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Background: Evidence-based point-of-care information (POCI) tools can facilitate patient safety and care by helping clinicians to answer disease state and drug information questions in less time and with less effort. However, these tools may also be visually challenging to navigate or lack the comprehensiveness needed to sufficiently address a medical issue., Objective: This study aimed to collect clinicians' feedback and directly observe their use of the combined POCI tool DynaMed and Micromedex with Watson, now known as DynaMedex. EBSCO partnered with IBM Watson Health, now known as Merative, to develop the combined tool as a resource for clinicians. We aimed to identify areas for refinement based on participant feedback and examine participant perceptions to inform further development., Methods: Participants (N=43) within varying clinical roles and specialties were recruited from Brigham and Women's Hospital and Massachusetts General Hospital in Boston, Massachusetts, United States, between August 10, 2021, and December 16, 2021, to take part in usability sessions aimed at evaluating the efficiency and effectiveness of, as well as satisfaction with, the DynaMed and Micromedex with Watson tool. Usability testing methods, including think aloud and observations of user behavior, were used to identify challenges regarding the combined tool. Data collection included measurements of time on task; task ease; satisfaction with the answer; posttest feedback on likes, dislikes, and perceived reliability of the tool; and interest in recommending the tool to a colleague., Results: On a 7-point Likert scale, pharmacists rated ease (mean 5.98, SD 1.38) and satisfaction (mean 6.31, SD 1.34) with the combined POCI tool higher than the physicians, nurse practitioner, and physician's assistants (ease: mean 5.57, SD 1.64, and satisfaction: mean 5.82, SD 1.60). Pharmacists spent longer (mean 2 minutes, 26 seconds, SD 1 minute, 41 seconds) on average finding an answer to their question than the physicians, nurse practitioner, and physician's assistants (mean 1 minute, 40 seconds, SD 1 minute, 23 seconds)., Conclusions: Overall, the tool performed well, but this usability evaluation identified multiple opportunities for improvement that would help inexperienced users., (©Angela Rui, Pamela M Garabedian, Marlika Marceau, Ania Syrowatka, Lynn A Volk, Heba H Edrees, Diane L Seger, Mary G Amato, Jacob Cambre, Sevan Dulgarian, Lisa P Newmark, Karen C Nanji, Petra Schultz, Gretchen Purcell Jackson, Ronen Rozenblum, David W Bates. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 17.04.2023.)

Background: Extended foster care programs help prepare transitional-aged youth (TAY) to step into adulthood and live independent lives. Aspiranet, one of California's largest social service organizations, used a social care management solution (SCMS) to meet TAY's needs., Objective: We aimed to investigate the impact of an SCMS, IBM Watson Care Manager (WCM), in transforming foster program service delivery and improving TAY outcomes., Methods: We used a mixed methods study design by collecting primary data from stakeholders through semistructured interviews in 2021 and by pulling secondary data from annual reports, system use logs, and data repositories from 2014 to 2021. Thematic analysis based on grounded theory was used to analyze qualitative data using NVivo software. Descriptive analysis of aggregated outcome metrics in the quantitative data was performed and compared across 2 periods: pre-SCMS implementation (before October 31, 2016) and post-SCMS implementation (November 1, 2016, and March 31, 2021)., Results: In total, 6 Aspiranet employees (4 leaders and 2 life coaches) were interviewed, with a median time of 56 (IQR 53-67) minutes. The majority (5/6, 83%) were female, over 30 years of age (median 37, IQR 32-39) with a median of 6 (IQR 5-10) years of experience at Aspiranet and overall field experience of 10 (IQR 7-14) years. Most (4/6, 67%) participants rated their technological skills as expert. Thematic analysis of participants' interview transcripts yielded 24 subthemes that were grouped into 6 superordinate themes: study context, the impact of the new tool, key strengths, commonly used features, expectations with WCM, and limitations and recommendations. The tool met users' initial expectations of streamlining tasks and adopting essential functionalities. Median satisfaction scores around pre- and post-WCM workflow processes remained constant between 2 life coaches (3.25, IQR 2.5-4); however, among leaders, post-WCM scores (median 4, IQR 4-5) were higher than pre-WCM scores (median 3, IQR 3-3). Across the 2 study phases, Aspiranet served 1641 TAY having consistent population demographics (median age of 18, IQR 18-19 years; female: 903/1641, 55.03%; race and ethnicity: Hispanic or Latino: 621/1641, 37.84%; Black: 470/1641, 28.64%; White: 397/1641, 24.19%; Other: 153/1641, 9.32%). Between the pre- and post-WCM period, there was an increase in full-time school enrollment (359/531, 67.6% to 833/1110, 75.04%) and a reduction in part-time school enrollment (61/531, 11.5% to 91/1110, 8.2%). The median number of days spent in the foster care program remained the same (247, IQR 125-468 years); however, the number of incidents reported monthly per hundred youth showed a steady decline, even with an exponentially increasing number of enrolled youth and incidents., Conclusions: The SCMS for coordinating care and delivering tailored services to TAY streamlined Aspiranet's workflows and processes and positively impacted youth outcomes. Further enhancements are needed to better align with user and youth needs., (©Rubina F Rizvi, Courtney B VanHouten, Van C Willis, Bedda L Rosario, Brett R South, Megan Sands-Lincoln, David Brotman, Jeffery Lenert, Jane L Snowdon, Gretchen P Jackson. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 16.12.2022.)

