ReHouSED: A novel measurement of Veteran housing stability using natural language processing

Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study

BACKGROUND

Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations.

OBJECTIVE

We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs.

METHODS

We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient.

RESULTS

The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs.

CONCLUSIONS

Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.

Addressing Housing Issues Among People With Kidney Disease: Importance, Challenges, and Recommendations

Kidney disease disproportionately impacts people with low socioeconomic status, and low socioeconomic status is associated with worse outcomes for people with kidney disease. Unstable housing, which includes housing insecurity and homelessness, is increasing due to rising housing costs. There is mounting evidence that unstable housing and other health-related social needs are partially driving worse outcomes for people with low socioeconomic status. In this perspective, we consider the challenges to addressing housing for people with kidney disease, such as difficulty with identification of those with unstable housing, strict eligibility criteria for housing support, inadequate supply of affordable housing, and flaws in communities' prioritization of affordable housing. We discuss ways to tailor management for people experiencing unstable housing with kidney disease, and the importance of addressing safety, trauma, and emotional concerns as a part of care. We identify opportunities for the nephrology community to surmount challenges through increased screening, investment in workforce dedicated to community resource navigation, advocacy for investment in affordable housing, restructuring of communities' prioritization of affordable housing, and conducting needed research. Identifying and addressing housing needs among people with kidney disease is critical to eliminating kidney health disparities.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Temporary Financial Assistance Reduced The Probability Of Unstable Housing Among Veterans For More Than 1 Year

The Department of Veterans Affairs (VA) aims to reduce homelessness among veterans through programs such as Supportive Services for Veteran Families (SSVF). An important component of SSVF is temporary financial assistance. Previous research has demonstrated the effectiveness of temporary financial assistance in reducing short-term housing instability, but studies have not examined its long-term effect on housing outcomes. Using data from the VA's electronic health record system, we analyzed the effect of temporary financial assistance on veterans' housing instability for three years after entry into SSVF. We extracted housing outcomes from clinical notes, using natural language processing, and compared the probability of unstable housing among veterans who did and did not receive temporary financial assistance. We found that temporary financial assistance rapidly reduced the probability of unstable housing, but the effect attenuated after forty-five days. Our findings suggest that to maintain long-term housing stability for veterans who have exited SSVF, additional interventions may be needed.

Using natural language processing to study homelessness longitudinally with electronic health record data subject to irregular observations

The Electronic Health Record (EHR) contains information about social determinants of health (SDoH) such as homelessness. Much of this information is contained in clinical notes and can be extracted using natural language processing (NLP). This data can provide valuable information for researchers and policymakers studying long-term housing outcomes for individuals with a history of homelessness. However, studying homelessness longitudinally in the EHR is challenging due to irregular observation times. In this work, we applied an NLP system to extract housing status for a cohort of patients in the US Department of Veterans Affairs (VA) over a three-year period. We then applied inverse intensity weighting to adjust for the irregularity of observations, which was used generalized estimating equations to estimate the probability of unstable housing each day after entering a VA housing assistance program. Our methods generate unique insights into the long-term outcomes of individuals with a history of homelessness and demonstrate the potential for using EHR data for research and policymaking.

Development of a social and environmental determinants of health informatics maturity model

Introduction

Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps.

Methods

We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration's Informatics Enterprise Committee, and a publicly available online self-assessment tool.

Results

We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels.

Conclusion

The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities.

Using natural language processing to identify the status of homelessness and housing instability among serious illness patients from clinical notes in an integrated healthcare system

Background

Efficiently identifying the social risks of patients with serious illnesses (SIs) is the critical first step in providing patient-centered and value-driven care for this medically vulnerable population.

Objective

To apply and further hone an existing natural language process (NLP) algorithm that identifies patients who are homeless/at risk of homeless to a SI population.

Methods

Patients diagnosed with SI between 2019 and 2020 were identified using an adapted list of diagnosis codes from the Center for Advance Palliative Care from the Kaiser Permanente Southern California electronic health record. Clinical notes associated with medical encounters within 6 months before and after the diagnosis date were processed by a previously developed NLP algorithm to identify patients who were homeless/at risk of homelessness. To improve the generalizability to the SI population, the algorithm was refined by multiple iterations of chart review and adjudication. The updated algorithm was then applied to the SI population.

Results

Among 206 993 patients with a SI diagnosis, 1737 (0.84%) were identified as homeless/at risk of homelessness. These patients were more likely to be male (51.1%), age among 45-64 years (44.7%), and have one or more emergency visit (65.8%) within a year of their diagnosis date. Validation of the updated algorithm yielded a sensitivity of 100.0% and a positive predictive value of 93.8%.

Conclusions

The improved NLP algorithm effectively identified patients with SI who were homeless/at risk of homelessness and can be used to target interventions for this vulnerable group.

Generalizability and portability of natural language processing system to extract individual social risk factors

OBJECTIVE

The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which was originally developed at a different institution.

MATERIALS AND METHODS

A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 months at another institution. 10% of positively-classified notes by NLP and the same number of negatively-classified notes were manually annotated. The NLP model was adjusted to accommodate notes at the new site. Accuracy, positive predictive value, sensitivity, and specificity were calculated.

RESULTS

More than 6 million notes were processed at the receiving site by the NLP model, which resulted in about 13,000 and 19,000 classified as positive for financial insecurity and housing instability, respectively. The NLP model showed excellent performance on the validation dataset with all measures over 0.87 for both social factors.

