Natural language processing can be used for automated extraction of social work interventions from electronic health records, thereby supporting social work staffing and resource allocation decisions.
Objectives: Health care organizations are increasingly employing social workers to address patients’ social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs), making measurement and analysis difficult. This study aims to extract and classify, from EHR notes, interventions intended to address patients’ social needs using natural language processing (NLP) and machine learning (ML) algorithms.
Study Design: Secondary data analysis of a longitudinal cohort.
Methods: We extracted 815 SW encounter notes from the EHR system of a federally qualified health center. We reviewed the literature to derive a 10-category classification scheme for SW interventions. We applied NLP and ML algorithms to categorize the documented SW interventions in EHR notes according to the 10-category classification scheme.
Results: Most of the SW notes (n = 598; 73.4%) contained at least 1 SW intervention. The most frequent interventions offered by social workers included care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services and organizations (17.1%), and supportive counseling (15.3%). High-performing classification algorithms included the kernelized support vector machine (SVM) (accuracy, 0.97), logistic regression (accuracy, 0.96), linear SVM (accuracy, 0.95), and multinomial naive Bayes classifier (accuracy, 0.92).
Conclusions: NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. Health care administrators can leverage this automated approach to gain better insight into the most needed social interventions in the patient population served by their organizations. Such information can be applied in managerial decisions related to SW staffing, resource allocation, and patients’ social needs.
Am J Manag Care. 2021;27(1):e24-e31. https://doi.org/10.37765/ajmc.2021.88580
Social work (SW) interventions are increasingly important in health care.
Traditionally, efforts to improve health outcomes have primarily focused on medical care services.1 However, consistent evidence suggests that social needs and risk factors have a more profound effect on individual and population health than medical care.2-5 Given their professional training and workflows, social workers within health care settings are uniquely positioned to deliver interventions to address patients’ social needs.6 Moreover, social workers are increasingly becoming embedded in health care organizations to help address patients’ social needs.7 However, tracking and evaluating interventions instituted by social workers remains challenging because social work (SW) interventions are largely documented as unstructured text data within electronic health records (EHRs).8,9 Unstructured documentation makes it difficult to systematically monitor and study SW interventions and services, as manual chart reviews are expensive and time-consuming and often require expert reviewers.10-12 These limitations underscore the need for using novel information extraction methods, such as natural language processing (NLP) and machine learning (ML), to identify and classify interventions documented in unstructured EHR notes, such as SW notes.
Existing research has been successful in using NLP methods to identify and classify social needs with EHR data. Dorr et al13 demonstrated that NLP could be used to identify chronic stress, social isolation, financial insecurity, and housing insecurity in the clinical notes of primary care doctors. Conway et al14 developed the Moonstone system, which used rule-based classification to identify housing situations and social support within EHRs. Also, Cook et al15 used NLP and ML techniques to predict suicidal ideation from a text message intervention. These studies demonstrated the feasibility and applicability of using NLP approaches to detect social needs and risks in unstructured clinical data. Although a small number of studies have developed classification schemes for SW interventions using manual chart reviews, no study has sought to utilize NLP methods to identify and/or classify SW interventions.16-21
The purpose of this study was to extract and categorize SW interventions aimed at addressing patients’ social needs by developing and applying NLP and ML algorithms. Development of such classification tools supports health care organizations’ assessment and measurement of a growing part of the nonmedical workforce. Additionally, this study highlights methodological approaches to examine SW interventions in situations in which only unstructured data are accessible. Health care organizations armed with data on SW interventions that have been instituted for their patient population will make more informed resource allocation, staffing, quality improvement, and program design decisions to address their patients’ social needs. Moreover, automation of the classification of interventions will offer stakeholders an enhanced ability to quantify the specific impact of social workers’ interventions on patients’ health outcomes.
We developed a classification scheme from the literature16-21 to categorize interventions instituted by social workers to address patients’ social needs at Eskenazi Health, an urban safety-net health system. Eskenazi Health employs in-house social workers to address patients’ social needs. These needs are identified through multiple pathways. For example, physicians and nurse practitioners may identify patients’ social needs during consultations and refer patients to social workers. Additionally, social workers may review records of patients with scheduled appointments to identify social needs.
We used NLP and ML algorithms for automated categorization of SW interventions based on the classification scheme we developed. The Figure describes, in brief, the process and pipeline used for this classification (see eAppendix A [eAppendices available at ajmc.com]). This study was approved by the Indiana University Institutional Review Board.
Setting and Sample
We used patient record data from Eskenazi Health, a safety-net provider with a 300-bed hospital and a federally qualified health center serving the Indianapolis, Indiana, metropolitan area. The study sample included 408 patients with 815 SW encounters between October 1, 2016, and September 30, 2019.
