OBJECTIVES: Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions. TARGET AUDIENCE: This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey. SCOPE: We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.

OBJECTIVE: We conducted a systematic review to assess the effect of natural language processing (NLP) systems in improving the accuracy and efficiency of eligibility prescreening during the clinical research recruitment process. MATERIALS AND METHODS: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards of quality for reporting systematic reviews, a protocol for study eligibility was developed a priori and registered in the PROSPERO database. Using predetermined inclusion criteria, studies published from database inception through February 2021 were identified from 5 databases. The Joanna Briggs Institute Critical Appraisal Checklist for Quasi-experimental Studies was adapted to determine the study quality and the risk of bias of the included articles. RESULTS: Eleven studies representing 8 unique NLP systems met the inclusion criteria. These studies demonstrated moderate study quality and exhibited heterogeneity in the study design, setting, and intervention type. All 11 studies evaluated the NLP system's performance for identifying eligible participants; 7 studies evaluated the system's impact on time efficiency; 4 studies evaluated the system's impact on workload; and 2 studies evaluated the system's impact on recruitment. DISCUSSION: NLP systems in clinical research eligibility prescreening are an understudied but promising field that requires further research to assess its impact on real-world adoption. Future studies should be centered on continuing to develop and evaluate relevant NLP systems to improve enrollment into clinical studies. CONCLUSION: Understanding the role of NLP systems in improving eligibility prescreening is critical to the advancement of clinical research recruitment.

OBJECTIVE: Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on the use of NLP to process or analyze symptom information documented in EHR free-text narratives. MATERIALS AND METHODS: Our search of 1964 records from PubMed and EMBASE was narrowed to 27 eligible articles. Data related to the purpose, free-text corpus, patients, symptoms, NLP methodology, evaluation metrics, and quality indicators were extracted for each study. RESULTS: Symptom-related information was presented as a primary outcome in 14 studies. EHR narratives represented various inpatient and outpatient clinical specialties, with general, cardiology, and mental health occurring most frequently. Studies encompassed a wide variety of symptoms, including shortness of breath, pain, nausea, dizziness, disturbed sleep, constipation, and depressed mood. NLP approaches included previously developed NLP tools, classification methods, and manually curated rule-based processing. Only one-third (n = 9) of studies reported patient demographic characteristics. DISCUSSION: NLP is used to extract information from EHR free-text narratives written by a variety of healthcare providers on an expansive range of symptoms across diverse clinical specialties. The current focus of this field is on the development of methods to extract symptom information and the use of symptom information for disease classification tasks rather than the examination of symptoms themselves. CONCLUSION: Future NLP studies should concentrate on the investigation of symptoms and symptom documentation in EHR free-text narratives. Efforts should be undertaken to examine patient characteristics and make symptom-related NLP algorithms or pipelines and vocabularies openly available.