A study on user willingness to participate in an adverse drug event information sharing system.

BACKGROUND: To prevent the recurrence of Adverse Drug Events (ADEs), particularly drug allergies, it is essential to avoid re-exposure to causative drugs. Awareness of previous ADEs is crucial for patients because they can share accurate information with healthcare providers (HCPs). This study aims to assess users' willingness to share ADE information and evaluate the factors related to this willingness by utilizing a prospective ADE information-sharing system currently under consideration in South Korea. METHODS: In September 2023, a self-administered questionnaire was collected from a sex-, age-, and regionally stratified nationwide convenience sample of adults recruited through a commercial panel in South Korea. Factors contributing to the willingness to share ADE information and create electronic ADE cards (e-ADE cards) were investigated using multivariate logistic regression analysis. RESULTS: Among the 1,000 respondents, 458 (45.8%) were willing to share ADE information, and 521 (52.1%) were willing to create e-ADE cards. The willingness to share personal ADE information and create e-ADE cards was positively associated with the perceived benefits of sharing ADE, trust in HCPs and positive experiences. Notably, older adult patients demonstrated a higher willingness to share information and use e-ADE cards, with rates of 56% and 62%, respectively. CONCLUSIONS: Our findings indicate that the approach to sharing personal ADE information should be distinct from that of sharing comprehensive health information. Notably, users are likely to willingly disclose their personal information even if they are not anonymized, owing to the significant perceived benefits of sharing. The findings of this study can enhance awareness about sharing personal ADE information and contribute to the successful establishment of an ADE information-sharing system, thereby improving the patient safety environment.

Measuring empathic stress - A systematic review of methodology and practical considerations for future research.

Aside from stressors that each of us experience directly, we also share the stress of the people around us. Such empathic stress exists on psychological and physiological levels, including subjective, sympathetic, parasympathetic and endocrine activation. The objective of this review is to offer an overview of methodology over the past fifteen years of empathic stress research and derive practical considerations for future research endeavors in the field. We used a keyword search strategy in the databases Web of Science, PsycInfo and PubMed to find empathic stress studies published until December 2023, and included 17 studies into our review. The reviewed laboratory studies provide initial yet consistent evidence for the existence of empathic stress across different populations, in intimate and stranger dyads, with direct and virtual contact, across multiple levels of the stress system, and based on diverse statistical analysis methods. We discuss all findings and derive practical considerations for future empathic stress research. The diversity of methods established provides a solid foundation upon which future studies can expand.

Building and Sustaining Public Trust in Health Data Sharing for Musculoskeletal Research: Semistructured Interview and Focus Group Study.

BACKGROUND: Although many people are supportive of their deidentified health care data being used for research, concerns about privacy, safety, and security of health care data remain. There is low awareness about how data are used for research and related governance. Transparency about how health data are used for research is crucial for building public trust. One proposed solution is to ensure that affected communities are notified, particularly marginalized communities where there has previously been a lack of engagement and mistrust. OBJECTIVE: This study aims to explore patient and public perspectives on the use of deidentified data from electronic health records for musculoskeletal research and to explore ways to build and sustain public trust in health data sharing for a research program (known as "the Data Jigsaw") piloting new ways of using and analyzing electronic health data. Views and perspectives about how best to engage with local communities informed the development of a public notification campaign about the research. METHODS: Qualitative methods data were generated from 20 semistructured interviews and 8 focus groups, comprising 48 participants in total with musculoskeletal conditions or symptoms, including 3 carers. A presentation about the use of health data for research and examples from the specific research projects within the program were used to trigger discussion. We worked in partnership with a patient and public involvement group throughout the research and cofacilitated wider community engagement. RESULTS: Respondents were supportive of their health care data being shared for research purposes, but there was low awareness about how electronic health records are used for research. Security and governance concerns about data sharing were noted, including collaborations with external companies and accessing social care records. Project examples from the Data Jigsaw program were viewed positively after respondents knew more about how their data were being used to improve patient care. A range of different methods to build and sustain trust were deemed necessary by participants. Information was requested about: data management; individuals with access to the data (including any collaboration with external companies); the National Health Service's national data opt-out; and research outcomes. It was considered important to enable in-person dialogue with affected communities in addition to other forms of information. CONCLUSIONS: The findings have emphasized the need for transparency and awareness about health data sharing for research, and the value of tailoring this to reflect current and local research where residents might feel more invested in the focus of research and the use of local records. Thus, the provision for targeted information within affected communities with accessible messages and community-based dialogue could help to build and sustain public trust. These findings can also be extrapolated to other conditions beyond musculoskeletal conditions, making the findings relevant to a much wider community.

