Health risks associated with urinary incontinence among older Korean Americans living in subsidized senior housing.

OBJECTIVES: We examined the association of urinary incontinence (UI) with physical, mental, and social health among older Korean Americans living in subsidized senior housing. DESIGN: Data were obtained from surveys conducted in 2023 with older Korean Americans residing in subsidized senior housing in the Los Angeles area (n = 313). UI was measured using a question about the frequency of involuntary urine loss. Physical, mental, and social health risks were assessed with a single item for self-rated health (fair/poor rating), the Patient Health Questionnaire-9 (probable depression), and the Lubben Social Network Scale-6 (isolation from family and friends). RESULTS: Over half of the sample reported UI, with 46.3% experiencing it infrequently (i.e. seldom) and 10.3% frequently (i.e. sometimes or often). UI was significantly associated with physical and mental health indicators; the odds of reporting fair or poor health and having probable depression were 1.94-7.32 times higher among those with either infrequent or frequent UI compared to those without UI. While family isolation was not associated with UI, the odds of being isolated from friends were 2.85 times greater among those with frequent UI compared to those without UI. CONCLUSION: Our findings confirm the adverse impact of UI on physical and mental health and highlight its unique role in social health. UI-associated social isolation was significant only in relationships with friends, providing new insights into the distinction between isolation from family and friends. These findings enhance our understanding of the health risks associated with UI and inform strategies for health management and promotion within the senior housing context.

Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.

OBJECTIVE: The study aimed to develop natural language processing (NLP) algorithms to automate extracting patient-centred breast cancer treatment outcomes from clinical notes in electronic health records (EHRs), particularly for women from under-represented populations. METHODS: The study used clinical notes from 2010 to 2021 from a tertiary hospital in the USA. The notes were processed through various NLP techniques, including vectorisation methods (term frequency-inverse document frequency (TF-IDF), Word2Vec, Doc2Vec) and classification models (support vector classification, K-nearest neighbours (KNN), random forest (RF)). Feature selection and optimisation through random search and fivefold cross-validation were also conducted. RESULTS: The study annotated 100 out of 1000 clinical notes, using 970 notes to build the text corpus. TF-IDF and Doc2Vec combined with RF showed the highest performance, while Word2Vec was less effective. RF classifier demonstrated the best performance, although with lower recall rates, suggesting more false negatives. KNN showed lower recall due to its sensitivity to data noise. DISCUSSION: The study highlights the significance of using NLP in analysing clinical notes to understand breast cancer treatment outcomes in under-represented populations. The TF-IDF and Doc2Vec models were more effective in capturing relevant information than Word2Vec. The study observed lower recall rates in RF models, attributed to the dataset's imbalanced nature and the complexity of clinical notes. CONCLUSION: The study developed high-performing NLP pipeline to capture treatment outcomes for breast cancer in under-represented populations, demonstrating the importance of document-level vectorisation and ensemble methods in clinical notes analysis. The findings provide insights for more equitable healthcare strategies and show the potential for broader NLP applications in clinical settings.

Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

PURPOSE: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain. DESIGN: This study was a retrospective, observational study. METHODS: We used demographic, diagnosis, and social survey data from the NIH 'All of Us' program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model. RESULTS: The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance. CONCLUSION: Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes. CLINICAL RELEVANCE: Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.

Sleep disturbances and interrelationship between persons with dementia and family caregivers: The lived experiences of Korean American Dyads.

BACKGROUND: Little research has investigated sleep quality in dyadic interrelationships between persons with dementia (PWD) and family caregivers, particularly among immigrant ethnic minorities, such as Korean Americans. PURPOSE: The study aimed to describe lived experiences of sleep disturbances and sleep interrelationships between Korean American PWD and their family caregivers. METHODS: A descriptive qualitative design used semi-structured interviews with cohabitating PWD-caregiver dyads. RESULTS: Eleven Korean American dyads participated (PWD mean age: 82.7, SD=2.3; caregivers mean age: 69.1, SD=10.2). Major themes included (1) linked sleep disturbances between PWD and caregivers, (2) interrelationship in dyads, (3) language challenges within and outside the dyads, and (4) strategies that improve sleep quality for dyads. CONCLUSION: Findings demonstrated bidirectional influences in dyadic sleep disturbances, where caregiving reciprocally impacted PWD sleep as part of an interactional unit. Communication barriers and limited community resources posed challenges for these dyads. Future sleep interventions should consider culturally competent, dyadic approaches.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.

OBJECTIVES: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools. METHODS: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework. RESULTS: Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care. CONCLUSION: In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

Intestinal Peyer's Patches: Structure, Function, and In Vitro Modeling.

