Depending on your settings, sessions with multiple presentations included will not export properly to Outlook (Posters, Oral Presentations). We advise manually blocking the time for these particular sessions in your Outlook calendar.
Biomedical health informatics is positioned to be a power-player in the future of healthcare by transforming care delivery and accelerating knowledge discovery. AMIA, professional home of the informatics community, has a large role to play in supporting the current and next generation of informaticians. This panel will share information from five different perspectives: AMIA Membership and Outreach Committee which provides services/benefits and fosters collaborations; AMIA Student Working Group which connects informatics students to share education experiences and career opportunities; AMIA High School Scholar Program which supports high school students conducting biomedical informatics research to showcase their work at the AMIA Annual Symposium; AMIA First Look Program which introduces undergraduate women in STEM (Science, Technology, Engineering, Mathematics) to the informatics field by supporting attendance at the AMIA Annual Symposium. The panel will include an overarching Diversity, Equity and Inclusion (DEI) perspective as AMIA builds inclusive pathways to informatics education and careers.
This panel discussion will delve into the potential of automation to support clinicians in clinical workflows, while also examining the unique challenges it presents. Informatics experts from varied backgrounds and perspectives will provide insights into automation progress in different healthcare contexts, and evaluate key considerations, such as the concept of agency and distributed cognition between humans and technology. The panel will discuss when automation may be appropriate, how to ensure its robustness in complex sociotechnical systems, and how to measure its impact effectively. Furthermore, the discussion will evaluate potential risks associated with automation, and explore ways to ensure equitable deployment. The intended audience for this panel includes healthcare professionals, researchers, policymakers, and vendors.
Stigma and discrimination within health care and public health institutions continue to deny people access to fair, culturally safe, and affirming care. Health and biomedical informatics can aid in understanding, detecting, and reducing stigma and discrimination in health care and public health. Panelists will draw from studies with multiple marginalized populations (e.g. African Americans, LGBTQ+, patients with stigmatized illnesses) and digital tools (e.g. Twitter, patient portals, electronic health records, electronic wearable devices, sensing technology). We will discuss how stigma and discrimination can negatively impact the adoption and use of digital health technologies, be enacted through the collection, use and analysis of health-related data, and be detected through emerging informatics methods (e.g. natural language processing and machine learning) and socio-technical strategies. Critically, we will discuss how health informatics can be part of addressing stigma and discrimination at individual, cultural, and institutional levels to promote fair, culturally safe and affirming care.
The US Supreme Court’s decision to strike down the rights of women to have an abortion also struck down the rights of clinicians to make reproductive healthcare decisions without state involvement. Privacy protection is a concern for patients with birthing capacity, healthcare providers, and institutions. Electronic health information (EHI) associated with perceived abortion-related care may be legal evidence incriminating clinicians. Fear of legal repercussions may impact medical care and lead to unequal access due to privacy concerns. There are multiple sources of privacy risks and multiple strategies to mitigate these risks, including data segmentation which is currently used to protect sensitive EHI and to prevent unintended disclosures of adolescents’ sensitive data in the portal. However, there are multiple challenges and risks to the use of data segmentation. It is important for the informatics community to discuss how sensitive EHI is protected within a healthcare organization and during health information exchange.
In response to the 21st Century Cures Act and the growing importance of Real-world evidence (RWE), the FDA has released guidance on RWE data for regulatory decision-making. Despite RWE's potential to improve clinical studies, challenges remain in rapidly utilizing RWE for decision-making due to the volume and diversity of real-world data, further emphasized by the COVID-19 pandemic. The Observational Health Data Science and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) to address these challenges and ensure the quality of RWE. OMOP focuses on the development of a common data model and standardized analytics to facilitate meaningful comparisons across different RWE data sources and research studies. Building on the need to better understand RWE and OMOP, the workshop gathers leading experts from various fields to discuss three major themes: (1) understanding the origin and barriers of real-world data for healthcare research and the role that OHDSI/OMOP has played in improving the use of RWE for healthcare research; (2) showcasing the potential of RWE analysis across multiple data types with OMOP CDM; and (3) discussing the challenges and opportunities to adopt RWE for secondary use in research and development. Participants will engage with in-depth topics such as data transformation, cohort definitions, diagnostic methods, visualization techniques, and practical applications of cohort diagnostics in real-world scenarios. This event aligns closely with the broader informatics interests of the attendees. It aims to enhance their understanding of the synergies and opportunities at the intersection of OHDSI, OMOP CDM, and healthcare.
