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Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.

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The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.

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As large-scale genomic screening becomes increasingly prevalent, understanding the influence of actionable results on healthcare utilization is key to estimating the potential long-term clinical impact. The eMERGE network sequenced individuals for actionable genes in multiple genetic conditions and returned results to individuals, providers, and the electronic health record. Differences in recommended health services (laboratory, imaging, and procedural testing) delivered within 12 months of return were compared among individuals with pathogenic or likely pathogenic (P/LP) findings to matched individuals with negative findings before and after return of results. Of 16,218 adults, 477 unselected individuals were found to have a monogenic risk for arrhythmia (n = 95), breast cancer (n = 96), cardiomyopathy (n = 95), colorectal cancer (n = 105), or familial hypercholesterolemia (n = 86). Individuals with P/LP results more frequently received services after return (43.8%) compared to before return (25.6%) of results and compared to individuals with negative findings (24.9%; p < 0.0001). The annual cost of qualifying healthcare services increased from an average of $162 before return to $343 after return of results among the P/LP group (p < 0.0001); differences in the negative group were non-significant. The mean difference-in-differences was $149 (p < 0.0001), which describes the increased cost within the P/LP group corrected for cost changes in the negative group. When stratified by individual conditions, significant cost differences were observed for arrhythmia, breast cancer, and cardiomyopathy. In conclusion, less than half of individuals received billed health services after monogenic return, which modestly increased healthcare costs for payors in the year following return.

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Influenza A Virus (IAV) is a recurring respiratory virus with limited availability of antiviral therapies. Understanding host proteins essential for IAV infection can identify targets for alternative host-directed therapies (HDTs). Using affinity purification-mass spectrometry and global phosphoproteomic and protein abundance analyses using three IAV strains (pH1N1, H3N2, H5N1) in three human cell types (A549, NHBE, THP-1), we map 332 IAV-human protein-protein interactions and identify 13 IAV-modulated kinases. Whole exome sequencing of patients who experienced severe influenza reveals several genes, including scaffold protein AHNAK, with predicted loss-of-function variants that are also identified in our proteomic analyses. Of our identified host factors, 54 significantly alter IAV infection upon siRNA knockdown, and two factors, AHNAK and coatomer subunit COPB1, are also essential for productive infection by SARS-CoV-2. Finally, 16 compounds targeting our identified host factors suppress IAV replication, with two targeting CDK2 and FLT3 showing pan-antiviral activity across influenza and coronavirus families. This study provides a comprehensive network model of IAV infection in human cells, identifying functional host targets for pan-viral HDT.

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Functional assessments identify biomechanical issues which may indicate risk for injury and can be used to monitor functional recovery after an injury or surgery. Although the gold standard to assess functional movements is marker-based motion capture systems, these are cost prohibitive and have high participant burden. As such, this study was conducted to determine if a markerless motion capture system could detect preinjury differences in functional movements between those who did and did not experience a noncontact lower extremity injury (NCLEI). A three-dimensional markerless motion capture system comprised an area of 3 m × 5 m × 2.75 m was used. Participants were Division I collegiate athletes wearing plain black long-sleeve shirts, pants, and running shoes of their choice. Functional assessments were the bilateral squat, right and left squat, double leg drop vertical jump, static vertical jump, right and left vertical jump, and right and left 5 hop. Measures were recorded once and the first NCLEI was recorded during the first year after measurement. Two-factor analysis of variance models were used for each measure with factors sex and injury status. Preinjury functional measures averaged 8.4 ± 3.4 minutes capture time. Out of the 333 participants recruited, 209 were male and 124 were female. Of those, 127 males (61%) and 92 females (74%) experienced later NCLEI. The most common initial NCLEI was nonanterior cruciate ligament knee injury in 38 females (41.3%) and 80 males (62.0%). Females had decreased flexion and lower valgus/varus displacement during the bilateral squat (p < 0.006). In addition, knee loading flexion for those who were not injured were more than that seen in the injured group, and was more pronounced for injured females (p < 0.03). The markerless motion capture system can efficiently provide data that can identify preinjury functional differences for lower extremity noncontact injuries. This method holds promise for effectively screening patients or other populations at risk of injury, as well as for monitoring pre-/postsurgery function, without the large costs or participant burden.

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OBJECTIVE: Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. MATERIALS AND METHODS: We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. RESULTS: Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. DISCUSSION: As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. CONCLUSION: A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.

