Gammarus minus is an important component of surface spring and cave ecosystems throughout Appalachia, and is a useful indicator of the hydrology and gene flow in freshwater communities. Gammarus minus populations occupying large cave passages (> 2 km) are usually troglomorphic, having reduced eyes, fewer ommatidia, larger body size, longer antennae, and reduced pigmentation relative to surface populations. We surveyed five cave stream and 10 surface spring populations for DNA sequence variation in the cytochromec oxidase I (COI) and internal transcribed spacer 1 (ITS-1) genes with an aim towards characterizing phylogeographical structure and comparing the nature of genetic variation in cave vs. surface populations. Although standing variation at both loci was rather low within populations, a significant degree of divergence and spatial structuring of populations was observed. Levels of genetic variation within cave and spring populations differed substantially, with caves harbouring significantly less variation at the COI locus than surface springs. Codon usage bias was significantly lower in caves, indicating that cave streams harbour smaller and/or more recently colonized populations. Overall these data indicate limited gene flow among populations and suggest that the cave populations sampled in this study are prone to bottlenecks, possibly due to larger temperature fluctuations and more frequent incidence of drought conditions associated with these particular cave habitats.

Objective This article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices. Methods A systematic literature review was conducted following PRISMA guidelines. PubMed searches were conducted in February 2017 using search terms representing key concepts of interest: health care settings, visualization, and evaluation. References were also screened for eligibility. Data were extracted from included studies and analyzed using a PICOS framework: Participants, Interventions, Comparators, Outcomes, and Study Design. Results After screening, 76 publications met the review criteria. Publications varied across all PICOS dimensions. The most common audience was healthcare providers (n = 43), and the most common data gathering methods were direct observation (n = 30) and surveys (n = 27). About half of the publications focused on static, concentrated views of data with visuals (n = 36). Evaluations were heterogeneous regarding setting and measurements used. Discussion When evaluating data visualizations and visual analytics technologies, a variety of approaches have been used. Usability measures were used most often in early (prototype) implementations, whereas clinical outcomes were most common in evaluations of operationally-deployed systems. These findings suggest opportunities for both (1) expanding evaluation practices, and (2) innovation with respect to evaluation methods for data visualizations and visual analytics technologies across health settings. Conclusion Evaluation approaches are varied. New studies should adopt commonly reported metrics, context-appropriate study designs, and phased evaluation strategies.

To reduce the risk of wrong-patient errors, safety experts recommend allowing only one patient chart to be open at a time. Due to the lack of empirical evidence, the number of allowable open charts is often based on anecdotal evidence or institutional preference, and hence varies across institutions. Using an interrupted time series analysis of intercepted wrong-patient medication orders in an emergency department during 2010-2016 (83.6 intercepted wrong-patient events per 100 000 orders), we found no significant decrease in the number of intercepted wrong-patient medication orders during the transition from a maximum of 4 open charts to a maximum of 2 (b = -0.19, P = .33) and no significant increase during the transition from a maximum of 2 open charts to a maximum of 4 (b = 0.08, P = .67). These results have implications regarding decisions about allowable open charts in the emergency department in relation to the impact on workflow and efficiency.

Objective Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider–patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs). Methods We prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow. Results We surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19–278.56; ED: OR = 19.06, 95% CI: 9.53–41.65' inpatient: OR = 2.95, 95% CI: 2.23–3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%). Conclusion Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.

Introduction: Key measures in preventing spread of the virus that causes coronavirus disease 2019 (COVID-19) are social distancing and stay-at-home mandates. These measures along with other stressors have the potential to increase incidences of intimate partner violence (IPV), sexual assault, and child maltreatment. Methods: We performed a retrospective review of county police dispatches, emergency department (ED) visits, Sexual Assault Nurse Examiner (SANE) consults, Domestic Violence Healthcare Project (DVHP) team consults, and Child Protection Team consults at a large, tertiary, Level I trauma center. We queried International Classification of Diseases Revision 10 codes most specific to IPV, sexual assault, and child maltreatment from March–October 2020 compared to 2019. Similarly, the number of consults performed by SANE, DVHP, and our Child Protection Team were collected. We compared all ED visits and consultations to total ED visits for the reviewed time period. Finally, the total number of calls and referrals to a child advocacy center and resource call line for victims were recorded during this timeframe. Results: Police dispatches for IPV-related assaults increased by 266 reports from 2019 to 2020 (P = 0.015). Emergency department visits related to IPV increased from 0.11% of visits in 2019 to 0.15% in 2020 (P = 0.032), and DVHP consults increased from 0.31% in 2019 to 0.48% in 2020 of ED visits in the first three months (P < 0.001). Child maltreatment visits increased from 0.47% of visits in 2019 to 0.81% of visits in 2020 (P = 0.028), and a higher percentage of patients required Child Protection team consults from 1% in 2019 to 1.6% in 2020 (P = 0.004). Sexual assault-related visits and SANE consults both showed a small increase that was not statistically significant. Fewer calls and referrals were made to our child advocacy center and resource call line, decreasing by 99 referrals and 252 calls, respectively. Conclusion: Despite decreased ED volumes throughout the pandemic, we observed an increase in police dispatches, ED visits, and utilization of hospital consult services related to IPV and child maltreatment following the initiation of stay-at-home orders. However, use of community resources, such as the local child advocacy center, declined.