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Objective - The Vanderbilt Children's Hospital launched an innovative Technology-Based Patient and Family Engagement Consult Service in 2014. This paper describes our initial experience with this service, characterizes health-related needs of families of hospitalized children, and details the technologies recommended to promote engagement and meet needs.
Materials and Methods - We retrospectively reviewed consult service documentation for patient characteristics, health-related needs, and consultation team recommendations. Needs were categorized using a consumer health needs taxonomy. Recommendations were classified by technology type.
Results - Twenty-two consultations were conducted with families of patients ranging in age from newborn to 15 years, most with new diagnoses or chronic illnesses. The consultation team identified 99 health-related needs (4.5 per consultation) and made 166 recommendations (7.5 per consultation, 1.7 per need). Need categories included 38 informational needs, 26 medical needs, 23 logistical needs, and 12 social needs. The most common recommendations were websites (50, 30%) and mobile applications (30, 18%). The most frequent recommendations by need category were websites for informational needs (39, 50%), mobile applications for medical needs (15, 40%), patient portals for logistical needs (12, 44%), and disease-specific support groups for social needs (19, 56%).
Discussion - Families of hospitalized pediatric patients have a variety of health-related needs, many of which could be addressed by technology recommendations from an engagement consult service.
Conclusion - This service is the first of its kind, offering a potentially generalizable and scalable approach to assessing health-related needs, meeting them with technologies, and promoting patient and family engagement in the inpatient setting.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: firstname.lastname@example.org
The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pK determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.
© 2017 The Protein Society.
Automated software improves the accuracy and reliability of blood velocity, vessel diameter, blood flow, and shear rate ultrasound measurements, but existing software offers limited flexibility to customize and validate analyses. We developed FloWave.US-open-source software to automate ultrasound blood flow analysis-and demonstrated the validity of its blood velocity (aggregate relative error, 4.32%) and vessel diameter (0.31%) measures with a skeletal muscle ultrasound flow phantom. Compared with a commercial, manual analysis software program, FloWave.US produced equivalent in vivo cardiac cycle time-averaged mean (TAMean) velocities at rest and following a 10-s muscle contraction (mean bias <1 pixel for both conditions). Automated analysis of ultrasound blood flow data was 9.8 times faster than the manual method. Finally, a case study of a lower extremity muscle contraction experiment highlighted the ability of FloWave.US to measure small fluctuations in TAMean velocity, vessel diameter, and mean blood flow at specific time points in the cardiac cycle. In summary, the collective features of our newly designed software-accuracy, reliability, reduced processing time, cost-effectiveness, and flexibility-offer advantages over existing proprietary options. Further, public distribution of FloWave.US allows researchers to easily access and customize code to adapt ultrasound blood flow analysis to a variety of vascular physiology applications.
Copyright © 2016 the American Physiological Society.
Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987-2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed "RosettaScripts" for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta's ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.
BACKGROUND - Electronic health records (EHRs), computerized provider order entry (CPOE), and patient portals have experienced increased adoption by health care systems. The objective of this study was to review evidence regarding the impact of such health information technologies (HIT) on surgical practice.
MATERIALS AND METHODS - A search of Medline, EMBASE, CINAHL, and the Cochrane Library was performed to identify data-driven, nonsurvey studies about the effects of HIT on surgical care. Domain experts were queried for relevant articles. Two authors independently reviewed abstracts for inclusion criteria and analyzed full text of eligible articles.
RESULTS - A total of 2890 citations were identified. Of them, 32 observational studies and two randomized controlled trials met eligibility criteria. EHR or CPOE improved appropriate antibiotic administration for surgical procedures in 13 comparative observational studies. Five comparative observational studies indicated that electronically generated operative notes had increased accuracy, completeness, and availability in the medical record. The Internet as an information resource about surgical procedures was generally inadequate. Surgical patients and providers demonstrated rapid adoption of patient portals, with increasing proportions of online versus inperson outpatient surgical encounters.
CONCLUSIONS - The overall quality of evidence about the effects of HIT in surgical practice was low. Current data suggest an improvement in appropriate perioperative antibiotic administration and accuracy of operative reports from CPOE and EHR applications. Online consumer health educational resources and patient portals are popular among patients and families, but their impact has not been studied well in surgical populations. With increasing adoption of HIT, further research is needed to optimize the efficacy of such tools in surgical care.
Copyright © 2016 Elsevier Inc. All rights reserved.
Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. It is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.
BACKGROUND AND OBJECTIVES - Electronic health record (EHR) patient portals allow individuals to access their medical information with the intent of patient empowerment. However, little is known about portal use in nephrology patients. We addressed this gap by characterizing adoption of an EHR portal, assessing secular trends, and examining the association of portal adoption and BP control (<140/90 mmHg).
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS - Patients seen between January 1, 2010, and December 31, 2012, at any of four university-affiliated nephrology offices who had at least one additional nephrology follow-up visit before June 30, 2013, were included. Sociodemographic characteristics, comorbidities, clinical measurements, and office visits were abstracted from the EHR. Neighborhood median household income was obtained from the American Community Survey 2012.