Supporting patients to receive adequate social services such as food, housing, transportation, commonly known as Social Determinants of Health (SDOH), requires coordination between health and social services providers, and patients. Through a national consensus building collaboration the HL7 Gravity Project has defined standard data terminologies to support the coordinated delivery of SDOH services using electronic systems. Funded by the Office of the National Coordinator for Health IT (ONC) through its Leading Edge Acceleration Projects (LEAP), the Dell Medical School at the University of Texas at Austin partnered with healthcare providers and the Central Texas Food Bank to implement a standards-based, interoperable, workflow-integrated, and scalable decision support system that optimizes the referral for Supplemental Nutrition Assistance Program (SNAP) applications and efficiently closes the loop of those referrals. This panel will describe the results of this first pilot of Gravity Project’s HL7 SDOH Implementation Guide.
The utilization of digital health solutions in mental health is not without its challenges. In this three-hour collaborative workshop, we will increase the audience’s understanding of the challenges in designing digital health solutions by conducting a robust discussion on best practices in designing and developing digital mental health solutions. In addition, we will use two case studies to illustrate the research and evaluation challenges of digital mental health research. Researchers and practitioners will provide interdisciplinary perspectives on the ethical, research, evaluation, and social challenges and considerations when designing digital health solutions for people with mental health concerns. An interdisciplinary panel representing researchers and user experience and interface (UIX) designers and developers will present the best practices for creating and implementing digital health solutions for those with mental health concerns. Our panel will cover best practices for bringing users into the design process (co-design and participatory research); identifying and addressing user needs (user research, design strategy, and user stories); and creating intuitive mobile or software user interfaces (UIX design).
Global health informatics (GHI) is a branch of biomedical informatics that utilizes e-Health practices to provide healthcare resources, services, and information to underserved populations to promote clinical and public health outcomes in low- and middle-income countries. Developing interventions with community buy-in and local ownership is critical to ensuring successful GHI projects. Building strong community relationships along the GHI pipeline is vital and should be incorporated into protocols and implementation efforts from a study’s inception. This workshop aims to identify key components of developing community-based, sustainable global health informatics partnerships and interventions using a health equity lens. We will articulate challenges to stakeholder participation, factors that impact community engagement and sustainability, and closing the publication gap in the GHI field and researchers from LMICs. In our workshop, a diverse panel will discuss specific data driven GHI projects. Experts will share knowledge into the underlying principles that facilitated success or led to failures. Workshop participants will act on this knowledge to identify feasible and appropriate applications of these principles to their own GHI initiatives. The principles discussed in this workshop should be generalizable both to international GHI projects, as well as domestic informatics interventions that work with vulnerable populations and to address health disparities.
With developments in the SNOMED CT concept model, the recent announcement of the LOINC-SNOMED extension1,2 and work at the NLM to prepare an RxNorm OWL dataset, semantic integration in support of interoperation between the US national healthcare vocabularies of SNOMED CT–LOINC-RxNorm is becoming a reality. This three hour instructional workshop for the advanced participant will explain the shared concept model that integrates the systems and interactively explore with participants use cases in healthcare data interoperation that employs the integrated ontologies in support of decision analytics and population health.
The importance of Registry Science is increasing for clinicians, researchers, and patients as they seek to collect and prepare complex data types for research and analysis. Next-generation multimodal registries are necessary to collect high-quality longitudinal data across multiple domains, including genomics, transcriptomics, and patient-reported outcomes, to understand diseases and their natural progression. However, as the number of data types increases, the complexity of creating and maintaining these systems also grows. One perspective and manager are no longer sufficient to ensure long-term success. This panel will discuss the challenges of creating and sustaining next-generation registries, including insights from informatics leaders who have successfully built and managed such systems.
