For updated information please visit the online program.
Collaborative Workshops are intended to encourage participation and collaboration using mechanisms such as discussion forums (e.g., shorter didactic presentations or panels followed by group discussions), research forums (e.g., presentations and discussions related to scientific advancements in a targeted areas of informatics innovation and practice), or a challenge/competition (e.g., individual or group submissions addressing a specific topic or scientific/technical challenge).
Instructional Workshops include a combination of didactic and interactive content delivery, as well as participant interaction. They are designed to address the needs and interests of individuals at all levels of experience relative to the selected topic area(s), and are led by individuals with appropriate subject matter expertise.
- Half-day workshops = 3 CME/CE credits
- Full-day workshops = 6 CME/CE credits
For sessions offering MOC-II credit, check the Self-assessment Booklet of Multiple Choice Questions.
Saturday, November 3, 8:30 a.m. – 12:00 p.m.
W01: How Health Systems Should Be Thinking About Clinical Integration of Electronic Patient Reported Outcomes (sponsored by Consumer and Pervasive Health Informatics Working Group)
E. Austin, University of Washington; C. LeRouge, Florida International University; A. Hartzler, D. Lavallee, University of Washington; A. Chung, University of North Carolina
Patient-reported outcomes (PROs) are a type of patient-generated data that can often provide clinically-meaningful insight into screening, diagnosis, and response to treatment. In response to changing healthcare and policy environments, health systems are beginning to prioritize the electronic capture and presentation of PROs, leveraging health information technology (i.e., ePRO systems) to transform healthcare and improve patient and population health. Yet a more comprehensive and integrated approach is necessary to understand and address diverse uses of ePRO systems within complex healthcare organizations. Not only must ePRO systems adapt to the needs and constraints of large healthcare organizations to remain sustainable, they must be designed with the flexibility to facilitate effective ePRO data collection and review across the patient’s care continuum. Such an approach will ensure that clinicians hear their patients’ “PRO voice” to promote mutual understanding, shared decision making, and use of collective PRO data to support quality of care. Workshop leaders experienced in the research and application of electronic patient-reported outcomes tools across health systems will share their perspectives and design principles for ePRO systems based on: 1) planning for ePROs at the health system level, 2) designing tools for ePRO data reporting and visualization, 3) deploying ePROs across diverse clinical environments, and 4) evaluating and sustaining ongoing use of electronic ePRO systems. The leadership team represents multiple areas of expertise (e.g., health services research, human-centered design, information systems, systems engineering, health and clinical informatics, implementation science) and diverse healthcare systems.
W02: Effective Change Management Techniques for Health IT Implementation and Optimization (sponsored by People and Organizational Issues Working Group)
R. Murphy, University of Texas Health Science Center at Houston; C. Shea, R. Kitzmiller, The University of North Carolina at Chapel Hill
Leadership and management of change is one of the four core content areas of the clinical informatics subspecialty and is frequently referenced as a key determinant of success in transformational efforts. This tutorial will provide attendees with practical guidance and tools for leading technology-driven change efforts to improve quality in their organizations. These change efforts may involve implementation of new technology or optimization of existing technology and may be inspired by internally derived quality improvement goals, external reporting demands and/or reimbursement requirements.
The instructors will begin by demonstrating the importance of aligning technology-driven change efforts to an organization’s strategic goals as well as establishing the business case for managing such change efforts effectively. Using an interactive, case-oriented approach as well as Poll Everywhere technology to facilitate participation, the instructors will then guide attendees through key aspects of leading implementation and optimization, including governance and stakeholder analysis, organizational readiness assessment, workflow modeling, and communication strategy. Through guided discussion of these topics, attendees will (1) identify barriers to implementation and optimization; (2) apply tools useful for leading their organizations through these barriers; and (3) gain insight into various methods for evaluating the level of success of an implementation or optimization effort.
Content for the tutorial will be informed by the complementary fields of informatics, innovation theory, implementation science, and change management. The instructors will also draw upon examples from their experience with efforts to implement and optimize health information systems in both inpatient and ambulatory care settings. The combination of case study, polling technology, and guided discussions will enable attendees to reflect individually upon the content, learn from the experiences and insights of fellow attendees, and apply practical tools to specific implementation and optimization scenarios.
