Natural language processing of lifestyle modification documentation

Shoenbill, Kimberly; Song, Younghwan; Gress, Lisa; Johnson, Heather M.; Smith, Maureen A.; Mendonça, Eneida A.

doi:10.1177/1460458218824742

Cited by 23 publications

(22 citation statements)

References 52 publications

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“… 36 , 37 Our investigation found that diagnosis codes from ICD-9 and ICD-10 and concepts identified using cTAKES and UMLS, were extensively used as features for substance use, alcohol use, and smoking status. 17 , 26 , 38 , 39 …”

Section: Resultsmentioning

confidence: 99%

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Patra

Sharma

Vekaria

et al. 2021

Journal of the American Medical Informatics Association

128

101

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Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

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Section: Resultsmentioning

confidence: 99%

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Patra

Sharma

Vekaria

et al. 2021

Journal of the American Medical Informatics Association

128

101

View full text Add to dashboard Cite

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“…37 So, in addition to medication-based treatment, telemonitoring applications should also strive for a behavior-related risk factor reduction by, for example, motivating for increasing sport activities following the principles of gamification. 38,39 A lack of viable business models hampers successful integration of these innovative diabetes telemonitoring services in Austrian standard care. 10,11,15 Despite experiencing positive effects on health parameters in the course of a longer term pilot telemonitoring project, diabetes patients were not willing to pay for these services for themselves.…”

Section: Discussionmentioning

confidence: 99%

Telemonitoring readiness among Austrian diabetic patients: A cross-sectional validation study

Muigg¹,

Duftschmid

Kästner³

et al. 2020

Health Informatics J

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Digitalized healthcare services offer remote and cost-effective treatment of diabetes patients. Thus, the present online study analyzed the readiness to use telemonitoring among Austrian diabetes patients. We developed and validated a German version of the patient telehealth readiness assessment tool and performed quantitative context analysis of free-text comments on perceived barriers and benefits of telemonitoring. Participants (n = 41, 42.6% females) achieved a medium average readiness level for telemonitoring. The three top benefits were intensified care, shorter travel and waiting times, and better therapy adjustment. The top three barriers were data privacy issues, loss of personal communication and focus on blood sugar, and teledoctor competence. Diabetes patients represent a suitable target group for remote treatment opportunities. However, a shift from traditional face-to-face medical care to exclusive telemonitoring treatment from diagnosis to consultation and treatment requires fundamental new legal framework conditions.

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“…[96]. cTAKES was used to identify subtypes of patients with opioid misuse [46] and lifestyle modification [62]. Shoenbill et al combine cTAKES with rules and regular expressions on selected EHRs to make previously unseen data on lifestyle modification documentation visible.…”

Section: Relations Between Sbdh and Health Outcomesmentioning

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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Natural language processing of lifestyle modification documentation

Cited by 23 publications

References 52 publications

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Telemonitoring readiness among Austrian diabetic patients: A cross-sectional validation study

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

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