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Introduction/Purpose Predictive models incorporating relevant clinical and social features can provide meaningful insights into complex interrelated mechanisms of cardiovascular disease (CVD) risk and progression and the influence of environmental exposures on adverse outcomes. The purpose of this targeted review (2018–2019) was to examine the extent to which present-day advanced analytics, artificial intelligence, and machine learning models include relevant variables to address potential biases that inform care, treatment, resource allocation, and management of patients with CVD. Methods PubMed literature was searched using the prespecified inclusion and exclusion criteria to identify and critically evaluate primary studies published in English that reported on predictive models for CVD, associated risks, progression, and outcomes in the general adult population in North America. Studies were then assessed for inclusion of relevant social variables in the model construction. Two independent reviewers screened articles for eligibility. Primary and secondary independent reviewers extracted information from each full-text article for analysis. Disagreements were resolved with a third reviewer and iterative screening rounds to establish consensus. Cohen's kappa was used to determine interrater reliability. Results The review yielded 533 unique records where 35 met the inclusion criteria. Studies used advanced statistical and machine learning methods to predict CVD risk (10, 29%), mortality (19, 54%), survival (7, 20%), complication (10, 29%), disease progression (6, 17%), functional outcomes (4, 11%), and disposition (2, 6%). Most studies incorporated age (34, 97%), sex (34, 97%), comorbid conditions (32, 91%), and behavioral risk factor (28, 80%) variables. Race or ethnicity (23, 66%) and social variables, such as education (3, 9%) were less frequently observed. Conclusions Predictive models should adjust for race and social predictor variables, where relevant, to improve model accuracy and to inform more equitable interventions and decision making.

Patients with advanced head and neck squamous cell carcinoma (HNSCC) associated with human papillomavirus (HPV) demonstrate favorable clinical outcomes compared to patients bearing HPV-negative HNSCC. We sought to characterize the association between HPV status and mutational profiles among patients served by the Veterans Health Administration (VHA).We performed a retrospective analysis of all Veterans with primary HNSCC tumors who underwent next-generation sequencing (NGS) through the VHA's National Precision Oncology Program between July 2016 and February 2019. HPV status was determined by clinical pathology reports of p16 immunohistochemical staining; gene variant pathogenicity was classified using OncoKB, an online precision oncology knowledge database, and mutation frequencies were compared using Fisher's exact test.A total of 79 patients met inclusion criteria, of which 48 (60.8%) had p16-positive tumors. Patients with p16-negative HNSCC were more likely to have mutations in TP53 (p0.0001), and a trend towards increased mutation frequency was observed within NOTCH1 (p = 0.032) and within the composite CDK/Rb pathway (p = 0.065). Mutations in KRAS, NRAS, HRAS, and FBXW7 were exclusively identified within p16-positive tumors, and a trend towards increased frequency was observed within the PI3K pathway (p = 0.051). No difference in overall mutational burden was observed between the two groups.In accordance with the previous studies, no clear molecular basis for improved prognosis among patients harboring HPV-positive disease has been elucidated. Though no targeted therapies are approved based upon HPV-status, current efforts to trial PI3K inhibitors and mTOR inhibitors across patients with HPV-positive disease bear genomic rationale based upon the current findings.

PURPOSE: Next-generation sequencing (NGS) gene panels are frequently completed for patients with advanced non–small-cell lung cancer (NSCLC). Patients with highly actionable gene variants have improved outcomes and reduced toxicities with the use of corresponding targeted agents. We sought to identify barriers to targeted agent use within the Veterans Health Affairs' National Precision Oncology Program (NPOP). METHODS: A retrospective evaluation of patients with NSCLC who underwent NGS multigene panels through NPOP between July 2015 and February 2019 was conducted. Patients who were assigned level 1 or 2A evidence for oncogenic gene variants by an artificial intelligence offering (IBM Watson for Genomics [WfG]) and NPOP staff were selected. Antineoplastic drug prescriptions and provider notes were reviewed. Reasons for withholding targeted treatments were categorized. RESULTS: Of 1,749 patients with NSCLC who successfully underwent NGS gene panel testing, 112 (6.4%) patients were assigned level 1 and/or 2A evidence for available targeted treatments by WfG and NPOP staff. All highly actionable gene variants were within ALK, BRAF, EGFR, ERBB2, MET, RET, and ROS1. Of these, 36 (32.1%) patients were not prescribed targeted agents. The three most common reasons were (1) patient did not carry a diagnosis of metastatic disease (33.3%), (2) treating provider did not comment on the NGS results (25.0%), and (3) provider felt that patient could not tolerate therapy (19.4%). No patients were denied access to level 1 or 2A targeted drugs because of rejection of a nonformulary drug request. CONCLUSION: A substantial minority of patients with NSCLC bearing highly actionable gene variants are not prescribed targeted agents. Further provider- and pathologist-directed educational efforts and implementation of health informatics systems to provide real-time decision support for test ordering and interpretation are needed.

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, employed a social care management solution (SCMS) to render effective, efficient, and tailored services to meet TAY’s needs. OBJECTIVE This study’s purpose was to investigate the impact of a SCMS, IBM Watson Care Manager (WCM)® in transforming foster program service delivery and improve TAY outcomes. METHODS A mixed-methods study was employed by collecting primary data from stakeholders through semi-structured interviews in 2021 and pulling secondary data from various resources including annual reports, system usage logs, and data repositories from 2014 to 2021. Thematic analysis based on grounded theory was employed to analyze qualitative data using NVivo® software. Descriptive analysis of aggregated outcome metrics in the quantitative data was performed and compared across two time periods: Pre-SCMS (before 10/31/2016) and Post-SCMS (11/01/2016-03/31/2021). RESULTS Six Aspiranet employees (4 leaders, 2 life coaches) were interviewed with median time of 56 (53,67) minutes. The majority were female (5,83%), over 30 years of age (median, (Q1,Q3)=37, (32,39)) with 6 (5,10) median years of experience at Aspiranet, and overall field experience of 10 (7,14) years. The majority of participants (4,67%) rated their technology skills as expert. Thematic analysis of participants’ interview transcripts resulted in 24 sub-themes that were grouped into 6 super-ordinate themes: study context, 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 such as cross-team communications, intuitive dashboard, and advanced search engine. Median satisfaction scores around Pre- and Post-WCM workflow processes remained constant between two life coaches (3.25 (2.5, 4)); however, among leaders, Post-WCM scores (4 (4, 5)) were higher than Pre-WCM (3 (3, 3)) scores. Across the two study phases, Aspiranet served 1,641 TAY having consistent population demographics: median age of 18 years (18,19); female (903,55%); race/ethnicity (Hispanic/Latino (621,38%), Black (470,29%), White (397,24%), and Other (153,9%)). Between Pre- and Post-WCM, there was an increase in full-time (359(68%) to 833(75%)) and a reduction in part-time (61(11%) to 91(8%)) school enrollment. The median number of days spent in the foster care program remained the same (247 (125, 468)), however, the number of incidents monthly reported 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 for even better alignment with user and youth needs.

