Turning Good Ideas Into Outstanding Advancements: Winners of the 2019 Imaging Innovation Awards

Albert Einstein is credited with remarking that the true sign of intelligence is not knowledge but imagination. To that the winners of RBJ’s Second Annual Imaging Innovation Awards might collectively add that it takes exploration to turn imagination into innovation. 

Which is to say that this year’s competition brought out the best in a strong field. People didn’t just brainstorm new ways of doing things. They thought through details, tested approaches and refined plans as they went along. 

All entrants developed notably original breakthroughs in various aspects of medical imaging. The winners never lost sight of the ultimate point of all the extra effort: improving patient care while increasing efficiencies and, wherever possible, cutting or at least containing costs. 

Then they took the time to recount their work in writing. To that end, entrants used our online form to fill in free-text fields that allowed them to: 

•     Describe a project that launched with a clear aim, unfolded over a specified time period and concluded with a measurable or demonstrable improvement; 
•     Detail steps that could be readily adoptable or imitated by other radiology organizations around the U.S.; and 
•     Tell how they innovated—applying resourcefulness, inventiveness and/or creative thinking—as they pursued their project’s goals. 

RBJ’s panel of experts whittled a stack of entries to 10 finalists and then turned things over to our judges, who voted independently of one another and arrived at five clear standouts. 

The winners will receive trophies at RSNA and, in the pages that follow, they show how they earned top honors as we present their entries in full. 

Read on to get the rest of the story from the best of the best in 2019.

An Initiative to Power Clinical Decision Support with Structured Reporting of Positive Ultrasound Exams for Deep Vein Thrombosis 

By UT Southwestern Medical Center at Dallas and Parkland Health & Hospital System

Doppler ultrasound for suspected deep vein thrombosis (DVT) is a fairly targeted exam with limited outcome options, which made it ideal for the first use of a concept we’d been exploring: triggering events in the EHR based on how we report exams when using structured reporting. Although delayed treatment for identified DVT was not considered a leading concern, our institution is a training environment with numerous handoffs, each of which raises the potential for losing information. In discussions with the hospitalist service, we decided this would be a useful tryout of a helpful safety system—as long as it only alerted when apparent delays in treatment initiation occurred. We engaged a lead hospitalist during the conceptualization and implementation of this project. This, in turn, facilitated communication with the treating services.

Aims and objectives. We wanted to use the report created by the radiologist to trigger clinical decision support (CDS) within the EHR for inpatient and emergency department patients. Using structured reporting transmitted discretely as a data element, the EHR could identify when a Doppler ultrasound of the extremity was reported as positive for DVT. This then initiated automated assessment of the patient state by the electronic system. If the patient did not have anticoagulation medication orders two hours after a positive ultrasound exam, and did not have an International Normalized Ratio of greater than 2, the EHR’s CDS system would trigger a popup window for the next provider viewing the patient’s chart. This interruptive alert would display the date and result of the imaging exam, recent pertinent labs and links to orders for anticoagulation medications. 

There also are options to acknowledge receipt of treatment guidance, such as a patient’s contraindication to anticoagulation, which would close out the alert. The primary aim was to ensure timely treatment for patients testing positive for DVT. Secondary aims were to be able to track the positive rate for ultrasound testing for DVT at an enterprise level and lay the foundation for a standard framework for structured reporting to impact other clinical scenarios.

Leadership and project management. This was a true team effort. The radiology informaticist and a structured reporting champion championed the project. A lead hospitalist and deputy medical informaticist for the hospital also contributed key input. Radiology department leadership and the hospital’s chief medical information officer approved the project, and the CMIO provided access to the technical support services of the EHR for the CDS build. The imaging IT team managed the interface with the reporting application. The structured reporting champion, radiology division chiefs and radiology department chair promoted use of the structured reporting templates. The division chiefs monitored and reinforced adherence to use of the templates. Communication of this system to the hospital and emergency department teams was handled through the standard enterprise communication systems for the hospital used for all technical updates. Outcomes of the project were monitored through the EHR reporting systems. The lead hospitalist embedded with the care teams in the hospital and could “hear” the voice of the customer.