W04: Introduction to LOINC, the Global Vocabulary for Identifying Health Measurements, Observations, and Documents
D. Vreeman, S. Abhyankar, Indiana University and Regenstrief Institute
LOINC® (Logical Observation Identifiers Names and Codes) is a freely available international standard for identifying laboratory and other clinical observations. Presently, LOINC is used by more than 60,000 users from 171 countries and has been adopted as a national standard in 35 countries. LOINC has been translated into 18 variants of 12 languages and is used by healthcare organizations, reference laboratories, ministries of health and other federal agencies, professional societies, healthcare information exchange networks, insurance companies, healthcare IT vendors, instrument manufacturers, health application developers, and more. LOINC is now ubiquitous in health data systems worldwide, and is an essential ingredient of system interoperability.
This workshop will cover the basics of LOINC, including: its origin; how it is developed, distributed, and used; and its relationship to other vocabulary and messaging standards such as SNOMED CT and HL7. We will summarize the highlights of global adoption and use cases.
This workshop will explain the LOINC concept model and key features to help users pick out the differences between LOINC terms with subtle but important distinctions. Many informatics professionals are aware of LOINC’s coverage of laboratory tests, which we will explore, but we will also tour its rich content in other domains such as vital signs, clinical document titles, radiology procedures, patient assessment instruments, and patient reported outcomes measures.
This workshop will introduce the key tools and resources available for implementing LOINC. Our primary focus will be on how to get started with LOINC by mapping your local observation codes to LOINC codes. We will briefly describe some secondary uses of LOINC, such as automated electronic laboratory reporting to public health, use in clinical decision support systems, and clinical quality measurement. And last, we will describe some best practices for using LOINC that will prepare the participants for success in the long run.
W06: Evaluation and Interpretability in Deep Neural Networks
J. Lalor, A. Jagannatha, University of Massachusetts; H. Yu, University of Massachusetts Lowell/Department of Veterans Affairs
This instructional workshop will focus on the question of evaluating and interpreting deep neural networks (DNNs). We have previously organized workshops on deep learning at MedInfo 2015 and AMIA 2017. We have developed deep learning models for both biomedical and open-domain problems, and have introduced new methods for evaluating deep learning. In this workshop we will review deep learning and traditional methods for evaluation and interpretability, and introduce new methods for opening the black box of neural networks. Participants in the workshop will be able to experiment with evaluation and interpretation methods using models built for biomedical tasks in computer vision (e.g. image detection) and natural language processing (e.g. question answering).
W03: Citizen Science & Patient Voice in Research: An Informatics Perspective
R. Austin, University of Minnesota; P. Hsueh, IBM Watson; R. Valdez, University of Virgin; A. Solomonides, NorthShore University HealthSystem Research Institute; A. Chung, UNC School of Medicine; U. Backonja, University of Washington Tacoma
National organizations have been leading efforts related to citizen science. In 1996, the Federal Community of Practice on Crowdsourcing and Citizen Science (CCS) was established and the National Institute of Health (NIH) established a Citizen Science Working Group compromising 80 staff across several NIH agencies to promote and engage national agencies in this movement. Since 1996 and particularly 2010, citizen science has experienced exponential growth, improving research capacity, quality of knowledge, and public understanding of science. To date, however, the benefits of citizen science have primarily been limited to the fields of biology, conservation, and ecology. An unprecedented opportunity exists to pair the growing citizen science movement with the explosive growth of health informatics. The focus of this pre-symposium is to inform, educate, and update colleagues on citizen science and the patient voice in research from an informatics perspective. Drawing on these national initiatives, this pre-symposium will provide the opportunity to have a robust dialogue about how the informatics community can innovatively engage in citizen science in ways that meaningfully address challenges of inclusivity and ethics. This pre-symposium will explore: (1) current state-of-the-science related to citizen science; (2) vulnerable and marginalized populations’ facilitators and barriers to engaging in citizen science; (3) ethical, legal and social implications; (4) vision of citizen science and informatics. Participants will gain an understanding of citizen science from the four areas described and how this work is being implemented across institutions. The goal of this pre-symposium is to educate and engage participants’ in a deeper discussion with fellow colleagues to create a vision and strategy for citizen science within informatics.
