Development and assessment of a natural language processing model to identify residential instability in electronic health records’ unstructured data: a comparison of 3 integrated healthcare delivery systems

Abstract

Objective To evaluate whether a natural language processing (NLP) algorithm could be adapted to extract, with acceptable validity, markers of residential instability (ie, homelessness and housing insecurity) from electronic health records (EHRs) of 3 healthcare systems. Materials and methods We included patients 18 years and older who received care at 1 of 3 healthcare systems from 2016 through 2020 and had at least 1 free-text note in the EHR during this period. We conducted the study independently; the NLP algorithm logic and method of validity assessment were identical across sites. The approach to the development of the gold standard for assessment of validity differed across sites. Using the EntityRuler module of spaCy 2.3 Python toolkit, we created a rule-based NLP system made up of expert-developed patterns indicating residential instability at the lead site and enriched the NLP system using insight gained from its application at the other 2 sites. We adapted the algorithm at each site then validated the algorithm using a split-sample approach. We assessed the performance of the algorithm by measures of positive predictive value (precision), sensitivity (recall), and specificity. Results The NLP algorithm performed with moderate precision (0.45, 0.73, and 1.0) at 3 sites. The sensitivity and specificity of the NLP algorithm varied across 3 sites (sensitivity: 0.68, 0.85, and 0.96; specificity: 0.69, 0.89, and 1.0). Discussion The performance of this NLP algorithm to identify residential instability in 3 different healthcare systems suggests the algorithm is generally valid and applicable in other healthcare systems with similar EHRs. Conclusion The NLP approach developed in this project is adaptable and can be modified to extract types of social needs other than residential instability from EHRs across different healthcare systems.

Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases

“…Similarly, populational trend evaluations were performed for atrial fibrillation [ 58 ], left atrial appendage occlusion procedures [ 59 ], transcatheter aortic valve implantation and surgical aortic valve replacement operations [ 60 ], implantable cardioverter-defibrillators and cardiac resynchronization therapy [ 61 ]. Furthermore, NLP technology allows for the in-depth EHR assessment of social determinants, which are non-medical factors impacting patient health outcomes [ 62 , 63 , 64 ]. Leveraging this opportunity, AI systems can help to verify the correctness of the diagnoses, as well as provide valuable information on critical aspects associated with populational health.…”

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

“…A number of previous studies have used NLP to identify housing instability in clinical notes, but they did not attempt to repeatedly measure patient housing status over time. [5][6][7][8][9][10] In VA, some studies have used the results of a standardized homelessness screener administered to Veterans nationally to examine follow up housing outcomes for Veterans with a prior positive screen. [22][23][24][25] These studies are limited by the relatively infrequent use of the homelessness screener by clinicians, with only a small proportion of patients having at least one repeat screening.…”

“…In recent years, Electronic Health Records (EHR) have increasingly been utilized as a data source for studying homelessness and other adverse SDoH. [1][2][3][4][5][6][7][8][9][10] Many of these studies have utilized natural language processing (NLP), a set of techniques for extracting information from unstructured clinical texts. Improved measurement of housing instability offers a number of benefits to researchers and policymakers such as the ability to study risk factors for becoming homeless or the effectiveness of homelessness interventions.…”

Barriers and Facilitators of Obtaining Social Determinants of Health of Patients With Cancer Through the Electronic Health Record Using Natural Language Processing Technology: Qualitative Feasibility Study With Stakeholder Interviews

“…Since most navigators have access to the EHR and can enter their own notes, it is important to use unstructured data to capture SDoH. NLP has produced valid results [ 36 ], but as algorithms become more accurate, it is necessary to involve data analysts who have historically worked with SDoH to support health services research. NLP requires a different skill set than what most data analysts are accustomed to.…”