Objective: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach., Methods: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank., Results: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux., Conclusions: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes., (© 2022 The Obesity Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Background: The first surge of the COVID-19 pandemic entirely altered healthcare delivery. Whether this also altered the receipt of high- and low-value care is unknown., Objective: To test the association between the April through June 2020 surge of COVID-19 and various high- and low-value care measures to determine how the delivery of care changed., Design: Difference in differences analysis, examining the difference in quality measures between the April through June 2020 surge quarter and the January through March 2020 quarter with the same 2 quarters' difference the year prior., Participants: Adults in the MarketScan® Commercial Database and Medicare Supplemental Database., Main Measures: Fifteen low-value and 16 high-value quality measures aggregated into 8 clinical quality composites (4 of these low-value)., Key Results: We analyzed 9,352,569 adults. Mean age was 44 years (SD, 15.03), 52% were female, and 75% were employed. Receipt of nearly every type of low-value care decreased during the surge. For example, low-value cancer screening decreased 0.86% (95% CI, -1.03 to -0.69). Use of opioid medications for back and neck pain (DiD +0.94 [95% CI, +0.82 to +1.07]) and use of opioid medications for headache (DiD +0.38 [95% CI, 0.07 to 0.69]) were the only two measures to increase. Nearly all high-value care measures also decreased. For example, high-value diabetes care decreased 9.75% (95% CI, -10.79 to -8.71)., Conclusions: The first COVID-19 surge was associated with receipt of less low-value care and substantially less high-value care for most measures, with the notable exception of increases in low-value opioid use., (© 2022. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Background: Approximately 1.1 million people living with HIV live in the United States, and the incidence is highest in Southeastern United States. Electronic patient portal prevalence is increasing and can improve engagement in primary medical care. Retention in care and viral suppression-measures of engagement in HIV care-are associated with decreased HIV transmission, morbidity, and mortality., Objective: We aimed to determine if patient portal access among people living with HIV was associated with retention and viral suppression., Methods: We conducted an observational cohort study among people living with HIV in care at the Vanderbilt Comprehensive Care Clinic (Nashville, Tennessee) from 2011-2016. Individual access was defined as patient portal account registration at any point in the year prior. Retention was defined as ≥2 kept appointments or HIV lab measurements ≥3 months apart within a 12-month period. Viral suppression was defined as the last viral load in the calendar year <200 copies/mL. We calculated adjusted prevalence ratios (aPRs) and 95% CIs using modified Poisson regression with generalized estimating equations to estimate the association of portal access with retention and viral suppression., Results: We included 4237 people living with HIV contributing 16,951 person-years of follow-up (median 5, IQR 3-5 person-years). The median age was 43 (IQR 33-50) years. Of the 4237 people living with HIV, 78.1% (n=4237) were male, 40.8% (n=1727) were Black non-Hispanic, and 56.5% (n=2395) had access. Access was independently associated with retention (aPR 1.13, 95% CI 1.10-1.17) and viral suppression (aPR 1.18, 95% CI 1.14-1.22)., Conclusions: In this population, patient portal access was associated with retention and viral suppression. Future prospective studies should assess the impact of increasing portal access among people living with HIV on these HIV outcomes., (©Cassandra Oliver Schember, Sarah E Scott, Cathy A Jenkins, Peter F Rebeiro, Megan Turner, Sally S Furukawa, Carmen Bofill, Zhou Yan, Gretchen P Jackson, April C Pettit. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 25.07.2022.)

The combination of helium charge transfer dissociation mass spectrometry (He-CTD-MS) with ultrahigh performance liquid chromatography (UHPLC) is presented for the analysis of a complex mixture of acidic and neutral human milk oligosaccharides (HMOs). The research focuses on the identification of the monosaccharide sequence, the branching patterns, the sialylation/fucosylation arrangements, and the differentiation of isomeric oligosaccharides in the mixture. Initial studies first optimized the conditions for the UHPLC separation and the He-CTD-MS conditions. Results demonstrate that He-CTD is compatible with UHPLC timescales and provides unambiguous glycosidic and cross-ring cleavages from both the reducing and the nonreducing ends, which is not typically possible using collision-induced dissociation. He-CTD produces informative fragments, including 0,3An and 0,4An ions, which have been observed with electron transfer dissociation, electron detachment dissociation, and ultraviolet photodissociation (UVPD) and are crucial for differentiating the α-2,3- versus α-2,6-linked sialic acid (Neu5Ac) residues present among sialyllacto-N-tetraose HMOs. In addition to the linkage positions, He-CTD is able to differentiate structural isomers for both sialyllacto-N-tetraoses and lacto-N-fucopentaoses structures by providing unique, unambiguous cross-ring cleavages of types 0,2An, 0,2Xn, and 1,5An while preserving most of the labile Neu5Ac and fucose groups., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

After 25 years of service to the American Medical Informatics Association (AMIA), Ms Karen Greenwood, the Executive Vice President and Chief Operating Officer, is leaving the organization. In this perspective, we reflect on her accomplishments and her effect on the organization and the field of informatics nationally and globally. We also express our appreciation and gratitude for Ms Greenwood's role at AMIA., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2022. This work is written by a US Government employee and is in the public domain in the US.)

As technology continues to improve, health care systems have the opportunity to use a variety of innovative tools for decision-making, including artificial intelligence (AI) applications. However, there has been little research on the feasibility and efficacy of integrating AI systems into real-world clinical practice, especially from the perspectives of clinicians who use such tools. In this paper, we review physicians' perceptions of and satisfaction with an AI tool, Watson for Oncology, which is used for the treatment of cancer. Watson for Oncology has been implemented in several different settings, including Brazil, China, India, South Korea, and Mexico. By focusing on the implementation of an AI-based clinical decision support system for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally and particularly for low-middle-income countries. By doing so, we hope to highlight the need for additional research on user experience and the unique social, cultural, and political barriers to the successful implementation of AI in low-middle-income countries for cancer care., (©Srinivas Emani, Angela Rui, Hermano Alexandre Lima Rocha, Rubina F Rizvi, Sergio Ferreira Juaçaba, Gretchen Purcell Jackson, David W Bates. Originally published in JMIR Cancer (https://cancer.jmir.org), 07.04.2022.)

Competing Interests: Competing interests: DB reports grants and personal fees from EarlySense, personal fees from CDI Negev, equity from ValeraHealth, equity from Clew, equity from MDClone, personal fees and equity from AESOP, personal fees and equity from FeelBetter, and grants from IBM Watson Health, outside the submitted work. KR was employed by IBM Watson Health and now is employed by CVS Health. GPJ is employed by IBM Watson Health.

Ultra-high-performance liquid chromatography (UHPLC) with charge transfer dissociation mass spectrometry (CTD-MS) is presented for the analysis of a mixture of complex sulfated oligosaccharides. The mixture contained kappa (κ), iota (ι), and lambda (λ) carrageenans that contain anhydro bridges, different degrees of sulfation ranging from one to three per dimer, different positioning of the sulfate groups along the backbone, and varying degrees of polymerization (DP) between 4 and 12. Optimization studies using standard mixtures of carrageenans helped establish the optimal conditions for online UHPLC-CTD-MS/MS analysis. Optimization included (1) UHPLC conditions; (2) ion source conditions, such as the capillary voltage, drying gas and nebulizing gas temperature, and flow rate; and (3) CTD-MS conditions, including data-dependent CTD-MS. The UHPLC-CTD results were contrasted with UHPLC-CID results of the same mixture on the same instrument. Whereas CID tends to produce B/Y and C/Z ions with many neutral losses, CTD produced more abundant A/X ions and less abundant neutral losses, which enabled more confident structural detail. The results demonstrate that He-CTD is compatible with the timescale of UHPLC and provides more structural information about carrageenans compared to state-of-the-art methods like UHPLC-CID analysis., (© 2021. Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Contact tracing in association with quarantine and isolation is an important public health tool to control outbreaks of infectious diseases. This strategy has been widely implemented during the current COVID-19 pandemic. The effectiveness of this nonpharmaceutical intervention is largely dependent on social interactions within the population and its combination with other interventions. Given the high transmissibility of SARS-CoV-2, short serial intervals, and asymptomatic transmission patterns, the effectiveness of contact tracing for this novel viral agent is largely unknown., Objective: This study aims to identify and synthesize evidence regarding the effectiveness of contact tracing on infectious viral disease outcomes based on prior scientific literature., Methods: An evidence-based review was conducted to identify studies from the PubMed database, including preprint medRxiv server content, related to the effectiveness of contact tracing in viral outbreaks. The search dates were from database inception to July 24, 2020. Outcomes of interest included measures of incidence, transmission, hospitalization, and mortality., Results: Out of 159 unique records retrieved, 45 (28.3%) records were reviewed at the full-text level, and 24 (15.1%) records met all inclusion criteria. The studies included utilized mathematical modeling (n=14), observational (n=8), and systematic review (n=2) approaches. Only 2 studies considered digital contact tracing. Contact tracing was mostly evaluated in combination with other nonpharmaceutical interventions and/or pharmaceutical interventions. Although some degree of effectiveness in decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality was observed, these results were highly dependent on epidemic severity (R0 value), number of contacts traced (including presymptomatic and asymptomatic cases), timeliness, duration, and compliance with combined interventions (eg, isolation, quarantine, and treatment). Contact tracing effectiveness was particularly limited by logistical challenges associated with increased outbreak size and speed of infection spread., Conclusions: Timely deployment of contact tracing strategically layered with other nonpharmaceutical interventions could be an effective public health tool for mitigating and suppressing infectious outbreaks by decreasing viral disease incidence, transmission, and resulting hospitalizations and mortality., (©Kelly Jean Thomas Craig, Rubina Rizvi, Van C Willis, William J Kassler, Gretchen Purcell Jackson. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 06.10.2021.)