The state of individual participant data sharing for the highest-revenue medicines.

BACKGROUND: Amid growing emphasis from pharmaceutical companies, advocacy groups, and regulatory bodies for sharing of individual participant data, recent audits reveal limited sharing, particularly for high-revenue medicines. Therefore, this study aimed to assess the individual participant data-sharing eligibility of clinical trials supporting the Food and Drug Administration approval of the top 30 highest-revenue medicines for 2021. METHODS: A cross-sectional analysis was conducted on 316 clinical trials supporting approval of the top 30 revenue-generating medicines of 2021. The study assessed whether these trials were eligible for individual participant data sharing, defined as being publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. Information was gathered from various sources including ClinicalTrials.gov, the European Union Clinical Trials Register, and PubMed. Key factors such as the trial phase, completion dates, and the nature of the data-sharing process were also examined. RESULTS: Of the 316 trials, 201 (64%) were confirmed eligible for sharing, meaning they were either publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. A total of 102 (32%) were confirmed ineligible, and for 13 (4%), the sponsor indicated that a full research proposal would be required to determine eligibility. The analysis also revealed a higher rate of individual participant data sharing among companies that utilized independent platforms, such as Vivli, for managing their individual participant data-sharing process. Trials not marked as completed had significantly lower eligibility for individual participant data sharing. CONCLUSION: This study highlights that a substantial portion of trials for top revenue-generating medicines are eligible for individual participant data sharing. However, challenges persist, particularly for trials that are marked as ongoing and for trials where the sharing processes are managed internally by pharmaceutical companies. Data-sharing rates could be improved by adopting open-access individual participant data-sharing models or using independent platforms. Standardizing policies to facilitate immediate individual participant data availability for approved medicines is necessary.

Collaborative Joint Perception and Prediction for Autonomous Driving.

Collaboration among road agents, such as connected autonomous vehicles and roadside units, enhances driving performance by enabling the exchange of valuable information. However, existing collaboration methods predominantly focus on perception tasks and rely on single-frame static information sharing, which limits the effective exchange of temporal data and hinders broader applications of collaboration. To address this challenge, we propose CoPnP, a novel collaborative joint perception and prediction system, whose core innovation is to realize multi-frame spatial-temporal information sharing. To achieve effective and communication-efficient information sharing, two novel designs are proposed: (1) a task-oriented spatial-temporal information-refinement model, which filters redundant and noisy multi-frame features into concise representations; (2) a spatial-temporal importance-aware feature-fusion model, which comprehensively fuses features from various agents. The proposed CoPnP expands the benefits of collaboration among road agents to the joint perception and prediction task. The experimental results demonstrate that CoPnP outperforms existing state-of-the-art collaboration methods, achieving a significant performance-communication trade-off and yielding up to 11.51%/10.34% Intersection over union and 12.31%/10.96% video panoptic quality gains over single-agent PnP on the OPV2V/V2XSet datasets.

A risk sharing model for old community renewal project based on bargaining game model.

