Applying natural language processing to identify emergency department and observation encounters for worsening heart failure

Abstract

AbstractAimsWorsening heart failure (WHF) events occurring in non‐inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)‐based approach compared with traditional diagnostic coding for non‐inpatient clinical encounters and left ventricular ejection fraction (LVEF).Methods and resultsWe compared characteristics for encounters that did vs. did not meet WHF criteria, stratified by care setting [i.e. emergency department (ED) and observation stay]. Overall, 8407 (22%) encounters met NLP‐based criteria for WHF (3909 ED visits and 4498 observation stays). The use of an NLP‐derived definition adjudicated 3983 (12%) of non‐primary HF diagnoses as meeting consensus definitions for WHF. The most common diagnosis indicated in these encounters was dyspnoea. Results were primarily driven by observation stays, in which 2205 (23%) encounters with a secondary HF diagnosis met the WHF definition by NLP.ConclusionsThe use of standard claims‐based adjudication for primary diagnosis in the non‐inpatient setting may lead to misclassification of WHF events in the ED and overestimate observation stays. Primary diagnoses alone may underestimate the burden of WHF in non‐hospitalized settings.