Detecting Language Associated With Home Healthcare Patient’s Risk for Hospitalization and Emergency Department Visit

Abstract

Background: About one in five patients receiving home healthcare (HHC) services are hospitalized or visit an emergency department (ED) during a home care episode. Early identification of at-risk patients can prevent these negative outcomes. However, risk indicators, including language in clinical notes that indicate a concern about a patient, are often hidden in narrative documentation throughout their HHC episode.Objective: The aim of the study was to develop an automated natural language processing (NLP) algorithm to identify concerning language indicative of HHC patients' risk of hospitalizations or ED visits.Methods: This study used the Omaha System-a standardized nursing terminology that describes problems/signs/symptoms that can occur in the community setting. First, five HHC experts iteratively reviewed the Omaha System and identified concerning concepts indicative of HHC patients' risk of hospitalizations or ED visits. Next, we developed and tested an NLP algorithm to identify these concerning concepts in HHC clinical notes automatically. The resulting NLP algorithm was applied on a large subset of narrative notes (2.3 million notes) documented for 66,317 unique patients (n = 87,966 HHC episodes) admitted to one large HHC agency in the Northeast United States between 2015 and 2017.Results: A total of 160 Omaha System signs/symptoms were identified as concerning concepts for hospitalizations or ED visits in HHC. These signs/symptoms belong to 31 of the 42 available Omaha System problems. Overall, the NLP algorithm showed good performance in identifying concerning concepts in clinical notes. More than 18% of clinical notes were detected as having at least one concerning concept, and more than 90% of HHC episodes included at least one Omaha System problem. The most frequently documented concerning concepts were pain, followed by issues related to neuromusculoskeletal function, circulation, mental health, and communicable/infectious conditions. Conclusion:Our findings suggest that concerning problems or symptoms that could increase the risk of hospitalization or ED visit were frequently documented in narrative clinical notes. NLP can automatically extract information from narrative clinical notes to improve our understanding of care needs in HHC. Next steps are to evaluate which concerning concepts identified in clinical notes predict hospitalization or ED visit.

Home Healthcare Patients With Distinct Psychological, Cognitive, and Behavioral Symptom Profiles and At-Risk Subgroup for Hospitalization and Emergency Department Visits Using Latent Class Analysis

“…Previous studies have used various methodologies such as k-means cluster analysis to identify risk factors for hospitalization or emergency department visits among the home healthcare patients (Song, Chae, et al, 2022; Song, Ojo, et al, 2022; Topaz et al, 2020). While these studies offer important insights, they took a variable-centered approach to identify risk factors.…”

“…Fourth, we selected only a few variables that represent psychological, cognitive, and behavioral symptoms due to limited data availability. It is recommended that future researchers include more variables or explore information extracted from the unstructured data (e.g., clinical notes) that might include more detailed symptom information not captured in the structured data (Song, Ojo, et al, 2022; Topaz et al, 2021). This will allow future researchers to further understand the association between psychological, cognitive, and behavioral symptoms with hospitalization and/or emergency department visits.…”

The identification of clusters of risk factors and their association with hospitalizations or emergency department visits in home health care

“…In a previous study, our team developed a natural language processing algorithm (NLP‐ an artificial intelligence field in which computers analyse, understand, and extract meaning from human language in a text form) to extract the risk factors for hospitalizations or ED visits from HHC clinical notes (Song, Ojo, et al, 2022). Details on our previous NLP development and validation are described elsewhere (Song, Ojo, et al, 2022).…”

“…Details on our previous NLP development and validation are described elsewhere (Song, Ojo, et al, 2022). In essence, based on the Omaha System-a standardized nursing terminology commonly utilized in community health (Martin, 2005)-a subset of 31 Omaha System problems, including "Circulation," "Respiration," "Healthcare supervision," etc., were identified as risk factors for hospitalizations or ED visits in HHC.…”

“…In a previous study, our team developed a natural language processing algorithm (NLP‐ an artificial intelligence field in which computers analyse, understand, and extract meaning from human language in a text form) to extract the risk factors for hospitalizations or ED visits from HHC clinical notes (Song, Ojo, et al, 2022). Details on our previous NLP development and validation are described elsewhere (Song, Ojo, et al, 2022). In essence, based on the Omaha System—a standardized nursing terminology commonly utilized in community health (Martin, 2005)—a subset of 31 Omaha System problems, including “Circulation,” “Respiration,” “Healthcare supervision,” etc., were identified as risk factors for hospitalizations or ED visits in HHC.…”