To ensure the successful implementation of the old community renewal project (OCRP), it is essential for the participants to allocate the project risks reasonably. Firstly, this study comprehensively identifies the 20 key risk factors of the OCRP. Secondly, an index system is established from three dimensions to evaluate the risk allocation ability of participants, including a total of nine evaluation indexes. Furthermore, a risk-sharing model based on TOPSIS method and bargaining game model is proposed to determine the optimal risk bearer and risk-taking ratio between the government and the private sector in OCRP. Finally, an OCRP in Chongqing is taken as a case study to verify the applicability of the developed model. The results indicate that in OCRP under PPP mode, the government need to independently bear 7 risks related to politics, law, policy, while the private sector needs to independently bear 8 risks mainly from project financing, design, construction, operation, and maintenance stages. In addition, the risk-taking ratios of 5 risks that require both parties to share are divided. The research findings provide references for ensuring the smooth implementation of urban renewal and sustainable development.

The origins of ADNI.

A brief history of events surrounding the conceptualization and original implementation of the Alzheimer's Disease Neuroimaging Initiative (ADNI) as a public-private partnership (PPP) is provided from the perspective of three individuals directly involved from the outset. Potential barriers and how they were addressed are summarized, especially the decision to make all data freely accessible in real-time. Decisions made at the beginning of ADNI are revisited in light of what has been learned over the past 20 years, especially the importance of the investment in cerebrospinal fluid (CSF) and blood measures and the commitment to data sharing. The key elements of ADNI's success from the authors' perspective are also summarized. HIGHLIGHTS: Informal interactions among colleagues were the beginning of something big. An NIH Director's personal decision on open data sharing has had perhaps the greatest impact of any single decision in the past several decades in terms of advancing clinical biomarker research. After 20 years, blood-based biomarkers of brain disease may soon take the place of brain imaging for purposes of diagnosis and drug development.

An investigation of perceived risk dimensions in acceptability of shared autonomous vehicles, a mediation-moderation analysis.

The integration of shared and autonomous mobility has led to the emergence of shared autonomous vehicles with ride-sharing services (SAVWRS), which have the potential to significantly reduce private car usage and promote sustainable transportation. Despite numerous studies on this topic, there is still no research examining the impact of all dimensions of perceived risk theory on usage intention. Therefore, we aim to investigate these relationships and gain deeper insights by examining the mediating effect of trust and the moderating effect of generation (Millennials vs. Baby Boomers) to address potential disparities across generations. To gather insights, we design an online survey that was completed by a random sample of 723 individuals in 2021. The estimation results of the structural equation model reveal that all perceived risk dimensions (social, performance, time, physical, security, and financial risks, in descending order) are negatively related to consumers' intention. Additionally, trust fully mediates the relationships between performance, physical, financial, and security risks and usage intention, whereas it partially mediates the relationships between social and time risks and the intention to use. Furthermore, moderation analysis revealed that Millennials are less concerned about most dimensions of perceived risk theory, except for social and time risks. In conclusion, our study contributes to a deeper understanding of the complex relationships between perceived risk dimensions, trust, and usage intention in SAVWRS. Our findings suggest that policymakers and industry stakeholders should consider strategies to address these concerns to promote widespread acceptance of SAVWRS.

Construction of knowledge sharing network indicator system for medication therapy management service training teams based on social network analysis.

BACKGROUND: Based on the perspective of social network theory, this study explored the network indicator system that facilitated optimal knowledge sharing effect in Medication Therapy Management Services (MTMS) training teams. The aim was to provide a reference for optimizing MTMS training and improving training quality. METHODS: Utilizing social network analysis combined with a questionnaire survey, a knowledge sharing matrix for MTMS training teams was constructed. Knowledge sharing behavior was assessed from three perspectives: individual networks, whole networks, and cohesive subgroups. RESULTS: Individual network analysis showed that the knowledge sharing effect within the training team reached its peak when the out-degree centrality was ≥ 3.5, in-degree centrality was ≥ 2.5, eigenvector centrality was ≥ 0.065, and closeness centrality was ≥ 7.86. Whole network analysis indicated that the optimal knowledge sharing effect occurred when the network density of the training team was higher than 0.0343 and the training size was less than 117 participants. Cohesion subgroups analysis demonstrated that knowledge sharing was more effective when members with similar working years participated in training together. CONCLUSIONS: The knowledge sharing indicator system developed for MTMS training teams, based on social network analysis, can assist in optimizing the MTMS training model and improving training effectiveness.