Introduction: Clinicians rely on pharmacologic knowledge bases to answer medication questions and avoid potential adverse drug events. In late 2018, an artificial intelligence-based conversational agent, Watson Assistant (WA), was made available to online subscribers to the pharmacologic knowledge base, Micromedex®. WA allows users to ask medication-related questions in natural language. This study evaluated search method-dependent differences in the frequency of information accessed by traditional methods (keyword search and heading navigation) vs conversational agent search., Materials and Methods: We compared the proportion of information types accessed through the conversational agent to the proportion of analogous information types accessed by traditional methods during the first 6 months of 2020., Results: Addition of the conversational agent allowed early adopters to access 22 different information types contained in the 'quick answers' portion of the knowledge base. These information types were accessed 117,550 times with WA during the study period, compared to 33,649,651 times using traditional search methods. The distribution across information types differed by method employed (c 2 test, P < .0001). Single drug/dosing, FDA/non-FDA uses, adverse effects, and drug administration emerged as 4 of the top 5 information types accessed by either method. Intravenous compatibility was accessed more frequently using the conversational agent (7.7% vs. 0.6% for traditional methods), whereas dose adjustments were accessed more frequently via traditional methods (4.8% vs. 1.4% for WA)., Conclusion: In a widely used pharmacologic knowledge base, information accessed through conversational agents versus traditional methods differed. User-centered studies are needed to understand these differences., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Objectives: Non-pharmaceutical interventions (e.g. quarantine and isolation) are used to mitigate and control viral infectious disease, but their effectiveness has not been well studied. For COVID-19, disease control efforts will rely on non-pharmaceutical interventions until pharmaceutical interventions become widely available, while non-pharmaceutical interventions will be of continued importance thereafter., Methods: This rapid evidence-based review provides both qualitative and quantitative analyses of the effectiveness of social distancing non-pharmaceutical interventions on disease outcomes. Literature was retrieved from MEDLINE, Google Scholar, and pre-print databases (BioRxiv.org, MedRxiv.org, and Wellcome Open Research)., Results: Twenty-eight studies met inclusion criteria ( n  = 28). Early, sustained, and combined application of various non-pharmaceutical interventions could mitigate and control primary outbreaks and prevent more severe secondary or tertiary outbreaks. The strategic use of non-pharmaceutical interventions decreased incidence, transmission, and/or mortality across all interventions examined. The pooled attack rates for no non-pharmaceutical intervention, single non-pharmaceutical interventions, and multiple non-pharmaceutical interventions were 42% (95% confidence interval = 30% - 55%), 29% (95% confidence interval = 23% - 36%), and 22% (95% confidence interval = 16% - 29%), respectively., Conclusion: Implementation of multiple non-pharmaceutical interventions at key decision points for public health could effectively facilitate disease mitigation and suppression until pharmaceutical interventions become available. Dynamics around R 0 values, the susceptibility of certain high-risk patient groups to infection, and the probability of asymptomatic cases spreading disease should be considered., Competing Interests: Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer-related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high-value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user-design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration., (© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Objective: To conduct a systematic review identifying workplace interventions that mitigate physician burnout related to the digital environment including health information technologies (eg, electronic health records) and decision support systems) with or without the application of advanced analytics for clinical care., Materials and Methods: Literature published from January 1, 2007 to June 3, 2020 was systematically reviewed from multiple databases and hand searches. Subgroup analysis identified relevant physician burnout studies with interventions examining digital tool burden, related workflow inefficiencies, and measures of burnout, stress, or job satisfaction in all practice settings., Results: The search strategy identified 4806 citations of which 81 met inclusion criteria. Thirty-eight studies reported interventions to decrease digital tool burden. Sixty-eight percent of these studies reported improvement in burnout and/or its proxy measures. Burnout was decreased by interventions that optimized technologies (primarily electronic health records), provided training, reduced documentation and task time, expanded the care team, and leveraged quality improvement processes in workflows., Discussion: The contribution of digital tools to physician burnout can be mitigated by careful examination of usability, introducing technologies to save or optimize time, and applying quality improvement to workflows., Conclusion: Physician burnout is not reduced by technology implementation but can be mitigated by technology and workflow optimization, training, team expansion, and careful consideration of factors affecting burnout, including specialty, practice setting, regulatory pressures, and how physicians spend their time., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Charge transfer dissociation mass spectrometry (CTD-MS) has been shown to induce high energy fragmentation of biological ions in the gas phase and provide fragmentation spectra similar to extreme ultraviolet photodissociation (XUVPD). To date, CTD has typically employed helium cations with kinetic energies between 4-10 keV to initiate radical-directed fragmentation of analytes. However, as a reagent, helium has recently been listed as a critical mineral that is becoming scarcer and more expensive, so this study explored the potential for using cheaper and more readily available reagent gases. A model peptide, bradykinin, and a model oligosaccharide, κ-carrageenan with a degree of polymerization of 4, were fragmented using a variety of CTD reagent gases, which included helium, hydrogen, oxygen, nitrogen, argon and lab air. The CTD results were also contrasted with low-energy collision-induced dissociation (LE-CID), which were collected on the same 3D ion trap. Using constant reagent ion fluxes and kinetic energies, all five alterative reagent gases generated remarkably consistent sequence coverage and fragmentation efficiencies relative to He-CTD, which suggests that the ionization energy of the reagent gas has a negligible effect on the activation of the biological ions. The CTD efficiencies of all the gases ranged from 11-13% for bradykinin and 7-8% for κ-carrageenan. Within these tight ranges, the abundance of the CTnoD peak of bradykinin and the efficiency of CTD fragmentation of bradykinin both correlated with the ionization energy of the CTD reagent gas, which suggests that resonant charge transfer plays a small role in the activation of this peptide. The majority of the excitation energy for bradykinin and for κ-carrageenan comes from an electron stopping mechanism, which is described by long-range interactions between the reagent cations and electrons in the highest occupied molecular orbitals (HOMOs) of the biological ions. The CTD spectra do not provide any evidence for covalently bound products between the biological ions and the more-reactive gases like hydrogen, oxygen and nitrogen, which implies that the high kinetic energies of the reagent ions make them unavailable for covalent reactions. This work demonstrates that any of the substitute reagent gases tested are viable options for future CTD-MS experiments., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Background: Screening patients for eligibility for clinical trials is labor intensive. It requires abstraction of data elements from multiple components of the longitudinal health record and matching them to inclusion and exclusion criteria for each trial. Artificial intelligence (AI) systems have been developed to improve the efficiency and accuracy of this process., Objective: This study aims to evaluate the ability of an AI clinical decision support system (CDSS) to identify eligible patients for a set of clinical trials., Methods: This study included the deidentified data from a cohort of patients with breast cancer seen at the medical oncology clinic of an academic medical center between May and July 2017 and assessed patient eligibility for 4 breast cancer clinical trials. CDSS eligibility screening performance was validated against manual screening. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for eligibility determinations were calculated. Disagreements between manual screeners and the CDSS were examined to identify sources of discrepancies. Interrater reliability between manual reviewers was analyzed using Cohen (pairwise) and Fleiss (three-way) κ, and the significance of differences was determined by Wilcoxon signed-rank test., Results: In total, 318 patients with breast cancer were included. Interrater reliability for manual screening ranged from 0.60-0.77, indicating substantial agreement. The overall accuracy of breast cancer trial eligibility determinations by the CDSS was 87.6%. CDSS sensitivity was 81.1% and specificity was 89%., Conclusions: The AI CDSS in this study demonstrated accuracy, sensitivity, and specificity of greater than 80% in determining the eligibility of patients for breast cancer clinical trials. CDSSs can accurately exclude ineligible patients for clinical trials and offer the potential to increase screening efficiency and accuracy. Additional research is needed to explore whether increased efficiency in screening and trial matching translates to improvements in trial enrollment, accruals, feasibility assessments, and cost., (©Tufia Haddad, Jane M Helgeson, Katharine E Pomerleau, Anita M Preininger, M Christopher Roebuck, Irene Dankwa-Mullan, Gretchen Purcell Jackson, Matthew P Goetz. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.03.2021.)

