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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.

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OBJECTIVES: To phenotype the psychoneurologic (PN) symptom cluster in individuals with metastatic breast cancer and associate those phenotypes with individual characteristics and cancer genomic variables from circulating tumor DNA. SAMPLE & SETTING: This study included 201 individuals with metastatic breast cancer recruited in western Pennsylvania. METHODS & VARIABLES: A descriptive, cross-sectional design was used. Symptom data were collected via the MD Anderson Symptom Inventory, and cancer genomic data were collected via ultra-low-pass whole-genome sequencing of circulating tumor DNA from participant blood. RESULTS: Three distinct PN symptom phenotypes were described in a population with metastatic breast cancer: mild symptoms, moderate symptoms, and severe mood-related symptoms. Breast cancer TP53 deletion was significantly associated with membership in a moderate to severe symptoms phenotype (p = 0.013). IMPLICATIONS FOR NURSING: Specific cancer genomic changes associated with increased genomic instability may be predictive of PN symptoms. This finding may enable proactive treatment or reveal new therapeutic targets for symptom management.

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OBJECTIVES: To explore genes in the nuclear factor E2-related factor 2 antioxidative response elements (Nrf2-ARE) signaling pathway using a multiomics approach for associations with variability of cancer-related fatigue (CRF) in postmenopausal women with early-stage hormone receptor-positive breast cancer. SAMPLE & SETTING: Postmenopausal women (N = 116) with early-stage hormone receptor-positive breast cancer were recruited from western Pennsylvania. METHODS & VARIABLES: Candidate genes from the Nrf2-ARE pathway were investigated for associations with CRF occurrence and severity. Associations were evaluated using logistic regression for occurrence and linear regression for severity. RESULTS: The rs2706110 TT genotype in NFE2L2 was associated with a 3.5-fold increase in odds of CRF occurrence. The cytosine-phosphate-guanine (CpG) site cg22820568 in PRDX1 was associated with CRF occurrence and severity. IMPLICATIONS FOR NURSING: Biomarkers based on Nrf2-ARE genes may help to identify women at increased risk for more severe CRF and to develop targeted interventions.

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BACKGROUND: Pain remains a global health problem affecting all populations. There is limited knowledge, however, about the effect of limited English proficiency (LEP) on pain care and outcomes. AIM: This systematic review determines the current state of pain research for LEP populations. METHOD: We searched peer-reviewed studies in PubMed, CINAHL, PsychInfo, and Google Scholar from 1970 to 2021. Two authors independently screened abstracts and full texts, evaluated the quality of the studies using the Mixed Methods Appraisal Tool, and extracted study characteristics, content, and findings into Microsoft Excel. RESULTS: Twenty-five studies met our inclusion criteria. Of the 25 articles, 15 were quantitative, three were mixed methods, five were qualitative, one was quasi-experimental, and one was a randomized controlled trial. Four studies addressed all items of the Mixed Methods Appraisal Tool. Most pain research among patients with LEP was conducted in the United States (n = 17) and in hospital settings (n = 16). The majority of studies focused on one language (n = 15) with Spanish (n = 8) being the most studied language. Sample sizes ranged from seven to 18,593. Studies focused on three main themes: pain communication (n = 14), pain management (n =5) and/or outcomes (n = 1), and pain prevalence (n = 3). CONCLUSIONS: The findings revealed that the pain research on LEP populations is still in its infancy, with varied areas of focus using descriptive research designs. More pain intervention research for LEP populations is needed to reduce pain disparities.

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Pain is a problem affecting women with breast cancer (HR+BrCa) receiving aromatase inhibitor (AI) therapy. We investigated the relationship between single-nucleotide polymorphisms (SNPs) in DNA repair and oxidative stress genes and perceived worst pain after 6 months of AI therapy. We explored 39 SNPs in genes involved in DNA repair (ERCC2, ERCC3, ERCC5, and PARP1) and oxidative stress (CAT, GPX1, SEPP1, SOD1, and SOD2) in women with HR+BrCa receiving adjuvant therapy (AI ± chemotherapy; n = 138). Pain was assessed via the Brief Pain Inventory. Hurdle regression was used to evaluate the relationship between each associated allele and (1) the probability of pain and (2) the severity of worst pain. ERCC2rs50872 and ERCC5rs11069498 were associated with the probability of pain and had a significant genetic risk score (GRS) model (p = 0.003). ERCC2rs50872, ERCC5rs11069498, ERCC5rs4771436, ERCC5rs4150360, PARP1rs3219058, and SEPP1rs230819 were associated with the severity of worst pain, with a significant GRS model (conditional mean estimate = 0.45; 95% CI = 0.29, 0.60; p < 0.001). These results suggest DNA repair and oxidative stress pathways may play a role in the probability of pain and the severity of worst pain. As healthcare delivery moves towards the model of precision healthcare, nurses may, in the future, be able to use these results to tailor patient care based on GRS.