Current state of data stewardship tools in life science.

In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.

A secure healthcare data sharing scheme based on two-dimensional chaotic mapping and blockchain.

Enhancing data privacy security in medical data sharing is crucial for the informatization development in the healthcare sector. This paper proposes a healthcare data sharing scheme based on two-dimensional chaotic mapping and blockchain (2DCM-DS). Specifically, a new two-dimensional chaotic mapping is proposed, which demonstrates superior chaotic performance. Then, by incorporating biometric audio information as an identity credential and integrating it with the proposed two-dimensional chaotic mapping, we design a data encryption method that establishes a strongly coupled and bi-directionally verifiable data ownership relationship in healthcare data sharing. Finally, we employ blockchain as the underlying network and design corresponding smart contracts to support 2DCM-DS. This approach addresses potential issues of unauthorized access, malicious tampering, and single points of failure in centralized data sharing. Experimental results demonstrate that 2DCM-DS effectively protects data security under the specified attack models. The results validate the security and efficiency of the 2DCM-DS, proving its application potential in healthcare insurance data sharing scenarios.

The Canadian VirusSeq Data Portal and Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology.

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform the public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). In addition, the portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. From inception to execution, the portal was developed with a conscientious focus on strong data governance principles and practices. Extensive efforts ensured a commitment to Canadian privacy laws, data security standards, and organizational processes. This portal has been coupled with other resources, such as Viral AI, and was further leveraged by the Coronavirus Variants Rapid Response Network (CoVaRR-Net) to produce a suite of continually updated analytical tools and notebooks. Here we highlight this portal (https://virusseq-dataportal.ca/), including its contextual data not available elsewhere, and the Duotang (https://covarr-net.github.io/duotang/duotang.html), a web platform that presents key genomic epidemiology and modelling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the portal (COVID-MVP, CoVizu), are all open source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

Dynamic pollution management strategies and coordination in aquatic product supply chain under eco-compensation: Perspective of extreme weather crisis.

The ecological compensation mechanism emerges as a solution to reduce pollution for aquatic product supply chain. To comprehend the dynamic pollution management strategies and coordination under the ecological compensation mechanism, this study develops a stochastic differential game between farmers and firms from the perspective of extreme weather crisis. The subjects' optimal decisions are explored in three cases including cooperative case, noncooperative case, and land transfer-cost sharing contract case. Then we simulate theoretical results and illustrate a case study to demonstrate the applicability of ecological compensation collaboration. The results indicate that the extreme weather crisis changes the evolutionary trend and steady state of pollution accumulation. Moreover, the land transfer-cost sharing contract could realize the Pareto improvement in high-risk environment especially after the crisis. But its coordination relies on the ecological compensation coefficient, which gets larger with the interval of [4.9,6.3] after the crisis, compared to the range of [4,5.4] before the crisis. Notably, the contract and ecological compensation mechanism exhibit a complementary relationship in response to the crisis. The findings could provide new inspirations for aquatic product supply chain to balance production and environmental protection.

Carrying capacity-based benchmarks for Belgian residential buildings.

Environmental benchmarks are a means to stimulate lowering environmental impacts of buildings. These benchmarks may be based on a bottom-up or top-down approach. While bottom-up benchmarks are derived from the analysis of reference buildings and represent current building practice, staying within the Earth's carrying capacity requires top-down benchmarks representing long-term environmental goals. Top-down benchmarks are derived by allocating a share of the global carrying capacity to objects, e.g. buildings, using so-called sharing principles. Various sharing principles exist, which significantly influence benchmark values. This study applies a wide range of sharing principles, including novel principles, to define top-down benchmark values for Belgian residential buildings based on global carrying capacities. An environmental budget was first allocated to Belgium, then to households and finally to the housing function. In each step, multiple sharing principles were applied, resulting in 32 combinations of sharing principles. For a single-person household, the minimum and maximum budget resulting from the combinations differ by a factor 42. Based on data availability and quality as well as ethical considerations, the authors of this paper give preference to "right to development" for the allocation to Belgium; "household composition" for the allocation to households and "final consumption expenditures" for the allocation to the housing function. The comparison of the top-down benchmark values with bottom-up benchmarks reveals that various measures are required to remain within the Earth's carrying capacity. The top-down benchmark values presented in this paper can hence guide policymakers in establishing environmental targets and related roadmaps for residential buildings in Belgium.