Disparities in cardiovascular disease (CVD) and associated health and healthcare delivery outcomes have been partially attributed to differential risk factors, and to prevention and treatment inequities within racial and ethnic (including language) minority groups and low socioeconomic status (SES) populations in urban and rural settings. Digital health interventions (DHIs) show promise in promoting equitable access to high-quality care, optimal utilization, and improved outcomes; however, their potential role and impact has not been fully explored. The role of DHIs to mitigate drivers of the health disparities listed above in populations disproportionately affected by atherosclerotic-related CVD was systematically reviewed using published literature (January 2008–July 2020) from multiple databases. Study design, type and description of the technology, health disparities information, type of CVD, outcomes, and notable barriers and innovations associated with the technology utilized were abstracted. Study quality was assessed using the Oxford Levels of Evidence. Included studies described digital health technologies in a disparity population with CVD and reported outcomes. DHIs significantly improved health (eg, clinical, intermediate, and patient-reported) and healthcare delivery (eg, access, quality, and utilization of care) outcomes in populations disproportionately affected by CVD in 24 of 38 included studies identified from 2104 citations. Hypertension control was the most frequently improved clinical outcome. Telemedicine, mobile health, and clinical decision support systems were the most common types of DHIs identified. DHIs improved CVD-related health and healthcare delivery outcomes in racial/ethnic groups and low SES populations in both rural and urban geographies globally.

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013–2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users’ health behaviors., Digital health technologies can effectively reach individuals within the context of their daily lives to improve health behaviors.

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less-common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

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.

OBJECTIVE: Identify how novel datasets and digital health technology, including both analytics-based and artificial intelligence (AI)-based tools, can be used to assess non-clinical, social determinants of health (SDoH) for population health improvement. METHODS: A state-of-the-art literature review with systematic methods was performed on MEDLINE, Embase, and the Cochrane Library databases and the grey literature to identify recently published articles (2013-2018) for evidence-based qualitative synthesis. Following single review of titles and abstracts, two independent reviewers assessed eligibility of full-texts using predefined criteria and extracted data into predefined templates. RESULTS: The search yielded 2,714 unique database records of which 65 met inclusion criteria. Most studies were conducted retrospectively in a United States community setting. Identity, behavioral, and economic factors were frequently identified social determinants, due to reliance on administrative data. Three main themes were identified: 1) improve access to data and technology with policy – advance the standardization and interoperability of data, and expand consumer access to digital health technologies; 2) leverage data aggregation – enrich SDoH insights using multiple data sources, and use analytics-based and AI-based methods to aggregate data; and 3) use analytics-based and AI-based methods to assess and address SDoH – retrieve SDoH in unstructured and structured data, and provide contextual care management sights and community-level interventions. CONCLUSIONS: If multiple datasets and advanced analytical technologies can be effectively integrated, and consumers have access to and literacy of technology, more SDoH insights can be identified and targeted to improve public health. This study identified examples of AI-based use cases in public health informatics, and this literature is very limited.

Introduction: Clinical models of cardiovascular disease (CVD) that incorporate relevant predictors can provide powerful insights into the complex, inter-related mechanisms of CVD pathophysiology, risk for adverse outcomes, and correlations with social determinants of health (SDoH) to inform practice guidelines and targeted interventions. Despite the growing body of evidence demonstrating the role of race, ethnicity and SDoH on CVD outcomes, limited evidence exists about incorporating these factors into models. Hypothesis: We assessed the hypotheses that clinical predictive studies on CVD risk, outcomes, prognosis and treatment interventions: (1) do not fully examine race, ethnicity and SDoH factors, and (2) may not leverage adequate data sources fully representative of a population of interest or may lack the decisional context to ensure robustness. These are critical limitations that could lead to inaccurate predictions, perpetuating bias and ultimately health and racial inequities. Methods: This targeted literature review yielded 533 potentially relevant PubMed articles (2018-2019) after duplicate removal. Article titles and abstracts were reviewed for eligibility where 375 met the exclusion criteria (age Results: Studies employed advanced statistical and machine learning methods to predict CVD risk (10/29%), mortality (19/54%), survival (7/20%), complication (10/29%), disease progression (6/17%), functional outcomes (4/11%) and disposition (2/6%). Most studies incorporated sex (34/97%), co-morbid conditions (32/91%), and behavioral risk factor (28/80%) variables. Race/ethnicity (23/66%) and SDoH variables (e.g. education (3/9%)) were less frequently observed. Conclusions: In conclusion, clinical risk and predictive models often do not adjust for or consider race, ethnicity and SDoH to improve model accuracy to inform more equitable interventions and decision-making.

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.

