Ambulatory Quality Improvement Despite COVID-19: Blueprint for a Successful System for Continuous Improvement.

ABSTRACT: In this article, we describe our experience developing and implementing a multipronged approach to improve performance across a strategic subset of quality measures within primary care. Detailed techniques include data visualization and analytics, process reengineering, team engagement, visual project management, continuous improvement methods and training, and incentives and recognition. We achieved positive change across 12 high priority measures which we deemed the "High Value Framework (HVF)" by fostering a collaborative, nonpunitive, problem-solving culture. We focused on measures that had the greatest potential for impact from a clinical, reimbursement, and reputational perspective. More importantly, we sustained gains despite the challenges posed by the COVID-19 pandemic, thereby demonstrating programmatic resilience and high process reliability. This systematic approach serves as a practical blueprint for other healthcare entities seeking to navigate the complexities of quality improvement in a dynamic environment. The model provides a strategic framework for prioritizing and standardizing quality measures, effectively engaging stakeholders, and managing organizational change. Our model emerged from a need to address real-world operational challenges, rather than as an academic or theoretical exercise, and was developed independently of existing literature on measure prioritization and standardization at the time of its inception.

Digital Otoscopy Videos Versus Composite Images: A Reader Study to Compare the Accuracy of ENT Physicians.

OBJECTIVES/HYPOTHESIS: With the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video. STUDY DESIGN: Diagnostic survey analysis. METHODS: Five Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image. RESULTS: There were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate. CONCLUSIONS: Equal to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:E1668-E1676, 2021.

OtoMatch: Content-based eardrum image retrieval using deep learning.

Acute infections of the middle ear are the most commonly treated childhood diseases. Because complications affect children's language learning and cognitive processes, it is essential to diagnose these diseases in a timely and accurate manner. The prevailing literature suggests that it is difficult to accurately diagnose these infections, even for experienced ear, nose, and throat (ENT) physicians. Advanced care practitioners (e.g., nurse practitioners, physician assistants) serve as first-line providers in many primary care settings and may benefit from additional guidance to appropriately determine the diagnosis and treatment of ear diseases. For this purpose, we designed a content-based image retrieval (CBIR) system (called OtoMatch) for normal, middle ear effusion, and tympanostomy tube conditions, operating on eardrum images captured with a digital otoscope. We present a method that enables the conversion of any convolutional neural network (trained for classification) into an image retrieval model. As a proof of concept, we converted a pre-trained deep learning model into an image retrieval system. We accomplished this by changing the fully connected layers into lookup tables. A database of 454 labeled eardrum images (179 normal, 179 effusion, and 96 tube cases) was used to train and test the system. On a 10-fold cross validation, the proposed method resulted in an average accuracy of 80.58% (SD 5.37%), and maximum F1 score of 0.90 while retrieving the most similar image from the database. These are promising results for the first study to demonstrate the feasibility of developing a CBIR system for eardrum images using the newly proposed methodology.

Digital otoscopy versus microscopy: How correct and confident are ear experts in their diagnoses?

Introduction With the growing popularity of telemedicine and tele-diagnostics, clinical validation of new devices is essential. This study sought to investigate whether high-definition digital still images of the eardrum provide sufficient information to make a correct diagnosis, as compared with the gold standard view provided by clinical microscopy. Methods Twelve fellowship-trained ear physicians (neurotologists) reviewed the same set of 210 digital otoscope eardrum images. Participants diagnosed each image as normal or, if abnormal, they selected from seven types of ear pathology. Diagnostic percentage correct for each pathology was compared with a gold standard of diagnosis using clinical microscopy with adjunct audiometry and/or tympanometry. Participants also rated their degree of confidence for each diagnosis. Results Overall correctness of diagnosis for ear pathologies ranged from 48.6-100%, depending on the type of pathology. Neurotologists were 72% correct in identifying eardrums as normal. Reviewers' confidence in diagnosis varied substantially among types of pathology, as well as among participants. Discussion High-definition digital still images of eardrums provided sufficient information for neurotologists to make correct diagnoses for some pathologies. However, some diagnoses, such as middle ear effusion, were more difficult to diagnose when based only on a still image. Levels of confidence of reviewers did not generally correlate with diagnostic ability.