The study data were derived from Eskenazi Health’s EHR (Epic). The EHR data included patients’ clinical notes and their demographic and clinical characteristics. We obtained unstructured data containing free-text descriptions of the reason(s) for patient visits, the intervention(s) instituted by the social worker, and future visits or engagement plans from social workers’ notes. We extracted SW notes, using an existing pipeline (nDepth22), by searching clinical notes for the following search terms: “social work” or “MSW” or “LCSW”, or “LSW”, or “LMSW”, or “LBSW.” nDepth, an NLP tool developed by the Regenstrief Institute, conducts information retrieval and extraction from textual documents by adapting its existing pipeline to customizable search queries. nDepth has been used in other clinical domains, including gastrointestinal diseases and sarcopenia.23,24 For this study, nDepth was used to extract a sample of SW notes from the EHR. Our search with nDepth retrieved 1289 clinical notes. Three abstractors (A.T.B., H.L.T., K.W.) manually reviewed the retrieved notes and categorized them into notes written by a social worker and notes written by other professionals. Notes were deemed as written by a social worker if they were signed off by a social worker or if the wordings and structure of the notes clearly indicated documentation of activities by a social worker. The final sample for this study included 815 SW encounters with notes that were determined to be written by social workers during distinct SW encounters (see eAppendix B for details and examples).
Based on literature review and expert consultations, 3 members of the research team (A.T.B., H.L.T., K.W.) developed a classification scheme for SW interventions, with 10 non–mutually exclusive categories of SW interventions briefly defined as follows:
Inspired by the ConText algorithm,30 3 coders (A.T.B., H.L.T., K.W.) independently reviewed 100 randomly selected SW notes to identify verbs likely to indicate the presence of SW interventions in a sentence. The abstractors also identified contextual terms and negation terms associated with each verb, used to describe a social worker (social worker terms), as well as unique terms that map to each of the 10 intervention categories (see eAppendices C and D for lexicon of terms). The abstractors manually coded the selected notes into their respective non–mutually exclusive SW intervention categories.We calculated the rate of agreement among the 3 coders using Fleiss’s kappa coefficients (see eAppendix E).
To categorize the notes based on our 10-category scheme, we used (1) rule-based classification algorithms using Python’s regular expressions31; (2) ML algorithms such as the multinomial naive Bayes classification algorithm32 and multilabel (one vs rest, binary relevance, classifier chains, and label powerset)33,34 classification algorithms using logistic regression33 and kernelized support vector machine (SVM) with radial basis function35; and (3) a deep learning algorithm for multilabel classification: the long short-term memory (LSTM) recurrent neural network with a sigmoid activation function and binary cross-entropy loss function.36 The LSTM model has a single input layer, an embedding layer, 1 LSTM layer with 128 nodes, and 1 output layer with 10 nodes, with each node in the output dense layer representing 1 of the SW intervention categories.
For the rule-based algorithm, we used Python’s31 in-built regular expressions to extract sentences that include a social worker term and one of the intervention verb terms, where the verb term is not preceded by one of the negative-lookbehind terms for intervention and not followed by one of the negative-lookahead terms. A review of SW notes and consultation with clinical social workers revealed that these sentences are likely to contain information about the type of intervention instituted. An SW note was deemed to indicate the presence of an intervention category if the extracted sentences in the note also contain at least 1 of the intervention key terms for that category.
For the implementation of the ML and deep learning algorithms, we randomly divided the data into training (68%), validation (12%), and test (20%) sets (see Figure). We preprocessed the text in each of these data sets as follows: We tokenized sentences in each SW note into individual words (tokens), removed stop words (eg, 1- and 2-letter words, compositions, prepositions), applied part of speech tagging to the tokens, and stemmed the tokens down to their root words. Next, for implementation of the ML algorithms, we created feature vectors using the term frequency–inverse document frequency vectorizer with single words (unigrams), 2 consecutive words (bigrams), and 3 consecutive words (trigrams). Relatedly, for the LSTM model, we created word embeddings using the GloVe word embeddings37 to convert text inputs to their numeric counterparts.
We used the preprocessed feature vectors (or word embeddings in case of the LSTM model) from the training data set to initially train each classifier and the validation data set to test the accuracy of the trained classifier. We implemented multiple iterations of training and validation for each classifier while tuning and optimizing the hyperparameters of the classification algorithm to attain higher accuracy. Upon achieving the highest accuracy score possible for each classifier, we coalesced the training and validation sets and trained a final classifier on them using the optimal hyperparameters. We also used 5-fold cross-validation to evaluate the performance of the logistic regression, kernelized SVM, linear SVM, and multinomial naive Bayes algorithms on the full training data (see eAppendix F [A and B]). Finally, for each intervention category, we evaluated the performance of the rule-based, logistic regression, kernelized SVM, linear SVM, and multinomial naive Bayes algorithms on test data using accuracy; precision or positive predictive value (PPV); recall (sensitivity); F1-score, which is the harmonic mean of precision (PPV) and recall (sensitivity); specificity; and area under the curve (AUC).