Objective: To develop a conceptual model and novel, comprehensive framework that encompass the myriad ways informatics and technology can support public health response to a pandemic. Method: The conceptual model and framework categorize informatics solutions that could be used by stakeholders (e.g., government, academic institutions, healthcare providers and payers, life science companies, employers, citizens) to address public health challenges across the prepare, respond, and recover phases of a pandemic, building on existing models for public health operations and response. Results: Mapping existing solutions, technology assets, and ideas to the framework helped identify public health informatics solution requirements and gaps in responding to COVID-19 in areas such as applied science, epidemiology, communications, and business continuity. Two examples of technologies used in COVID-19 illustrate novel applications of informatics encompassed by the framework. First, we examine a hub from The Weather Channel, which provides COVID-19 data via interactive maps, trend graphs, and details on case data to individuals and businesses. Second, we examine IBM Watson Assistant for Citizens, an AI-powered virtual agent implemented by healthcare providers and payers, government agencies, and employers to provide information about COVID-19 via digital and telephone-based interaction. Discussion: Early results from these novel informatics solutions have been positive, showing high levels of engagement and added value across stakeholders. Conclusion: The framework supports development, application, and evaluation of informatics approaches and technologies in support of public health preparedness, response, and recovery during a pandemic. Effective solutions are critical to success in recovery from COVID-19 and future pandemics.

COVID-19 caused by SARS-CoV-2 was first identified in Japan on January 15th, 2020, soon after the pandemic originated in Wuhan, China. Subsequently, Japan experienced three distinct waves of the outbreak in the span of a year and has been attributed to new exogenous strains and evolving existing strains. Japan engaged very early on in tracking different COVID-19 strains and have sequenced approximately 5% of all confirmed cases. While Japan has enforced stringent airport surveillance on cross-border travelers and returnees, some carriers appear to have advanced through the quarantine stations undetected. In this study 30493 genomes sampled in Japan were analyzed to understand the strains, heterogeneity and temporal evolution of different SARS-CoV-2 strains. We identified 12 discrete strains with a substantial number of cases with most strains possessing the spike (S) D614G and nucleocapsid (N) 203_204delinsKR mutations. 155 distinct strains have been introduced into Japan and 39 of them were introduced after strict quarantine policy was implemented. In particular, the B.1.1.7 strain, that emerged in the United Kingdom (UK) in September 2020, has been circulating in Japan since late 2020 after eluding cross-border quarantine stations. Similarly, the B.1.351 strain dubbed the South African variant, P.1 Brazilian strain and R.1 strain with the spike E484K mutation have been detected in Japan. At least 14 exogenous B.1.1.7 sub-strains have been independently introduced in Japan as of late March 2021, and these strains carry mutations that give selective advantage including N501Y, H69_V70del, and E484K that confer increased transmissibility, reduced efficacy to vaccines and possible increased virulence. Furthermore, various strains, which harbor multiple variants in the PCR primers and the probe developed by National Institute of Infectious Disease (NIID), are emerging. It is imperative that the quarantine policy be revised, cross-border surveillance reinforced, and new public health measures implemented to mitigate further transmission of this deadly disease and to identify strains that may engender resistance to vaccines.

In personalized cancer genomic medicine, characterizing a patient’s molecular profile based on comprehensive information is important for maximizing treatment benefits. However, current cancer genome analysis is centered on single nucleotide variation (SNV), gene expression, and copy number variation (CNV) but places little emphasis on structural variations (SV) beside fusions. To date, investigation of SVs has been limited because SV analysis entails a cumbersome annotation process. This study describes the design, development, and implementation of an annotation tool for SV, termed SVAnnotator. Detailed annotation was performed on the results of SV detection of 2,781 whole genome samples from the ICGC/TCGA PanCancer Analysis of Whole Genomes (PCAWG) with identifications of fusion, exon skipping, gene disruption, and tandem duplication SVs. These annotations of SVs will facilitate understanding of molecular events and further enhance utilities of precision medicine in stratifications, pathogenicity assessments and drug responses. Frequent novel SV events in MACROD2, FHIT, WWOX and CCSER1 were observed across many cancers. Importantly, SV events were frequently identified in well-established tumor suppressor genes including RB1, NF1, PTEN and TP53. As such, it is plausible that potential therapeutic opportunities are overlooked when SV analysis is not appropriately performed. Given the frequency of SVs detected in our study, SVanalysis with detailed annotation should be a routine part of comprehensive precision medicine analysis, and further studies are warranted to enhance clinical benefits as well as our understanding of uncharacterized SV events.

Background Care-management tools are typically utilized for chronic disease management. Sonoma County government agencies employed advanced health information technologies, artificial intelligence (AI), and interagency process improvements to help transform health and health care for socially disadvantaged groups and other displaced individuals. Objectives The objective of this case report is to describe how an integrated data hub and care-management solution streamlined care coordination of government services during a time of community-wide crisis. Methods This innovative application of care-management tools created a bridge between social and clinical determinants of health and used a three-step approach—access, collaboration, and innovation. The program Accessing Coordinated Care to Empower Self Sufficiency Sonoma was established to identify and match the most vulnerable residents with services to improve their well-being. Sonoma County created an Interdepartmental Multidisciplinary Team to deploy coordinated cross-departmental services (e.g., health and human services, housing services, probation) to support individuals experiencing housing insecurity. Implementation of a data integration hub (DIH) and care management and coordination system (CMCS) enabled integration of siloed data and services into a unified view of citizen status, identification of clinical and social determinants of health from structured and unstructured sources, and algorithms to match clients across systems. Results The integrated toolset helped 77 at-risk individuals in crisis through coordinated care plans and access to services in a time of need. Two case examples illustrate the specific care and services provided individuals with complex needs after the 2017 Sonoma County wildfires. Conclusion Unique application of a care-management solution transformed health and health care for individuals fleeing from their homes and socially disadvantaged groups displaced by the Sonoma County wildfires. Future directions include expanding the DIH and CMCS to neighboring counties to coordinate care regionally. Such solutions might enable innovative care-management solutions across a variety of public, private, and nonprofit services.

Background Social programs are services provided by governments, nonprofits, and other organizations to help improve the health and well-being of individuals, families, and communities. Social programs aim to deliver services effectively and efficiently, but they are challenged by information silos, limited resources, and the need to deliver frequently changing mandated benefits. Objective We aim to explore how an information system designed for social programs helps deliver services effectively and efficiently across diverse programs. Methods This viewpoint describes the configurable and modular architecture of Social Program Management (SPM), a system to support efficient and effective delivery of services through a wide range of social programs and lessons learned from implementing SPM across diverse settings. We explored usage data to inform the engagement and impact of SPM on the efficient and effective delivery of services. Results The features and functionalities of SPM seem to support the goals of social programs. We found that SPM provides fundamental management processes and configurable program-specific components to support social program administration; has been used by more than 280,000 caseworkers serving more than 30 million people in 13 countries; contains features designed to meet specific user requirements; supports secure information sharing and collaboration through data standardization and aggregation; and offers configurability and flexibility, which are important for digital transformation and organizational change. Conclusions SPM is a user-centered, configurable, and flexible system for managing social program workflows.

