#Pathology report manual

Relevant simply means that the pathology report (or reports) makes sense in the context of what was described in the radiology report (or reports). It should be noted that candidate matching can be many-to-many meaning that more than one pathology report can be relevant to a candidate radiology report and more than one radiology report may match a candidate pathology report. The reports are then processed by the AI model to determine if they should be considered relevant matches or not. Reports are likely pre-filtered to at least be sure they belong to the same patient and potentially limited to a reasonable time period (perhaps 30-60 days), but these pre-filtering steps are subject to debate and it may be valuable to consider longer time periods if model performance characteristics and radiologist interest allow it. Radiology and pathology reports are consumed electronically by some mechanism such as health level 7 (HL7) report extraction, fast healthcare interoperability resources (FHIR) interface, or other custom database query. The radiologist also provides feedback on whether the pathology report was concordant with their imaging interpretation if not, further workup may be required to ensure proper patient care.

The radiologist reviews both reports and provides feedback on whether the matching algorithm worked and whether any potential discrepancy was appropriately or inappropriately flagged this feedback is used to iteratively improve the matching algorithm. The application flags potential discrepancies between the radiology and pathology reports. These radiology and pathology reports are automatically correlated as relevant and presented to the radiologist for review. The pathology report confirms the presence of cancer. The patient undergoes a targeted prostate biopsy because of these imaging findings. The radiologist reports a prostate imaging reporting and data system (PI-RADS) score of 4 for a lesion identified in the MRI examination indicating a high chance of cancer. The doctor orders a screening magnetic resonance imaging (MRI) examination of the prostate.

Narrative(s)Ī doctor notices the prostate-specific antigen (PSA) level of a 55-year-old male is higher than normal and suspects there may be cancer.

#Pathology report manual

Modern artificial intelligence and machine learning techniques can help us realize the benefits of rad-path correlation without the manual effort. Early work has been performed for rad-path correlation using language modeling, but further work is needed to develop mechanisms for identifying discrepancies and timely user feedback. Historically, this has been a manual process requiring cumbersome paper-based documentation, but with the widespread use of Electronic health records, this information is now available in a computer consumable format. Correlating radiology with pathology has long been of interest to radiologists as it helps with peer learning, continuing education, ensures multidisciplinary patient care and helps meet regulatory requirements.