Application of a rules-based natural language parser to critical value reporting in anatomic pathology

Critical values in anatomic pathology are uncommon and challenging to define precisely. Accrediting bodies require timely and effective communication of these values. Some provisional criteria have been proposed for identifying potentially critical diagnoses, with limited success in implementation. Manual identification of such cases is impractical due to their rarity. Prior research has shown that programmatic solutions could improve communication reliability. However, no prior work had resolved how to automate the detection of critical diagnoses in pathology reports. That uncertainty drove the need for a system that could automatically flag cases requiring clinical discussion. The development of such a system could help avoid missed communication opportunities. This gap motivated the creation of a new tool based on natural language processing.

The aim of this study was to develop and evaluate a system for automatically identifying critical diagnoses in anatomic pathology reports. The specific problem addressed is the difficulty of manually identifying rare but clinically significant cases. Effective communication of these findings is essential for patient care. The motivation stems from the limitations of manual review processes. The researchers sought a solution that could flag cases requiring clinical follow-up. The system was designed to integrate with existing laboratory information systems. It uses a rules-based natural language parser to detect qualifying cases. This approach aims to improve the detection rate of critical diagnoses.

The team created a laboratory information system-based tool using a tiered natural language processing predicate calculus inference engine. The system was designed to identify cases meeting criteria for critical diagnoses. It scanned pathology reports for qualifying terms and patterns. Cases flagged by the system were reviewed manually. A total of 13,790 cases were analyzed over 49 months. The electronic medical records of each patient were examined. Only 35 cases were found to require clinical follow-up. The system was tested for its ability to detect rare but actionable diagnoses.

The system identified 13,790 cases over a 49-month period. Of these, only 35 (0.3%) were flagged for clinical follow-up. Eight of the 35 cases led to new information or a change in patient care. The overall rate of actionable cases was 0.058%. This low percentage confirms the rarity of such cases. The system successfully avoided missed communication opportunities. It demonstrated the feasibility of automated detection in pathology reports. The results suggest that manual identification alone is insufficient.

The automated system proved useful in identifying rare critical diagnoses in anatomic pathology. It successfully flagged cases requiring clinical follow-up. The very low incidence of such cases supports the need for automated tools. The system helped avoid missed communication opportunities. The findings suggest that manual approaches are unreliable for this task. The use of a rules-based parser improved detection accuracy. The system is generically deployable across laboratory settings. These results support the value of programmatic solutions in pathology communication.