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Patterns of Access to Radiology Reports and Images Through a Patient Portal

Wang, Jason; Goldberg, Julia E; Block, Tobias; Ostrow, Dana; Carbone, Dan; Recht, Michael; Doshi, Ankur
Access to radiology reports and images through a patient portal offers several advantages. The purpose of this study was to characterize patient's interactions with their radiology results. This was a retrospective study that evaluated radiography, ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography, exams performed between July 2020 and June 2021 for patients aged 12 and older. Exam information, access logs of radiology reports and images, and patient demographics were obtained from the electronic health record and image viewing software. Descriptive statistics were computed. The study included 1,685,239 exams. A total of 54.1% of reports were viewed. MRI and PET reports were viewed with the greatest frequency (70.2% and 67.6%, respectively); 25.5% of exam images were viewed, with the greatest frequency for MRI (40.1%). Exams were shared a total of 17,095 times and downloaded 8409 times; 64% of reports were viewed for patients aged 18-39 and 34% for patients aged 80 and greater. The rate of reports viewed was greater for patients with English as their preferred language (57.1%) compared to other languages (33.3%). Among those viewed, 56.5% of reports and 48.2% of images were viewed multiple times; 72.8% of images were viewed on smartphones, 25.8% on desktop computers, and 1.4% on tablets. Patients utilize a portal to view reports and view and share images. Continued efforts are warranted to promote the use of portals and create patient-friendly imaging results to help empower patients.
PMID: 38315344
ISSN: 2948-2933
CID: 5632732

Patient-centered radiology: a roadmap for outpatient imaging

Recht, Michael P; Donoso-Bach, Lluís; Brkljačić, Boris; Chandarana, Hersh; Jankharia, Bhavin; Mahoney, Mary C
Creating a patient-centered experience is becoming increasingly important for radiology departments around the world. The goal of patient-centered radiology is to ensure that radiology services are sensitive to patients' needs and desires. This article provides a framework for addressing the patient's experience by dividing their imaging journey into three distinct time periods: pre-exam, day of exam, and post-exam. Each time period has aspects that can contribute to patient anxiety. Although there are components of the patient journey that are common in all regions of the world, there are also unique features that vary by location. This paper highlights innovative solutions from different parts of the world that have been introduced in each of these time periods to create a more patient-centered experience. CLINICAL RELEVANCE STATEMENT: Adopting innovative solutions that help patients understand their imaging journey and decrease their anxiety about undergoing an imaging examination are important steps in creating a patient centered imaging experience. KEY POINTS: • Patients often experience anxiety during their imaging journey and decreasing this anxiety is an important component of patient centered imaging. • The patient imaging journey can be divided into three distinct time periods: pre-exam, day of exam, and post-exam. • Although components of the imaging journey are common, there are local differences in different regions of the world that need to be considered when constructing a patient centered experience.
PMID: 38047974
ISSN: 1432-1084
CID: 5595272

Fast and Frictionless: A Novel Approach to Radiology Appointment Scheduling Using a Mobile App and Recommendation Engine

Doshi, Ankur M; Ostrow, Dana; Gresens, August; Grimmelmann, Rachel; Mazhar, Salman; Neto, Eduardo; Woodriff, Molly; Recht, Michael
Many outpatient radiology orders are never scheduled, which can result in adverse outcomes. Digital appointment self-scheduling provides convenience, but utilization has been low. The purpose of this study was to develop a "frictionless" scheduling tool and evaluate the impact on utilization. The existing institutional radiology scheduling app was configured to accommodate a frictionless workflow. A recommendation engine used patient residence, past and future appointment data to generate three optimal appointment suggestions. For eligible frictionless orders, recommendations were sent in a text message. Other orders received either a text message for the non-frictionless app scheduling approach or a call-to-schedule text. Scheduling rates by type of text message and scheduling workflow were analyzed. Baseline data for a 3-month period prior to the launch of frictionless scheduling showed that 17% of orders that received an order notification text were scheduled using the app. In an 11-month period after the launch of frictionless scheduling, the rate of app scheduling was greater for orders that received a text message with recommendations (frictionless approach) versus app schedulable orders that received a text without recommendations (29% vs. 14%, p < 0.01). Thirty-nine percent of the orders that received a frictionless text and scheduled using the app used a recommendation. The most common recommendation rules chosen for scheduling included location preference of prior appointments (52%). Among appointments that were scheduled using a day or time preference, 64% were based on a rule using the time of the day. This study showed that frictionless scheduling was associated with an increased rate of app scheduling.
PMID: 37145249
ISSN: 1618-727x
CID: 5509182