BACKGOUND: Considering the important role of the Peyer's patches (PPs) in gut immune balance, understanding of the detailed mechanisms that control and regulate the antigens in PPs can facilitate the development of immune therapeutic strategies against the gut inflammatory diseases. METHODS: In this review, we summarize the unique structure and function of intestinal PPs and current technologies to establish in vitro intestinal PP system focusing on M cell within the follicle-associated epithelium and IgA+ B cell models for studying mucosal immune networks. Furthermore, multidisciplinary approaches to establish more physiologically relevant PP model were proposed. RESULTS: PPs are surrounded by follicle-associated epithelium containing microfold (M) cells, which serve as special gateways for luminal antigen transport across the gut epithelium. The transported antigens are processed by immune cells within PPs and then, antigen-specific mucosal immune response or mucosal tolerance is initiated, depending on the response of underlying mucosal immune cells. So far, there is no high fidelity (patho)physiological model of PPs; however, there have been several efforts to recapitulate the key steps of mucosal immunity in PPs such as antigen transport through M cells and mucosal IgA responses. CONCLUSION: Current in vitro PP models are not sufficient to recapitulate how mucosal immune system works in PPs. Advanced three-dimensional cell culture technologies would enable to recapitulate the function of PPs, and bridge the gap between animal models and human.

Higher Protein Digestibility of Chicken Thigh than Breast Muscle in an In Vitro Elderly Digestion Model.

This study investigated the protein digestibility of chicken breast and thigh in an in vitro digestion model to determine the better protein sources for the elderly in terms of bioavailability. For this purpose, the biochemical traits of raw muscles and the structural properties of myofibrillar proteins were monitored. The thigh had higher pH, 10% trichloroacetic acid-soluble α-amino groups, and protein carbonyl content than the breast (p<0.05). In the proximate composition, the thigh had higher crude fat and lower crude protein content than the breast (p<0.05). Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) of myofibrillar proteins showed noticeable differences in the band intensities of tropomyosin α-chain and myosin light chain-3 between the thigh and breast. The intrinsic tryptophan fluorescence intensity of myosin was lower in the thigh than in the breast (p<0.05). Moreover, circular dichroism spectroscopy of myosin revealed that the thigh had higher α-helical and lower ß-sheet structures than the breast (p<0.05). The cooked muscles were then chopped and digested in the elderly digestion model. The thigh had more α-amino groups than the breast after both gastric and gastrointestinal digestion (p<0.05). SDS-PAGE analysis of the gastric digesta showed that more bands remained in the digesta of the breast than that of the thigh. The content of proteins less than 3 kDa in the gastrointestinal digesta was also higher in the thigh than in the breast (p<0.05). These results reveal that chicken thigh with higher in vitro protein digestibility is a more appropriate protein source for the elderly than chicken breast.

Evaluation of race/ethnicity-specific survival machine learning models for Hispanic and Black patients with breast cancer.

OBJECTIVES: Survival machine learning (ML) has been suggested as a useful approach for forecasting future events, but a growing concern exists that ML models have the potential to cause racial disparities through the data used to train them. This study aims to develop race/ethnicity-specific survival ML models for Hispanic and black women diagnosed with breast cancer to examine whether race/ethnicity-specific ML models outperform the general models trained with all races/ethnicity data. METHODS: We used the data from the US National Cancer Institute's Surveillance, Epidemiology and End Results programme registries. We developed the Hispanic-specific and black-specific models and compared them with the general model using the Cox proportional-hazards model, Gradient Boost Tree, survival tree and survival support vector machine. RESULTS: A total of 322 348 female patients who had breast cancer diagnoses between 1 January 2000 and 31 December 2017 were identified. The race/ethnicity-specific models for Hispanic and black women consistently outperformed the general model when predicting the outcomes of specific race/ethnicity. DISCUSSION: Accurately predicting the survival outcome of a patient is critical in determining treatment options and providing appropriate cancer care. The high-performing models developed in this study can contribute to providing individualised oncology care and improving the survival outcome of black and Hispanic women. CONCLUSION: Predicting the individualised survival outcome of breast cancer can provide the evidence necessary for determining treatment options and high-quality, patient-centred cancer care delivery for under-represented populations. Also, the race/ethnicity-specific ML models can mitigate representation bias and contribute to addressing health disparities.

A Data-Driven Approach to Identify the Predictors of Perceived Health Status Among Chinese and Korean Americans.

