Reasons for Social Work Referrals in an Urban Safety-Net Population: A Natural Language Processing and Market Basket Analysis Approach

Bako, Abdulaziz; Walter-McCabe, Heather A.; Kasthurirathne, Suranga Nath; Halverson, Paul K.; Vest, Joshua R.

doi:10.1080/01488376.2020.1817834

Cited by 9 publications

(8 citation statements)

References 55 publications

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“…While this study focused on structural data, health care providers have historically recorded their patients' social risk factors within patient records as text [53,54] with social history as a standard portion of health records [55]. As a result, unstructured text constitutes an important and rich source for information [56], and multiple researchers and institutions are increasingly leveraging natural language processing (NLP) to extract a variety of social risk factors into structured data elements [57][58][59][60]. Future work would be necessary to see if leveraging the existing screening questions and ICD-10 Z code data collected by health systems could yield informative social risk factor measures.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity and Specificity of Real-World Social Factor Screening Approaches

Vest

Mendonça

2021

J Med Syst

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Section: Discussionmentioning

confidence: 99%

Sensitivity and Specificity of Real-World Social Factor Screening Approaches

Vest

Mendonça

2021

J Med Syst

Self Cite

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“…Rule-based methods. Rule-based methods were widely used (7 studies) [15], [32], [38], [76], [78], [81], [83] for extracting SDOH information from clinical records. Rules including key terms were usually manually curated by domain experts.…”

Section: Nlp Methods To Extract Sdoh From Clinical Textsmentioning

confidence: 99%

“…(2.1%) [89][90][91][92][93]. Other significant SBDH factors include geographic location [24,31,44,70], health literacy [43,47,88,94], social patterns (sexual health, adverse experiences and behavioral attitudes) [33,52,73,95], social environment [41,56,77], health access [21,54,88], living condition [20,31,35], social behavior [54,63,82], and financial insecurity [81,86].…”

Section: Sbdh Typementioning

confidence: 99%

Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

et al. 2021

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Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches. Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes. Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.

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“…Using explanatory analytics, they found that such an effort reduces hospital 30-day readmission rates and increases discharge knowledge among the discharged patients. Using natural language processing (NLP) and market basket analysis, Bako et al (2021) show that safety-net patients tend to be referred to social work-ers a lot, signifying the complexities of social needs among patients and the potential role for social workers in addressing those needs.…”

Section: Cross-functional Care Coordination Mechanismmentioning

confidence: 99%

“…Using natural language processing (NLP) and market basket analysis, Bako et al. (2021) show that safety‐net patients tend to be referred to social workers a lot, signifying the complexities of social needs among patients and the potential role for social workers in addressing those needs.…”

Section: Cooperative Team Of Providers’ Perspectivementioning

confidence: 99%

Putting analytics into action in care coordination research: Emerging issues and potential solutions

Kumar

Qiu

Sen

et al. 2022

Production and Operations Management

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Care coordination involves shaping patient care activities and sharing information among all participants concerned with a patient's care to achieve safe and effective care. The objectives of care coordination are to promote sharing of patients' clinical information, keep patients and families informed, and manage effective referrals and care transitions. Failures in care coordination account for a large amount of waste per year in the United States. Many innovative healthcare organizations have recently recognized the danger of poorly coordinated care and have implemented analytics to improve it. Therefore, more analytics-based research (especially combining explanatory analytics with predictive analytics) is needed to direct efforts to improve care coordination. This paper focuses on systematically studying the extant literature to understand how analytics play a role in improving care coordination. Our goal is to identify a set of key research questions that would lead to new research areas in the use of analytics for care coordination. Based on these questions, we offer new analytics solution pathways to care coordination problems. K E Y W O R D Sbusiness analytics, care coordination, care transition, referral management … care coordination is not just a value proposition (higher quality, lower costs) but a patientsafety issue. Patients can be harmed when the many moving parts of their care are out of sync. We owe it to them to coordinate the care we provide and prevent this type of medical error.

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Reasons for Social Work Referrals in an Urban Safety-Net Population: A Natural Language Processing and Market Basket Analysis Approach

Cited by 9 publications

References 55 publications

Sensitivity and Specificity of Real-World Social Factor Screening Approaches

Sensitivity and Specificity of Real-World Social Factor Screening Approaches

Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

Putting analytics into action in care coordination research: Emerging issues and potential solutions

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