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Polygenic risk scores (PRS) have potential to improve health care by identifying individuals that have elevated risk for common complex conditions. Use of PRS in clinical practice, however, requires careful assessment of the needs and capabilities of patients, providers, and health care systems. The electronic Medical Records and Genomics (eMERGE) network is conducting a collaborative study which will return PRS to 25,000 pediatric and adult participants. All participants will receive a risk report, potentially classifying them as high risk (∼2-10% per condition) for 1 or more of 10 conditions based on PRS. The study population is enriched by participants from racial and ethnic minority populations, underserved populations, and populations who experience poorer medical outcomes. All 10 eMERGE clinical sites conducted focus groups, interviews, and/or surveys to understand educational needs among key stakeholders-participants, providers, and/or study staff. Together, these studies highlighted the need for tools that address the perceived benefit/value of PRS, types of education/support needed, accessibility, and PRS-related knowledge and understanding. Based on findings from these preliminary studies, the network harmonized training initiatives and formal/informal educational resources. This paper summarizes eMERGE's collective approach to assessing educational needs and developing educational approaches for primary stakeholders. It discusses challenges encountered and solutions provided.

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The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

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PURPOSE: Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk. METHODS: To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results. RESULTS: GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022. CONCLUSION: Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.

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The chr12q24.13 locus encoding OAS1-OAS3 antiviral proteins has been associated with coronavirus disease 2019 (COVID-19) susceptibility. Here, we report genetic, functional and clinical insights into this locus in relation to COVID-19 severity. In our analysis of patients of European (n = 2,249) and African (n = 835) ancestries with hospitalized versus nonhospitalized COVID-19, the risk of hospitalized disease was associated with a common OAS1 haplotype, which was also associated with reduced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clearance in a clinical trial with pegIFN-λ1. Bioinformatic analyses and in vitro studies reveal the functional contribution of two associated OAS1 exonic variants comprising the risk haplotype. Derived human-specific alleles rs10774671-A and rs1131454 -A decrease OAS1 protein abundance through allele-specific regulation of splicing and nonsense-mediated decay (NMD). We conclude that decreased OAS1 expression due to a common haplotype contributes to COVID-19 severity. Our results provide insight into molecular mechanisms through which early treatment with interferons could accelerate SARS-CoV-2 clearance and mitigate against severe COVID-19.

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Background@#Missed appointments or “no-show” is a widespread problem faced both by the private and public sectors of the health care community. Identifying factors leading up to no shows will help the movers of health to understand the effects of no show in the utilization of essential health services especially during a pandemic and putting up with a plan to manage the exaggerated decline in both primary consultation and follow-up appointments@*Objective@#To determine the associated factors of future no show among patients seen in Cebu Institute of Medicine – Community Medico Social Services (CIM-CMSS).@*Methods@#Analytic, cross-sectional study was adopted by the study and was conducted at Cebu Institute of Medicine – Community Medico-Social Services Center from July to November 2021 to 165 participants chosen via purposive random sampling. Data collection was done using a validated, researcher-developed two-part questionnaire@*Results@#One hundred sixty-five patients participated in the study. The demographics were gathered, associated with the factors that could affect the no-show rate. The top 5 contributing factors are as follows: Fear of COVID-19; Weather; Long waiting time during consult or follow-up; Work or school; Transportation Problems. Other non-patient and non-institution-related factors obtained the highest mean ratings. Only civil status and income showed significant difference. These may indicate that the decision to seek follow up is affected by factors outside the control of patient or the institution in relation to the different civil status and income levels.@*Conclusion@#The fear for the COVID-19 infection still holds the primary reason for not showing up for the scheduled follow up. Majority of the factors belonged to patient-related and other non-patient related and non-institution related factors. The long waiting hours could be addressed by CIM-CMSS to improve its services to patients. The ongoing pandemic will continue to affect the already-struggling follow up rates of CIM-CMSS