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PURPOSE: Investigate potential relationships between pre-treatment cancer-related fatigue (CRF) in women with early-stage breast cancer and variation in genes involved with oxidative stress and DNA repair. METHODS: Investigated 39 functional and tagging single nucleotide polymorphisms (SNPs) in genes involved in oxidative stress (CAT, GPX1, SEPP1, SOD1, and SOD2) and DNA repair (ERCC2, ERCC3, ERCC5, and PARP1) in a sample (N = 219) that included n = 138 postmenopausal women diagnosed with early-stage breast cancer before initiation of therapy and n = 81 age- and education-matched healthy controls. Using the Profile of Mood States Fatigue/Inertia Subscale, fatigue occurrence and severity were evaluated in both groups. Regression analysis was used to independently identify significant SNPs for three outcomes: 1) any fatigue versus no fatigue, 2) clinically meaningful versus non-clinically meaningful fatigue, and 3) fatigue severity. Using a weighted multi-SNP method, genetic risk scores (GRS) were calculated for each participant, and GRS models were constructed for each outcome. Models were adjusted for age, pain, and symptoms of depression and anxiety. RESULTS: SEPP1rs3877899, ERCC2rs238406, ERCC2rs238416, ERCC2rs3916874, and ERCC3rs2134794 were associated with fatigue occurrence and had a significant GRS model (OR = 1.317, 95%CI [1.067, 1.675], P ≤ 0.05). One SNP, SOD2rs5746136, was significant for clinically meaningful fatigue; therefore, a GRS model could not be constructed. ERCC3rs4150407, ERCC3rs4150477, and ERCC3rs2134794 were associated with fatigue severity with a significant GRS model (b = 1.010, 95%CI [1.647, 4.577], R2 = 6.9%, P ≤ 0.01). CONCLUSIONS: These results may contribute to identifying patients who are at risk of developing CRF. Oxidative stress and DNA repair biological pathways may be involved with CRF.

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Variability in the symptom experience in patients diagnosed with chronic conditions may be related to social determinants of health (SDoH). The purpose of this critical review was to (1) summarize the existing literature on SDoH and symptom clusters (i.e., multiple, co-occurring symptoms) in patients diagnosed with common chronic conditions, (2) evaluate current variables and measures used to represent SDoH, (3) identify gaps in the evidence base, and (4) provide recommendations for the incorporation of SDoH into future symptom cluster research. We identified 118 articles including information on SDoH in chronic condition symptom cluster research. Articles primarily focused on cancer populations. Few articles had the explicit purpose of investigating relationships between SDoH and symptom clusters, and the inclusion of SDoH was often limited to variables used to describe samples. Future studies should be designed to "move beyond Table 1" in their utilization of SDoH as variables and examine relationships between SDoH and symptom clusters. Attention should be paid to the appropriateness of measures being used to collect information on SDoH, and analysis methods that estimate causal connections between variables should be considered. Research regarding the relationship of SDoH with symptom clusters in patients with chronic conditions has the potential to reveal mechanisms of symptom disparities and guide changes to alleviate these disparities.

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Scientific data visualization is a critical aspect of fully understanding data patterns and trends. To date, the majority of data visualizations in nursing research - as with other biomedical fields - have been static. The availability of electronic scientific journal articles (which are quickly becoming the norm) has created new opportunities for dynamic and interactive data visualization which carry added cognitive benefits and support the ability to understand data more fully. Therefore, here we highlight the benefits of R, an open-source programming language, for scientific data visualization, with a specific focus on creating dynamic, interactive figures using the R shiny package. For R users, we have included a tutorial with example code to create three increasingly complex shiny applications. For individuals more interested in understanding the potential of R shiny as an innovative tool to interact with research data, we have included links to online versions of the examples that do not require any programming or R experience. We believe that widespread adoption of dynamic and interactive scientific data visualization will further support nurse scientists' higher-level mission of advancing our understanding of health and wellness of individuals and communities.