Advances in Musculoskeletal Imaging: Recent Developments and Predictions for the Future [Editorial]

Recht, Michael P; White, Lawrence M; Fritz, Jan; Resnick, Donald L
PMID: 37642575
ISSN: 1527-1315
CID: 5618402

Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI

Lin, Dana J; Schwier, Michael; Geiger, Bernhard; Raithel, Esther; von Busch, Heinrich; Fritz, Jan; Kline, Mitchell; Brooks, Michael; Dunham, Kevin; Shukla, Mehool; Alaia, Erin F; Samim, Mohammad; Joshi, Vivek; Walter, William R; Ellermann, Jutta M; Ilaslan, Hakan; Rubin, David; Winalski, Carl S; Recht, Michael P
BACKGROUND:Detection of rotator cuff tears, a common cause of shoulder disability, can be time-consuming and subject to reader variability. Deep learning (DL) has the potential to increase radiologist accuracy and consistency. PURPOSE:The aim of this study was to develop a prototype DL model for detection and classification of rotator cuff tears on shoulder magnetic resonance imaging into no tear, partial-thickness tear, or full-thickness tear. MATERIALS AND METHODS:This Health Insurance Portability and Accountability Act-compliant, institutional review board-approved study included a total of 11,925 noncontrast shoulder magnetic resonance imaging scans from 2 institutions, with 11,405 for development and 520 dedicated for final testing. A DL ensemble algorithm was developed that used 4 series as input from each examination: fluid-sensitive sequences in 3 planes and a sagittal oblique T1-weighted sequence. Radiology reports served as ground truth for training with categories of no tear, partial tear, or full-thickness tear. A multireader study was conducted for the test set ground truth, which was determined by the majority vote of 3 readers per case. The ensemble comprised 4 parallel 3D ResNet50 convolutional neural network architectures trained via transfer learning and then adapted to the targeted domain. The final tear-type prediction was determined as the class with the highest probability, after averaging the class probabilities of the 4 individual models. RESULTS:The AUC overall for supraspinatus, infraspinatus, and subscapularis tendon tears was 0.93, 0.89, and 0.90, respectively. The model performed best for full-thickness supraspinatus, infraspinatus, and subscapularis tears with AUCs of 0.98, 0.99, and 0.95, respectively. Multisequence input demonstrated higher AUCs than single-sequence input for infraspinatus and subscapularis tendon tears, whereas coronal oblique fluid-sensitive and multisequence input showed similar AUCs for supraspinatus tendon tears. Model accuracy for tear types and overall accuracy were similar to that of the clinical readers. CONCLUSIONS:Deep learning diagnosis of rotator cuff tears is feasible with excellent diagnostic performance, particularly for full-thickness tears, with model accuracy similar to subspecialty-trained musculoskeletal radiologists.
PMID: 36728041
ISSN: 1536-0210
CID: 5502202

AUR Radiology Resident Core Curriculum Lecture Series - A Model for Multi-Society Collaborative Virtual Education

Fefferman, Nancy R; Recht, Michael P
To fulfill ACGME requirements, radiology residency programs are required to provide an educational experience that includes a core didactic curriculum for each subspecialty. Although developing and delivering such a core curriculum may not present a problem for large academic programs, it can present a significant challenge for smaller programs with limited faculty in each subspecialty area. Success of the core curriculum lectures series developed for cardiothoracic radiology by the Society of Thoracic Radiology and for musculoskeletal radiology by the International Skeletal Society in collaboration with the Society for Skeletal Radiology prompted the idea of creating a comprehensive core curriculum lecture series encompassing all subspecialties. This paper aims to describe the multi-society collaborative effort entailed in building a curated, on line resident focused core curriculum lecture series detailing the barriers encountered, effects of the COVID-19 pandemic and impact of the finished project.
PMID: 36639275
ISSN: 1878-4046
CID: 5410542

Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI

Johnson, Patricia M; Lin, Dana J; Zbontar, Jure; Zitnick, C Lawrence; Sriram, Anuroop; Muckley, Matthew; Babb, James S; Kline, Mitchell; Ciavarra, Gina; Alaia, Erin; Samim, Mohammad; Walter, William R; Calderon, Liz; Pock, Thomas; Sodickson, Daniel K; Recht, Michael P; Knoll, Florian
Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep learning (DL) methods have provided accelerated high-quality image reconstructions from undersampled data, but it is unclear if DL image reconstruction can be reliably translated to everyday clinical practice. Purpose To determine the diagnostic equivalence of prospectively accelerated DL-reconstructed knee MRI compared with conventional accelerated MRI for evaluating internal derangement of the knee in a clinical setting. Materials and Methods A DL reconstruction model was trained with images from 298 clinical 3-T knee examinations. In a prospective analysis, patients clinically referred for knee MRI underwent a conventional accelerated knee MRI protocol at 3 T followed by an accelerated DL protocol between January 2020 and February 2021. The equivalence of the DL reconstruction of the images relative to the conventional images for the detection of an abnormality was assessed in terms of interchangeability. Each examination was reviewed by six musculoskeletal radiologists. Analyses pertaining to the detection of meniscal or ligament tears and bone marrow or cartilage abnormalities were based on four-point ordinal scores for the likelihood of an abnormality. Additionally, the protocols were compared with use of four-point ordinal scores for each aspect of image quality: overall image quality, presence of artifacts, sharpness, and signal-to-noise ratio. Results A total of 170 participants (mean age ± SD, 45 years ± 16; 76 men) were evaluated. The DL-reconstructed images were determined to be of diagnostic equivalence with the conventional images for detection of abnormalities. The overall image quality score, averaged over six readers, was significantly better (P < .001) for the DL than for the conventional images. Conclusion In a clinical setting, deep learning reconstruction enabled a nearly twofold reduction in scan time for a knee MRI and was diagnostically equivalent with the conventional protocol. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roemer in this issue.
PMID: 36648347
ISSN: 1527-1315
CID: 5462122

Work From Home in Academic Radiology Departments: Advantages, Disadvantages and Strategies for the Future

Recht, Michael P
To achieve necessary social distancing during the Covid-19 pandemic, working from home was introduced at most if not all academic radiology departments. Although initially thought to be a temporary adaptation, the popularity of working from home among faculty has made it likely that it will remain a component of radiology departments for the long term. This paper will review the potential advantages and disadvantages of working from home for an academic radiology department and suggest strategies to try to preserve the advantages and minimize the disadvantages.
PMCID:9791330
PMID: 36577604
ISSN: 1878-4046
CID: 5426242

Video Radiology Reports: A Valuable Tool to Improve Patient-Centered Radiology

Recht, Michael P; Westerhoff, Malte; Doshi, Ankur M; Young, Matthew; Ostrow, Dana; Swahn, Dawn-Marie; Krueger, Sebastian; Thesen, Stefan
PMID: 35441532
ISSN: 1546-3141
CID: 5218302

Artificial intelligence in musculoskeletal imaging: a perspective on value propositions, clinical use, and obstacles

Fritz, Jan; Kijowski, Richard; Recht, Michael P
Artificial intelligence and deep learning (DL) offer musculoskeletal radiology exciting possibilities in multiple areas, including image reconstruction and transformation, tissue segmentation, workflow support, and disease detection. Novel DL-based image reconstruction algorithms correcting aliasing artifacts, signal loss, and noise amplification with previously unobtainable effectiveness are prime examples of how DL algorithms deliver promised value propositions in musculoskeletal radiology. The speed of DL-based tissue segmentation promises great efficiency gains that may permit the inclusion of tissue compositional-based information routinely into radiology reports. Similarly, DL algorithms give rise to a myriad of opportunities for workflow improvements, including intelligent and adaptive hanging protocols, speech recognition, report generation, scheduling, precertification, and billing. The value propositions of disease-detecting DL algorithms include reduced error rates and increased productivity. However, more studies using authentic clinical workflow settings are necessary to fully understand the value of DL algorithms for disease detection in clinical practice. Successful workflow integration and management of multiple algorithms are critical for translating the value propositions of DL algorithms into clinical practice but represent a major roadblock for which solutions are critically needed. While there is no consensus about the most sustainable business model, radiology departments will need to carefully weigh the benefits and disadvantages of each commercially available DL algorithm. Although more studies are needed to understand the value and impact of DL algorithms on clinical practice, DL technology will likely play an important role in the future of musculoskeletal imaging.
PMID: 33983500
ISSN: 1432-2161
CID: 4867662