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Transgender and nonbinary (TGNB) individuals have an increased risk of certain mental health outcomes, such as depression and suicide attempts. This population skews younger in the United States and prior studies have not included TGNB patients for the entire pediatric age range in an emergency department (ED) setting. The present study aimed to examine gender identity documentation in the electronic health record and then use that information to identify and further characterize the pediatric TGNB population presenting to a psychiatric emergency service. Preliminary findings include a greater percentage of TGNB patients compared to non-TGNB individuals who had repeat visits to the ED for high acuity psychiatric concerns. A larger portion of TGNB patients also had at least one evaluation that included suicidal ideation. These results call for increased attention on the quality of mental healthcare for TGNB youth both inside and outside of the ED., (©2024 AMIA - All rights reserved.)

Diabetes constitutes a significant health problem that leads to many long term health issues including renal, cardiovascular, and neuropathic complications. Many of these problems can result in increased health care costs, as well risk of ICU stay and mortality. To date, no published study has used predictive modeling to examine the relative influence of diabetes, diabetic health maintenance, and comorbidities on outcomes in ICU patients. Using the MIMIC-III database, machine learning and binomial logistic regression modeling were applied to predict risk of mortality. The final models achieved good fit with AUC values of 0.787 and 0.785 respectively. Additionally, this study demonstrated that robust classification can be done as a combination of five variables (HbA1c, mean glucose during stay, diagnoses upon admission, age, and type of admission) to predict risk as compared with other machine learning models that require nearly 35 variables for similar risk assessment and prediction.

Public health and clinical practice pattern trends are often analyzed using complex survey data. Use of statistical approaches that do not account for survey design predisposes to error, potentially leading to resource misdirection and inefficiency. This study examined two techniques for analyzing trends in complex survey data: (1) design-corrected logistic regression and (2) jackknife re-weighted linear regression. These approaches were compared toweighted least squares regression, as well as non-design corrected techniques. Data were obtained from NEISS, a complex survey of emergency departments that can be weighted to produce national estimates of injury occurrence. Trends were analyzed in rug-related injuries among male versus female patients ≥65 years of age. All design-corrected techniques performed comparably in assessment of trend within sex-based subgroups. In almost all cases, design-corrected approaches contrasted profoundly with standard statistical techniques. Future analyses may employ these design-corrected approaches to appropriately account for estimate variance in complex survey data.

Social determinants of health (SDOH) are important considerations in diagnosis, prevention, and health outcomes. However, they are often not well documented in the EHR and found primarily in unstructured or semi-structured text. Building upon previous work, we analyzed all flowsheet data in 2013 for information related to the SDOH topic areas of Residence, Living Situation, and Living Conditions. Overall, 91 rows were identified as being related to the topics areas resulting in 604,616 unique observations. Individual rows contained SDOH data often covered multiple concepts especially free-text entries. These data included most often references to the residence, residence details, and with whom the patient lives. Very few contained living condition references. Additionally, there was significant duplication and inconsistency of row labels, as well as variation in value list content for rows collecting the same concepts. Our findings demonstrate significant opportunities to improve and achieve better standardization in documentation around these SDOH.

Obstructive sleep apnea (OSA) is one of the most common diseases among Americans, affecting between 5 and 20% of the population. While there is existing evidence of numerous comorbid conditions, such as obesity, diabetes, and high blood pressure, the vast majority of this evidence has focused explicitly on cardiovascular morbidities and excluded any mental or behavioral disorders. The goal of this study was to examine psychiatric comorbidities of OSA in two types of sources: (1) biomedical literature in the MEDLINE/PubMed database (focusing on MeSH descriptors) and Semantic MEDLINE Database (SemMedDB; for semantic predications), and (2) electronic health record data in the MIMIC-III database. Approximately 300 unique psychiatric comorbidities were identified, ranked, and compared across MEDLINE/PubMed, SemMedDB, and MIMIC-III. The preliminary results highlight the potential of this multi-angled approach for suggesting opportunities for further investigation that may contribute to improving mental health in persons afflicted with OSA.

Epilepsy is a prevalent chronic neurological disorder afflicting about 50 million people worldwide. There is evidence of a strong relationship between familial risk factors and epilepsy, as well as associations with substance use. The goal of this study was to explore the interactions between familial risk factors and substance use based on structured data from the family and social history modules of an electronic health record system for adult epilepsy patients. A total of 8,957patients with 38,802 family history entries and 8,822 substance use entries were gathered and mined for associations at different levels of granularity for three age groupings (>18, 18-64, and ≥65 years old). Our results demonstrate the value of an association rule mining approach to validate knowledge of familial risk factors. The preliminary findings also suggest that substance use does not demonstrate significant association between social and familial risk factors for epilepsy.

The debate regarding potential negative health effects of electronic nicotine delivery systems (ENDS), which include electronic cigarettes, has received much recent attention. Currently, it is unknown whether ENDS pose a real health risk to users or those passively exposed to their vapor. With the increased use of these devices, the goal of this study was to examine if and how their use is being documented in the electronic health record (EHR) and the associated implications for clinical research. Analysis of five years of progress notes and tobacco use comments revealed that ENDS use is documented at an increasing rate with variable associated information, most often consisting of the status, purpose, and side effects of ENDS use. These results highlight that improved and consistent EHR discrete data entry for ENDS with associated clinical standards for documentation and representation of potential exposures are needed for enabling effective population health surveillance and research.

Fairview Health Services is an affiliated integrated health system partnering with the University of Minnesota to establish a secure research-oriented clinical data repository that includes large numbers of clinical documents. Standardization of clinical document names and associated attributes is essential for their exchange and secondary use. The HL7/LOINC Document Ontology (DO) was developed to provide a standard representation of clinical document attributes with a multi-axis structure. In this study, we evaluated the adequacy of DO to represent documents in the clinical data repository from legacy and current EHR systems across community and academic practice sites. The results indicate that a large portion of repository data items can be mapped to the current DO ontology but that document attributes do not always link consistently with DO axes and additional values for certain axes, particularly "Setting" and "Role" are needed for better coverage. To achieve a more comprehensive representation of clinical documents, more effort on algorithms, DO value sets, and data governance over clinical document attributes is needed.

Within large sequence repositories such as GenBank there is a wealth of metadata providing contextual information that may enhance search and retrieval of relevant sequences for a range of subsequent analyses. One challenge is the use of free-text in these metadata fields where approaches are needed to extract, structure, and encode essential information. The goal of the present study was to explore the feasibility of using a combination of existing resources for annotating unstructured GenBank metadata, initially focusing on the "host" and "isolation_source" fields. This paper summarizes early results for 10 host organisms that include a characterization of associated isolation sources with respect to biomedical ontologies and semantic types. The findings from this preliminary study provide insights to the rich amount of information captured within these unstructured metadata, guidance for addressing the challenges and issues encountered, and highlight the potential value for enriching comparative biological studies towards improving human health.