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BACKGROUND AND OBJECTIVES: The COVID-19 pandemic resulted in significant changes to the US residency application process for medical school graduates. Due to the lack of in-person activities, family medicine programs have utilized various social media platforms to connect with their applicants. In this paper, we describe how family medicine residency programs have adapted for the 2021 application cycle by using social media platforms. METHODS: We evaluated all family residency programs listed on the Electronic Residency Application Service (ERAS) for the presence of departmental and residency Twitter, Instagram, and Facebook accounts. We reviewed programs' websites and social media posts for posts regarding virtual opportunities for prospective applicants. We noted family medicine virtual subinternship opportunities on the Visiting Student Application Service (VSAS). We collected data from October 17, 2020 through November 2, 2020. RESULTS: Of 675 identified family medicine residency programs, 372 (55%) had some form of social media presence. Open house opportunities were offered by 46 (6.8%) programs on Twitter, 60 (8.9%) programs on Instagram, and 64 (9.5%) programs on Facebook. One hundred ninety-five of 578 residency-specific accounts were created after March 1, 2020; Instagram accounts (103 of 195) represented most of these; five virtual subinternships were identified on VSAS. CONCLUSIONS: Family medicine residency programs have adapted to the challenges that came with the COVID-19 pandemic by increasing social media outreach, particularly through Instagram. This has allowed residency programs to virtually communicate with prospective applicants during an unprecedented application cycle.

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Genomic regions have been associated with COVID-19 susceptibility and outcomes, including the chr12q24.13 locus encoding antiviral proteins OAS1-3. Here, we report genetic, functional, and clinical insights into genetic associations within this locus. In Europeans, the risk of hospitalized vs. non-hospitalized COVID-19 was associated with a single 19Kb-haplotype comprised of 76 OAS1 variants included in a 95% credible set within a large genomic fragment introgressed from Neandertals. The risk haplotype was also associated with impaired spontaneous but not treatment-induced SARS-CoV-2 clearance in a clinical trial with pegIFN-λ1. We demonstrate that two exonic variants, rs10774671 and rs1131454, affect splicing and nonsense-mediated decay of OAS1 . We suggest that genetically-regulated loss of OAS1 expression contributes to impaired spontaneous clearance of SARS-CoV-2 and elevated risk of hospitalization for COVID-19. Our results provide the rationale for further clinical studies using interferons to compensate for impaired spontaneous SARS-CoV-2 clearance, particularly in carriers of the OAS1 risk haplotypes.

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Genomic regions have been associated with COVID-19 susceptibility and outcomes, including the chr12q24.13 locus encoding antiviral proteins OAS1-3. Here, we report genetic, functional, and clinical insights into genetic associations within this locus. In Europeans, the risk of hospitalized vs. non-hospitalized COVID-19 was associated with a single 19Kb-haplotype comprised of 76 OAS1 variants included in a 95% credible set within a large genomic fragment introgressed from Neandertals. The risk haplotype was also associated with impaired spontaneous but not treatment-induced SARS-CoV-2 clearance in a clinical trial with pegIFN-{lambda}1. We demonstrate that two exonic variants, rs10774671 and rs1131454, affect splicing and nonsense-mediated decay of OAS1. We suggest that genetically-regulated loss of OAS1 expression contributes to impaired spontaneous clearance of SARS-CoV-2 and elevated risk of hospitalization for COVID-19. Our results provide the rationale for further clinical studies using interferons to compensate for impaired spontaneous SARS-CoV-2 clearance, particularly in carriers of the OAS1 risk haplotypes.

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The use of race and ethnicity in medicine has become a matter of intense debate. Currently the social construct of race and ethnicity is far from precise, serving as a poor proxy for ancestry. In this issue of Cell, Belbin et al. provide an interesting framework for better characterizing population substructure in a diverse urban environment.

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Implementing an effective software solution is an important step in managing a portfolio of core facilities. Though commercial options are available, developing or adopting a custom platform is a viable path for many institutions. At Northwestern University (NU), the cores program was reorganized beginning in 2008, and we pursued the latter path in order to retain control of the development priorities and to ensure integration with other enterprise systems. This manuscript describes our experience and results after a decade of effort. The platform, named NUcore, began enrollment in 2011, and full enrollment was achieved in 2019. Key features of NUcore include a stable and secure environment, a responsive and intuitive interface for users and core staff, seamless integration with the university financial system, rules and restrictions to ensure compliance, and both core-specific and enterprise reporting. NUcore now supports nearly half of all sponsored award dollars at NU at a cost of only 1 cent per dollar of business transacted. On average, there are over 4000 active users each year. NUcore is managed as an open-source project, available at no cost to any organization. Five academic organizations currently use the NUcore code base. For NU, NUcore has been a substantial success, and continuous development will ensure that it meets the future needs of our university and its cores.

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