Open Science requires that research data be Findable, Accessible, Interoperable, and Reusable (FAIR). Although the FAIR Guiding Principles provide a framework for making datasets broadly useful to the scientific community, most investigators either disregard the expectations for FAIR data or they blithely claim that their datasets are already FAIR, simply because the data are archived online. The CEDAR Workbench provides a Web-based approach that simplifies the use of reporting guidelines and ontologies to create the standards-adherent metadata that are essential for data to be FAIR. This panel discussion will introduce CEDAR, and it will describe several large-scale projects where the technology has been deployed in different ways. Representatives of consortia studying topics as diverse as opioid addiction in the United States, COVID-19 in Africa, and the creation of reusable knowledge objects will reflect on their successes and challenges in using CEDAR to achieve the goal of data “FAIRness.”
The prevalence of multiple chronic conditions, called multimorbidities, is a growing problem in healthcare leading to increased costs, unpredictable outcomes, and decreased quality of life for patients. Confounding these challenges is the lack of knowledge on optimal methods to treat patients suffering from multimorbidities and a lack of understanding of the impact they have. This panel describes four informatics based approaches to addressing multimorbidities including modeling patient cost, computable phenotypes for multimorbidity, informatics infrastructure for multimorbidity research at a single institution, and informatics-driven systems to support self-management of symptoms in patients with multimorbidities.
Using maturity models across a community of institutions has been shown to be an effective method for guiding planning and development of a range of IT and data driven processes in health care[MAT]. For the past several years the CTSA consortium has promoted maturity models as tools to track and develop informatics resources that accelerate translational science through a project sponsored by the Center for Data to Health. This workshop is an extension of this work to develop maturity models and examine issues in implementation. The workshop will provide an introduction to maturity model concepts and present the Clinical Trial Management Ecosystems (CTME) Maturity Model currently under development by a CTSA working group and the Research Data Governance Maturity model currently being drafted by the AMIA Informatics Maturity Workgroup.
Translational research efforts increasingly rely on up-to-date and comprehensive resources and associated analytics to accelerate preclinical and clinical discoveries. While many different types of resources exist, they can be broadly categorized as those providing information on diseases, molecular/omic phenotypes, target annotations, and ingredient/drug regulatory information. Our group at NCATS is developing, maintaining, and providing access and analytics to such resources in collaboration with other domain experts in academia and other federal agencies. The goal of this instructional workshop is to provide an overview of four public resources: 1) Rare Disease Alert System (RDAS) for annotations on rare diseases from biomedical literature, grant funding, and clinical trials; 2) Relational database of Metabolomics Pathways (RaMP-DB) for chemical, biological, and ontology annotations on human analytes (metabolites, genes, proteins); 3) Pharos for annotations related to targets, including the widely used Target Development Level; and 4) Inxight/Global Substance Registration System (GSRS) for integrated and curated data supplied by the FDA and private companies on the regulatory status and drug ingredient definitions and annotations. For each of these resources, an overview of the underlying datasets, their provenance and how they could be useful will be discussed, followed by a more detailed and interactive session on how to interact with the data (through web applications, R packages, and/or APIs). Further, we will provide details on the underlying standards used within each resource, as well as examples on how we integrate these resources for generating comprehensive knowledge graphs.
Systematically designed Data Quality Assessment (DQA) is important for secondary use of clinical data, as collection cannot usually be altered to fit a specific use case. The goal of this panel will be to describe recent advances in data quality beyond traditional frameworks. We will discuss methods for designing and automating effective DQA by merging research requirements with reusable DQ principles, yielding testing that extends beyond simple structure of data to assess its fit for intended analyses. We will review deployment of DQA in large networks and datasets, to identify issues early and efficiently, allowing changes to data acquisition or analytic methods before effort is lost in studies. Finally, we will explore interpretation and causes of DQA findings, not just to fix data extraction errors but to understand how the characteristics of the data influence the validity and usability of study outcomes.
With the increasing polarization of U.S. politics, states are passing laws pertaining to reproductive health, educational curricula, gender minorities, and other divisive issues. These laws are affecting informatics in multiple ways, from data collection and curation to research to education to workforce. In this wide-ranging discussion, panelists from multiple states will report on recent developments in their state, explore effects on informatics, and discuss solutions and responses from the informatics community. We will focus on the question: Can informatics do anything to help address this divide?