W05: Enabling Innovation and Collaboration in Informatics and Health Care Research
M. Edmunds, B. Johnson, AcademyHealth; P. Payne, Washington University in St. Louis; S. Greene, Health Care Systems Research Network
This interactive, collaborative workshop will describe best practices and offer practical guidance about how to innovate and enable collaboration to accelerate progress in biomedical and clinical informatics and health systems research. Intended for all career stages and organizational settings, this hands-on session builds on last year’s successful workshop focusing on this area and will explore challenges and opportunities for enabling meaningful collaborative initiatives that can advance progress in the health research and informatics communities. This session will be organized into three parts. First, presenters will present a landscape of initiatives to enable collaboration and showcase two types of innovative approaches to collaboration including: CIELO, an open science platform; and DALHS, a community of practice for researchers embedded in health care delivery systems. During the second hour, participants will engage in small group activity and design approaches for cultivating spaces of innovation and collaboration at their own institutions. In the final hour, the group will discuss key lessons learned and takeaways to bring back to their institutions.
W07: Clinicians on FHIR®: Zero Percent Contained
L. Heermann Langford, Intermountain Healthcare; R. Leftwich, Intersystems; V. Nguyen, Stratametrics; C. Parker, PAREXEL; J. McClay, UNMC; R. Handzel, University of Pittsburgh
Beginning in 2010 HL7 created Fast Healthcare Interoperability Resources (FHIR) as a next generation standard to address clinical data interoperability. Clinicians on FHIR evolved in 2014 as an event held at each HL7 Working Group Meeting (3 times yearly) to educate clinicians about HL7 FHIR and provide feedback to the HL7 FHIR team regarding the clinical viability and usability of the FHIR standard. This workshop will bring the Clinicians on FHIR activity to a broader clinical audience attending the Annual Symposium. It is designed to educate attendees about HL7 FHIR and tools available to access, review, and provide feedback to the HL7 FHIR team regarding the evolving HL7 FHIR standard. It is also intended to make the audience aware of the potential of FHIR for innovation in their organizations. The faculty will provide lectures describing HL7 FHIR history, background, and fundamental principles. Examples of applications using the SMART on FHIR platform will also be discussed. After this initial overview of FHIR, the attendees will be guided through using online tools to examine HL7 FHIR Resources (the basic building blocks of FHIR) and build FHIR Profiles (implementation guides for specific use cases) for care plan and care coordination use cases. Additionally, the facilitators will demonstrate the use of the SMART Python Client and instruct users on developing FHIR-based applications in Python.
Saturday, November 3, 8:30 a.m. – 4:30 p.m.
W08: Seeing is Believing: Making Nursing Data Visible (sponsored by Nursing Informatics Working Group)
K. Monsen, University of Minnesota; L. Hardy, Ohio State University; D. Ariosto, Vanderbilt University Medical Center; R. Yang, University of Utah; J. Role, Loma Linda University Health/University of Minnesota; T. Fan, University of New Mexico Hospital/University of Minnesota
Data visualization is a method to enhance interpretation of data by placing it in a visual context allowing for information trending that may be overlooked. It is placing data in a visual framework to increase understanding of its significance Data visualization synthesizes large sets of data providing an alternative view supporting predictive analytics. The use case shift to predicative analytics increases efficiency, real-time reporting for decision making and quality of care. Providing a visual environment often deepens the meaning and urgency for patient care and/or practice change. Images can replace a thousand words. Data visualization allows customizable reports for real-time information that impact the patient, provider and practice. Providers have the ability to track data points local to a patient or facility or globally to populations or disease. Nurse leaders benefit from the ability to visualize data and explore patterns in service delivery. Data visualization is pushing healthcare forward by increasing the ability for standardized data sharing. It enhances reporting through real-time processes and alerts thereby increasing safety, quality and decision-making. The right information to the right person at the right time.
This workshop will provide an overview of data visualization design principles, demonstrate exemplars in clinical practice as well as facilitate hands on activity to explore, filter, and visualize clinical data sets of high interest to nursing.