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OBJECTIVE: To explore experiences of symptoms of suspected or confirmed COVID-19 illness among women using the CovidWatcher mobile citizen science app. DESIGN: Convergent parallel mixed-methods design. PARTICIPANTS: Twenty-eight self-identified women consented for follow-up after using CovidWatcher. Participants' ages ranged from 18 to 83 years old. METHODS: We collected data via semistructured, virtual interviews and surveys: the COVID-19 Exposure and Family Impact Survey and Patient-Reported Outcomes Measurement Information System measures. We used directed content analysis to develop codes, categories, themes, and subthemes from the qualitative data and summarized survey data with descriptive statistics. RESULTS: We derived five themes related to symptom experiences: (a) Physical Symptoms, (b) Mental Health Symptoms, (c) Symptom Intensity, (d) Symptom Burden, and (e) Symptom Trajectories. Subthemes reflected more nuanced experiences of suspected or confirmed COVID-19 disease. For those without COVID-19, anxiety and mental health symptoms were still present. Of those who attested to one of the PROMIS-measured symptoms, all but one had at least mild severity in one of their reported symptoms. CONCLUSION: This study demonstrates the cross-cutting impact of the COVID-19 pandemic on individuals who identify as women. Future research and clinical practice guidelines should focus on alleviating physical and mental health symptoms related to the ongoing pandemic, regardless of COVID-19 diagnosis. Furthermore, clinicians should consider how patients can use symptom reconciliation apps and tracking systems.

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CONTEXT: Over half of American adults are diagnosed with a chronic condition, with an increasing prevalence being diagnosed with multiple chronic conditions. These adults are at higher risk for having unrelieved, co-occurring symptoms, known as symptom clusters. OBJECTIVES: To identify symptom phenotypes of patients diagnosed with four common chronic conditions, specifically, cancer, chronic obstructive pulmonary disease, heart failure, and/or type 2 diabetes mellitus, and to understand factors that predict membership in symptomatic phenotypes. METHODS: We conducted a retrospective, cross-sectional analysis using participant responses (N=14,127) to All of Us Research Program, a National Institutes of Health biomedical database, survey questions. We performed hierarchical clustering to generate symptom phenotypes of fatigue, emotional distress, and pain and used multinomial regression to determine if demographic, healthcare access and utilization, and health-related variables predict symptom phenotype. RESULTS: Four phenotypes, one asymptomatic or mildly symptomatic and three highly symptomatic (characterized by severe symptoms, severe pain, and severe emotional distress), were identified. The percentage of participants belonging to the severe symptoms phenotype increased with the number of chronic conditions. Most notably, foregoing or delaying medical care and rating mental health as poor or fair increased the odds of belonging to a highly symptomatic phenotype. CONCLUSION: We found meaningful relationships between demographic, healthcare access and utilization, and health-related factors and symptom phenotypes. With the increasing trends of American adults with one or more chronic conditions and a demand to individualize care in the precision health era, it is critical to understand the factors that lead to unrelieved symptoms.