Key steps. One of the linchpins to the success of this project was the enterprise-wide adoption of system standard report templates by the radiology practice. This took a few years and many discussions. In fact, the radiology department had accepted the process as a standard best practice by the time of this project’s inception. The report template for Doppler ultrasound for suspected DVT had already gone through several iterations during active clinic use. Prior to the initiation of this project, the impression field for this template was fixed into a set picklist with six categories of interpretive outcomes separating out negative, positive (four variant options) and superficial thrombus-only results. With a now-codified impression field in hand, we activated transmission of this report element into the EHR as a discrete data element separate from the report text. We then engaged the hospitalist informaticist in discussions on how this data element could be utilized. From these conversations we decided the EHR’s CDS would be a good target.

Knowing that people generally dislike interruptive alerts, we outlined logic to prevent any alerting where providers were practicing as expected: We would only alert for apparent delays in treatment. Project leaders presented the idea to radiology and hospital leadership, who approved and authorized use of technical support personnel time. We built the EHR alert to guidelines and activated it in “silent” mode, meaning it would trigger but no one would see it in production. We then reviewed the times for alerting. The radiology informaticist reviewed to make certain it alerted for positive exams and did not alert for negative exams. The hospital informaticist reviewed to make sure it only alerted in the correct clinical scenario. These reviews resulted in a few rounds of logic modification. Once alert triggering appeared accurate in production, we took the alert out of silent mode and made it visible to treating providers. The EHR reporting application monitored rates of alerting, and the hospitalist informaticist informally surveyed his coworkers with regards to the alerting. No additional modifications were identified as needed, and the system currently remains active.

Positive outcomes. In the first six months of 2018, we performed 4,024 Doppler ultrasound exams on inpatient and ER patients for suspected DVT. Of these exams, 3,982 (99%) were reported with the system structured report template. Of those, 455 (11.4%) were reported as positive for DVT. Of those patients with a positive imaging test, 358 (78.7%) received anticoagulation in less than two hours; no alert was triggered. Of the other 97 (21.3%) patients, 68 (70.1%) went on to receive anticoagulation after providers saw the DVT notification alert. To date, no treating-provider has complained about this alert. When we asked the hospitalist how things went with the alert go-live, he responded: “I really haven’t heard anything about it.” That’s a pretty positive outcome when it comes to activating disruptive alerts for providers.

Submitted by Travis Browning, MD, director of quality in UTSW’s radiology department. 



By Radiology Partners

In 2015, our practice began a quality-improvement project aimed at decreasing variability in our radiology reports. We used a change management process to help our radiologists provide evidence-based Best Practice Recommendations (BPRs) for several incidental pathologies. Because no tools were available to help at the time, our radiologists had to remember to use our BPRs whenever they came across a relevant incidental lesion. If they remembered, they needed to find and apply the correct logic to identify the appropriate follow-up recommendation and input this recommendation into their report. To provide feedback to our rads, analysts in our practice evaluated hundreds of relevant radiologist reports for adherence to our BPRs and provided those scores to our local practices. This feedback process is an essential component of change management and worked well for some time. However, as we scaled both our practice size (i.e., more reports to review), the number of BPRs (i.e., more reports to review), and the complexity of our BPRs (e.g., incidental adrenal lesions) we realized both our rads and our  analysts would soon get overwhelmed, and change management on its own would not be effective. We needed a tool to assist both groups in order to scale.

Aims and objectives. The main aim of the project was to improve the consistency and accuracy of follow-up recommendations—specifically, to ensure reported recommendations are population health-driven and evidence-based. If we were able to do this, we knew we would not only be able to improve patient care (all BPRs) but also to save lives (abdominal aortic aneurysm BPR), decrease costs (incidental thyroid nodule BPR), improve referring physician satisfaction (all BPRs), and improve radiologist workflow and efficiency (all BPRs). In order to accomplish this lofty goal, we set out to create an artificial intelligence (AI) tool based on natural language processing (NLP) that would a.) provide the appropriate follow-up recommendation at the point of dictation and within the radiologists’ workflow using the dictated pathology and BPR logic, and b.) assist our analysts in evaluating radiologist BPR compliance. 

If both components succeeded, we knew we could scale our BPR program from few to many and include BPRs that were previously too complicated to deploy with change management alone. We also would be able to continue to scale the size of our practice and roll out our BPR program to our new practices without overwhelming our radiologists or the internal resources who were evaluating radiologist BPR compliance.