Asian Americans are the country's fastest-growing racial group, and several studies have focused on the health outcomes of Asian Americans, including perceived health status. Perceived health status provides a summarized view of the health of populations for diverse domains, such as the psychological, social, and behavioral aspects. Given its multifaceted nature, perceived health status should be carefully approached when examining any variables' influence because it results from interactions among many variables. A data-driven approach using machine learning provides an effective way to discover new insights when there are complex interactions among multiple variables. To date, there are not many studies available that use machine learning to examine the effects of diverse variables on the perceived health status of Chinese and Korean Americans. This study aims to develop and evaluate three prediction models using logistic regression, random forest, and support vector machines to find the predictors of perceived health status among Chinese and Korean Americans from survey data. The prediction models identified specific predictors of perceived health status. These predictors can be utilized when planning for effective interventions for the better health outcomes of Chinese and Korean Americans.

Effects of CYP2D6*10 allele on the pharmacokinetics of tolperisone.

Tolperisone, a muscle relaxant used for post-stroke spasticity, has been reported to have a very wide interindividual pharmacokinetic variability. It is metabolized mainly by CYP2D6 and, to a lesser extent, by CYP2C19 and CYP1A2. CYP2D6 is a highly polymorphic enzyme, and CYP2D6*wt/*wt, CYP2D6*wt/*10 and CYP2D6*10/*10 genotypes constitute more than 90% of the CYP2D6 genotypes in the Korean population. Thus, effects of the CYP2D6*10 on tolperisone pharmacokinetics were investigated in this study to elucidate the reasons for the wide interindividual variability. Oral tolperisone 150 mg was given to sixty-four healthy Koreans, and plasma concentrations of tolperisone were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The CYP2D6*10/*10 and CYP2D6*wt/*10 groups had significantly higher Cmax and lower CL/F values than the CYP2D6*wt/*wt group. The AUCinf of CYP2D6*10/*10 and CYP2D6*wt/*10 groups were 5.18-fold and 2.25-fold higher than the CYP2D6*wt/*wt group, respectively. There were considerable variations in the Cmax and AUC values within each genotype group, and the variations were greater as the activity of CYP2D6 decreased. These results suggest that the genetic polymorphism of CYP2D6 significantly affected tolperisone pharmacokinetics and factor(s) other than CYP2D6 may also have significant effects on the pharmacokinetics of tolperisone.

Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.

PURPOSE: The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE). DESIGN: This study was a retrospective, observational study. METHODS: We extracted and preprocessed the structured electronic health records (EHRs) from a single academic hospital. Then we developed and evaluated three prediction models using logistic regression, the balanced random forest model, and the multilayer perceptron. RESULTS: The study sample included 158,804 total admissions; VTE-positive cases accounted for 2,080 admissions from among 1,695 patients (1.31%). Based on the evaluation results, the balanced random forest model outperformed the other two risk prediction models. CONCLUSIONS: This study delivered a high-performing, validated risk prediction tool using machine learning and EHRs to identify patients at high risk for VTE after discharge. CLINICAL RELEVANCE: The risk prediction model developed in this study can potentially guide treatment decisions for discharged patients for better patient outcomes.

Development and external validation of a prognostic tool for COVID-19 critical disease.

BACKGROUND: The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. METHODS: This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. RESULTS: Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21-88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27-88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87-1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. CONCLUSIONS AND RELEVANCE: We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

Knowledge Discovery With Machine Learning for Hospital-Acquired Catheter-Associated Urinary Tract Infections.

Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics. The purpose of this article is to explain a knowledge discovery and data mining approach that was employed to discover knowledge about hospital-acquired catheter-associated urinary tract infections from multiple data sources, including electronic health records and nurse staffing data. Three different machine learning techniques are described: decision trees, logistic regression, and support vector machines. The decision tree model created rules to interpret relationships among associated factors of hospital-acquired catheter-associated urinary tract infections. The logistic regression model showed what factors were related to a higher risk of hospital-acquired catheter-associated urinary tract infections. The support vector machines model was included to compare performance with the other two interpretable models. This article introduces the examples of cutting-edge machine learning approaches that will advance secondary use of electronic health records and integration of multiple data sources as well as provide evidence necessary to guide nursing professionals in practice.

Mediator subunit MDT-15/MED15 and Nuclear Receptor HIZR-1/HNF4 cooperate to regulate toxic metal stress responses in Caenorhabditis elegans.