BackgroundTo 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 methodsUsing 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.ResultsOf 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.ConclusionMedical 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.

To assess the impact of organization-directed workplace interventions on physician burnout, including stress or job satisfaction in all settings, we conducted a systematic review of the literature published from January 1, 2007, to October 3, 2018, from multiple databases. Manual searches of grey literature and bibliographies were also performed. Of the 633 identified citations, 50 met inclusion criteria. Four unique categories of organization-directed workplace interventions were identified. Teamwork involved initiatives to incorporate scribes or medical assistants into electronic health record (EHR) processes, expand team responsibilities, and improve communication among physicians. Time studies evaluated the impact of schedule adjustments, duty hour restrictions, and time-banking initiatives. Transitions referred to workflow changes such as process improvement initiatives or policy changes within the organization. Technology related to the implementation or improvement of EHRs. Of the 50 included studies, 35 (70.0%) reported interventions that successfully improved the 3 measures of physician burnout, job satisfaction, and/or stress. The largest benefits resulted from interventions that improved processes, promoted team-based care, and incorporated the use of scribes/medical assistants to complete EHR documentation and tasks. Implementation of EHR interventions to improve clinical workflows worsened burnout, but EHR improvements had positive effects. Time interventions had mixed effects on burnout. The results of our study suggest that organization-directed workplace interventions that improve processes, optimize EHRs, reduce clerical burden by the use of scribes, and implement team-based care can lessen physician burnout. Benefits of process changes can enhance physician resiliency, augment care provided by the team, and optimize the coordination and communication of patient care and health information.

Background: Genomic sequencing of tumor samples is often considered for patients diagnosed with metastatic malignancies. In July 2016, the Veterans Health Administration (VHA) created the VA National Precision Oncology Program (NPOP) to offer next generation sequencing (NGS) multigene panels for veterans with advanced solid tumors. We sought to assess the perceptions of NPOP among medical oncologists across VHA. Methods: Semi-structured interviews were designed to evaluate the following concepts: expectations for NGS testing, required workflow to conduct testing, applicability of testing results, and summative views of genomic sequencing. VHA medical oncologists who had previously sent at least one sample for testing through NPOP were solicited to participate for an in-person or telephonic conversation. Interviews were analyzed by an inductive narrative approach to code responses, which was then followed by thematic analysis for key findings that emerged. Results: 17 medical oncologists were interviewed from 16 different VA medical centers (VAMCs) in 12 states. 16 (94.1%) oncologists reported sending at least 5 samples for NGS testing; 4 (23.5%) oncologists practiced at VAMCs that sent over 100 samples. Clinicians collectively expected that testing would determine all clinically relevant genomic alterations in a reasonable time. Testing was expedited for oncologists who maintained a collaborative relationship with their local pathologists and proceduralists. 8 (47.1%) oncologists felt that testing reports should provide greater insight into the clinical significance of uncommon gene variants. 6 (35.3%) respondents expressed that educational efforts are warranted to describe optimal sample processing, indications for testing, and/or relevance of rare mutations. Twelve (70.6%) respondents felt strongly that NGS testing would improve outcomes for their patients, while 3 (17.6%) oncologists were wary that the current number of actionable mutations is too limited to offer widespread benefit. Conclusions: VHA medical oncologists opined that NGS testing through VA NPOP improved outcomes. The testing process is expedited with multidisciplinary involvement. Designed approaches to semi-algorithmically report testing results may improve efficiency of clinical decision-making. More education is warranted to detail the procedural requirements to conduct testing, indications for test ordering, and interpretation of results.

This work presents the first segmentation study of both diseased and healthy skin in standard camera photographs from a clinical environment. Challenges arise from varied lighting conditions, skin types, backgrounds, and pathological states. For study, 400 clinical photographs (with skin segmentation masks) representing various pathological states of skin are retrospectively collected from a primary care network. 100 images are used for training and fine-tuning, and 300 are used for evaluation. This distribution between training and test partitions is chosen to reflect the difficulty in amassing large quantities of labeled data in this domain. A deep learning approach is used, and 3 public segmentation datasets of healthy skin are collected to study the potential benefits of pre-training. Two variants of U-Net are evaluated: U-Net and Dense Residual U-Net. We find that Dense Residual U-Nets have a 7.8% improvement in Jaccard, compared to classical U-Net architectures (0.55 vs. 0.51 Jaccard), for direct transfer, where fine-tuning data is not utilized. However, U-Net outperforms Dense Residual U-Net for both direct training (0.83 vs. 0.80) and fine-tuning (0.89 vs. 0.88). The stark performance improvement with fine-tuning compared to direct transfer and direct training emphasizes both the need for adequate representative data of diseased skin, and the utility of other publicly available data sources for this task., Accepted to IEEE EMBC 2018