Leadership and project management. We organized a multidisciplinary team with representation from IT infrastructure, IT applications, data scientists, radiologists and analysts from our clinical value team (CVT). A radiologist who oversees our clinical data science and analytics team provided strategic guidance, and a project manager kept the group on track. Interestingly, we learned that communication between IT, data science (DS) and radiologists is not automatic. It takes work to ensure group members deliver the message in a way that other members can understand. As an example, when we first began working with the DS team, the initial product was not functioning as desired. With that realization, we revisited how we communicated with the DS team. 

Instead of remotely providing information about the BPRs, we flew onsite and spent two to three days with the DS team discussing anatomy, physiology and terminology of the relevant structures before delving into the BPR logic. We spent the final day reviewing radiology reports clarifying how the logic would be applied. This communication change transformed our interaction and enabled the DS team to produce a highly accurate product. We call our AI product “recoMD.” We promoted it by creating a video advertisement, discussing it at our all-practice meetings, advertising it through blog posts and demonstrating it with multiple local practices. We also were intentional about training each radiologist to ensure a successful rollout. Our attention to process has been effective: Radiologists and practices are now requesting recoMD.

Key steps. Deploying recoMD with our radiologists revolved around radiologist education and communication. Specifically, we provided each radiologist with information about why the tool was created, its capabilities, how the tool fits into the normal radiologist workflow and how the radiologists and the care they provide could benefit by using it. We looked at the education from the radiologist’s perspective—what was important to them—and created our training materials and training process with that in mind. We begin every rollout with an onsite group presentation between our clinical lead (a radiologist) and the local radiologists. This one-hour session focuses on why recoMD was created and how it helps the radiologists. The clinical lead also reviews recoMD’s functionality, describes how to use it and discusses potential future developments. The session ends with a live demo. Over the subsequent week(s), the radiologists are individually trained on the tool. Each spends anywhere from 15 to 30 minutes with a clinical trainer and IT staff member who show them how to use recoMD on their workstation and let them practice using it on live cases. Contacts are shared for follow-up questions. We provide additional hardcopy FAQs and cheat sheets, and we make digital information about the tool available to all radiologists on our internal practice-wide website. Interaction between the radiologist and our training team continues through the many feedback mechanisms available within the tool. The CVT evaluates and responds to every piece of feedback sent by a radiologist. 

Positive outcomes. After the implementation of our recoMD tool, radiologist adherence to BPRs and billing conditions significantly increased. In fact, the first practice for which recoMD was implemented improved their BPR adherence by up to 83%. Specifically, performance in this practice increased from 75% to 100% on ovarian cysts, from 61% to 92% on abdominal aortic aneurysms and from 54% to 99% on incidental thyroid nodules. Practices also improved their adherence to required billing conditions (including MIPS measures) by up to 86%, with 58% improvement on MIPS measure 436, 61% improvement on MIPS 145, 81% improvement on properly documenting CTA studies and 86% improvement in providing a billable history. In addition, both user logs and radiologist feedback have validated that our radiologists are consistently using the tool. Although it seems to be well accepted since implementation, this project is far from complete. Our goal is to continually add functionality and make improvements. Aside from feedback we receive from the radiologists through the tool, surveys have been used to gather additional feedback. This information has directed improvements to both the data science logic and the user interface. New BPRs also are being consistently added.

Submitted by Nina Kottler, MD, VP of clinical operations for Radiology Partners in El Segundo, Calif.  


Process Improvement for Follow-up Radiology Report Recommendations of Lung Nodules

By Radiology Group of Abington

Communication of imaging results and follow-up recommendations to patients and primary care providers (PCPs) is a challenge for healthcare systems. In addition, tracking whether a patient’s follow-up has been completed is another significant gap in care coordination. Patients are often unaware of or cannot even understand the significance of radiology findings or follow-up recommendations reported after imaging procedures. In addition, patients may not have a primary physician listed at time of imaging if the first encounter is in the emergency room or if their primary care physician or specialist works in a different EHR platform. Communication of imaging results to different healthcare providers is challenging with the myriad of existing EHR systems, which often lack interoperability with other clinical entities. Description of lung nodules in radiology reports can vary widely if a standardized lexicon is not used. Moreover, follow-up recommendations by radiologists can be varied for certain size lung nodules because an individual’s risk factors to develop lung cancer may not be known at the time of dictation.

Aims and objectives. The goal of this project was to develop a better automated tracking method and communication tool to reduce the likelihood that needed follow-up studies are missed by patients and clinicians. Secondary outcomes we hoped to achieve were to track the patient condition at follow-up imaging. Also, we tried to determine which communication pathway was better at increasing the likelihood of follow-up being completed: a letter to the PCP or a letter to the patient.

