Background: The quality of healthcare in the US has been progressively addressed by 3 reports from the National Academy of Medicine, the latest of which, entitled "Improving Diagnosis in Health Care," was issued in 2015 from a 21-member panel (the author of this report was a member). The report is a review of the longstanding problem of diagnostic error. The infrastructure of healthcare delivery in the US has inadvertently made diagnostic error a major contributor to the high cost of care and preventable poor patient outcomes.
Content: This review describes the failures in US healthcare delivery that have led to the overwhelming number of deaths attributable to diagnostic error. Each failure is associated with recommendations to eliminate it. The review begins with a description of the scope of the diagnostic error problem and then discusses each of the issues that need to be addressed to reduce the number of misdiagnoses.
Summary: The problem of diagnostic error in the US is a large one. Some the contributing factors to this large problem can be resolved at a small expense and with modest change; others require a major overhaul of aspects of medical practice. For the first time, Americans have a "to-do list" to reduce our diagnostic error problem and be on par with other developed countries that are recognized as providing less costly care with better patient outcomes.
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http://dx.doi.org/10.1373/jalm.2017.025882 | DOI Listing |
PLoS One
January 2025
Medical Image Processing Research Group (MIPRG), Dept. of Elect. & Comp. Engineering, COMSATS University Islamabad, Islamabad, Pakistan.
Recovering diagnostic-quality cardiac MR images from highly under-sampled data is a current research focus, particularly in addressing cardiac and respiratory motion. Techniques such as Compressed Sensing (CS) and Parallel Imaging (pMRI) have been proposed to accelerate MRI data acquisition and improve image quality. However, these methods have limitations in high spatial-resolution applications, often resulting in blurring or residual artifacts.
View Article and Find Full Text PDFMedicine (Baltimore)
January 2025
Department of Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, People's Republic of China.
Rationale: Enteral nutrition is a critical component of care for critically ill patients. However, the blind insertion of a nasoenteric tube, despite being a simple procedure, carries inherent risks that necessitate a reevaluation of the technique.
Patient Concerns: A case of a 60-year-old female experienced the rare yet critical complication of a misplaced nasoenteric tube entering the thoracic cavity during a blind insertion procedure for enteral nutrition following a liver transplant.
Medicine (Baltimore)
January 2025
Department of Bone and Joint Surgery, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Rationale: Synovial sarcoma (SS) is a rare and highly malignant soft tissue sarcoma. When SS occurs in atypical locations, it can present significant diagnostic challenges. We report a case of paraspinal SS initially misdiagnosed as spinal tuberculosis, highlighting the diagnostic difficulties and the importance of considering SS in the differential diagnosis.
View Article and Find Full Text PDFSci Immunol
January 2025
Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
Human recombination-activating gene (RAG) deficiency can manifest with distinct clinical and immunological phenotypes. By applying a multiomics approach to a large group of -mutated patients, we aimed at characterizing the immunopathology associated with each phenotype. Although defective T and B cell development is common to all phenotypes, patients with hypomorphic variants can generate T and B cells with signatures of immune dysregulation and produce autoantibodies to a broad range of self-antigens, including type I interferons.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Importance: Determining spectacle-corrected visual acuity (VA) is essential when managing many ophthalmic diseases. If artificial intelligence (AI) evaluations of macular images estimated this VA from a fundus image, AI might provide spectacle-corrected VA without technician costs, reduce visit time, or facilitate home monitoring of VA from fundus images obtained outside of the clinic.
Objective: To estimate spectacle-corrected VA measured on a standard eye chart among patients with diabetic macular edema (DME) in clinical practice settings using previously validated AI algorithms evaluating best-corrected VA from fundus photographs in eyes with DME.
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