Artificial intelligence (AI) represents a valuable tool that could be used to improve the safety of care. Major adverse events in healthcare include: healthcare-associated infections, adverse drug events, venous thromboembolism, surgical complications, pressure ulcers, falls, decompensation, and diagnostic errors. The objective of this scoping review was to summarize the relevant literature and evaluate the potential of AI to improve patient safety in these eight harm domains. A structured search was used to query MEDLINE for relevant articles. The scoping review identified studies that described the application of AI for prediction, prevention, or early detection of adverse events in each of the harm domains. The AI literature was narratively synthesized for each domain, and findings were considered in the context of incidence, cost, and preventability to make projections about the likelihood of AI improving safety. Three-hundred and ninety-two studies were included in the scoping review. The literature provided numerous examples of how AI has been applied within each of the eight harm domains using various techniques. The most common novel data were collected using different types of sensing technologies: vital sign monitoring, wearables, pressure sensors, and computer vision. There are significant opportunities to leverage AI and novel data sources to reduce the frequency of harm across all domains. We expect AI to have the greatest impact in areas where current strategies are not effective, and integration and complex analysis of novel, unstructured data are necessary to make accurate predictions; this applies specifically to adverse drug events, decompensation, and diagnostic errors.

The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data-here referred to as Adaptive CDS-present unique challenges and considerations. Although Adaptive CDS represents an expected progression from earlier work, the activities needed to appropriately manage and support the establishment and evolution of Adaptive CDS require new, coordinated initiatives and oversight that do not currently exist. In this AMIA position paper, the authors describe current and emerging challenges to the safe use of Adaptive CDS and lay out recommendations for the effective management and monitoring of Adaptive CDS., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice., Methods: This study included patients treated between 1/2017 and 7/2018 for breast, colon, lung, and rectal cancers at Bumrungrad International Hospital (BIH), Thailand. Treatments selected by clinicians were paired with therapeutic options presented by the CDSS and coded to mask the origin of options presented. The panel rated the acceptability of each treatment in the pair by consensus, with acceptability defined as compliant with BIH's institutional practices. Descriptive statistics characterized the study population and treatment-decision evaluations by cancer type and stage., Results: Nearly 60% (187) of 313 treatment pairs for breast, lung, colon, and rectal cancers were identical or equally acceptable, with 70% (219) of WfO therapeutic options identical to, or acceptable alternatives to, BIH therapy. In 30% of cases (94), 1 or both treatment options were rated as unacceptable. Of 32 cases where both WfO and BIH options were acceptable, WfO was preferred in 18 cases and BIH in 14 cases. Colorectal cancers exhibited the highest proportion of identical or equally acceptable treatments; stage IV cancers demonstrated the lowest., Conclusion: This study demonstrates that a system designed in the US to support, rather than replace, cancer-treating clinicians provides therapeutic options which are generally consistent with recommendations from oncologists outside the US., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

The rapidly evolving science about the Coronavirus Disease 2019 (COVID-19) pandemic created unprecedented health information needs and dramatic changes in policies globally. We describe a platform, Watson Assistant (WA), which has been used to develop conversational agents to deliver COVID-19 related information. We characterized the diverse use cases and implementations during the early pandemic and measured adoption through a number of users, messages sent, and conversational turns (ie, pairs of interactions between users and agents). Thirty-seven institutions in 9 countries deployed COVID-19 conversational agents with WA between March 30 and August 10, 2020, including 24 governmental agencies, 7 employers, 5 provider organizations, and 1 health plan. Over 6.8 million messages were delivered through the platform. The mean number of conversational turns per session ranged between 1.9 and 3.5. Our experience demonstrates that conversational technologies can be rapidly deployed for pandemic response and are adopted globally by a wide range of users., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: People with complex needs, such as those experiencing homelessness, require concurrent, seamless support from multiple social service agencies. Sonoma County, California has one of the nation's largest homeless populations among largely suburban communities. To support client-centered care, the county deployed a Care Management and Coordination System (CMCS). This system comprised the Watson Care Manager (WCM), a front-end system, and Connect 360, which is an integrated data hub that aggregates information from various systems into a single client record., Objective: The aim of this study is to evaluate the perceived impact and usability of WCM in delivering services to the homeless population in Sonoma County., Methods: A mixed methods study was conducted to identify ways in which WCM helps to coordinate care. Interviews, observations, and surveys were conducted, and transcripts and field notes were thematically analyzed and directed by a grounded theory approach. Responses to the Technology Acceptance Model survey were analyzed., Results: A total of 16 participants were interviewed, including WCM users (n=8) and department leadership members (n=8). In total, 3 interdisciplinary team meetings were observed, and 8 WCM users were surveyed. WCM provided a central shared platform where client-related, up-to-date, comprehensive, and reliable information from participating agencies was consolidated. Factors that facilitated WCM use were users' enthusiasm regarding the tool functionalities, scalability, and agency collaboration. Constraining factors included the suboptimal awareness of care delivery goals and functionality of the system among the community, sensitivities about data sharing and legal requirements, and constrained funding from government and nongovernment organizations. Overall, users found WCM to be a useful tool that was easy to use and helped to enhance performance., Conclusions: WCM supports the delivery of care to individuals with complex needs. Integration of data and information in a CMCS can facilitate coordinated care. Future research should examine WCM and similar CMCSs in diverse populations and settings., (©Rubina Rizvi, Courtney VanHouten, Tiffani J Bright, Mollie M McKillop, Shira Alevy, David Brotman, Megan Sands-Lincoln, Jane Snowdon, Barbie J Robinson, Carolyn Staats, Gretchen P Jackson, William J Kassler. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 12.03.2021.)