Breast cancer has a high annual incidence among females in India, with an age adjusted rate of 25.8 cases per 100,000 women and a mortality rate of 12.7 per 100,000 women1. Approximately 75% of breast cancers are estrogen receptor positive (ER+), and unfortunately, a significant number will develop resistance to first line hormonal therapy. Although several mechanisms have been linked to hormonal therapy resistance, no biomarker in clinical use currently exists to predict hormonal therapy resistance in a reliable manner. Comprehensive genomic profiling (CGP) is not routinely performed for metastatic breast carcinoma (MBC) in a relapsed setting in India. This research highlights utility of CGP on a MBC sample combined with interpretation by Watson™ for Genomics (WfG), an artificial-intelligence (AI)-based decision-support system (DSS), when a patient in a middle-income country developed resistance to endocrine therapy. A 33-year-old Indian female was diagnosed with ER+, progesterone-receptor-negative (PR-), human-epidermal-receptor-2-negative (HER2-) breast cancer. Two years later, she noted an axillary mass which was biopsied and revealed an ER+, PR-, HER2- MBC metastasis. CGP was performed with a panel from Illumina designed to detect genomic alterations in 170 genes, including 148 genes for substitutions and indels, 55 genes for fusion and splice variants, and 59 genes for copy number variation detection. Variant annotation and interpretation performed using WfG revealed co-existing ESR1 D538G mutation and ESR1-CCDC170 fusion. In addition, alterations in CCND1 resulting in activation of the CDK signaling pathway, an oncogenic mutation in MTOR resulting in activation of the MTOR/PI3K signaling pathway, and amplification of FGFR1, a poor prognostic factor in MBC, were also identified. While ESR1 D538 and ESR1-CCDC170 fusion individually have been reported to confer resistance to endocrine therapy, to the best of our knowledge, our report is the first HR+ MBC case with these two oncogenic alterations co-occurring in a metastatic tumor suggesting a synergistic role. CGP in this patient revealed a possible mechanism of resistance to endocrine therapy informing the physician to change treatment to a generic mTOR inhibitor based on the oncogenic MTOR mutation. In conclusion, assessment of DNA/RNA tumor variants in one assay along with a robust AI-based DSS can provide relevant information for consideration by the clinician when developing a treatment plan, may create efficiencies in tumor sample usage, time, and cost of treatment. Further research is needed to explore the functional synergy of the oncogenic ESR1 mutation and fusion detected in this patient's tumor and the role of identifying these pathogenic variants through circulating tumor cell or cell-free DNA assays to detect minimal residual disease. Citation Format: Ravindra Kolhe, Ashis Mondal, Nikhil Sahajpal, Meenakshi Ahluwalia, Yull Arriaga, Allan Njau, David Brotman, Jane Snowdon, Gretchen Jackson, Dilhan Weeraratne. Co-existing ESR1 D538G mutation and ESR1-CCDC170 fusion in a patient with metastatic hormone receptor positive breast cancer with progression after hormonal therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4105.

Next-generation sequencing (NGS) is increasingly used to inform diagnostic, therapeutic, and prognostic decisions in AML at the time of first presentation. We highlight the utility of NGS combined with Watson™ for Genomics (WfG), an artificial-intelligence-based decision-support system, in identifying new clinically actionable alterations as a result of clonal evolution in the relapsed disease setting. In less than 3 minutes, WfG identified an IDH1 R132H pathogenic mutation in the relapsed sample sequenced with the Illumina TruSight Tumor 170-gene panel leading to the compassionate use of ivosidenib. In addition, mutations in two genes resulting in increased sensitivity to PARP inhibitors and mutations in PTEN resulting in activation of the MTOR/PI3K signaling pathway were detected by WfG. In February 2018, a previously healthy 23-year old Caucasian female presented with AML consisting of 80% blasts with positive FLT3 mutation. She received induction cytarabine plus daunorubicin (7+3) followed by multikinase inhibitor therapy with midostaurin on days 8 to 21. A 28-day bone marrow biopsy showed persistent disease with 40% blasts. In March 2018, the patient underwent re-induction chemotherapy with mitoxantrone, etoposide, and cytarabine (MEC) followed by midostaurin on days 8 to 21. A bone marrow biopsy after completion of re-induction therapy showed complete remission with < 5% blasts (CR1). In May 2018, a matched donor was not found on a bone marrow registry, and the patient underwent a post-remission dual unrelated umbilical cord blood (UCB) hematopoietic stem cell transplantation (HSCT). Engraftment was not achieved. In July 2018, the patient had disease relapse with the presence of circulating blasts and 7% of blasts in the bone marrow. While NGS is typically not performed on relapsed samples, WfG identified IDH1 R132H and PTEN C78T pathogenic mutations using the 170-gene panel. Based on these results, the patient initiated azacytidine plus the IDH1 inhibitor ivosidenib. In August 2018, a bone marrow biopsy showed less than 5% blasts positive for a FLT3-ITD mutation. In September 2018, the patient underwent reduced-intensity conditioning with fludarabine, cyclophosphamide, and total body irradiation followed by haploidentical allogeneic HSCT from her mother. Maintenance therapy with azacytidine plus ivosidenib was continued until the present time. Currently, the patient is in remission for over 15 months without evidence of AML minimal residual disease. She has developed mild skin chronic graft-versus-host disease that is controlled with standard treatment. She works on a full-time basis and has excellent functional status. The combination of CGP, artificial intelligence, and expert care has resulted in an excellent outcome in a patient with relapsed AML. In conclusion, our experience suggests that CGP testing should be considered at different time points, at least in the relapsed setting, to help treating physicians alter or help improve clinical outcome. CGP testing in a relapsed setting is precluded because it is not covered by payers. In support of optimal care, we have initiated a new program for compassionate use of genomic testing, where such testing is medically necessary, but not covered by insurance or payer supported. Citation Format: Ravindra Kolhe, Ashis Mondal, Vamsi Kota, Nkhil Sahajpal, Meenakshi Ahluwalia, Allan Njau, Dilhan Weeraratne, Yull Arriaga, David Brotman, Gretchen Jackson, Jane Snowdon. Clinical utility of comprehensive genomic testing with artificial-intelligence-based analysis to identify targetable sub-clonal events in relapsed acute myeloid leukemia (AML) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6579.

