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Per-/poly-fluoroalkyl substances (PFAS) are a group of manmade compounds with known human toxicity and evidence of contamination in drinking water throughout the US. We augmented our electronic health record data with geospatial information to classify PFAS exposure for our patients living in New Jersey. We explored the utility of three different methods for classifying PFAS exposure that are popularly used in the literature, resulting in different boundary types: public water supplier service area boundary, municipality, and ZIP code. We also explored the intersection of the three boundaries. To study the potential for bias, we investigated known PFAS exposure-disease associations, specifically hypertension, thyroid disease and parathyroid disease. We found that both the significance of the associations and the effect size varied by the method for classifying PFAS exposure. This has important implications in knowledge discovery and also environmental justice as across cohorts, we found a larger proportion of Black/African-American patients PFAS-exposed., (©2023 AMIA - All rights reserved.)

As of August 2020, there were ~6 million COVID-19 cases in the United States of America, resulting in ~200,000 deaths. Informatics approaches are needed to better understand the role of individual and community risk factors for COVID-19. We developed an informatics method to integrate SARS-CoV-2 data with multiple neighborhood-level factors from the American Community Survey and opendataphilly.org. We assessed the spatial association between neighborhood-level factors and the frequency of SARS-CoV-2 positivity, separately across all patients and across asymptomatic patients. We found that neighborhoods with higher proportions of individuals with a high-school degree and/or who were identified as Hispanic/Latinx were more likely to have higher SARS-CoV-2 positivity rates, after adjusting for other neighborhood covariates. Patients from neighborhoods with higher proportions of individuals receiving public assistance and/or identified as White were less likely to test positive for SARS-CoV-2. Our approach and its findings could inform future public health efforts., (©2021 AMIA - All rights reserved.)

Many diseases have been linked with birth seasonality, and these fall into four main categories: mental, cardiovascular, respiratory and women's reproductive health conditions. Informatics methods are needed to uncover seasonally varying infectious diseases that may be responsible for the increased birth month-dependent disease risk observed. We have developed a method to link seasonal infectious disease data from the USA to birth month dependent disease data from humans and canines. We also include seasonal air pollution and climate data to determine the seasonal factors most likely involved in the response. We test our method with osteosarcoma, a rare bone cancer. We found the Lyme disease incidence was the most strongly correlated significant factor in explaining the birth month-osteosarcoma disease pattern (R=0.418, p=2.80X10-23), and this was true across all populations observed: canines, pediatric, and adult populations., (©2021 AMIA - All rights reserved.)

Mapping local terminologies to standardized terminologies facilitates secondary use of electronic health records (EHR). Penn Medicine comprises multiple hospitals and facilities within the Philadelphia Metropolitan area providing services from primary to quaternary care. Our Penn Medicine (PennMed) data include medications collected during both inpatient and outpatient encounters at multiple facilities. Our goal was to map 941,198 unique medication terms to RxNorm, a standardized drug nomenclature from the National Library of Medicine (NLM). We chose three popular tools for mapping: NLM's RxMix and RxNav-in-a-Box, OHDSI's Usagi and Mayo Clinic's MedXN. We manually reviewed 400 mappings obtained from each tool and evaluated their performance for drug name, strength, form, and route. RxMix performed the best with an F1 score of 90% for drug name versus Usagi's 82% and MedXN's 74%. We discuss the strengths and limitations of each method and tips for other institutions seeking to map a local terminology to RxNorm., (©2020 AMIA - All rights reserved.)

Prenatal and perinatal exposures vary seasonally (e.g., sunlight, allergens) and many diseases are linked with variance in exposure. Epidemiologists often measure these changes using birth month as a proxy for seasonal variance. Likewise, Genome-Wide Association Studies have associated or implicated these same diseases with many genes. Both disparate data types (epidemiological and genetic) can provide key insights into the underlying disease biology. We developed an algorithm that links 1) epidemiological data from birth month studies with 2) genetic data from published gene-disease association studies. Our framework uses existing data repositories - PubMed, DisGeNET and Gene Ontology - to produce a bipartite network that connects enriched seasonally varying biofactorss with birth month dependent diseases (BMDDs) through their overlapping developmental gene sets. As a proof-of-concept, we investigate 7 known BMDDs and highlight three important biological networks revealed by our algorithm and explore some interesting genetic mechanisms potentially responsible for the seasonal contribution to BMDDs.

Adverse drug events (ADEs) are responsible for unnecessary patient deaths making them a major public health issue. Literature estimates 1% of ADEs recorded in Electronic Health Records (EHRs) are reported to federal databases making EHRs a vital source of ADE-related information. Using Columbia University Medical Center (CUMC)'s EHRs, we developed an algorithm to mine for vaccine-related ADEs occurring within 3 months of vaccination. In phase one, we measured the association between vaccinated patients with an ADE (cases) against those vaccinated without an ADE. To adjust for healthcare-process effects, phase two compared cases against those who returned to CUMC within 3 months without an ADE. We report 7 results passing multiplicity correction after demographic confounder adjustment. We observed an association, having some literature support, between swine flu vaccination and ADEs (H1N1v-like, OR=9.469, p<0.001; H1N1/H3N2, OR=3.207, p<0.001). Our algorithm could inform clinicians of the risks/benefits of vaccinations towards improving clinical care.

User needs understanding is critical for developing useful and usable clinical research decision support. Existing methods largely depend on self-reporting and often fail to elicit implicit or fine-grained user needs. We hypothesized that functional software would address this problem by presenting to users existing technology while simultaneously encouraging users to optimize workflow. Using clinical research visit scheduling as an example, we used a piece of software under development that was called IMPACT to reveal user needs iteratively. The identified user needs explained why most clinical research coordinators still rely on paper to schedule clinical research visits. The common user needs themes such as information completeness for software to be useful may generalize to other clinical decision support. This paper contributes valuable firsthand knowledge about user needs for decision support for clinical research visit scheduling among clinical research coordinators and a generalizable methodology for collecting and analyzing software usage data to inform user needs elicitation.

To most medical researchers, databases are obscure black boxes. Query analysts are often indispensable guides aiding researchers to perform mediated data queries. However, this approach does not scale up and is time-consuming and expensive. We analyzed query mediation dialogues to inform future designs of intelligent query mediation systems. Thirty-one mediated query sessions for 22 research projects were recorded and transcribed. We analyzed 10 of these to develop an annotation schema for dialogue acts through iterative refinement. Three coders independently annotated all 3160 dialogue acts. We assessed the inter-rater agreement and resolved disagreement by group consensus. This study contributes early knowledge of the query negotiation space for medical research. We conclude that research data query formulation is not a straightforward translation from researcher data needs to database queries, but rather iterative, process-oriented needs assessment and refinement.

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria.