Understanding the Utility of Social Risk Factors Documented in Clinical Notes to Predict Hospitalization and Emergency Department Visits in Home Healthcare

Background: Approximately 5 million older adults receive home healthcare (HHC) annually in the United Sates, and nearly 90% of HHC recipients are 65 years or older. HHC encompasses in-home interdisciplinary services such as skilled nursing, social work, and physical, speech, and occupational therapy. One in every five patients is hospitalized during their time in HHC. Researchers have explored machine learning models that use data in the electronic health record (EHR) to aid clinicians in identifying patients at high risk for hospitalization and emergency department (ED) visits. Failure to consider social risk factors can exacerbate health inequities. Some studies suggest that including social risk factors in machine learning models can help to mitigate bias in model performance among individuals from racial and ethnic minority groups. Prior literature has reported that a majority of social information is documented in clinical notes. In the HHC setting, there is a gap in understanding how social risk factors are documented in clinical notes and whether adding social risk factors in machine learning models can improve model performance. Thus, this dissertation aims to: 1) summarize the literature on machine learning conducted in the HHC setting, 2) extract social risk factors documented in HHC clinical notes, and 3) examine how social risk factors influence machine learning model performance. Methods: The data from this dissertation is from one HHC agency in New York, New York, including approximately 65,000 unique patients and 2.3 million clinical notes. The Biopsychosocial Model guided this study by providing a framework to report the features included in the machine learning models. To address the first aim, a scoping review was conducted to summarize the literature on machine learning applied to EHR data in the HHC setting. To address the second aim, a natural language processing system was developed to extract social risk factors from HHC clinical notes. Then, logistic regression was utilized to examine the association between the social risk factors documented in clinical notes and hospitalization and ED visits. Finally, to address the third aim, social risk factors were included in four machine learning models to predict hospitalization and ED visit risk in HHC. A sub-analysis was conducted to explore the utility of social risk factors in machine learning models across individuals from different racial and ethnic groups. Results: The results from all three aims suggest that there has been a rise in machine learning applied in HHC, but few studies have incorporated clinical notes. There are gaps in implementing machine learning models in practice and standardizing social risk factors in documentation. HHC clinicians are documenting the following social risk factors in 4% of their clinical notes: Social Environment, Physical Environment, Education and Literacy, Food Insecurity, and Access to Care. These social risk factors are significantly associated with hospitalization and ED visits; however, their contribution showed minimal differences in machine learning model performance. Conclusion: This dissertation study demonstrates the feasibility and utility of leveraging HHC clinicians’ clinical notes to understand social risk factors. Further exploration is needed to tease out the nuances in how HHC clinicians perceive, assess, and document social risk factors in the EHR. Stakeholders are encouraged to standardize social risk factors and develop informatics tools tailored to the HHC setting to improve the identification of patients at risk for hospitalization and ED visits

Characterising symptom clusters in patients with atrial fibrillation undergoing catheter ablation

Objective This study aims to leverage natural language processing (NLP) and machine learning clustering analyses to (1) identify co-occurring symptoms of patients undergoing catheter ablation for atrial fibrillation (AF) and (2) describe clinical and sociodemographic correlates of symptom clusters.Methods We conducted a cross-sectional retrospective analysis using electronic health records data. Adults who underwent AF ablation between 2010 and 2020 were included. Demographic, comorbidity and medication information was extracted using structured queries. Ten AF symptoms were extracted from unstructured clinical notes (n=13 416) using a validated NLP pipeline (F-score=0.81). We used the unsupervised machine learning approach known as Ward’s hierarchical agglomerative clustering to characterise and identify subgroups of patients representing different clusters. Fisher’s exact tests were used to investigate subgroup differences based on age, gender, race and heart failure (HF) status.Results A total of 1293 patients were included in our analysis (mean age 65.5 years, 35.2% female, 58% white). The most frequently documented symptoms were dyspnoea (64%), oedema (62%) and palpitations (57%). We identified six symptom clusters: generally symptomatic, dyspnoea and oedema, chest pain, anxiety, fatigue and palpitations, and asymptomatic (reference). The asymptomatic cluster had a significantly higher prevalence of male, white and comorbid HF patients.Conclusions We applied NLP and machine learning to a large dataset to identify symptom clusters, which may signify latent biological underpinnings of symptom experiences and generate implications for clinical care. AF patients’ symptom experiences vary widely. Given prior work showing that AF symptoms predict adverse outcomes, future work should investigate associations between symptom clusters and postablation outcomes

Eliciting Insights From Chat Logs of the 25X5 Symposium to Reduce Documentation Burden: Novel Application of Topic Modeling

BackgroundAddressing clinician documentation burden through “targeted solutions” is a growing priority for many organizations ranging from government and academia to industry. Between January and February 2021, the 25 by 5: Symposium to Reduce Documentation Burden on US Clinicians by 75% (25X5 Symposium) convened across 2 weekly 2-hour sessions among experts and stakeholders to generate actionable goals for reducing clinician documentation over the next 5 years. Throughout this web-based symposium, we passively collected attendees’ contributions to a chat functionality—with their knowledge that the content would be deidentified and made publicly available. This presented a novel opportunity to synthesize and understand participants’ perceptions and interests from chat messages. We performed a content analysis of 25X5 Symposium chat logs to identify themes about reducing clinician documentation burden. ObjectiveThe objective of this study was to explore unstructured chat log content from the web-based 25X5 Symposium to elicit latent insights on clinician documentation burden among clinicians, health care leaders, and other stakeholders using topic modeling. MethodsAcross the 6 sessions, we captured 1787 messages among 167 unique chat participants cumulatively; 14 were private messages not included in the analysis. We implemented a latent Dirichlet allocation (LDA) topic model on the aggregated dataset to identify clinician documentation burden topics mentioned in the chat logs. Coherence scores and manual examination informed optimal model selection. Next, 5 domain experts independently and qualitatively assigned descriptive labels to model-identified topics and classified them into higher-level categories, which were finalized through a panel consensus. ResultsWe uncovered ten topics using the LDA model: (1) determining data and documentation needs (422/1773, 23.8%); (2) collectively reassessing documentation requirements in electronic health records (EHRs) (252/1773, 14.2%); (3) focusing documentation on patient narrative (162/1773, 9.1%); (4) documentation that adds value (147/1773, 8.3%); (5) regulatory impact on clinician burden (142/1773, 8%); (6) improved EHR user interface and design (128/1773, 7.2%); (7) addressing poor usability (122/1773, 6.9%); (8) sharing 25X5 Symposium resources (122/1773, 6.9%); (9) capturing data related to clinician practice (113/1773, 6.4%); and (10) the role of quality measures and technology in burnout (110/1773, 6.2%). Among these 10 topics, 5 high-level categories emerged: consensus building (821/1773, 46.3%), burden sources (365/1773, 20.6%), EHR design (250/1773, 14.1%), patient-centered care (162/1773, 9.1%), and symposium comments (122/1773, 6.9%). ConclusionsWe conducted a topic modeling analysis on 25X5 Symposium multiparticipant chat logs to explore the feasibility of this novel application and elicit additional insights on clinician documentation burden among attendees. Based on the results of our LDA analysis, consensus building, burden sources, EHR design, and patient-centered care may be important themes to consider when addressing clinician documentation burden. Our findings demonstrate the value of topic modeling in discovering topics associated with clinician documentation burden using unstructured textual content. Topic modeling may be a suitable approach to examine latent themes presented in web-based symposium chat logs

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

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.N