Evaluation of falls detected by natural language processing algorithm and not coded external cause of morbidity.

Objective: Falls are a leading cause of morbidity and mortality among older adults. Common methods for identifying fall-related ED visits within both claims and electronic health record datasets rely on diagnosis code-based definitions, which underestimate the true prevalence of falls. This study applies a natural language processing (NLP) algorithm to ED provider notes to identify patients presenting due to falls and compares the characteristics of NLP-identified cases to those identified through diagnosis codes to identify the impact of identification strategy. Materials and Methods: This cross-sectional study analyzed ED encounter data from older adult patients who visited an ED between December 2016 and 2020. The NLP algorithm identified falls based on provider notes, searching for keywords related to falls and excluding negated and spurious matches. We also applied common ICD code methods to identify falls. Results: We processed 50 153 ED encounters and the NLP approach identified 14 604 encounters for patients who fell. Of those, 7086 (49%) were not identified using external cause of morbidity ICD codes. Patients identified by just the NLP algorithm exhibited higher Elixhauser comorbidity scores and increased likelihood of 30-day mortality. Patients identified by NLP algorithm but not ICD codes were more likely to have severe underlying conditions such as sepsis or acute kidney disease rather than traumatic injuries. Discussion: The NLP algorithm identifies many fall-related visits not identified by traditional methods. Conclusion: If the causal relationships between falls and comorbid conditions are not considered in NLP algorithms, they can easily identify patients who fell, but the fall was a sequela of underlying medical illness.