e14051 Background: Adoption of next-generation sequencing (NGS) technology in routine clinical practice has enabled the detection of genetic aberrations such as single nucleotide variants, copy number alterations, and gene fusions. Pathway and network analyses (PNA) are key components for evaluation of NGS data in a clinical setting to explain findings involving thousands of altered genes and proteins with a smaller and more interpretable set of altered processes. Though PNA have been applied to identify driver genes and pathways in cohort-based analyses, its application in precision oncology remains unexplored. We investigate the potential utility of the Watson for Genomics (WfG) pathway analyses tool in interpreting complex and multiple genomic alterations in individual cancers. Methods: DNA and RNA isolated from 70 patient tumors across 30 different cancer types were processed with Illumina’s TST170 NGS platform. WfG’s feature of pathway analyses was used to identify gene variants, signaling pathways, networks, and the drugs targeting these alterations based on evidence in the clinical literature and FDA drug databases. Results: Analyses defined 5 different pathway/network models: 1) downstream therapeutic targets, 2) synthetic lethality, 3) combinatorial downstream targets + synthetic lethality, 4) two or more pathways converging to downstream targets, and 5) complex profile analyses. The five PNA models are illustrated by the following unique cases. 1) A thyroid cancer case with HRAS variant and activated RAF1 downstream pathway showed MAPK1/3 were suggestive of relevant targets. 2) An acute myeloid leukemia case with BRCA1, BRCA2 and PTEN variants, targeting a common synthetic lethal partner PARP1 was ideal for therapy. 3) A penile carcinoma case with BRAF, CDKN2A and TP53 variants, targeting the BRAF downstream pathway in combination with either CDKN2A or TP53 were the likely choice for therapy. 4) A glioma case with activated PI3K and MEK downstream pathway, targeting a common downstream marker would block both pathways. 5) A breast carcinoma case with a complex pathogenic variant profile provided relevant clinical information and levels of evidence for multiple drug targets. Conclusions: We discovered that the integrated WfG pathway analyses tool is ideal for visualization of the variants with levels of evidence from clinical literature and FDA drug databases that can help inform treatment options and provides a holistic understanding of a specific tumor profile allowing the treating clinician to select personalized targeted therapy.

2005 Background: Next-Generation Sequencing (NGS) gene panels are often completed to guide therapeutic decisions for patients with advanced stage non-small cell lung cancer (NSCLC). Patients with highly-actionable gene variants may experience improved therapeutic treatments and reduced toxicities with use of targeted agents. Ensuring appropriate prescription of targeted therapies is therefore of high importance. We sought to identify barriers to targeted agent use within the Veterans Health Affairs’ (VHA) National Precision Oncology Program (NPOP). Methods: A retrospective evaluation examined the cohort of NSCLC patients who underwent NGS multi-gene panels through NPOP between July 2015 and February 2019. A level of evidence for drug actionability was assigned to each observed oncogenic gene variant using an artificial intelligence offering (IBM Watson for Genomics: WfG). WfG level 1 and 2A evidence was reviewed by NPOP staff to exclude gene variants that did not conform to NPOP level 1 and 2A definitions. Anti-neoplastic drug prescriptions and oncology provider notes were obtained for all included patients from the VHA Corporate Data Warehouse. Review of clinical notes of patients who did not receive targeted agents was performed to categorize the reason(s). Results: Of 1764 NSCLC patients who successfully underwent NGS gene panel testing, 156 (8.9%) received therapeutic level 1 (7.3%) or 2A (1.6%) options for targeted agents based on WfG evidence analysis. In total, 117 (6.6%) patients had NPOP level 1 and 2A gene variants, all within ALK, BRAF, EGFR, ERBB2, MET, and RET. Of these, 49 (41.2%) patients were not prescribed available targeted agents. The three most common reasons were: (1) treating provider did not comment on NGS results (30.7%), (2) patient did not carry a diagnosis of advanced stage disease (18.4%), and (3) patient had begun an alternative systemic therapy prior to completion of sequencing (16.3%). No patient was denied access to a level 1 or 2A targeted drug due to utilization-management review. Conclusions: A substantial minority of patients with advanced NSCLC bearing highly-actionable gene variants are not prescribed available targeted agents. Further provider- and pathologist-directed educational effort are needed, as well as implementation of health informatics systems to provide near real-time decision support for test ordering and interpretation.

An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms “diabetes” and “artificial intelligence.” The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers.

e13023 Background: Next generation sequencing (NGS) in hematological tumors is increasingly shaping clinical treatment decisions at the point of care. While the impact of NGS panels in solid tumors is largely therapeutic, targeted sequencing in hematological tumors can additionally provide diagnostic and prognostic insights. Additional data generated in hematological tumor sequencing makes manual interpretation and annotation of variants tedious and non-scalable. In this study we compared hematological tumor variant interpretation using an artificial intelligence decision-support system, Watsonä for Genomics (WfG), with expert guided manual curation. Methods: Patients with hematological tumors at Hallym University, College of Medicine between December 2017 and December 2018, were sequenced using the 54 gene Illumina TruSight Myeloid Panel. WfG interpreted and annotated all patients’ sequencing results, a subset of which were assessed manually to ascertain concordance. Results: 54 South Korean patients with hematological malignancies were analyzed (23 Acute Myeloid Leukemia, 12 myeloproliferative neoplasm, 5 myelodysplastic syndrome, 5 multiple myeloma and 9 others). Comparison of manual and WfG interpretation of 10 randomly selected cases yielded 90% (9/10) concordance and identification of 9 clinically actionable variants (33%) not found in manual interpretation. In total, WfG identified that 71% (38/54) of all cases had at least one clinically actionable therapeutic alteration (a variant targeted by a US FDA approved drug, off-label drug, or clinical trial). 33% (18/54) of cases had genes that were targeted by a US FDA approved therapy including JAK2, IDH1, IDH2, and FLT3. In cases without therapeutic alterations, WfG identified diagnostic or prognostic insights in an additional 20% (11/54) of patients. 9% (5/54) had no clinically actionable information. Conclusions: WfG variant interpretation correlated well with manually curated expert opinion and identified clinically actionable insights missed by manual interpretation. WfG has obviated the need for labor-intensive manual curation of clinical trials and therapy, enabling our center to exponentially scale our NGS operations.