Our final sample included 408 patients with 815 SW encounters. Descriptive information about the sample and categories of social worker interventions is available in eAppendix G. Briefly, 43% of the patients were Hispanic; their mean age was 38.7 years; and the majority (64%) were women.
Of the 815 SW encounters, almost three-fourths (n = 598; 73.4%) contained at least 1 SW intervention. More specifically, 217 (26.6%) did not include any description of a SW intervention, 295 (36.2%) included 1 SW intervention, 207 (25.4%) included 2 interventions, and 96 (11.8%) included 3 or more interventions. The highest number of interventions in a single SW note was 6, which was observed in only 5 of the 815 encounters. The most common SW interventions in the notes included care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services and organizations (17.1%), and supportive counseling (15.3%) (Table 1).
Models with the highest accuracy included multilabel (one vs rest) classifier with kernelized SVM (accuracy, 0.97), multilabel (one vs rest) classifier with logistic regression (accuracy, 0.96), linear SVC (accuracy, 0.95), and multinomial naive Bayes classifier (accuracy, 0.92) (see eAppendix H). Precision (PPV) score was generally higher than the recall (sensitivity) score in the kernelized SVM, logistic regression, and linear SVM for most of the intervention categories (Table 2 [A and B]). However, for the multinomial naive Bayes classifier, the recall score was higher than the sensitivity score in the financial planning, care coordination, community service, and education intervention categories. Moreover, the multinomial naive Bayes classifier offered the highest recall scores for the financial planning (recall, 0.86) and community service (recall, 0.92) categories (Table 2 [B]). Linear SVM provided the best evaluation metrics for the financial planning category (accuracy, 0.96; precision, 0.91, recall, 0.91; F1-score, 0.91; specificity, 0.98; AUC, 0.94). The F1-scores for the logistic regression and kernelized SVM algorithms in the care coordination, community services, and transportation categories were high (0.82-0.94). The multinomial naive Bayes algorithms had the worst F1-score (0.00) in the legal intervention category. Linear SVM offered the best evaluation metrics for the housing (accuracy, 1.00; precision, 1.00; recall, 1.00; F1-score, 1.00; specificity, 1.00; AUC, 1.00) and durable medical equipment (accuracy, 0.99; precision, 0.80; recall, 1.00; F1-score, 0.89; specificity, 0.99; AUC, 0.90) categories (Table 2 [A-C]).
This study described the extent and nature of SW activities in primary care using NLP algorithms. These findings illustrate the key roles that social workers play in connecting patients to other aspects of the health care system, brokering connections with non–health care–related organizations, and providing patient education.
As brokers, social workers link patients to critically needed resources and services.38,39 This broker function was evident in our study, in which care coordination interventions and referrals to community-based organizations were frequently offered. SW interventions require brokering services for patients among different organizations/providers, maintaining communication, and identifying needed resources from referral partners. These efforts may lead to improved clinical outcomes. For instance, Bronstein et al40 found that social worker involvement in postdischarge care coordination, which involved directing patients to organizations that address food insecurity, clothing, and peer advocacy needs, was associated with fewer 30-day hospital readmissions. Pruitt et al41 showed that referrals to community-based organizations offered by social workers were associated with lower health care costs. Our study provided evidence that social workers in primary care offer interventions known to aid health care organizations’ efforts to improve quality of care, reduce costs, and effectively manage population health. We intend to investigate the value of SW interventions toward improving quality outcomes in our future studies.
In our study, social workers educated patients, in 21% of encounters, about the availability of resources and interpretation of medical information. Patient education contributes significantly toward better health outcomes.42 The role of social workers as providers of patient education thus highlights the significance of social workers in primary care teams. Also, financial planning, which involves assisting patients with medication, insurance, and benefits, was a common SW intervention in this study. Similar to previous studies,38,43 our study found that social workers primarily address the impact of finances on health care access by providing assistance with medication, insurance, and benefits, rather than directly providing monetary assistance.
