Technological Model to Optimize the Request for Radiological Studies

TL;DR

A diagnostic algorithm was tested to help doctors choose the right imaging tests, improving accuracy and communication while reducing unnecessary requests.

Contribution

A new diagnostic algorithm was evaluated for its ability to optimize radiological study requests in clinical practice.

Findings

The algorithm improved diagnostic accuracy and decision-making for abdominal emergencies and complex cases.

Participants noted challenges with contrast safety evaluation and limited access to high-cost imaging.

Overall, the algorithm was seen as a useful tool to reduce inappropriate imaging requests and enhance patient care.

Abstract

Inappropriate requests for imaging studies are a frequent problem in clinical practice, leading to diagnostic errors, unnecessary costs, and patient dissatisfaction. These errors often arise from insufficient dissemination of clinical guidelines, limited training of referring physicians, and variability in request formats. The result is delayed diagnoses, duplication of studies, increased radiation exposure, and inefficient use of healthcare resources. To address this issue, technological tools such as diagnostic algorithms have been proposed to support physicians in selecting the most appropriate imaging tests, especially in time-sensitive conditions.

This study evaluated a diagnostic algorithm through a cross-sectional survey of 111 participants, including physicians, residents, interns, medical students, and dental professionals. The questionnaire explored perceptions of the algorithm’s clinical utility, clarity, and feasibility of integration into daily workflows. Respondents consistently highlighted its capacity to improve diagnostic accuracy, expedite decision-making, and facilitate clearer communication between physicians and radiologists. Specific strengths included its applicability to abdominal emergencies and complex scenarios such as right upper quadrant pain, jaundice, pancreatitis, and trauma.

At the same time, participants pointed out challenges, including difficulties in evaluating contrast safety, limited access to high-cost imaging, and the need for broader diagnostic coverage and personalization by age group. Despite these concerns, the algorithm was positively received overall and was recognized as a useful support tool for reducing inappropriate requests, enhancing diagnostic confidence, and ultimately improving patient care.

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