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Homepage > AJR OnTrend > Increasing Quality of Imaging Histories

Increasing Quality of Imaging Histories

Richard E. Sharpe, Jr.
Corresponding Author

Collaborative research has not only standardized the definition of a complete imaging history, but also engineered systems to include supportive prompts in the order entry interface with a single keystroke—sustainably improving the overall quality of imaging histories. A Kaiser Permanente multidisciplinary team—physicians, both primary care and medical imaging providers, as well as information technology and practice improvement professionals—first defined the various components of a complete imaging history, a process that underwent several improvement cycles where consensus audits were regularly performed. The final apparatus of the collaborative team’s complete imaging history definition included the following responses: what happened; when it happened; where the patient was experiencing pain; and the ordering provider’s concern. These four prompts were then inserted into the electronic physician order entry process, and performance was monitored for an additional 18 months. From March 13, 2017, to December 16, 2018, 10,236 total orders were placed by ordering providers in the study clinic. Of the orders audited in the baseline period, 16.0% (64/397) contained all four history components, which increased to 52.0% (2200/4234; absolute increase of 36.0%, relative increase of 225.0%; p < 0.0001) in the subsequent time periods. Moreover, the mean number of characters ordering providers entered into the imaging histories they submitted increased from 45.4 characters per order during the baseline period to 75.4 (66.1% increase, p < 0.0001) after the intervention. “We have also noticed that increased clinical information results in radiologists feeling less of a need to recommend additional downstream or repeat imaging,” wrote Daniel S. Bor from Kaiser Permanente’s department of medical imaging. Bor noted, too, that the project team remains optimistic that the emergence of artificial intelligence could facilitate an automated method to improve imaging histories and support high-quality radiologist interpretations

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