Maximizing Value in Radiology Through Quality and Safety Improvements

Oct 21st, 2010Oct 22nd, 2010
Phoenix, AZ
US
https://www.acr.org/SecondaryMainMenuCategories/MeetingsandEvents/acr_meetings/MaxValue.aspx
In today's rapidly changing healthcare environment -- with mounting concerns over healthcare costs -- radiologists must demonstrate their commitment to quality patient care and their dedication to the highest levels of professional excellence. With quality experts from leading radiology practices across the nation as your guide, you’ll learn specific ways you can implement a successful quality and safety program.
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Example cases illustrate the double-edged role of large language model (LLM) rationale quality. (A) A 50-year-old female patient with a palpable anterior chest wall mass (correct diagnosis: solitary fibrous tumor of the pleura). The LLM incorrectly suggested synovial sarcoma but provided a persuasive rationale (quality score: 4 of 5), describing a well-defined nodule at axial contrast-enhanced CT spanning the pleura and intercostal space with heterogeneous strong enhancement. Four of five readers (80%) accepted the incorrect suggestion. FDG = fluorodeoxyglucose. (B) A 61-year-old male patient with diabetes presented with cough, sputum, and dyspnea (correct diagnosis: mucormycosis). The LLM correctly suggested mucormycosis with an excellent rationale (quality score: 5 of 5), integrating the reverse halo sign on serial axial CT images and rapidly progressive cavitary lesions. Three of five readers initially selected incorrect diagnoses; after LLM output, all five selected the correct diagnosis (100% acceptance). Blue and orange reader icons denote high- and low-expertise readers, respectively.
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