RESULTS - Of 2803 patients, 1098 (39%) accessed the portal. Over 87% of users reviewed laboratory results, 85% reviewed their medical information (e.g., medical history), 85% reviewed or altered appointments, 77% reviewed medications, 65% requested medication refills, and 31% requested medical advice from their renal provider. In adjusted models, older age, African-American race (odds ratio [OR], 0.50; 95% confidence interval [95% CI], 0.39 to 0.64), Medicaid status (OR, 0.53; 95% CI, 0.36 to 0.77), and lower neighborhood median household income were associated with not accessing the portal. Portal adoption increased over time (2011 versus 2010: OR, 1.38 [95% CI, 1.09 to 1.75]; 2012 versus 2010: OR, 1.95 [95% CI, 1.44 to 2.64]). Portal adoption was correlated with BP control in patients with a diagnosis of hypertension; however, in the fully adjusted model this was somewhat attenuated and no longer statistically significant (OR, 1.11; 95% CI, 0.99 to 1.24).
CONCLUSION - While portal adoption appears to be increasing, greater attention is needed to understand why vulnerable populations do not access it. Future research should examine barriers to the use of e-health technologies in underserved patients with CKD, interventions to address them, and their potential to improve outcomes.
Copyright © 2015 by the American Society of Nephrology.
How much do people differ in their abilities to recognize objects, and what is the source of these differences? To address the first question, psychologists have created visual learning tests including the Cambridge Face Memory Test (Duchaine & Nakayama, 2006) and the Vanderbilt Expertise Test (VET; McGugin et al., 2012). The second question requires consideration of the influences of both innate potential and experience, but experience is difficult to measure. One solution is to measure the products of experience beyond perceptual knowledge-specifically, nonvisual semantic knowledge. For instance, the relation between semantic and perceptual knowledge can help clarify the nature of object recognition deficits in brain-damaged patients (Barton, Hanif, & Ashraf, Brain, 132, 3456-3466, 2009). We present a reliable measure of nonperceptual knowledge in a format applicable across categories. The Semantic Vanderbilt Expertise Test (SVET) measures knowledge of relevant category-specific nomenclature. We present SVETs for eight categories: cars, planes, Transformers, dinosaurs, shoes, birds, leaves, and mushrooms. The SVET demonstrated good reliability and domain-specific validity. We found partial support for the idea that the only source of domain-specific shared variance between the VET and SVET is experience with a category. We also demonstrated the utility of the SVET-Bird in experts. The SVET can facilitate the study of individual differences in visual recognition.
BACKGROUND - Biomedical research has traditionally been conducted via surveys and the analysis of medical records. However, these resources are limited in their content, such that non-traditional domains (eg, online forums and social media) have an opportunity to supplement the view of an individual's health.
OBJECTIVE - The objective of this study was to develop a scalable framework to detect personal health status mentions on Twitter and assess the extent to which such information is disclosed.
METHODS - We collected more than 250 million tweets via the Twitter streaming API over a 2-month period in 2014. The corpus was filtered down to approximately 250,000 tweets, stratified across 34 high-impact health issues, based on guidance from the Medical Expenditure Panel Survey. We created a labeled corpus of several thousand tweets via a survey, administered over Amazon Mechanical Turk, that documents when terms correspond to mentions of personal health issues or an alternative (eg, a metaphor). We engineered a scalable classifier for personal health mentions via feature selection and assessed its potential over the health issues. We further investigated the utility of the tweets by determining the extent to which Twitter users disclose personal health status.
RESULTS - Our investigation yielded several notable findings. First, we find that tweets from a small subset of the health issues can train a scalable classifier to detect health mentions. Specifically, training on 2000 tweets from four health issues (cancer, depression, hypertension, and leukemia) yielded a classifier with precision of 0.77 on all 34 health issues. Second, Twitter users disclosed personal health status for all health issues. Notably, personal health status was disclosed over 50% of the time for 11 out of 34 (33%) investigated health issues. Third, the disclosure rate was dependent on the health issue in a statistically significant manner (P<.001). For instance, more than 80% of the tweets about migraines (83/100) and allergies (85/100) communicated personal health status, while only around 10% of the tweets about obesity (13/100) and heart attack (12/100) did so. Fourth, the likelihood that people disclose their own versus other people's health status was dependent on health issue in a statistically significant manner as well (P<.001). For example, 69% (69/100) of the insomnia tweets disclosed the author's status, while only 1% (1/100) disclosed another person's status. By contrast, 1% (1/100) of the Down syndrome tweets disclosed the author's status, while 21% (21/100) disclosed another person's status.
CONCLUSIONS - It is possible to automatically detect personal health status mentions on Twitter in a scalable manner. These mentions correspond to the health issues of the Twitter users themselves, but also other individuals. Though this study did not investigate the veracity of such statements, we anticipate such information may be useful in supplementing traditional health-related sources for research purposes.