Social Risk Factors are Associated with Risk for Hospitalization in Home Health Care: A Natural Language Processing Study

Building a Time-Series Model to Predict Hospitalization Risks in Home Health Care: Insights Into Development, Accuracy, and Fairness

OBJECTIVES

Home health care (HHC) serves more than 5 million older adults annually in the United States, aiming to prevent unnecessary hospitalizations and emergency department (ED) visits. Despite efforts, up to 25% of patients in HHC experience these adverse events. The underutilization of clinical notes, aggregated data approaches, and potential demographic biases have limited previous HHC risk prediction models. This study aimed to develop a time-series risk model to predict hospitalizations and ED visits in patients in HHC, examine model performance over various prediction windows, identify top predictive variables and map them to data standards, and assess model fairness across demographic subgroups.

SETTING AND PARTICIPANTS

A total of 27,222 HHC episodes between 2015 and 2017.

METHODS

The study used health care process modeling of electronic health records, including clinical notes processed with natural language processing techniques and Medicare claims data. A Light Gradient Boosting Machine algorithm was used to develop the risk prediction model, with performance evaluated using 5-fold cross-validation. Model fairness was assessed across gender, race/ethnicity, and socioeconomic subgroups.

RESULTS

The model achieved high predictive performance, with an F1 score of 0.84 for a 5-day prediction window. Twenty top predictive variables were identified, including novel indicators such as the length of nurse-patient visits and visit frequency. Eighty-five percent of these variables mapped completely to the US Core Data for Interoperability standard. Fairness assessment revealed performance disparities across demographic and socioeconomic groups, with lower model effectiveness for more historically underserved populations.

CONCLUSIONS AND IMPLICATIONS

This study developed a robust time-series risk model for predicting adverse events in patients in HHC, incorporating diverse data types and demonstrating high predictive accuracy. The findings highlight the importance of considering established and novel risk factors in HHC. Importantly, the observed performance disparities across subgroups emphasize the need for fairness adjustments to ensure equitable risk prediction across all patient populations.

Documentation of social determinants of health across individuals from different racial and ethnic groups in home healthcare

INTRODUCTION

Home healthcare (HHC) enables patients to receive healthcare services within their homes to manage chronic conditions and recover from illnesses. Recent research has identified disparities in HHC based on race or ethnicity. Social determinants of health (SDOH) describe the external factors influencing a patient's health, such as access to care and social support. Individuals from racially or ethnically minoritized communities are known to be disproportionately affected by SDOH. Existing evidence suggests that SDOH are documented in clinical notes. However, no prior study has investigated the documentation of SDOH across individuals from different racial or ethnic backgrounds in the HHC setting. This study aimed to (1) describe frequencies of SDOH documented in clinical notes by race or ethnicity and (2) determine associations between race or ethnicity and SDOH documentation.

DESIGN

Retrospective data analysis.

METHODS

We conducted a cross-sectional secondary data analysis of 86,866 HHC episodes representing 65,693 unique patients from one large HHC agency in New York collected between January 1, 2015, and December 31, 2017. We reported the frequency of six SDOH (physical environment, social environment, housing and economic circumstances, food insecurity, access to care, and education and literacy) documented in clinical notes across individuals reported as Asian/Pacific Islander, Black, Hispanic, multi-racial, Native American, or White. We analyzed differences in SDOH documentation by race or ethnicity using logistic regression models.

RESULTS

Compared to patients reported as White, patients across other racial or ethnic groups had higher frequencies of SDOH documented in their clinical notes. Our results suggest that race or ethnicity is associated with SDOH documentation in HHC.

CONCLUSION

As the study of SDOH in HHC continues to evolve, our results provide a foundation to evaluate social information in the HHC setting and understand how it influences the quality of care provided.

CLINICAL RELEVANCE

The results of this exploratory study can help clinicians understand the differences in SDOH across individuals from different racial and ethnic groups and serve as a foundation for future research aimed at fostering more inclusive HHC documentation practices.

Topic modeling on clinical social work notes for exploring social determinants of health factors

Objective

Existing research on social determinants of health (SDoH) predominantly focuses on physician notes and structured data within electronic medical records. This study posits that social work notes are an untapped, potentially rich source for SDoH information. We hypothesize that clinical notes recorded by social workers, whose role is to ameliorate social and economic factors, might provide a complementary information source of data on SDoH compared to physician notes, which primarily concentrate on medical diagnoses and treatments. We aimed to use word frequency analysis and topic modeling to identify prevalent terms and robust topics of discussion within a large cohort of social work notes including both outpatient and in-patient consultations.

Materials and methods

We retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181 644 patients at the University of California, San Francisco. We conducted word frequency analysis related to ICD-10 chapters to identify prevalent terms within the notes. We then applied Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion, which was further stratified by note types and disease groups.

Results

Word frequency analysis primarily identified medical-related terms associated with specific ICD10 chapters, though it also detected some subtle SDoH terms. In contrast, the LDA topic modeling analysis extracted 11 topics explicitly related to social determinants of health risk factors, such as financial status, abuse history, social support, risk of death, and mental health. The topic modeling approach effectively demonstrated variations between different types of social work notes and across patients with different types of diseases or conditions.

Discussion

Our findings highlight LDA topic modeling's effectiveness in extracting SDoH-related themes and capturing variations in social work notes, demonstrating its potential for informing targeted interventions for at-risk populations.

Conclusion

Social work notes offer a wealth of unique and valuable information on an individual's SDoH. These notes present consistent and meaningful topics of discussion that can be effectively analyzed and utilized to improve patient care and inform targeted interventions for at-risk populations.

Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review

OBJECTIVES

To determine the scope of the application of natural language processing to free-text clinical notes in post-acute care and provide a foundation for future natural language processing-based research in these settings.

DESIGN

Scoping review; reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.

SETTING AND PARTICIPANTS

Post-acute care (ie, home health care, long-term care, skilled nursing facilities, and inpatient rehabilitation facilities).

METHODS

PubMed, Cumulative Index of Nursing and Allied Health Literature, and Embase were searched in February 2023. Eligible studies had quantitative designs that used natural language processing applied to clinical documentation in post-acute care settings. The quality of each study was appraised.

RESULTS

Twenty-one studies were included. Almost all studies were conducted in home health care settings. Most studies extracted data from electronic health records to examine the risk for negative outcomes, including acute care utilization, medication errors, and suicide mortality. About half of the studies did not report age, sex, race, or ethnicity data or use standardized terminologies. Only 8 studies included variables from socio-behavioral domains. Most studies fulfilled all quality appraisal indicators.

CONCLUSIONS AND IMPLICATIONS

The application of natural language processing is nascent in post-acute care settings. Future research should apply natural language processing using standardized terminologies to leverage free-text clinical notes in post-acute care to promote timely, comprehensive, and equitable care. Natural language processing could be integrated with predictive models to help identify patients who are at risk of negative outcomes. Future research should incorporate socio-behavioral determinants and diverse samples to improve health equity in informatics tools.