was set to determine the background of residents that would most benefit from such training. MATERIALS AND METHODS: This was a prospective pilot study involving 42 radiology residents at two separate residency programs who participated in the Radiological Society of North America Imaging AI Foundational Certificate course over a four-month period. The course consisted of 6 online modules that contained didactic lectures followed by end-of-module quizzes to assess knowledge gained from these lectures. Pre- and post-course assessments were conducted to evaluate the residents' knowledge and skills in AI. Additionally, a post-course survey was performed to assess participants' overall satisfaction with the course. RESULTS: All participating residents completed the certificate program. The mean pre-course assessment score was 37 %, which increased to 73 % after completing the modules (p < 0.001). 74 % (31/42) endorsed the belief the course improved familiarity with artificial intelligence in radiology. Residency program, residency year, and reported prior familiarity with AI were not found to influence pre-course score, post-course score, nor score improvement. 57 % (24/42) endorsed interest in pursuing further certification in AI. CONCLUSION: Our pilot study suggests that a certificate course can effectively enhance the knowledge and skills of radiology residents in the application of AI in radiology. The benefits of such a course can be found regardless of program, resident year, and self-reported prior resident understanding of radiology in AI.

New Frontiers in Breast Cancer Imaging: The Rise of AI.

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

Pulmonary nontuberculous mycobacterial disease, Ontario, Canada, 1998-2010.

We measured the prevalence and temporal trends of pulmonary nontuberculous mycobacterial disease among residents of Ontario, Canada, during 1998-2010. Five-year prevalence increased from 29.3 cases/100,000 persons in 1998-2002 to 41.3/100,000 in 2006-2010 (p<0.0001). Improved laboratory methods did not explain this increase, suggesting a surge in disease prevalence.

A Phase I, open-label, dose-escalation study of continuous once-daily oral treatment with afatinib in patients with advanced solid tumors.

BACKGROUND: This trial evaluated the safety, tolerability and maximum tolerated dose (MTD) of afatinib, a novel ErbB Family Blocker. METHODS: In this open-label, dose-escalation Phase I study, afatinib was administered continuously, orally, once-daily for 28 days to patients with advanced or metastatic solid tumors. Dose escalation was performed in a 3 + 3 design, with a starting dose of 10 mg/day (d); doses were doubled for each successive cohort until the MTD was defined. The MTD cohort was expanded to a total of 19 patients. Incidence and severity of adverse events (AEs), antitumor activity and pharmacokinetics were assessed. RESULTS: Thirty patients received at least one dose of afatinib. Twenty-nine patients were evaluable for response. Dose-limiting toxicities (DLTs) consisting of Grade 3 diarrhea were observed in two out of three patients treated at 60 mg/d. The MTD was determined at 40 mg/d. The most frequent treatment-related AEs were diarrhea and mucosal inflammation reported in 76.7% and 43.3% of patients respectively. Five patients had stable disease with a median progression-free survival of 111 days. No objective responses occurred. Pharmacokinetic data showed no deviation from dose-proportionality and steady-state was reached on Day 8 at the latest. CONCLUSIONS: Afatinib was well tolerated with manageable side effects when administered once-daily, continuously at a dose of 40 mg.

A phase 1 study of TRC102, an inhibitor of base excision repair, and pemetrexed in patients with advanced solid tumors.

INTRODUCTION: TRC102 potentiates the activity of cancer therapies that induce base excision repair (BER) including antimetabolite and alkylating agents. TRC102 rapidly and covalently binds to apurinic/apyrimidinic (AP) sites generated during BER, and TRC102-bound DNA causes topoisomerase II-dependent irreversible strand breaks and apoptosis. This study assessed the safety, maximum-tolerated dose (MTD), pharmacokinetics and pharmacodynamics of TRC102 alone and in combination with pemetrexed. PURPOSE: Patients with advanced solid tumors received oral TRC102 daily for 4 days. Two weeks later, patients began standard-dose pemetrexed on day 1 in combination with oral TRC102 on days 1 to 4. The pemetrexed-TRC102 combination was repeated every 3 weeks until disease progression. METHODS: Twenty-eight patients were treated with TRC102 at 15, 30, 60 or 100 mg/m(2)/d. The MTD was exceeded at 100 mg/m(2)/d due to grade 3 anemia in 50 % of patients. TRC102 exposure increased in proportion to dose with a mean t1/2 of 28 h. A pharmacodynamic assay confirmed that TRC102 binds to pemetrexed-induced AP sites at all doses studied. Stable disease or better was achieved in 15 of 25 patients evaluable for response (60 %), including one patient with recurrent metastatic oropharyngeal carcinoma that expressed high levels of thymidylate synthase, who achieved a partial response and was progression free for 14 months. CONCLUSIONS: When administered with pemetrexed, the maximum tolerated dose of oral TRC102 is 60 mg/m(2)/d for 4 days. Randomized controlled studies are planned to evaluate the clinical benefit of adding TRC102 to pemetrexed and other agents that induce BER.

Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation.

Purpose: Breast ultrasound suffers from low positive predictive value and specificity. Artificial intelligence (AI) proposes to improve accuracy, reduce false negatives, reduce inter- and intra-observer variability and decrease the rate of benign biopsies. Perpetuating racial/ethnic disparities in healthcare and patient outcome is a potential risk when incorporating AI-based models into clinical practice; therefore, it is necessary to validate its non-bias before clinical use. Approach: Our retrospective review assesses whether our AI decision support (DS) system demonstrates racial/ethnic bias by evaluating its performance on 1810 biopsy proven cases from nine breast imaging facilities within our health system from January 1, 2018 to October 28, 2021. Patient age, gender, race/ethnicity, AI DS output, and pathology results were obtained. Results: Significant differences in breast pathology incidence were seen across different racial and ethnic groups. Stratified analysis showed that the difference in output by our AI DS system was due to underlying differences in pathology incidence for our specific cohort and did not demonstrate statistically significant bias in output among race/ethnic groups, suggesting similar effectiveness of our AI DS system among different races (p>0.05 for all). Conclusions: Our study shows promise that an AI DS system may serve as a valuable second opinion in the detection of breast cancer on diagnostic ultrasound without significant racial or ethnic bias. AI tools are not meant to replace the radiologist, but rather to aid in screening and diagnosis without perpetuating racial/ethnic disparities.

A phase II study of afatinib (BIBW 2992), an irreversible ErbB family blocker, in patients with HER2-positive metastatic breast cancer progressing after trastuzumab.

Afatinib is an oral, ErbB family blocker, which covalently binds and irreversibly blocks all kinase-competent ErbB family members. This phase II, open-label, single-arm study explored afatinib activity in human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients progressing after trastuzumab treatment. Patients had stage IIIB/IV HER2-positive metastatic breast cancer, with progression following trastuzumab or trastuzumab intolerance and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2. Patients received 50 mg afatinib once-daily until disease progression. Primary endpoint was objective response rate (Response Evaluation Criteria in Solid Tumors 1.0), with tumor assessments every 8 weeks. Forty-one patients were treated. Patients had received a median of three prior chemotherapy lines (range, 0-15) and 68.3% had received trastuzumab for >1 year. Four patients (10% of 41 treated; 11% of evaluable patients) had partial response. Fifteen patients (37% of 41) had stable disease as best response and 19 (46% of 41) achieved clinical benefit. Median progression-free survival was 15.1 weeks (95% confidence interval [CI]: 8.1-16.7); median overall survival was 61.0 weeks (95% CI: 56.7-not evaluable). Most frequent common terminology criteria for adverse events grade 3 treatment-related adverse events were diarrhea (24.4%) and rash (9.8%). Afatinib monotherapy was associated with promising clinical activity in extensively pretreated HER2-positive breast cancer patients who had progressed following trastuzumab treatment.

Moving Toward Seamless Interinstitutional Electronic Image Transfer.

The fact that medical images are still predominately exchanged between institutions via physical media is unacceptable in the era of value-driven health care. Although better solutions are technically possible, problems of coordination and market dynamics may be inhibiting progress more than technical factors. We provide a macrosystem analysis of the problem of interinstitutional medical image exchange and propose a strategy for nudging the market toward a patient-friendly solution. The system can be viewed as a network, with autonomous nodes interconnected by links through which information is exchanged. A variety of potential network configurations include those that depend on individual carriers, peer-to-peer links, one or multiple hubs, or a hybrid of models. We find the linked multihub model, in which individual institutions are connected to other institutions via image exchange companies, to be the configuration most likely to create a patient-friendly electronic image exchange system. To achieve this configuration, image exchange companies, which operate in a competitive marketplace, must exchange images with each other. We call on these vendors to immediately commit to coordinating in this manner. We call on all other stakeholders, including local care provider institutions, medical societies, payers, and regulators, to actively encourage and facilitate this behavior. Specifically, we call on institutions to create appropriate market incentives by only contracting with image exchange vendors who are committed to begin vendor-to-vendor image exchange by no later than 2024.