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INTRODUCTION: There are 25 million individuals in the United States with limited English proficiency (LEP). Language barriers contribute to poorer patient assessment, misdiagnosed and/or delayed treatment, and inadequate understanding of the patient condition or prescribed treatment. LEP also has been shown to result in inadequate pain control, yet there are significant gaps in our knowledge related to pain documentation and pain management in primary care settings. The objective of this study is to describe and compare pain documentation for LEP racial/ethnic minority patients - Hmong-speaking Asian and Spanish-speaking Latinx - to English-speaking White patients with moderate to severe pain at an academic primary care clinic. METHODS: We conducted a retrospective mixed methods electronic health record study of patients age ≥ 18 with a pain score of ≥ 6; preferred language of Hmong, Spanish, or English; and evaluation in a primary care clinic. Abstracted data included characteristics of the provider, patient, interpreter, and pain care process. Descriptive statistics, analysis of variance, and chi-square tests were used. Clinician subjective assessment was analyzed using directed content analysis. RESULTS: Three hundred forty-two patient visits were included. Pain score distribution differed by patient language and race/ethnic group (P < 0.001), with an average pain score of 7.66 (SD 1.25). Interpreter type varied between the LEP groups (P = 0.009). Pain location was documented in a higher percentage of visits overall (87%) and more frequently for English-speaking White and Spanish-speaking Latinx patient visits than Hmong-speaking Asian visits (P < 0.001). Pain quality, onset, and duration were documented more frequently in LEP patient visits than English-speaking White patient visits (all P < 0.001). While overall opioid prescription rates were low, opioids were prescribed 3 times more frequently to English-speaking White patients than LEP patients (P = 0.002). Approximately 20% of patients were prescribed nonpharmacological treatment. CONCLUSION: Pain care process and treatment documentation varied by patient language and race/ethnicity. Future studies could evaluate the impact of pain assessment and treatment documentation on pain outcomes for LEP patients.

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BACKGROUND: Patient race, ethnicity, and culture including language are intertwined and may influence patient reporting of pain severity. PURPOSE: To describe documentation of patient's self-reported pain presence and severity by race, ethnicity, and language, specifically, Spanish, Hmong, Lao, or Khmer requiring an interpreter or English. DESIGN AND SAMPLE: Retrospective, electronic health record clinical data mining study of 79,195 patient visits with documented pain scores from one primary care clinic. METHODS: Hurdle regression was used to explore the effect of race, ethnicity, and language on the chances of having any pain (vs. no pain) and pain severity for visits with pain scores ≥1, controlling for age, sex, and documentation of a pain diagnosis. Mann-Whitney tests were used to explore the influence of English vs. non-English language on pain severity within a race or ethnicity category. RESULTS: Pain scores were higher for limited English proficiency, compared with English-speaking, patients within the Asian race or Hispanic/Latino ethnicity category. Older age, female sex, pain diagnosis, Black or African American race, and Spanish or Lao language increased the chance of having any pain. These same factors, plus American Indian or Alaska Native race, contributed to higher pain severity. Asian race, in contrast, decreased the chance of reporting any pain and contributed to lesser pain severity. CONCLUSIONS: Race, in addition to a new area of focus, language, impacted both the chances of reporting any pain and pain severity. Additional research is needed on the impact of language barriers on pain severity reporting, documentation, and differences in pain outcomes and disparities.

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AIM: To explore whether the relationship between mental health diagnosis (i.e., mood or neurotic, stress-related, or somatoform disorder) and pain is moderated by language in patients with limited English proficiency (LEP). Southeast Asian languages (i.e., Hmong, Lao, Khmer) and Spanish were compared with English. METHOD: A retrospective data mining study was conducted (n = 79,109 visits). Pain scores, language, mental health diagnoses, age, sex, race, ethnicity, and pain diagnosis were obtained from electronic medical records. Cragg two-equation hurdle regression explored: (1) the effect of patient language and mental health diagnosis on pain and (2) the interaction between language and mental health diagnosis on pain. RESULTS: Visits were primarily for female (62.45%), White (80.10%), not Hispanic/Latino (96.06%), and English-speaking (97.85%) patients. Spanish or Southeast Asian language increased chances of reporting any pain (i.e., pain score of 0 versus ≥1) and pain severity in visits with pain scores ≥1, whereas mental health diagnosis decreased chances of reporting any pain and pain severity. The combination of Southeast Asian language and mood disorder contributed to higher chances of reporting any pain (odds ratio [OR] = 1.78, p<.001) but no difference in severity. A similar trend was observed for Southeast Asian language and neurotic disorder (OR = 1.29, p=.143). In contrast, the combination of Spanish language and mood (p = .066) or neurotic (p = .289) disorder contributed to lower pain severity but did not change the chances of reporting any pain. CONCLUSIONS: LEP and patient language should be considered during pain assessment within the context of mental health.