Zinc is essential for cellular functions as it is a catalytic and structural component of many proteins. In contrast, cadmium is not required in biological systems and is toxic. Zinc and cadmium levels are closely monitored and regulated as their excess causes cell stress. To maintain homeostasis, organisms induce metal detoxification gene programs through stress responsive transcriptional regulatory complexes. In Caenorhabditis elegans, the MDT-15 subunit of the evolutionarily conserved Mediator transcriptional coregulator is required to induce genes upon exposure to excess zinc and cadmium. However, the regulatory partners of MDT-15 in this response, its role in cellular and physiological stress adaptation, and the putative role for mammalian MED15 in the metal stress responses remain unknown. Here, we show that MDT-15 interacts physically and functionally with the Nuclear Hormone Receptor HIZR-1 to promote molecular, cellular, and organismal adaptation to cadmium and excess zinc. Using gain- and loss-of-function mutants and qRT-PCR and reporter analysis, we find that mdt-15 and hizr-1 cooperate to induce zinc and cadmium responsive genes. Moreover, the two proteins interact physically in yeast-two-hybrid assays and this interaction is enhanced by the addition of zinc or cadmium, the former a known ligand of HIZR-1. Functionally, mdt-15 and hizr-1 mutants show defective storage of excess zinc in the gut and are hypersensitive to zinc-induced reductions in egg-laying. Furthermore, mdt-15 but not hizr-1 mutants are hypersensitive to cadmium-induced reductions in egg-laying, suggesting potential divergence of regulatory pathways. Lastly, mammalian MDT-15 orthologs bind genomic regulatory regions of metallothionein and zinc transporter genes in a cadmium and zinc-stimulated fashion, and human MED15 is required to induce a metallothionein gene in lung adenocarcinoma cells exposed to cadmium. Collectively, our data show that mdt-15 and hizr-1 cooperate to regulate cadmium detoxification and zinc storage and that this mechanism is at least partially conserved in mammals.

Factors Associated With Healthcare-Acquired Catheter-Associated Urinary Tract Infections: Analysis Using Multiple Data Sources and Data Mining Techniques.

PURPOSE: The purpose of this study was to identify factors associated with healthcare-acquired catheter-associated urinary tract infections (HA-CAUTIs) using multiple data sources and data mining techniques. SUBJECTS AND SETTING: Three data sets were integrated for analysis: electronic health record data from a university hospital in the Midwestern United States was combined with staffing and environmental data from the hospital's National Database of Nursing Quality Indicators and a list of patients with HA-CAUTIs. METHODS: Three data mining techniques were used for identification of factors associated with HA-CAUTI: decision trees, logistic regression, and support vector machines. RESULTS: Fewer total nursing hours per patient-day, lower percentage of direct care RNs with specialty nursing certification, higher percentage of direct care RNs with associate's degree in nursing, and higher percentage of direct care RNs with BSN, MSN, or doctoral degree are associated with HA-CAUTI occurrence. The results also support the association of the following factors with HA-CAUTI identified by previous studies: female gender; older age (>50 years); longer length of stay; severe underlying disease; glucose lab results (>200 mg/dL); longer use of the catheter; and RN staffing. CONCLUSIONS: Additional findings from this study demonstrated that the presence of more nurses with specialty nursing certifications can reduce HA-CAUTI occurrence. While there may be valid reasons for leaving in a urinary catheter, findings show that having a catheter in for more than 48 hours contributes to HA-CAUTI occurrence. Finally, the findings suggest that more nursing hours per patient-day are related to better patient outcomes.

An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing.

Digital rectal examination (DRE) is considered a quality metric for prostate cancer care. However, much of the DRE related rich information is documented as free-text in clinical narratives. Therefore, we aimed to develop a natural language processing (NLP) pipeline for automatic documentation of DRE in clinical notes using a domain-specific dictionary created by clinical experts and an extended version of the same dictionary learned by clinical notes using distributional semantics algorithms. The proposed pipeline was compared to a baseline NLP algorithm and the results of the proposed pipeline were found superior in terms of precision (0.95) and recall (0.90) for documentation of DRE. We believe the rule-based NLP pipeline enriched with terms learned from the whole corpus can provide accurate and efficient identification of this quality metric.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

Big data science: A literature review of nursing research exemplars.

BACKGROUND: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge. PURPOSE: The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals. METHODS: A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. DISCUSSION: Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. CONCLUSION: There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.

Expanding Interprofessional EHR Data in i2b2.

Emerging issues of team-based care, precision medicine, and big data science underscore the need for health information technology (HIT) tools for integrating complex data in consistent ways to achieve the triple aims of improving patient outcomes, patient experience, and cost reductions. The purpose of this study was to demonstrate the feasibility of creating a hierarchical flowsheet ontology in i2b2 using data-derived information models and determine the underlying informatics and technical issues. This study is the first of its kind to use information models that aggregate team-based care across time, disciplines, and settings into 14 information models that were integrated into i2b2 in a hierarchical model. In the process of successfully creating a hierarchical ontology for flowsheet data in i2b2, we uncovered a variety of informatics and technical issues described in this paper.