Evaluation of a risk-sharing agreement for atezolizumab treatment in patients with non-small cell lung cancer: a strategy to improve access in low-income countries.

BACKGROUND: Using immune checkpoint inhibitors (IO) is a promising approach to maximize clinical benefits for patients with non-small cell lung cancer (NSCLC). PD-L1 expression serves as a predictive factor for treatment outcomes with IO. However, the high cost of this treatment creates significant barriers to access. Substantial evidence demonstrates the sustained clinical benefits experienced by patients who respond to immunotherapy. While IOs show promise in NSCLC treatment, their high cost poses access barriers. AIM: This study focused on a prospective cost analysis conducted at a high-specialty health facility to assess the economic implications of implementing a risk-sharing agreement (RSA) for atezolizumab in NSCLC. METHODS: The study included 30 patients with advanced NSCLC, with the pharmaceutical company funding the initial cycles. If patients responded, a government program covered costs until disease progression. RESULTS: A median progression-free survival of 4.67 months across populations, rising to 9.4 months for responders. The 2-year overall survival rate for the response group was 64%, significantly higher than for non-response. Without an RSA, a total treatment cost of $881 859.36 ($29 395.31/patient) was reported, compared to $530 467.12 ($17 682.24/patient) with an RSA, representing a 40% cost reduction. In responders, the average cost per year of life per patient dropped by 22%. Risk-sharing, assessed through non-parametric tests, showed a statistically significant difference in pharmacological costs (P < .001). CONCLUSION: Implementing RSAs can optimize resource allocation, making IO treatment more accessible, especially in low-income countries.

Transactional leadership and intellectual capital, the mediating role of knowledge sharing: The study of customs employees in Sulaymaniyah governorate.

This study aims to examine the impact of transactional leadership style on intellectual capital, and knowledge sharing as a mediator between transactional leadership and each of the intellectual capital components (human capital, structural capital, and relational capital) in the public sector, in Sulaymaniyah governorate in north of Iraq. The research model was settled based on the previous investigations on transactional leadership, knowledge sharing, and intellectual capital. To collect the data, convenience sampling was utilized, questionnaires were sent to employees in five customs directorates, and 355 responses were received. The research model analytical estimation was conducted through the employment of (SEM) Structural Equation Modeling by using Partial Least Square (PLS). The findings of the research show that transactional leadership has a significant relationship with knowledge sharing as well as with all three components of intellectual capital. Knowledge sharing also has a significant correlation with the components of intellectual capital. Moreover, the study's results show that knowledge sharing mediates the relationship between transactional leadership with human capital, structural capital, and relational capital.

The association of social networks with the job performance of primary health care professionals: the mediating effect of knowledge sharing.