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AIMS: Digital health can transform the management of atrial fibrillation (AF) and enable patients to take a central role in detecting symptoms and self-managing AF. There is a gap in understanding factors that support sustained use of digital health tools for patients with AF. This study identified predictors of Alivecor® KardiaMobile ECG monitor usage among patients with AF enrolled in the iPhone®Helping Evaluate Atrial fibrillation Rhythm through Technology (iHEART) randomized controlled trial. METHODS AND RESULTS: We analysed data from 105 English and Spanish-speaking adults with AF enrolled in the intervention arm of the iHEART trial. The iHEART intervention included smartphone-based electrocardiogram self-monitoring with Alivecor® KardiaMobile and triweekly text messages for 6 months. The primary outcome was use of Alivecor® categorized as: infrequent (≤5 times/week), moderate (>5 times and ≤11 times/week), and frequent (>11 times/week). We applied multinomial logistic regression modelling to characterize frequency and predictors of use. Of the 105 participants, 25% were female, 75% were White, and 45% were ≥65 years of age. Premature atrial contractions (PACs) [adjusted odds ratio (OR): 1.23, 1.08-1.40, P = 0.002] predicted frequent as compared to infrequent use. PACs (adjusted OR: 1.17, 95% confidence interval 1.06-1.30, P = 0.003), lower symptom burden (adjusted OR: 1.06, 1.01-1.11, P = 0.02), and less treatment concern (adjusted OR: 0.96, 0.93-0.99, P = 0.02) predicted moderate as compared to infrequent use. CONCLUSIONS: Frequent use of AliveCor® is associated with AF symptoms and potentially symptomatic cardiac events. Symptom burden and frequency should be measured and incorporated into analyses of future digital health trials for AF management.

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Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery. This study aims to computationally describe symptom documentation from electronic nursing notes and compare symptom clusters among patients diagnosed with four chronic conditions-chronic obstructive pulmonary disease (COPD), heart failure, type 2 diabetes mellitus, and cancer. Nursing notes (N = 504,395; 133,977 patients) were obtained for the 2016 calendar year from a single medical center. We used NimbleMiner, a natural language processing application, to identify the presence of 56 symptoms. We calculated symptom documentation prevalence by note and patient for the corpus. Then, we visually compared documentation for a subset of patients (N = 22,657) diagnosed with COPD (n = 3339), heart failure (n = 6587), diabetes (n = 12,139), and cancer (n = 7269) and conducted multiple correspondence analysis and hierarchical clustering to discover underlying groups of patients who have similar symptom profiles (i.e., symptom clusters) for each condition. As expected, pain was the most frequently documented symptom. All conditions had a group of patients characterized by no symptoms. Shared clusters included cardiovascular symptoms for heart failure and diabetes; pain and other symptoms for COPD, diabetes, and cancer; and a newly-identified cognitive and neurological symptom cluster for heart failure, diabetes, and cancer. Cancer (gastrointestinal symptoms and fatigue) and COPD (mental health symptoms) each contained a unique cluster. In summary, we report both shared and distinct, as well as established and novel, symptom clusters across chronic conditions. Findings support the use of electronic health record-derived notes and NLP methods to study symptoms and symptom clusters to advance symptom science.

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BACKGROUND: Nurses often document patient symptoms in narrative notes. PURPOSE: This study used a technique called natural language processing (NLP) to: (1) Automatically identify documentation of seven common symptoms (anxiety, cognitive disturbance, depressed mood, fatigue, sleep disturbance, pain, and well-being) in homecare narrative nursing notes, and (2) examine the association between symptoms and emergency department visits or hospital admissions from homecare. METHOD: NLP was applied on a large subset of narrative notes (2.5 million notes) documented for 89,825 patients admitted to one large homecare agency in the Northeast United States. FINDINGS: NLP accurately identified symptoms in narrative notes. Patients with more documented symptom categories had higher risk of emergency department visit or hospital admission. DISCUSSION: Further research is needed to explore additional symptoms and implement NLP systems in the homecare setting to enable early identification of concerning patient trends leading to emergency department visit or hospital admission.