The novel severe acute respiratory syndrome coronavirus 2, the causal agent of coronavirus disease 2019 (COVID-19), quickly spread around the world, resulting in the most aggressive pandemic experienced in more than 100 years. Research on targeted therapies and vaccines has been initiated on an unprecedented scale and speed but will take months and even years to come to fruition. Meanwhile, the efficacy of emerging therapeutics for use in treating COVID-19 is feverishly being investigated to identify the best available treatment options for dealing with the current wave of disease. This review of publications with a "treatment" tag through June 29, 2020 in the National Library of Medicine's LitCovid literature hub, provides frontline clinicians with a pragmatic summary of the current state of the rapidly evolving evidence supporting emerging candidate therapeutics for COVID-19. Two main categories of pharmaceutical therapeutics are showing promise: those with antiviral activity directly addressing infection and those that counteract the inflammatory cytokine storm induced by severe disease. Preliminary results suggest that other approaches such as convalescent plasma therapy and lung radiation therapy may have some efficacy. The current clinical evidence for potential treatments is preliminary-often small retrospective series or early results of randomized trials-and the science is evolving rapidly. The long-term results from large, well-designed randomized controlled trials will provide definitive evidence for therapeutic effectiveness and are likely months away. The trial landscape for promising therapies is described., (© 2020 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc.)

With growth in consumer health technologies, patients and caregivers have become increasingly involved in their health and medical care. Such health-related engagement often occurs at home. Pregnancy is a common condition and, for many women, their first exposure to health management practices. This study examined how pregnant women and caregivers managed health in their homes. Participants completed sociodemographic surveys and semi-structured interviews about living situation, information needs, and technology use. Using an iterative, inductive coding approach, we identified themes about health management, including the physical home, help at home, community, the virtual home, and biggest concerns. Most expectant mothers encountered everyday problems with mobility and household management. Pregnant women desired more assistance from caregivers, who often did not know how to help. Caregivers who provided help took on new roles. Many expectant families did not trust advice found online. Over half of expectant families had biggest concerns that involved the home., Competing Interests: Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Gretchen Jackson is currently employed by IBM Watson Health. This conflict has been disclosed and managed by the VUMC Conflicts of Interest process., (© The Author(s) 2020.)

