Identification of Inpatient Falls Using Automated Review of Text-Based Medical Records.

Objectives: Although falls are among the most common adverse event in hospitals, they are difficult to measure and often unreported. Mechanisms to track falls include incident reporting and medical records review. Because of limitations of each method, researchers suggest multimodal approaches. Although incident reporting is commonly used, medical records review is limited by the need to read a high volume of clinical notes. Natural language processing (NLP) is 1 potential mechanism to automate this process.

Method: We compared automated NLP to manual chart review and incident reporting as a method to detect falls among inpatients. First, we developed an NLP algorithm to identify inpatient progress notes describing falls. Second, we compared the NLP algorithm to manual records review in identifying inpatient progress notes that describe falls. Third, we compared the NLP algorithm to the incident reporting system in identifying falls.

Results: When examining individual inpatient notes, our NLP algorithm was highly specific (0.97) but had low sensitivity (0.44) when compared with our manual records review. However, when considering groups of inpatient notes, all describing the same fall, our NLP algorithm had a large improvement in sensitivity (0.80) with some loss of specificity (0.65) compared with incident reporting.

Conclusions: National language processing represents a promising method to automate review of inpatient medical records to identify falls.