Leadership and project management. Monthly meetings with a larger multidisciplinary team included two physician patient safety officers, a radiology administrative director, a physician who served as chief medical information officer, a champion radiologist, a surgical resident and a senior hospital administrator. This group designed the process of letter notification to the PCP and patient, identifying which clinical radiologic follow-up was to be queried for proof of concept. We chose lung nodule(s) due for follow-up as the primary focus since this scenario has the most widely accepted, evidence-based recommendations. A smaller working group consisted of the champion radiologist and an analyst with IT and nursing experience (the latter employed by the billing company) who met weekly to review identified cases. The radiology champion met with the primary care physicians and their office managers at monthly meetings to educate them about these follow-up letters that they and their patients would be receiving in the mail. The chief medical officer strongly emphasized that the PCP would be responsible for determining if follow-up imaging would be needed after review of the patient’s risk factors and clinical history, even if the PCP was not the one ordering the original imaging study. The PCP was the most central care coordinator best equipped at managing patient problem lists and orchestrating needed follow-up since the PCP had the most complete clinical history and relationships with referring specialists.

Key steps. Initially using a natural language processing (NLP) commercial software, we could identify the radiology reports that were overdue for follow-up based on data from 2016. However, we also realized the laborious work needed to track the patients, opting instead to use an export into Excel spreadsheets from the NLP which contained the full report and other key elements needed for tracking. The export only contained NLP identified reports with follow-ups detected as overdue. As we began taking exports from older studies, we realized that the Excel spreadsheets became quite voluminous with multiple patients and that access to prior reports and their time stamps was needed. We also needed to reference the report data exported from the NLP with data from the billing system to get addresses for the patients and the PCPs. This was done manually in spreadsheets with lookup functions. At this point, we hired a computer programmer. 

The first iteration of the custom designed analytics system was used to review radiology reports and significantly decreased the time requirement of reviewing and assigning intervals for follow-up. Once brought into the new system, the profile logic in the customized analytics system was applied to data-mine the overdue reports for the lung profiles. We implemented automation of Lung-RADS report follow-up due dates to enhance system intelligence, using Lung-RADS categories 1 through 4 to calculate the due date and to automate closing when the patient returned for follow-up. We further enhanced the software by tracking which lung nodules had resolved, improved or worsened at the time of follow-up. With our tracking of clinical conditions such as worsening of lung finding at follow-up imaging, we could identify patients who returned for follow-up with new lung nodules so that they could be placed in a separate category within the analytics system as patients who are at higher risk.

Positive outcomes. We saw steady improvement in rates of completed imaging follow-ups over the course of the project. For example, the percentage of completed follow-ups more than doubled, rising from 26.5% in 2015 (baseline) to 41.3% in 2016 and, finally, to 59.7% in 2017. This coincided with our increase in targeted communications: Within 2017 alone, for example, we sent 43 letters in July and went on to average more than 250 per month from August through October. Later we refined our follow-up profiles to include searching for follow-up terminology within a certain word count. At this time, the system output averaged more than 200 letters sent per month. Further, while close to 70% of patients were stable at follow-up and 5% had improved, 11% were found to be worsening, while 6% had a new lung nodule and 4.2% had a new abnormality. We also measured the follow-up completed numbers by using the PCP letter send date or the patient letter send date to find which method of notification was more effective. We found the average return of patients improved by 10% when letters were sent to the PCP (45%) as compared to being only sent to the patient (35%).

Submitted by Philip Lim, MD, chair of radiology at Abington Hospital/Jefferson Health in Pennsylvania. 


Standardization of Clinical Imaging Through Novel Multi-Council Development Within a Large Integrated Healthcare System

By Integrated Imaging Consultants

The concept of standardization in medicine, both in terms of cost and quality, has risen to the forefront in the constant pursuit of optimized patient care. Multiple journal articles have advocated for a more standardized approach in the delivery of imaging in the pursuit of improved outcomes. To this end, Advocate Health Care (AHC), the largest integrated delivery network (IDN) in Illinois, developed a stated goal to standardize system-wide medical care across its 12 hospitals and multiple clinics. Given the wide variability of practice settings within the enterprise, including level 1 trauma centers, safety net and community hospitals, as well as outpatient imaging centers, standardizing imaging practices presented unique challenges that were not addressed in previous studies. Integrated Imaging Consultants (IIC) was formed in January 2016 to create a single independent corporate entity bringing together 140 radiologists practicing at various sites throughout the enterprise. In order to meet the system’s stated goals, IIC sought to innovate internally, with central buy-in from AHC, to improve the quality of care delivered primarily through standardization.