DISCUSSION

Our study illustrated the need to accommodate institution-specific note-writing templates as well as clinical terminology of emergent diseases when applying NLP model for social factors. A state machine is relatively simple to port effectively across institutions. Our study. showed superior performance to similar generalizability studies for extracting social factors.

CONCLUSION

Rule-based NLP model to extract social factors from clinical notes showed strong portability and generalizability across organizationally and geographically distinct institutions. With only relatively simple modifications, we obtained promising performance from an NLP-based model.

Assessing longitudinal housing status using Electronic Health Record data: a comparison of natural language processing, structured data, and patient-reported history

Introduction

Measuring long-term housing outcomes is important for evaluating the impacts of services for individuals with homeless experience. However, assessing long-term housing status using traditional methods is challenging. The Veterans Affairs (VA) Electronic Health Record (EHR) provides detailed data for a large population of patients with homeless experiences and contains several indicators of housing instability, including structured data elements (e.g., diagnosis codes) and free-text clinical narratives. However, the validity of each of these data elements for measuring housing stability over time is not well-studied.

Methods

We compared VA EHR indicators of housing instability, including information extracted from clinical notes using natural language processing (NLP), with patient-reported housing outcomes in a cohort of homeless-experienced Veterans.

Results

NLP achieved higher sensitivity and specificity than standard diagnosis codes for detecting episodes of unstable housing. Other structured data elements in the VA EHR showed promising performance, particularly when combined with NLP.

Discussion

Evaluation efforts and research studies assessing longitudinal housing outcomes should incorporate multiple data sources of documentation to achieve optimal performance.

Developing an operational definition of housing instability and homelessness in Veterans Health Administration's medical records

The main objective of this study was to examine how homelessness and housing instability is captured across data sources in the Veterans Health Administration (VHA). Data from 2021 were extracted from three data repositories, including the Corporate Data Warehouse (CDW), the Homeless Operations Management System (HOMES), and the Homeless Management Information System (HMIS). Using these three data sources, we identified the number of homeless and unstably housed veterans across a variety of indicators. The results showed that the use of diagnostic codes and clinic stop codes identified a large number of homeless and unstably housed veterans, but the use of HOMES and HMIS data identified additional homeless and unstably housed veterans to provide a complete count. A total of 290,431 unique veterans were identified as experiencing homelessness or housing instability in 2021 and there was regional variability in how homelessness and housing stability were captured across data sources, supporting the need for more uniform ways to operationalize these conditions. Together, these findings highlight the and encourage use of all available indicators and data sources to identify homelessness and housing instability in VHA. These methodologies applied to the largest healthcare system in the U.S. demonstrate their utility and possibilities for other healthcare systems. Transparent practices about data sources and indicators used to capture homelessness and housing instability should be shared to increase uniform use.

Development and evaluation of an interoperable natural language processing system for identifying pneumonia across clinical settings of care and institutions

Objective

To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions.

Materials and Methods

A rule-based NLP system was designed to identify assertions of pneumonia in 3 types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type. The system was first developed and evaluated using annotated notes from the Department of Veterans Affairs (VA). Interoperability was assessed using data from the University of Utah (UU).

Results

The NLP system was comprised of 782 rules and achieved moderate-to-high performance in all 3 note types in VA (precision/recall/f1: emergency = 88.1/86.0/87.1; radiology = 71.4/96.2/82.0; discharge = 88.3/93.0/90.1). When applied to UU data, performance was maintained in emergency and radiology but decreased in discharge summaries (emergency = 84.7/94.3/89.3; radiology = 79.7/100.0/87.9; discharge = 65.5/92.7/76.8). Customization with 34 additional rules increased performance for all note types (emergency = 89.3/94.3/91.7; radiology = 87.0/100.0/93.1; discharge = 75.0/95.1/83.4).

Conclusion

NLP can be used to accurately identify the diagnosis of pneumonia across different clinical settings and institutions. A limited amount of customization to account for differences in lexicon, clinical definition of pneumonia, and EHR structure can achieve high accuracy without substantial modification.

Substance use disorders and social determinants of health from electronic medical records obtained during Kentucky's "triple wave"

Social determinants of health (SDOH) play a critical role in the risk of harmful drug use. Examining SDOH as a means of differentiating populations with multiple co-occurring substance use disorders (SUDs) is particularly salient in the era of prevalent opioid and stimulant use known as the "Third Wave". This study uses electronic medical records (EMRs) from a safety net hospital system from 14,032 patients in Kentucky from 2017 to 2019 in order to 1) define three types of SUD cohorts with shared/unique risk factors, 2) identify patients with unstable housing using novel methods for EMRs and 3) link patients to their residential neighborhood to obtain quantitative perspective on social vulnerability. We identified patients in three cohorts with statistically significant unique risk factors that included race, biological sex, insurance type, smoking status, and urban/rural residential location. Adjusting for these variables, we found a statistically significant, increasing risk gradient for patients experiencing unstable housing by cohort type: opioid-only (n = 7385, reference), stimulant-only (n = 4794, odds ratio (aOR) 1.86 95 % confidence interval (CI): 1.66-2.09), and co-diagnosed (n = 1853, aOR = 2.75, 95 % CI: 2.39 to 3.16). At the neighborhood-level, we used 8 different measures of social vulnerability and found that, for the most part, increasing proportions of patients with stimulant use living in a census tract was associated with more social vulnerability. Our study identifies potentially modifiable factors that can be tailored by substance type and demonstrates robust use of EMRs to meet national goals of enhancing research on social determinants of health.