Background and aims: Social networks formed through social media platforms have facilitated knowledge sharing among primary health care professionals (PHCPs). However, the impact of these networks on PHCPs' job performance and the mediating role of knowledge sharing remain underexplored. This study aimed to investigate the association between social networks formed via social media and the job performance of PHCPs, and to explore the mediating role of knowledge sharing in this association. Methods: A cross-sectional survey was carried out among PHCPs in Henan Province, China, involving 655 valid responses. Validated scales measured the key variables, and structural equation modeling (SEM) tested the proposed hypotheses, including the mediating effect of knowledge sharing through bootstrap method. Statistical analysis was performed using SPSS 24.0 and AMOS 24.0. Results: The degree centrality (ß = 0.225; p = 0.001) and network heterogeneity (ß = 0.093; p = 0.043) of the social network had a significant direct association with job performance, whereas the direct associations of betweenness centrality and network tie strength with job performance were not significant. Knowledge sharing mediated the relationship between degree centrality (ß = 0.147; p = 0.001), network heterogeneity (ß = 0.251; p = 0.043), and job performance. Conclusion: The study revealed the internal mechanisms by which social network characteristics influence PHCPs' job performance, highlighting the mediating role of knowledge sharing. Social networks formed within social media contexts have multifaceted effects on job performance, with knowledge sharing as a critical mediating variable. These findings underscore the importance of leveraging social media for professional networking and knowledge exchange to enhance PHCPs' job performance.

Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by deep-learning from local information.

OBJECTIVE: Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cramér-Rao Lower Bound (CRLB) than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs). This work explores charge sharing compensation using local spatial coincidence counter information for MEICC detectors through a deep-learning method. Approach: By analyzing the impact of charge sharing on photon count detection, we designed our network with a focus on individual pixels. Employing MEICC data of patches centered on POIs as input, we utilized local information for effective charge sharing compensation. The output was VMAI at different energies to address real detector issues without knowledge of primary counts. To achieve data diversity, a fast and online data generation method was proposed to provide adequate training data. A new loss function was introduced to reduce bias for training with high-noise data. The proposed method was validated by Monte Carlo (MC) simulation data for MEICC detectors that were compared with conventional PCDs. Main-Results: For conventional data as a reference, networks trained on low-noise data yielded results with a minimal bias (about 0.7%) compared with > 3% for the polynomial fitting method. The results of networks trained on high-noise data exhibited a slightly increased bias (about 1.3%) but a significantly reduced standard deviation (STD) and normalized root mean square error (NRMSE). The simulation study of the MEICC detector demonstrated superior compared to the conventional detector across all the metrics. Specifically, for both networks trained on high-noise and low-noise data, their biases were reduced to about 1% and 0.6%, respectively. Meanwhile, the results from a MEICC detector were of about 10% lower noise than a conventional detector. Moreover, an ablation study showed that the additional loss function on bias was beneficial for training on high-noise data. Significance: We demonstrated that a network-based method could utilize local information in PCDs effectively by patch-based learning to reduce the impact of charge sharing. MEICC detectors provide very valuable local spatial information by additional coincidence counters. Compared with MEICC detectors, conventional PCDs only have limited local spatial information for charge sharing compensation, resulting in higher bias and standard deviation in VMAI estimation with the same patch strategy. .

The Clinical Genome Resource (ClinGen): Advancing genomic knowledge through global curation.

The Clinical Genome Resource (ClinGen) is a National Institutes of Health-funded program founded 10 years ago that defines the clinical relevance of genes and variants for medical and research use. ClinGen working groups develop standards for data sharing and curating genomic knowledge. Expert panels, with >2500 active members from 67 countries, curate the validity of monogenic disease relationships, pathogenicity of genetic variation, dosage sensitivity of genes, and actionability of gene-disease interventions using ClinGen standards, infrastructure, and curation interfaces. Results are available on clinicalgenome.org and classified variants are also submitted to ClinVar, a publicly available database hosted by the National Institutes of Health. As of January 2024, over 2700 genes have been curated (2420 gene-disease relationships for validity, 1557 genes for dosage sensitivity, and 447 gene-condition pairs for actionability), and 5161 unique variants have been classified for pathogenicity. New efforts are underway in somatic cancer, complex disease and pharmacogenomics, and a systematic approach to addressing justice, equity, diversity, and inclusion. ClinGen's knowledge can be used to build evidence-based genetic testing panels, interpret copy-number variation, resolve discrepancies in variant classification, guide disclosure of genomic findings to patients, and assess new predictive algorithms. To get involved in ClinGen activities go to https://www.clinicalgenome.org/start.