Objectives: Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs., Methods: New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus., Results: Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph., Conclusion: Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted approach beyond traditional in silico evaluation of AI. We propose an interdisciplinary, phased research framework for evaluation of AI implementations in healthcare. We draw analogies to and highlight differences from the clinical trial phases for drugs and medical devices, and we present study design and methodological guidance for each stage., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: To support the rising need for testing and to standardize tumor DNA sequencing practices within the U.S. Department of Veterans Affairs (VA)'s Veterans Health Administration (VHA), the National Precision Oncology Program (NPOP) was launched in 2016. We sought to assess oncologists' practices, concerns, and perceptions regarding Next-Generation Sequencing (NGS) and the NPOP., Materials and Methods: Using a purposive total sampling approach, oncologists who had previously ordered NGS for at least one tumor sample through the NPOP were invited to participate in semi-structured interviews. Questions assessed the following: expectations for the NPOP, procedural requirements, applicability of testing results, and the summative utility of the NPOP. Interviews were assessed using an open coding approach. Thematic analysis was conducted to evaluate the completed codebook. Themes were defined deductively by reviewing the direct responses to interview questions as well as inductively by identifying emerging patterns of data., Results: Of the 105 medical oncologists who were invited to participate, 20 (19%) were interviewed from 19 different VA medical centers in 14 states. Five recurrent themes were observed: (1) Educational Efforts Regarding Tumor DNA Sequencing Should be Undertaken, (2) Pathology Departments Share a Critical Role in Facilitating Test Completion, (3) Tumor DNA Sequencing via NGS Serves as the Most Comprehensive Testing Modality within Precision Oncology, (4) The Availability of the NPOP Has Expanded Options for Select Patients, and (5) The Completion of Tumor DNA Sequencing through the NPOP Could Help Improve Research Efforts within VHA Oncology Practices., Conclusion: Medical oncologists believe that the availability of tumor DNA sequencing through the NPOP could potentially lead to an improvement in outcomes for veterans with metastatic solid tumors. Efforts should be directed toward improving oncologists' understanding of sequencing, strengthening collaborative relationships between oncologists and pathologists, and assessing the role of comprehensive NGS panels within the battery of precision tests., Competing Interests: In regard to potential competing interests, authors JLS, VM, DW, and GPJ are employed by IBM Watson Health. In addition, author HGS was previously employed by IBM Watson Health and author VLP is a consultant for IBM Watson Health. As mentioned, the commercial interest did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objective: The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital., Methods: A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I-III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility., Results: The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53-100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243-4132). Median time for the system to run a query and return results was 15.5 s (range 7.2-37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen's kappa 0.70-1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%., Discussion and Conclusion: The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: This article describes the system architecture, training, initial use, and performance of Watson Assistant (WA), an artificial intelligence-based conversational agent, accessible within Micromedex ® ., Materials and Methods: The number and frequency of intents (target of a user's query) triggered in WA during its initial use were examined; intents triggered over 9 months were compared to the frequency of topics accessed via keyword search of Micromedex. Accuracy of WA intents assigned to 400 queries was compared to assignments by 2 independent subject matter experts (SMEs), with inter-rater reliability measured by Cohen's kappa., Results: In over 126 000 conversations with WA, intents most frequently triggered involved dosing ( N  = 30 239, 23.9%) and administration ( N  = 14 520, 11.5%). SMEs with substantial inter-rater agreement (kappa = 0.71) agreed with intent mapping in 247 of 400 queries (62%), including 16 queries related to content that WA and SMEs agreed was unavailable in WA. SMEs found 57 (14%) of 400 queries incorrectly mapped by WA; 112 (28%) queries unanswerable by WA included queries that were either ambiguous, contained unrecognized typographical errors, or addressed topics unavailable to WA. Of the queries answerable by WA (288), SMEs determined 231 (80%) were correctly linked to an intent., Discussion: A conversational agent successfully linked most queries to intents in Micromedex. Ongoing system training seeks to widen the scope of WA and improve matching capabilities., Conclusion: WA enabled Micromedex users to obtain answers to many medication-related questions using natural language, with the conversational agent facilitating mapping to a broader distribution of topics than standard keyword searches., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the majority of newborn deaths. We investigated the extent to which deep learning models that consider temporal relations documented in electronic health records (EHRs) can predict EPB., Study Design: EHR data were subject to word embedding and a temporal deep learning model, in the form of recurrent neural networks (RNNs) to predict EPB. Due to the low prevalence of EPB, the models were trained on datasets where controls were undersampled to balance the case-control ratio. We then applied an ensemble approach to group the trained models to predict EPB in an evaluation setting with a nature EPB ratio. We evaluated the RNN ensemble models with 10 years of EHR data from 25,689 deliveries at Vanderbilt University Medical Center. We compared their performance with traditional machine learning models (logistical regression, support vector machine, gradient boosting) trained on the datasets with balanced and natural EPB ratio. Risk factors associated with EPB were identified using an adjusted odds ratio., Results: The RNN ensemble models trained on artificially balanced data achieved a higher AUC (0.827 vs. 0.744) and sensitivity (0.965 vs. 0.682) than those RNN models trained on the datasets with naturally imbalanced EPB ratio. In addition, the AUC (0.827) and sensitivity (0.965) of the RNN ensemble models were better than the AUC (0.777) and sensitivity (0.819) of the best baseline models trained on balanced data. Also, risk factors, including twin pregnancy, short cervical length, hypertensive disorder, systemic lupus erythematosus, and hydroxychloroquine sulfate, were found to be associated with EPB at a significant level., Conclusion: Temporal deep learning can predict EPB up to 8 weeks earlier than its occurrence. Accurate prediction of EPB may allow healthcare organizations to allocate resources effectively and ensure patients receive appropriate care., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: Recurrent vaso-occlusive pain episodes, the most common complication of sickle cell disease (SCD), cause frequent health care utilization. Studies exploring associations between patient activation and acute health care utilization for pain are lacking. We tested the hypothesis that increased activation and self-efficacy are associated with decreased health care utilization for pain in SCD., Methods: In this cross-sectional study of adults with SCD at a tertiary medical center, we collected demographics, SCD phenotype, Patient Activation Measure levels, and self-efficacy scores using structured questionnaires. We reviewed charts to obtain disease-modifying therapy and acute health care utilization, defined as emergency room visits and hospitalizations, for vaso-occlusive pain episodes. Negative binomial regression analyses were used to test the hypothesis., Results: We surveyed 67 adults with SCD. The median age was 27.0 years, 53.7% were female, and 95.5% were African American. Median health care utilization for pain over one year (range) was 2.0 (0-24). Only one-third of participants (38.8%) were at the highest activation level (median [range] = 3 [1-4]). Two-thirds (65.7%) of participants had high self-efficacy (median [range] = 32.0 [13-45]). Regressions showed significant association between health care utilization and activation (incidence rate ratio [IRR] = 0.663, P = 0.045), self-efficacy (IRR = 0.947, P = 0.038), and male sex (IRR = 0.390, P = 0.003). Two outliers with high activation, self-efficacy, and health care utilization also had addictive behavior., Conclusions: Many individuals with SCD have suboptimal activation and reduced self-efficacy. Higher activation and self-efficacy were associated with lower health care utilization for pain. Additional studies are needed to evaluate interventions to improve activation and self-efficacy and reduce acute health care utilization for pain., (© 2018 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The significant role of lay caregivers has been explored in chronic and acute illnesses. In pregnancy, caregivers' (eg, the baby's father, friends, and family) roles in promoting the health of the mother and baby are not well understood., Objective: We characterize the activities and roles of pregnancy caregivers and offer opportunities for engaging this important group., Method: We conducted interviews with 29 pregnancy caregivers. Interview transcripts were analyzed inductively, resulting in a coding scheme of actions and roles that pregnancy caregivers perform., Results: The most common actions and roles included searching for information (97%), accompanying patients to medical appointments (69%), and being a source of emotional support (76%). Identified actions and roles fit a patient work framework, including work types identified by Corbin and Strauss: illness, everyday life, biographical, articulation, and invisible., Conclusion: The patient work framework can be employed to describe the activities and roles of pregnancy caregivers. We have contributed new insights into the experiences of pregnancy caregivers and recommendations for educational and technological interventions., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Adult blow flies are one of the first necrophagous insects to colonize fresh carcasses. The eggs they lay hatch into larvae, which then feed on the decomposing body. Like all organisms, blow flies "are what they eat," meaning that the isotopic composition of their body tissues reflects their diet. This manuscript combines ecology with a forensic application by using isotope ratio mass spectrometry (IRMS) to understand the relationship between the δ 13 C of amino acids in different carrion sources and the blow fly that feed on them. We also measure the amino acid-level fractionation that occurs at each major life stage of the blow flies. Adult blow flies from a commercial strain of Calliphora vicina (Robineau-Desvoidy) (Diptera: Calliphoridae) oviposited on raw pork muscle, beef muscle, or chicken liver. Larvae, pupae, and adult blow flies from each carrion were selected for amino acid compound-specific isotope analysis. Canonical discriminant analysis showed that flies were correctly classified to specific carrion types in 100% (original rules) and 96.8% (leave-one-out cross-validation [LOOCV]) of cases. Regarding life stages, we obtained 100% and 71% of correct classification in original rules and LOOCV, respectively. The isotope ratios of most of the essential amino acids did not significantly change between life stages (at 95% CI). However, some non-essential amino acids (Ala, Ser, and Glu) and some conditionally essential amino acids (Gly and Pro) were isotopically depleted in the adult stage. Except for the essential amino acids, the amino acids in larvae and pupae were enriched in 13 C, and adult blow flies were depleted in 13 C relative to the carrion on which they fed. These results make it possible to exclude potential sources of carrion as larval food. Amino acid-specific IRMS could help inform entomologists whether a fly has just arrived from another location to feed on a corpse or has emerged from a pupa whose feedstock was the corpse. Such insight could enhance the significance of blow flies for post-mortem interval determinations. The analytical ability to link organisms from one trophic level to another through the use of compound-specific isotope analysis of amino acids could have wide-reaching consequences in a variety of disciplines. Graphical abstract ᅟ.

Objective: Sickle cell disease (SCD) is a chronic condition affecting over 100 000 individuals in the United States, predominantly from vulnerable populations. Clinical practice guidelines, written for providers, have low adherence. This study explored knowledge about guidelines; desire for guidelines; and how technology could support guideline awareness and adherence, examining current technology uses, and user preferences to inform design of a patient-centered guidelines application in a chronic disease., Methods: This cross-sectional mixed-methods study involved semi-structured interviews, surveys, and focus groups of adolescents and adults with SCD. We evaluated interest, preferences, and anticipated benefits or barriers of a patient-centered adaptation of SCD practice guidelines; prospective technology uses for health; and barriers to technology utilization., Results: Forty-seven individuals completed surveys and interviews, and 39 participated in three separate focus groups. Most participants (91%) were unaware of SCD guidelines, but almost all (96%) expressed interest in a guidelines application, identifying benefits (knowledge, activation, individualization, and rewards), and barriers (poor information, low motivation, and resource limitations). Current technology health uses included information access, care coordination, and reminders about health-related actions. Prospective technology uses included informational messaging and timely alerts. Barriers to technology use included lack of interest, lack of utility, and preference for direct communication., Conclusions: This study's findings can inform the design of clinical practice guideline applications, suggesting a promising role for technology to engage patients, facilitate care decisions and actions, and improve outcomes.

