Identification of Preanesthetic History Elements by a Natural Language Processing Engine

Abstract

Background: Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes. We hypothesize that the NLP pipeline would identify a significant portion of pertinent history captured by a perioperative provider.

Methods: For each patient, we collected all pertinent notes from the institution's electronic medical record that were available no later than 1 day before their preoperative anesthesia clinic appointment. Pertinent notes included free-text notes consisting of history and physical, consultation, outpatient, inpatient progress, and previous preanesthetic evaluation notes. The free-text notes were processed by a Named Entity Recognition pipeline, an NLP machine learning model trained to recognize and label spans of text that corresponded to medical concepts. These medical concepts were then mapped to a list of medical conditions that were of interest for a preanesthesia evaluation. For each condition, we calculated the percentage of time across all patients in which (1) the NLP pipeline and the anesthesiologist both captured the condition; (2) the NLP pipeline captured the condition but the anesthesiologist did not; and (3) the NLP pipeline did not capture the condition but the anesthesiologist did.

Results: A total of 93 patients were included in the NLP pipeline input. Free-text notes were extracted from the electronic medical record of these patients for a total of 9765 notes. The NLP pipeline and anesthesiologist agreed in 81.24% of instances on the presence or absence of a specific condition. The NLP pipeline identified information that was not noted by the anesthesiologist in 16.57% of instances and did not identify a condition that was noted by the anesthesiologist's review in 2.19% of instances.

Conclusions: In this proof-of-concept study, we demonstrated that utilization of NLP produced an output that identified medical conditions relevant to preanesthetic evaluation from unstructured free-text input. Automation of risk stratification tools may provide clinical decision support or recommend additional preoperative testing or evaluation. Future studies are needed to integrate these tools into clinical workflows and validate its efficacy.

Figure 1.

Workflow of the study, in which free-text clinical notes from 93 patients were extracted from the electronic medical record system. These notes were processed by an NLP pipeline, and its output was compared to that captured by an anesthesiologist. NLP indicates natural language processing.

Figure 2.

Illustration of the algorithm followed by the NLP pipeline (KAID Health). cNLP indicates clinical natural language processing; CUI, concept unique identifier; EMR, electronic medical record; NER, named entity recognition; NLP‚ natural language processing; UMLS, Unified Medical Language System.

Figure 3.

Stacked bar plot illustrating the concordance rates between the NLP pipeline output and anesthesiologist review. CABG indicates coronary artery bypass graft; METs‚ metabolic equivalents; NLP, natural language processing; TIA‚ transient ischemic attack.