Social workers are the largest group of providers of mental health services in the United States.44 In Indiana, social workers can offer a wide range of mental health services, including counseling and treatment for substance use, provided that they obtain a state license to practice mental health.45 However, some authors have opined that social workers employed in health care settings may not have the time to directly provide mental health services, given the need to frequently coordinate and allocate resources for these patients.38,46 For example, in a study on patients with diabetes, Rabovsky et al38 found that social workers did not directly address patients’ mental health issues. Rather, they referred patients to mental health providers. The practice pattern and/or expertise of the social workers in Rabovsky et al38 may explain why social workers outsourced counseling interventions to external organizations rather than providing the counseling services themselves. In our study, social workers addressed mental and behavioral health issues by directly providing supportive counseling interventions to patients. Thus, it is conceivable, given our findings, that social workers can multitask and interchange roles, thereby providing patients in health care settings with direct counseling and tangible resources or referrals. Finally, in previous studies, the nature of the advocacy role of social workers was not evident and underexplored.38,39,47 However, in our study, we found that social workers helped patients in completing and filing insurance coverage or housing applications and in reporting child and adult domestic issues to the appropriate social agencies. This highlights the role of social workers as patient advocates, particularly for those most vulnerable.
Similar to the findings of Pooler et al,47 we found that approximately two-fifths of the SW notes included more than 1 SW intervention, indicating that a substantial number of patients have co-occurring social needs.47 The presence of a sizable proportion of patients receiving multiple social interventions indicates the need for effective SW staffing and differential use of more experienced social workers to manage patients with co-occurring social needs.
Although unstructured free-text narratives within EHRs are conducive to NLP- and ML-based classification methods, previous research suggests that social needs interventions are rarely explored using these methods.11,16-21 Our study shows that NLP and ML can effectively be used to determine the types of SW interventions suggested by social workers operating within health care systems. Consistent with the findings of previous studies, ML- and deep learning–based classification algorithms performed better than rule-based classification methods.48,49 However, the multilabel LSTM model did not perform as well as some ML approaches. This finding may be due to the small sample size in this study, as deep learning algorithms are known to perform poorly when the sample size is small.50
This study has several limitations. First, although we derived our SW intervention categories from consultation with experts and peer-reviewed literature, our classification scheme may not be exhaustive. Second, the small nature of our sample may limit the performance of our classification algorithms on new test data. However, for most of the intervention categories our evaluation metrics are satisfactory. In addition, our models were trained using data from a single health system, which weakens the generalizability of our findings to other hospital systems or other diverse populations. Lastly, the range of intervention categories and their relative frequencies in this study may be a reflection of the characteristics of the population under study and/or the practice pattern of the social workers in our study site, rather than the general primary care population.
Contextual details of interventions instituted by social workers, which are available in EHR notes, highlight how social needs are addressed by social workers. NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. These methods have the advantage of being scalable and can be automated and integrated into EHR systems. Thus, NLP and ML can be leveraged by health care administrators to gain better insight into the most needed social interventions in their patient populations, thereby helping organizations make better decisions related to SW staffing, resource allocation, and patients’ social needs.
The authors thank the Robert Wood Johnson Foundation for providing the financial support necessary for conducting this research through their Systems for Action National Coordinating Center (ID 73485). They also thank Data Core Services of the Regenstrief Institute for providing them with the data used in this study.
Research reported in this publication/press release was supported by the National Library of Medicine of the National Institutes of Health under award No. T15LM012502. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Library of Medicine.
Author Affiliations: Department of Health Policy & Management, Indiana University Richard M. Fairbanks School of Public Health (ATB, HLT, KW, JRV), Indianapolis, IN; Center for Outcomes Research, Houston Methodist Research Institute (ATB), Houston, TX; Center for Biomedical Informatics, Regenstrief Institute (HLT, KW, SNK, JRV), Indianapolis, IN; School of Informatics and Computing, Indiana University–Purdue University Indianapolis (JZ), Indianapolis, IN; Wayne State University School of Social Work (HW-M), Detroit, MI; Wayne State University School of Law (HW-M), Detroit, MI; Department of Pediatrics, Indiana University School of Medicine (SNK), Indianapolis, IN.
Source of Funding: Robert Wood Johnson Foundation (ID 73485) and National Library of Medicine of the National Institutes of Health (T15LM012502).
Author Disclosures: Dr Kasthurirathne is a founder of Uppstroms, LLC, and reports patents pending and stock ownership. Dr Vest is a founder and board member of Uppstroms, LLC. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (ATB, JZ, SNK, JRV); acquisition of data (HLT, SNK, JRV); analysis and interpretation of data (ATB, HLT, KW, HW-M, SNK); drafting of the manuscript (ATB, KW, SNK); critical revision of the manuscript for important intellectual content (ATB, HLT, KW, JZ, SNK, JRV); statistical analysis (ATB); provision of patients or study materials (JRV); administrative, technical, or logistic support (HW-M, SNK, JRV); and supervision (JZ, HW-M, SNK).
Address Correspondence to: Abdulaziz Tijjani Bako, MPH, Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University–Purdue University Indianapolis, 1050 Wishard Blvd, Indianapolis, IN 46202. Email: email@example.com.
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