Background: Informed and engaged parents may influence outcomes for childhood illness. Understanding the needs of the caregivers of pediatric patients is a critical first step in promoting engagement in their child's care. In 2014, we developed an Engagement Consultation Service at the Monroe Carell Jr. Children's Hospital at Vanderbilt. This service determines the health-related needs of the caregivers of hospitalized children and makes educational or technology recommendations to meet those needs and support engagement., Objectives: This report describes the most common health-related needs identified in the caregivers of hospitalized pediatric patients and details the recommended interventions to meet those needs., Methods: The most commonly reported consumer health-related needs from our 3-year experience with the Engagement Consultation Service were extracted from consultations notes. Each need was classified by semantic type using a taxonomy of consumer health needs. Typical recommendations for each need and their administration were detailed., Results: The most frequently recognized needs involved communicating with health care providers after discharge, using medical devices, distinguishing between benign and concerning symptoms, knowing what questions to ask providers and remembering them, finding trustworthy sources of information online, understanding disease prognosis, and getting emotional support. A variety of apps, Web sites, printed materials, and online groups were recommended., Conclusion: The parents of hospitalized patients share several common health-related needs that can be addressed with educational and technology interventions. An inpatient Engagement Consultation Service provides a generalizable framework for identifying health-related needs and delivers tools to meet those needs and promote engagement during and after hospitalizations., Competing Interests: None., (Georg Thieme Verlag KG Stuttgart · New York.)

Objectives: To build effective applications, technology designers must understand consumer health needs. Pregnancy is a common health condition, and expectant families have unanswered questions. This study examined consumer health-related needs in pregnant women and caregivers and determined the types of needs that were not met., Materials and Methods: We enrolled pregnant women <36 weeks' gestational age and caregivers from advanced maternal-fetal and group prenatal care settings. Participant characteristics were collected through surveys, and health-related needs were elicited in semi-structured interviews. Researchers categorized needs by semantic type and whether they were met (ie, met, partially met, or unmet). Inter-rater reliability was measured by Cohen's kappa., Results: Seventy-one pregnant women and 29 caregivers participated and reported 1054 needs, 28% unmet, and 49% partially met. Need types were 66.2% informational, 15.9% logistical, 8.9% social, 8.6% medical, and 0.3% other. Inter-rater reliability was near perfect ( κ =0.95, P  < 0.001)., Discussion: Common topics of unmet needs were prognosis, life management, and need for emotional support. For pregnant women, these unmet needs focused around being healthy, childbirth, infant care, and being a good mother; caregivers' needs involved caring for the mother, the natural course of pregnancy, and life after pregnancy., Conclusion: Pregnant women and caregivers have a rich set of health-related needs with many not fully met. Caregivers' needs differed from those of pregnant women and may not be adequately addressed by resources designed for mothers. Many unmet needs involved stress and life management. Knowledge about consumer health needs can inform the design of better technologies for pregnancy.

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: journals.permissions@oup.com)

Objective: Patients communicate with healthcare providers via secure messaging in patient portals. As patient portal adoption increases, growing messaging volumes may overwhelm providers. Prior research has demonstrated promise in automating classification of patient portal messages into communication types to support message triage or answering. This paper examines if using semantic features and word context improves portal message classification., Materials and Methods: Portal messages were classified into the following categories: informational, medical, social, and logistical. We constructed features from portal messages including bag of words, bag of phrases, graph representations, and word embeddings. We trained one-versus-all random forest and logistic regression classifiers, and convolutional neural network (CNN) with a softmax output. We evaluated each classifier's performance using Area Under the Curve (AUC)., Results: Representing the messages using bag of words, the random forest detected informational, medical, social, and logistical communications in patient portal messages with AUCs: 0.803, 0.884, 0.828, and 0.928, respectively. Graph representations of messages outperformed simpler features with AUCs: 0.837, 0.914, 0.846, 0.884 for informational, medical, social, and logistical communication, respectively. Representing words with Word2Vec embeddings, and mapping features using a CNN had the best performance with AUCs: 0.908 for informational, 0.917 for medical, 0.935 for social, and 0.943 for logistical categories., Discussion and Conclusion: Word2Vec and graph representations improved the accuracy of classifying portal messages compared to features that lacked semantic information such as bag of words, and bag of phrases. Furthermore, using Word2Vec along with a CNN model, which provide a higher order representation, improved the classification of portal messages., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care., Materials and Methods: We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers., Results: The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g., Temporal&#95;Concept, which maps to 'morning', 'evening' and Idea&#95;or&#95;Concept which maps to 'appointment' and 'refill'). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g., Telephone mapping to 'phone', 'insurance'). For social and informational communications, the most predictive variables were words (e.g., social: 'thanks', 'much', informational: 'question', 'mean')., Conclusions: This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types, although NLP variables were most predictive for medical communication types. As adoption of patient portals increases, automated techniques could assist in understanding and managing growing volumes of messages. Further work is needed to improve classification performance to potentially support message triage and answering., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Patient portal adoption has increased over the last two decades. Most research about patient portals has focused on adult populations in the primary care and medical specialty settings., Objective: We describe initial and long-term portal use by pediatric patients and their caregivers in a broadly deployed patient portal at an academic medical center., Methods: We analyzed portal usage for pediatric patients and their caregivers from 2008 to 2014. We recorded usage events with time stamps; user role defined as self, surrogate (i.e., parent or guardian), or delegate; and functions accessed. Usage events were grouped into sessions to calculate descriptive statistics by patient age, user role, and active use over time., Results: From 2008 to 2014, the number of portal accounts increased from 633 to 17,128. 15.9% of pediatric patients had their own account; 93.6%, a surrogate account; and 2.2% a delegate account. During the study period, 15,711 unique users initiated 493,753 sessions and accessed 1,491,237 functions. Most commonly used functions were secure messaging (accessed in 309,204 sessions; 62.6%); test results (174,239; 35.3%) and appointments (104,830; 21.2%). Function usage was greatest for patients ages 0-2 years (136,245 functions accessed; 23.1%) and 15-17 years (109,241;18.5%). Surrogate users conducted 83.2% of logins for adolescent patients. Portal accounts were actively used for < 1 year for 9,551 patients (55.8%), 1-2 years for 2,826 patients (16.5%), 2-3 years for 1,968 patients (11.5%) and over 3 years for 2,783 patients (16.3%)., Conclusion: Pediatric patients and caregivers have avidly used messaging, test result, and appointment functions. The majority of access was done by surrogates. Adolescent portal usage increased with age. Most accounts for pediatric patients were only used actively for a few years, with peak usage for patients in early childhood and late adolescence.