e14300 Background: Acral and mucosal melanoma are rare subtypes accounting for about 3% of all melanoma cases. The cutaneous melanoma genomic landscape is well defined; however, little is known about the acral and mucosal melanoma mutational spectrum. In this pilot study, we evaluated the genomic and neo-antigen profiles and tumor mutational burden (TMB) from acral and mucosal melanoma patients with the aim of designing personalized vaccines and longitudinally tracking patients’ clinical courses. Methods: Tumor whole exome sequencing and neo-antigen profiling of 5 acral and 3 mucosal melanoma patients at Shanghai Tenth Peoples Hospital, Tongji University, China between April 2018 and January 2019 was performed using YuceBio’s proprietary analytics platform. Watsonä for Genomics, an artificial intelligence decision-support system, was used for variant interpretation and annotation. A comparative analysis was performed on Chinese acral melanoma data with the published Caucasian acral cohort from the Translational Genomics Research Institute (TGen) and The Cancer Genome Atlas (TCGA) predominantly Caucasian cutaneous melanoma data set. Results: TMB in our acral/mucosal melanoma cohort was 2.26/Megabase (Mb) compared to over 20/Mb in published cutaneous melanoma studies. Tumor neo-antigen burden (TNAB) in our group was 1.03 neo-epitopes/Mb. Low TNAB levels were associated with low TMB levels in all tumors. Incidence of BRAF and NRAS mutant cases in our cohort was 0% (0/8) and 13% (1/8) respectively compared to 19% (5/27) and 7% (2/27) of the Caucasian acral population in the TGen dataset. Incidence of BRAF and NRAS mutations in the TCGA cutaneous melanoma dataset was 54% (237/440) and 28% (125/440), respectively. Conclusions: TMB was significantly lower in acral/mucosal than in cutaneous melanoma and may be a surrogate for TNAB. Detection of BRAF and NRAS mutations, the two most prevalent driver mutations in cutaneous melanoma, were significantly lower frequencies in both Chinese and Caucasian acral melanoma patients in this study, suggesting alternate cancer drivers may exist in this subtype. Strategies to address challenges of low TNAB in vaccine development are being explored.

e13025 Background: Next generation sequencing (NGS) studies on various cancer types have revealed clinical insights on the molecular etiology of tumors and defined new treatment paradigms. However, the genomic landscape of a tumor type may be predicated on race. Application of broad-brush findings from one population to another may lead to erroneous treatment decisions. While the majority of published NGS cancer studies focus on Caucasian populations, few studies evaluate the molecular profiles of a tumor type between different demographies. In this study, acute myeloid leukemia (AML) patients of East Asian descent were sequenced at a South Korean hospital, interpreted, and compared with the Caucasian population from The Cancer Genome Atlas (TCGA). Methods: 23 South Korean AML patients were sequenced in 2018 using the 54 gene Illumina TruSight Myeloid Panel at Hallym University, College of Medicine. Orthogonal testing for FLT3-internal tandem duplication (ITD) was done by Sanger sequencing. Watsonä for Genomics (WfG), an artificial intelligence decision-support system, was used for variant interpretation and annotation. Additionally, 181 AML patients of Caucasian descent from the TCGA dataset were analyzed for comparison. Results: WfG identified at least 1 clinically actionable therapeutic alteration in 70% (16/23) of all Asian cohort cases. FLT3-ITD or tyrosine kinase domain (TKD) mutations were reported in 27% (49/181) of cases in the TCGA cohort but only 9% (2/23) of the Asian cohort. DNMT3A mutations were detected in 25% (45/181) and 74% (17/23) of the TCGA and Asian cohorts, respectively. Other oncogenic mutations in AML including NRAS, IDH1, IDH2, CEBPA, TET2, and NPM1 were represented at comparable frequencies between the two populations. Conclusions: Tumor biomarkers that affect prognosis can be informative in the clinical setting. In our study, FLT3 and DNMT3A, predictors of poor prognosis in AML, demonstrated decreased and increased respective frequencies in South Koreans compared to Caucasians, suggesting that some mutational signatures that predict cancer outcome may vary by race.

Reducing energy consumption, improving energy efficiency, and reducing greenhouse gas (GHG) emissions are among the most important initiatives in today's world. Occupied buildings consume a substantial amount of energy, mounting to about 40% of overall energy consumption in most countries. The majority of the world's population either lives or works in buildings; therefore, everybody can contribute in reducing energy consumption, controlling GHG emissions, and mitigating climate change and its potential impact. We developed an analytical tool that can assist building owners, facility managers, operators, and tenants of buildings in assessing, benchmarking, diagnosing, tracking, forecasting, simulating, and optimizing energy consumption in building portfolios. Furthermore, for greater dissemination, we have made this analytic service available on demand in a flexible cloud environment. Cloud is an efficient and effective medium to provide building energy analytics capability to various functions and people in a variety of roles in buildings without investing a substantial amount of money in hardware, software, and information technology infrastructure. We present results of the building energy analytics developed for K–12 public school buildings and a commercial office building complex.

Smarter electrical power systems—which include the addition of local intelligence in transmission and distribution substations, intelligent electrical devices, and computerized control systems in generating plants—collectively provide additional resilience in the electricity production and consumption chain. As part of national critical infrastructure, this set of computing and network capabilities requires careful thought regarding cybersecurity as part of the overall system architecture and development. This paper provides a summary of current security engineering in the power system. Further, it addresses recent project developments from IBM on security of the electrical infrastructure. The real-time and distributed nature of the power grid is unique in its breadth of potential security issues. Actions from the U.S. Federal Government, beginning in 1996 and more recently in 2012, to identify the energy industry as the most popular threat target and thus encourage appropriate security practices testify to the importance of grid security. The European Union has reacted similarly. In this paper, we also provide a discussion of the resilience of the grid from a reliability perspective, as well as maintaining the physical security of electrical grid equipment.

This paper has two primary purposes: to motivate the need for machine learning in scheduling systems and to survey work on machine learning in scheduling. In order to motivate the need for machine learning in scheduling, we briefly motivate the need for systems employing artificial intelligence methods for scheduling. This leads to a need for incorporating adaptive methods-learning. >

Software as a Service (SaaS) is essentially about delivering a software application as a service over the Web with a usage-based charging plan. Existing Web service subscription models do not consider the complexity of the application and the business environment; for example, multiple service elements with mutual dependencies derived from one application, or various business partnership models for reselling and cross-selling services. Therefore the design of subscription management is critical for a SaaS provider to appropriately decompose its application into service elements and then package them into service offerings for a client or business partner to subscribe according to the nature of the application and the overall business design. In this paper, we propose a pattern-based approach for the subscription management design of SaaS. A subscription model is introduced first to capture the different entities and their relationships involved in SaaS subscription. Then a method supported with service structure patterns and business interaction patterns analysis is presented to empower a SaaS provider to design an appropriate subscription model for its service offering. A case study is made to demonstrate the effectiveness of the method at the end of this paper.