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PURPOSE: Investigate sexual identity and racial/ethnic differences in awareness of heart attack and stroke symptoms. DESIGN: Cross-sectional. SETTING: 2014 and 2017 National Health Interview Survey. SAMPLE: 54 326 participants. MEASURES: Exposure measures were sexual identity (heterosexual, gay/lesbian, bisexual, "something else") and race/ethnicity. Awareness of heart attack and stroke symptoms was assessed. ANALYSIS: Sex-stratified logistic regression analyses to examine sexual identity and racial/ethnic differences in awareness of heart attack and stroke symptoms. RESULTS: Gay men were more likely than heterosexual men to identify calling 911 as the correct action if someone is having a heart attack (adjusted odds ratio [AOR] = 2.16, 95% CI: 1.18-3.96). The majority of racial/ethnic minority heterosexuals reported lower rates of awareness of heart attack and stroke symptoms than White heterosexuals. Hispanic sexual minority women had lower awareness of heart attack symptoms than White heterosexual women (AOR = 0.43, 95% CI: 0.25-0.74), whereas Asian sexual minority women reported lower awareness of stroke symptoms (AOR = 0.25, 95% CI: 0.08-0.80). Hispanic (AOR = 0.52, 95% CI: 0.33-0.84) and Asian (AOR = 0.35, 95% CI: 0.14-0.84) sexual minority men reported lower awareness of stroke symptoms than White heterosexual men. CONCLUSION: Hispanic and Asian sexual minorities had lower rates of awareness of heart attack and stroke symptoms. Health information technology may be a platform for delivering health education and targeted health promotion for sexual minorities of color.

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BACKGROUND: Depression and anxiety in patients with atrial fibrillation (AF) and/or atrial flutter may influence the effectiveness of cardioversion and ablation. There is a lack of knowledge related to depressive symptoms and anxiety at the time of these procedures. OBJECTIVE: We aimed to describe the prevalence and explore potential covariates of depressive symptoms and anxiety in patients with AF at the time of cardioversion or ablation. We further explored the influence of depressive symptoms and anxiety on quality of life at the time of procedure and 6-month AF recurrence. METHODS: Depressive symptoms, anxiety, and quality of life were collected at the time of cardioversion or ablation using the Patient Health Questionnaire-9, State-Trait Anxiety Inventory, and Atrial Fibrillation Effect on Quality of Life questionnaire. Presence of AF recurrence within 6 months post procedure was evaluated. RESULTS: Participants (N = 171) had a mean (SD) age of 61.20 (11.23) years and were primarily male (80.1%) and white, non-Hispanic (81.4%). Moderate to severe depressive symptoms (17.2%) and clinically significant state (30.2%) and trait (23.6%) anxiety were reported. Mood/anxiety disorder diagnosis was associated with all 3 symptoms. Atrial fibrillation symptom severity was associated with both depressive symptoms and trait anxiety. Heart failure diagnosis and digoxin use were also associated with depressive symptoms. Trends toward significance between state and trait anxiety and participant race/ethnicity as well as depressive symptoms and body mass index were observed. Study findings support associations between symptoms and quality of life, but not 6-month AF recurrence. CONCLUSION: Depressive symptoms and anxiety are common in patients with AF. Healthcare providers should monitor patients with AF for depressive symptoms and anxiety at the time of procedures and intervene when indicated. Additional investigations on assessment, prediction, treatment, and outcome of depressive symptoms and anxiety in patients with AF are warranted.

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BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes. OBJECTIVES: We aim to describe a method that combines standardized vocabularies, clinical expertise, and natural language processing to generate comprehensive symptom vocabularies and identify symptom information in EHR notes. We piloted this method with five diverse symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations. METHODS: First, we obtained synonym lists for each pilot symptom concept from the Unified Medical Language System. Then, we used two large bodies of text (clinical notes from Columbia University Irving Medical Center and PubMed abstracts containing Medical Subject Headings or key words related to the pilot symptoms) to further expand our initial vocabulary of synonyms for each pilot symptom concept. We used NimbleMiner, an open-source natural language processing tool, to accomplish these tasks and evaluated NimbleMiner symptom identification performance by comparison to a manually annotated set of nurse- and physician-authored common EHR note types. RESULTS: Compared to the baseline Unified Medical Language System synonym lists, we identified up to 11 times more additional synonym words or expressions, including abbreviations, misspellings, and unique multiword combinations, for each symptom concept. Natural language processing system symptom identification performance was excellent. DISCUSSION: Using our comprehensive symptom vocabularies and NimbleMiner to label symptoms in clinical notes produced excellent performance metrics. The ability to extract symptom information from EHR notes in an accurate and scalable manner has the potential to greatly facilitate symptom science research.

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