Aims and objectives. The project’s main aim, shared by Integrated Imaging Consultants and Advocate Health Care, was to optimize and standardize imaging policies and protocols throughout the system. A focus on standardization strongly helps reduce human error and therefore safety events, refine imaging professionals’ skills, improve quality and add value. A consistent approach to patient care promotes more reliability and therefore less ambiguity in serving patients, thus enhancing the patient experience. Our efforts are aligned with the core values of Advocate Health Care and the principles of a high-reliability organization. Secondary goals included disseminating our success story and collaborating with other hospitals and/or imaging facilities to improve population health. Another important secondary aim was to promote consistency in performance behavior by hardwiring the attitudes, skills and behaviors required in a robust safety culture.

Leadership and project management. This project was a joint effort co-led by IIC radiologist physician leaders and their AHC dyad administrative partners. We brought together content experts to form subspecialty/modality councils in CT, MRI, ultrasound, nuclear medicine, breast imaging and interventional radiology, including a dedicated council for imaging safety. The project included recruitment of existing and newly created site-specific physician leaders and lead administrative directors to create these councils. Site-specific participation by these established dyad partnerships was mandated per council, assuring individual site buy-in, communication and implementation. We also established a feedback mechanism to report back to system-level leadership.

Key steps. After identifying physician and administrative leaders from each site to serve as dyad partner council leaders with expertise per modality/subspecialty, we established individual council charters with formal statement of goals, objectives and rules of engagement. We recruited radiologists, administrators and technologists at each site to participate as site-specific representatives on the councils, then standardized council workflows including, but not limited to: prioritization of policy/protocol standardization; identification of evidence-based best practices; review of existing practices per individual site; formulation of new policies/protocols; creation of a robust, reproducible and transparent communication platform on policy/procedure/protocol modifications and implementation throughout the system; and formalization of consistent monitoring and reporting of site implementation based on metrics established by the councils. 

Leveraging the talent, skills, funds of knowledge and experience provided by the dyad partnerships to engage in numerous collaborative grant-funded IRB-approved research projects, we presented our research and outcomes nationally and internationally at seven or so conferences and meetings. We published our results in scientific and business journals and presented them via webinars and videos. We provided/maintained a comprehensive educational platform for technologists mandating completion of didactic courses to maintain performance proficiency. Finally, we monitored outcomes to assess post-implementation changes.

Positive outcomes. Our efforts yielded standardization of multiple imaging workflows, which helped us achieve many good outcomes. These included: dose reduction strategies/protocols for pregnant/pediatric patients; using estimated glomerular filtration rate in determining renal function before administration of IV contrast for CT/MRI; electronic interference policy for implantable devices in CT; algorithmic weight-based delivery for administering CT contrast, resulting in decreased contrast volume administered; potential decreased contrast-induced nephropathy; decreasing the number of delayed-sequence abdomen/pelvis acquisitions, reducing up to 50% of administered dose in some facilities, with average delivered dose by as much as 40% less than the ACR’s recommended diagnostic reference levels for select studies; reducing repeat examinations on a single patient within a pre-determined time period, decreasing imaging utilization up to 80% at some facilities; CT contrast extravasation/pre-medication policies for at-risk patients; ultrasound study acquisition techniques system-wide, including renal, right upper quadrant and vascular studies, allowing error reduction, improved acquisition efficiency and optimized comparison studies between sites; CT protocols for imaging/reporting of specialized pancreatic and hepatobiliary studies and low-dose CT lung cancer screening; dictation templates; utilizing 3D breast tomosynthesis at every site; dense breast caveat for additional screening with ultrasound/MRI; pre- and post-patient education/instructions in therapeutic nuclear medicine studies system-wide; MRI screening sheets/gadolinium safety screening; and establishing radiation dose committees at each site. 

These efforts culminated in our obtaining ACR Imaging 3.0 site distinction, ACR Diagnostic Imaging Center of Excellence distinction twice and ACR CT Lung Cancer Screening Designation. 

Submitted by Abraham Bogachkov, MD, a radiology resident with Advocate Health Care in Chicago.