In today’s competitive global business environment, pressures for reducing costs, improving profits, increasing customer satisfaction, and decreasing cycle time confront many managers. Simulation models can play an important role in increasing a business’s ability to get the right product or service to the right location at the right time. Simulation models can also help in designing and developing streamlined software applications.

Two words best characterize the future for the travel industry: growth and change. Many global forces have driven companies in this industry to adapt quickly to survive and remain competitive. Information technology plays a vital role in the way the travel industry responds as the world is “getting connected” at almost every level. The purpose of this paper is three-fold. First, the effect of network computing, the combination of electronic ticketing and smart cards, corporate travel management systems, and other trends that are transforming the travel industry will be examined. Second, the public policy issues involved such as the liberalization of government regulations, affordability and ease-of-use, and data security and privacy will be examined. Finally, the future direction of underlying technologies such as displays, storage, and microprocessors and advancements in the global networking infrastructure, mobility, and speech recognition will be presented.

For most cities, it is very challenging to both collect information from throughout a city, irrespective of agency or city function, and integrate this information into a broad decision-support environment. For example, this is particularly challenging for mature cities of the size and complexity of New York City, Tokyo, and London and for their managing agencies. Resource management for a city spans multiple domains including water and sanitation, buildings, transportation, energy, security, citizen services, and regional development. Implementing processes, policies, or interoperability features across multiple domains in a legacy city, as defined in this paper, requires significant coordination and "permission" among the agency players. Here, we propose a novel smarter-city maturity model that depicts the cross-domain and agency-to-agency interactions in response to an event (such as a water-main break). We also propose an enabling system architecture and multidomain-aware command portal that provide a detailed plan to address and optimize the management of resources for complex events. Finally, we propose a value-modeling method for quantifying the estimated cost of alternative scenarios and of selecting the best response to an event. To illustrate the approach, an example is given for a rainfall event.

Cloud computing represents the latest phase in the evolution of Internet-based computing. In this paper, we describe the fundamental building blocks of cloud computing and the initiative undertaken by the IBM Research Division in this area, which includes work on Internet-scale data centers, virtualization, scalable storage, and cloud computing services. The focus of this project has been the Research Compute Cloud, an environment for cloud computing research that is also used as a computing resource by various groups in the IBM Research Division.

Airline passenger terminal congestion caused by increasing passenger traffic results in unsatisfactory levels of customer service. We discuss a simulation modelling tool to help airlines and airports to use advanced technology to improve service to passengers. The tool consists of custom designed, reusable modules that represent the most common airline and airport system data, logic and processes. A model of an actual airline operation based on this approach is described.

e19533 Background: Therapeutic, prognostic, and diagnostic insights gained from next generation sequencing (NGS) are a key premise of genomics-informed cancer care in hematological diseases. Particularly in BCR-ABL negative myeloproliferative neoplasms (MPN), insights gained from NGS is integral for risk stratification and prognostication. In this study, MPN patients of South Korean descent were sequenced, interpreted, and compared with a published validation cohort to identify variations in mutational profiles specific to demographics. Methods: 31 South Korean MPN patients including 12 essential thrombocythemia, 6 polycythemia vera, 6 primary myelofibrosis, and 7 chronic myelogenous leukemia were sequenced in 2018 and 2019 using the 54 gene Illumina TruSight Myeloid Panel at Hallym University College of Medicine. Orthogonal testing for CALR mutations was done by Sanger sequencing. Watson for Genomics (WfG), an artificial intelligence offering was used for variant interpretation and annotation. A cohort of 151 MPN patients previously published in the New England Journal of Medicine (NEJM) was used for comparison (PMID:24325359). Results: The table shows identified actionable mutations. Conclusions: Two novel pathogenic mutations in CALR (c.1162delG and c.1100_1145del)) were identified in Korean MPN patients. NOTCH1 pathogenic mutations were exclusive while TP53 mutations were significantly enriched in the Korean cohort suggesting that these pathways may play a role in MPN. TP53 mutations in MPN are clinically significant as they have been associated with increased risk for leukemic transformation. Of note, MPL mutations were not detected in the Korean cohort. In conclusion, race and ethnicity may contribute to some mutational signatures in cancer. [Table: see text]

e13547 Background: Improved scalability and affordability of next generation sequencing (NGS) has pivoted cancer care toward genomics-driven treatment decisions. Particularly in advanced-stage or refractory cancer, clinical insights gleaned from NGS have become an integral option as these patients have typically exhausted all lines of available therapy. As precision oncology evolves, NGS is expected to have a differential impact based on the cancer type. In this study, a comprehensive NGS panel was used to determine the strength of clinical evidence in various advanced stage tumor samples from The Cancer Genome Atlas (TCGA). Methods: A hybrid capture panel, Oncofoco, was developed to evaluate SNVs, INDELs, CNVs and TMB in 366 genes. The panel’s utility was validated by interrogating a broader cohort of 2847 TCGA samples (advanced tumors with T3 or T4; or N > = 1; or M > = 1). Watsonä for Genomics, an artificial intelligence offering, was used for variant interpretation and annotation of the 366 genes. A clinical evidence classification system that evaluated the strength of biomarker/drug response associations was used for annotation with level 1/R1 strongest and level 4 weakest from clinical literature, FDA drug labels and guidelines (PMID:28890946). Results: The highest level of evidence for the top nine frequently occurring advanced stage cancers in TCGA is shown in Table. Conclusions: Thyroid cancer and cutaneous melanoma have emerged as the cancer types with the most level 1 evidence (FDA approved drugs) owing to BRAF V600E mutations. Kidney and prostate cancers show no cases with level 1 evidence and also had the largest fraction of unactionable tumors. Over half of colorectal cancer cases had level R1 resistance evidence attributed to KRAS and NRAS mutations. The clinical utility of NGS in late-stage refractory cancer varies widely by tumor type. The presence of level 3 and level 4 evidence in all cancer types bodes well for the development of new targeted drugs. [Table: see text]