Federal efforts and local initiatives to increase adoption and use of electronic health records (EHRs) continue, particularly since the enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act. Roughly one in four hospitals not adopted even a basic EHR system. A review of the barriers may help in understanding the factors deterring certain healthcare organizations from implementation. We wanted to assemble an updated and comprehensive list of adoption barriers of EHR systems in the United States. Authors searched CINAHL, MEDLINE, and Google Scholar, and accepted only articles relevant to our primary objective. Reviewers independently assessed the works highlighted by our search and selected several for review. Through multiple consensus meetings, authors tapered articles to a final selection most germane to the topic (n = 27). Each article was thoroughly examined by multiple authors in order to achieve greater validity. Authors identified 39 barriers to EHR adoption within the literature selected for the review. These barriers appeared 125 times in the literature; the most frequently mentioned barriers were regarding cost, technical concerns, technical support, and resistance to change. Despite federal and local incentives, the initial cost of adopting an EHR is a common existing barrier. The other most commonly mentioned barriers include technical support, technical concerns, and maintenance/ongoing costs. Policy makers should consider incentives that continue to reduce implementation cost, possibly aimed more directly at organizations that are known to have lower adoption rates, such as small hospitals in rural areas.

BackgroundFitness trackers can engage users through automated self-monitoring of physical activity. Studies evaluating the utility of fitness trackers are limited among adolescents, who are often difficult to engage in weight management treatment and are heavy technology users.ObjectiveWe conducted a pilot randomized trial to describe the impact of providing adolescents and caregivers with fitness trackers as an adjunct to treatment in a tertiary care weight management clinic on adolescent fitness tracker satisfaction, fitness tracker utilization patterns, and physical activity levels.MethodsAdolescents were randomized to 1 of 2 groups (adolescent or dyad) at their initial weight management clinic visit. Adolescents received a fitness tracker and counseling around activity data in addition to standard treatment. A caregiver of adolescents in the dyad group also received a fitness tracker. Satisfaction with the fitness tracker, fitness tracker utilization patterns, and physical activity patterns were evaluated over 3 months.ResultsA total of 88 adolescents were enrolled, with 69% (61/88) being female, 36% (32/88) black, 23% (20/88) Hispanic, and 63% (55/88) with severe obesity. Most adolescents reported that the fitness tracker was helping them meet their healthy lifestyle goals (69%) and be more motivated to achieve a healthy weight (66%). Despite this, 68% discontinued use of the fitness tracker by the end of the study. There were no significant differences between the adolescent and the dyad group in outcomes, but adolescents in the dyad group were 12.2 times more likely to discontinue using their fitness tracker if their caregiver also discontinued use of their fitness tracker (95% CI 2.4-61.6). Compared with adolescents who discontinued use of the fitness tracker during the study, adolescents who continued to use the fitness tracker recorded a higher number of daily steps in months 2 and 3 of the study (mean 5760 vs 4148 in month 2, P=.005, and mean 5942 vs 3487 in month 3, P=.002).ConclusionsDespite high levels of satisfaction with the fitness trackers, fitness tracker discontinuation rates were high, especially among adolescents whose caregivers also discontinued use of their fitness tracker. More studies are needed to determine how to sustain the use of fitness trackers among adolescents with obesity and engage caregivers in adolescent weight management interventions.

BackgroundFingerprints represent a particular characteristic for each individual. Characteristic patterns are also formed on the palms of the hands and soles of the feet. Their origin and development is still unknown but it is believed to have a strong genetic component, although it is not the only thing determining its formation. Each fingerprint is a papillary drawing composed by papillae and rete ridges (crests). This paper proposes a phenomenological model describing fingerprint pattern formation using reaction diffusion equations with Turing space parameters.ResultsSeveral numerical examples were solved regarding simplified finger geometries to study pattern formation. The finite element method was used for numerical solution, in conjunction with the Newton-Raphson method to approximate nonlinear partial differential equations.ConclusionsThe numerical examples showed that the model could represent the formation of different types of fingerprint characteristics in each individual.

Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics—area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)—were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues.

Uno de los desafíos para el mercado de la energía renovable en Colombia es el suministro de energía en regiones apartadas. Si bien Colombia ha tenido iniciativas en la promoción de energía renovable, actualmente existen regiones que carecen de suministro de electricidad. La falta de infraestructura y suministro de electricidad conlleva problemas relacionados con el número de usuarios cubiertos, desarrollo industrial y condiciones ambientales que afectan la competitividad de las regiones.Este artículo propone un análisis de las implicaciones que se generan por la falta de cobertura de electricidad en las regiones apartadas de Colombia. Un análisis basado en un modelo de simulación es desarrollado en este artículo, con el fin de entender y proponer alternativas políticas a las implicaciones por la falta de suministro energético. Los resultados evidencian que la falta de suministro afecta los indicadores de competitividad de un país o región, lo cual no permite el desarrolloadecuado de tecnologías de energía no convencional tales como solar, biomasa y eólica.