Reduction of Unnecessary Gadolinium-Based Contrast in Multiple Sclerosis Patients

By the Department of Radiology at the Hospital of the University of Pennsylvania

Patients with multiple sclerosis (MS) can receive more than 60 contrast-enhanced MRI exams during the course of their lifetime. Growing evidence suggests that there is deposition of free gadolinium in the brain and other organs with gadolinium-based contrast agents (GBCAs), even in patients with normal renal function. In fact, the FDA recently issued a warning on all GBCAs, although the clinical significance still remains unknown. We recently conducted a study showing that contrast enhancement of MS lesions is only seen in patients with new disease activity on noncontrast imaging (Mattay et al., “Do All Patients with Multiple Sclerosis Benefit from the Use of Contrast on Serial Follow-Up MR Imaging? A Retrospective Analysis,” American Journal of Neuroradiology, October 2018). This highlighted an opportunity to administer GBCAs only in MS patients who show evidence of new disease activity on noncontrast imaging, which represents only 25% or so of patients.

Aims and objectives. We sought to minimize GBCA use in MS follow-up patients by only giving contrast to patients who have evidence of new disease activity on noncontrast imaging. This form of precision diagnostics can prevent possible side effects related to GBCA use while also decreasing patient scan time, cost and discomfort. In addition, it may significantly reduce costs to the healthcare system by minimizing unnecessary imaging and increasing the possibility of scanning other patients who need imaging sooner. As part of this project, we trained 3D laboratory technologists to perform preliminary, real-time analysis of the T2/FLAIR noncontrast images using our in-house computer-assisted-detection (CAD) software. Therefore, a secondary aim was to train the technologists to perform this task with high accuracy.

Leadership and project management. Prior to implementing a new protocol, we held several multidisciplinary meetings to discuss how to exactly limit IV contrast in MS follow-up patients. Several meetings were first held within the radiology department, which included the department chair, the neuroradiology division chief, the neuroradiologists in charge of MRI protocols and the MS CAD software, as well as MRI technologists and the director of the 3D lab. After arriving at a general agreement to proceed within the department, we held several meetings between individual radiologists and MS neurologists, and we met with the MS neurology group during their weekly group meeting. Some neurologists expressed hesitancy to start this new protocol, but they and all stakeholders agreed to pilot the new workflow during a two-month period to determine actual real-world performance and assess benefits and disadvantages.

Key steps. We created a training module to help the 3D lab technologists learn the MS CAD software. The module incorporated multiple examples and explanations of typical true positives and false positives. The project team decided that patients who agreed to participate in this new protocol would not receive an IV prior to being placed in the MRI scanner. After the 3D FLAIR sequence was obtained following a localizer scan, the scanning MRI technologist would send the exam to PACS and let the 3D lab know it could be processed. The 3D lab technologist would then run the CAD software, which takes approximately 10 minutes to process an exam. The 3D lab technologist would review the output of the CAD software for the presence or absence of new lesions in the brain and let the MRI technologist know whether the patient needed contrast-enhanced imaging or not. If the patient needed contrast, it would be injected by the technologist. If no contrast was needed, the MR technologist would omit the post-contrast sequences of the brain and spine, which consisted of 15 minutes of additional sequences when the patient was ordered for imaging of the brain, cervical spine and thoracic spine. 

During the first week of the pilot, a neuroradiologist reviewed each of the cases. After the first week, if the technologists felt unsure about any particular case, they had the option to call a neuroradiologist for their opinion of whether or not there were new lesions in the brain as shown by CAD and, thus, whether or not to inject contrast.

Positive outcomes. During the pilot period, 153 subjects were included in the new protocol. GBCA use in MS follow-up patients was reduced by 87%. As far as the secondary outcome of training 3D lab technologists to interpret the presence of new disease activity, the technologists’ overall accuracy was 94.8% with a sensitivity of 80.0% for detecting new lesions and a specificity of 97.0% to rule out the presence of new lesions.  

Submitted by Jeff Rudie, MD, PhD, a former Penn Medicine radiology resident who is now with UC-San Francisco.


3rd Annual IIAs on the Way

The 2020 Imaging Innovation Awards are soon to open for entries. Once again we’ll recognize radiology practices and hospital radiology departments that combined creative thinking with coordinated teamwork in a dedicated project to maximize care quality while boosting operational efficiency. Check RadiologyBusiness.com for details and an entry form.