Clinical report with an actionable recommendation

Abstract

A system (100) includes a parser engine (120), a context inferring engine (130), and an action selection engine (140). The parser engine (120) detects a recommendation in a received clinical report (112) for a patient. The context inferring engine (130) extracts and normalizes recommendation elements in the detected recommendation. The action selection engine (140) generates a patient actionable clinical report (142) based on the received clinical report and the extracted and normalized recommendation elements.

Description

technical field [0001] The following relates generally to clinical reports, and more specifically to clinical reports with actionable recommendations. Background technique [0002] Healthcare practitioners such as radiologists examine images of patients from imaging procedures or studies and prepare clinical reports. The prepared clinical report, eg formatted as a narrative text, includes relevant findings and / or incidental findings. For example, computed tomography (CT) images from a patient's abdominal CT imaging study include liver and lungs. In this example, the CT imaging study was ordered based on patient conditions that might indicate cirrhosis of the liver. Relevant findings included information confirming cirrhosis, and incidental findings included information indicating underlying pulmonary nodules. Based on these findings, healthcare practitioners include recommendations for follow-up, such as another patient's imaging tests, laboratory tests, or other medical ...

AI Technical Summary

Problems solved by technology

Sometimes the recommendations contained in the clinical report are not followed, for example, when the referring healthcare practitioner fails to act on the recommendation, the recommendation is not clearly identified or difficult to interpret, the patient fails to schedule an appointment for a scheduled test based on the recommendation, the patient Missing a scheduled appointment for an inspection scheduled in response to a referral, etc.

Unfortunately, this can lead to impacts on patient safety, for example, resulting in missed or delayed diagnoses, and / or increased risk of liability for clinicians and / or healthcare providers

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