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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 The causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management function is managing rapid growth in data volume and diversity. Methods Tufts Center for the Study of Drug Development (Tufts CSDD)—in collaboration with IBM Watson Health—conducted an online global survey between September and October 2020. Results One ninty four verified responses were analyzed. Planned and unplanned mid-study updates were the top challenges mentioned and their management was time intensive. Respondents reported an average of 4.1 planned and 3.7 unplanned mid-study updates per clinical trial. Conclusion Mid-study database updates are disruptive and present a major opportunity to accelerate cycle times and improve efficiency, particularly as protocol designs become more flexible and the diversity of data, most notably unstructured data, increases.

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 Cognitive computing point‐of‐care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence (RWE), might prove useful to expert and novice oncologists. The concordance of augmented intelligence systems with best medical practices and potential influences on physician behavior remain unknown. Methods Electronic health records from 88 breast cancer patients evaluated at a USA tertiary care center were presented to subspecialist experts and oncologists focusing on other disease states with and without reviewing the IBM Watson for Oncology with Cota RWE platform. Results The cognitive computing “recommended” option was concordant with selection by breast cancer experts in 78.5% and “for consideration” option was selected in 9.4%, yielding agreements in 87.9%. Fifty‐nine percent of non‐concordant responses were generated from 8% of cases. In the Cota observational database 69.3% of matched controls were treated with “recommended,” 11.4% “for consideration”, and 19.3% “not recommended.” Without guidance from Watson for Oncology (WfO)/Cota RWE, novice oncologists chose 75.5% recommended/for consideration treatments which improved to 95.3% with WfO/Cota RWE. The novices were more likely than experts to choose a non‐recommended option (P, Cognitive computing point‐of‐care decision support tools which ingest patient attributes from electronic health records and display treatment options based on expert training and medical literature, supplemented by real world evidence, might prove useful to expert and novice oncologists. We compared recommendations from one platform and noted concordance with disease expert opinions. The platform was also able to influence novice oncologists in test cases. Since nearly a fifth of similar real world cases received non‐recommended therapies. Our study demonstrates a need for decision support tools.

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.

New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat COVID-19. Several approaches are being used by scientists for investigation, including (1) various small molecule approaches targeting RNA polymerase, 3C-like protease, and RNA endonuclease; and (2) exploration of antibodies obtained from convalescent plasma from patients who have recovered from COVID-19. The coronavirus genome is highly prone to mutations that lead to genetic drift and escape from immune recognition; thus, it is imperative that sub-strains with different mutations are also accounted for during vaccine development. As the disease has grown to become a pandemic, B-cell and T-cell epitopes predicted from SARS coronavirus have been reported. Using the epitope information along with variants of the virus, we have found several variants which might cause drifts. Among such variants, 23403A>G variant (p.D614G) in spike protein B-cell epitope is observed frequently in European countries, such as the Netherlands, Switzerland, and France, but seldom observed in China.

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.