We herein demonstrate an approach to gas phase ion manipulation that provides MS 3 -level CID spectra of phospholipid radical cations that are almost independent of the original charging adduct ions. In the MS 2 He-MAD spectra of the protonated, sodiated and potassiated adducts of POPC, the different adducts induce different primary fragmentation pathways and provide significantly different spectra, as is commonly observed by other activation methods. In separate experiments, the even-electron adduct ions ([M+H] + , [M+Na] + , [M+K] + ) of 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) were first converted to radical cations [POPC] + • by using helium metastable atom-activated dissociation (He-MAD) to eject the charging adduct ions, then exposed to low-energy collision induced dissociation (CID) to induce extensive fragmentation along the acyl chains. Such charge-remote fragmentation is generally inaccessible through low-energy CID of the even-electron precursor ions. The combination of He-MAD and CID provides radical-induced spectra that show very major similarities and only minor differences, and therefore overcomes major differences in chemistry that are otherwise observed by the original adducting species. Collisional activation of even-electron [POPC+H] + required higher CID amplitudes than odd-electron [POPC] + • to effect fragmentation-as expected-and the latter provided fragments within the acyl chains that were influenced by the double bond position.

The fragmentation pattern of several protonated 1+ phosphatidylcholines (PCs) were studied using low energy collision induced dissociation (CID) and helium metastable atom-activated dissociation (He-MAD). He-MAD of the protonated compounds produced a dominant phosphocholine head group at m/z 184 as well as typical sn-1 and sn-2 glycerol fragments such as [M+H-R x-1 CHC=O] + and [M+H-R x-1 CO 2 H] + . Within the aliphatic chain, He-MAD showed fragments consistent with high-energy collision induced dissociation (HE-CID) and products/pathways consistent with Penning ionization of the 1+ precursor ions to their respective radical dications. These Penning ionization products included both singly and doubly charged radical fragments, and the fragment ions are related to the number and position of double bonds in the acyl chains. Fragments created through HE-CID-like fragmentation followed classic charge remote fragmentation pathways including ladder-like fragmentation along the acyl chain, except for additional or missing peaks due to predictable rearrangement reactions. He-MAD therefore shows utility in being able to effectively fragment singly charged lipids into a variety of useful product ions using both radical and high-energy processes in the confines of a 3D ion trap.

Objective: Patient portals are online applications that allow patients to interact with healthcare organizations. Portal adoption is increasing, and secure messaging between patients and healthcare providers is an emerging form of outpatient interaction. Research about portals and messaging has focused on medical specialties. We characterized adoption of secure messaging and the contribution of messaging to outpatient interactions across diverse clinical specialties after broad portal deployment., Methods: This retrospective cohort study at Vanderbilt University Medical Center examined use of patient-initiated secure messages and clinic visits in the three years following full deployment of a patient portal across adult and pediatric specialties. We measured the proportion of outpatient interactions (i.e., messages plus clinic visits) conducted through secure messaging by specialty over time. Generalized estimating equations measured the likelihood of message-based versus clinic outpatient interaction across clinical specialties., Results: Over the study period, 2,422,114 clinic visits occurred, and 82,159 unique portal users initiated 948,428 messages to 1,924 recipients. Medicine participated in the most message exchanges (742,454 messages; 78.3% of all messages sent), followed by surgery (84,001; 8.9%) and obstetrics/gynecology (53,424; 5.6%). The proportion of outpatient interaction through messaging increased from 12.9% in 2008 to 33.0% in 2009 and 39.8% in 2010 (p<0.001). Medicine had the highest proportion of outpatient interaction conducted through messaging in 2008 (23.3% of outpatient interactions in medicine). By 2010, this proportion was highest for obstetrics/gynecology (83.4%), dermatology (71.6%), and medicine (56.7%). Growth in likelihood of message-based interaction was greater for anesthesiology, dermatology, obstetrics/gynecology, pediatrics, and psychiatry than for medicine (p<0.001)., Conclusions: This study demonstrates rapid adoption of secure messaging across diverse clinical specialties, with messaging interactions exceeding face-to-face clinic visits for some specialties. As patient portal and secure messaging adoption increase beyond medicine and primary care, research is needed to understand the implications for provider workload and patient care.

Background: Patient portal adoption has rapidly increased over the last decade. Most patient portal research has been done in primary care or medical specialties, and few studies have examined their use in surgical patients or for recruiting research subjects. No known studies have compared portal messaging with other approaches of recruitment., Objectives: This case report describes our experience with patient portal versus telephone recruitment for a study involving long-term follow up of surgical patients., Methods: Participants were recruited for a study of recurrence after ventral hernia repair through telephone calls and patient portal messaging based on registration status with the portal. Potential subjects who did not have a portal account or whose portal messages were returned after 5 days were called. The proportion of participants enrolled with each method was determined and demographics of eligible patients, portal users, and participants were compared., Results: 1359 patients were eligible for the hernia study, and enrollment was 35% (n=465). Most participants were recruited by telephone (84%, n=391); 16% (n=74) were recruited through portal messaging. Forty-four percent of eligible participants had a registered portal account, and 14% of users responded to the recruitment message. Portal users were younger than non-users (55 vs. 58 years, p<0.001); participants recruited through the portal versus telephone were also younger (54 vs. 59 years, p=0.001). Differences in the sex and racial distributions between users and non-users and between portal and telephone recruits were not significant., Conclusions: Portal versus telephone recruitment for a surgical research study demonstrated modest portal recruitment rates and similar demographics between recruitment methods. Published studies of portal-only recruitment in primary care or medical-specialty patient populations have demonstrated higher enrollment rates, but this case study demonstrates that portal recruitment for research studies in the surgical population is feasible, and it offers convenience to patients and researchers.

The U.S. Department of Energy Portsmouth Gaseous Diffusion Plant is in the early stages of decommissioning and decontamination. During operations, the site drew a large amount of electric power and had multiple large switchyards on site. These are a source of polychlorinated biphenyls (PCB) contamination to both on-site and off-site streams. Some soil remediation has been completed in the main switchyard. During 2011 and 2012, fifteen sites were sampled at the surface (<10 cm) and subsurface (20-30 cm) to characterize the extent of PCB contamination, to identify weathering and migration of PCB contamination and to explore potential polychlorinated dibenzo-p-dioxins (PCDD) and polychlorinated dibenzofurans (PCDF) contamination due to transformer fires and explosions in the 1950s and 1960s. Stagnant sites tended to exhibit more migration of contamination to deeper sediments than sites with fast-moving waters, and the highest concentrations were found at the bottom of a settling pond. A signature set of five dioxin-like PCBs were consistently found across the site with higher concentrations in carbon rich surface sediments. PCB concentrations had a significant inverse correlation with clay content, suggesting that PCBs did not bind to clays at this site. Remediation has reduced PCB concentrations throughout the site compared to levels found in previous studies and long-term upkeep of sediment lagoons is necessary to retain PCB and dioxin-rich sediments. The flow regimen, organic carbon and clay content play a very important role in the fate of PCBs in the environment at the surface as well as downward migration., (Copyright © 2014 Elsevier Ltd. All rights reserved.)