There is increasing recognition of the vital importance of social drivers of health (SDoH) in order to better understand and improve health and healthcare. However, assessment of SDoH at the individual-level has been inconsistent, data are not standardized, and social-environmental data linked to the places patients live are rarely integrated with their clinical data to support clinical decision-making or research. In this panel, collaborators from the National COVID Cohort Collaborative (N3C) will present examples from their work within N3C as well as from their academic institutions to provide real-world examples of ongoing work that seeks to improve collection, interoperability, analysis, visualization, and community engagement for SDoH research in big data.
Digital inclusion is increasingly recognized as a social driver of health. There is significant variation in screening for digital inclusion with regards to what questions are asked, how data are recorded, who conducts the screening, and how connections to resources are made. To exacerbate these challenges, few well-validated screening questions exist for use in populations that are lower income, older, or have limited English proficiency. This panel will share insights learned from initiatives to optimize the screening process for digital needs at four health systems representing geographically and socioeconomically diverse populations: an urban safety net system in San Francisco, an integrated health system in Northern California, an urban safety net system in Los Angeles, and an academic health system in Boston. Lessons learned will focus on emerging best practices on screening questions to determine who needs help; when and how to screen patients; and what kind of help is desired.
Qualitative research is the collection and analysis of non-quantitative and non-quantifiable data through interviews, observations, and other methods to understand perspectives, beliefs, experiences, and contexts. Qualitative research can be used to generate nuanced insight into the patients and professionals who use informatics innovations, the settings in which they live and work, and the life experiences that produce medical, health, technology, and social media data. But despite the recognized value of qualitative research in informatics, researchers sometimes find it challenging to publish qualitative projects, which use different methods and terms than the ones most familiar to quantitative and computational researchers. Authors may struggle with selecting accurate language to report their work, determining what types and how much methodological detail to provide, and finding ways to present or visualize findings in a compelling way. Many qualitative authors also encounter challenges complying with journal word or page limits, as qualitative papers may require more space to effectively report methods and describe findings.
Social determinants of health (SDH) refer to the conditions in which people are born, live, work,
and age.1 They include socioeconomic status, nutrition, education, housing, physical
environment, safety, employment, social support, and access to healthcare.1 SDH are important
risk factors for a broad range of diseases and outcomes including diabetes, cancer,
cardiovascular diseases, Alzheimer’s disease, opioid overdose and suicide. The National
Institutes of Health (NIH) has recognized the importance of SDH.
SDH has been richly described in electronic health records (EHR). While International
Classification of Disease (ICD) codes can be used to identify SDH, a study has shown that EHR
notes contain about 90 times more information about SDH than the structured EHR data.2 Recent
studies have shown that advanced natural language processing (NLP) approaches can be
developed to extract SDH from EHR notes.3–5 The NLP-extracted SDH have shown to be
associated with opioid overdose4 and suicide death.5
In this workshop, we have invited world-leading experts to discuss AI technologies related to
detection of SDH, and how AI-identified SDH can support epidemiological, and clinical
applications. This event will cover in-depth topics such as AI technologies for SDH, challenges in
AI deployment in clinical settings. We will invite NIH program officers (we plan to invite Drs.
Khatipov and Haegerich) to join the discussion.
Communication is a frequently underappreciated, understated, and underestimated asset in the care of patients, particularly in the setting of informatics and technological solutions. Poor communication is often identified as the cause of poor outcomes in the medical environment. The workshop will discuss the importance and choice of information mode, techniques to tailor communication for diverse audiences, and failures caused by communication “encoding” (messaging) or “decoding” (interpretation) errors. We will examine the consequences of poor communication in project planning and implementation, as well as strategies to develop robust communication plans to support health IT change. Participants then will apply these learnings through interactive, small-group case discussions, to develop communication plans in scenarios that informaticists commonly face: 1. Implementing complex/nuanced technology and process changes - 21st Century Cures and multi-stakeholder communication planning (including patients, providers, and caregivers). 2. Time-Sensitive and Critical Communications - Unplanned EHR downtime and “real-time” communication to busy clinical providers. 3. Team Collaboration and Choice of Mode - Comparison of pros and cons of email, voice, in-person, video, team-based communication tools (e.g. Slack, Teams), and real-time communication (e.g., WhatsApp, Teams, Secure Text Messaging).