W09: Getting Creative About Informatics and Mental Health (sponsored by Mental Health Informatics Working Group)
A. Nathan, Brigham and Women's Hospital, Bentley University; A. Busch, McLean Hospital/Harvard Medical School; P. Ranallo, Six Aims for Behavioral Health
As the need for developing new models of mental health care delivery has grown more obvious in recent years, so has the attention this specialty is receiving. Informaticists across the country are beginning to explore how informatics can help. Unfortunately, most medical environments – even those actively seeking to be creative and innovative – fail to provide the necessary creative resources to sustain a culture that encourages this type of thinking over periods of time longer than a year. In this workshop we aim to provide a collaborative environment in which we can use creative ideation and idea convergence techniques, methods borrowed from the business world, to identify areas of need in mental health to which informatics can contribute, both products to be developed and research to be conducted. To accomplish these goals, we will implement a series of exercises that will take us through each stage of the Parnes-Osborne Creative Problem Solving (CPS) model: objective finding, fact finding, problem finding, idea finding, solution finding, and acceptance finding. This process focuses on encouraging participants to question their basic assumptions, remove the inherent bias that their experience provides, and leverage the variety of perspectives in the people around them as a means of redefining problems and generating ideas and solutions that they would not have otherwise identified.
W10: 9th Annual Workshop on Visual Analytics in Healthcare (VAHC)
A. Chen, University of Washington; J. Warner, Vanderbilt University; D. Wu, University of Cincinnati
In recent years, healthcare organizations have tackled numerous challenges including quality improvement in the face of rising healthcare costs, facilitating interoperability, innovation with health information technologies (HIT), and integration of data from novel data sources such as patient-generated health data (PGHD) and mobile app data. The increasing diversity and volume of healthcare data poses a challenging task for medical experts trying to make sense of patients’ health and illness conditions, for patients trying to make sense of their health data and their health, and for analysts to conduct outcomes and discovery research. Visual Analytics, the science of analytical reasoning facilitated by interactive visual interfaces, has the potential to provide great benefits to healthcare providers, patients, and data analysts.
W11: Knowledge Representation and Semantics Working Group Workshop: Doctoral Consortium, Highlights, and Late-breaking Research
D. Schlegel, SUNY Oswego; M. Brochhausen, University of Arkansas for Medical Science; J. Bian, University of Florida; L. Cui, University of Kentucky; T. Edinger, Oregon Health & Science University; Z. He, Florida State University; X. Jing, Ohio University; R. Sarmiento, University of the Philippines Manila; J. Schneider, University of Illinois at Urbana Champaign; R. Zhang, University of Minnesota
Knowledge representation and semantics represents a thriving and crucial subfield of biomedical informatics. The representation of healthcare data, information, and knowledge in a semantically rich manner which allows for diverse applications is critical to the present and future of healthcare. There have been great successes in areas ranging from drug discovery and -omics to public health and medication safety, but the job is not done. As we continue to build systems in the cognitive computing era, we face issues in ensuring the represented knowledge is up to date, that we have not made representational errors, that we can use and reason over the represented knowledge as needed for specific applications, and so forth. This pre-symposium event provides a forum for these and many other topics and issues dealing with knowledge representation and semantics to be discussed. The event will consist of three sessions: a doctoral consortium in which students will present their in-progress dissertation work; highlights in knowledge representation and semantics in which published work from a diverse set of venues will be presented to this wider audience; and late-breaking research posters and demonstrations in which extremely recent results and software systems under development will be presented for the first time.
W12: Natural Language Processing Working Group Workshop: Graduate Student Consortium, Year-in-Review, and Community Shared Tasks
H. Liu, Mayo Clinic; O. Uzuner, George Mason University; S. Arabandi, Ontopro; D. Demner-Fushman, National Library of Medicine; S. Meystre, Medical University of South Carolina; J. Patrick, Health Language Analytics; G. Savova, Harvard Medical School; K. Wagholikar, Harvard Medical School; C. Weng, Columbia University; H. Xu, University of Texas Health Science Center; R. Xu, Case Western Reserve University; M. Yetisgen, University of Washington; P. Zweigenbaum, LIMSI/CNRS
The application of Natural Language Processing (NLP) methods and resources to clinical and biomedical text has received growing attention over the past years, but progress has been constrained by difficulties to access shared tools and resources, partially caused by patient privacy and data confidentiality constraints. Efforts to increase sharing and interoperability of the few existing resources are needed to facilitate the progress observed in the general NLP domain. To address this need, the AMIA NLP working group workshop continues the tradition since its inception in 2012 to provide a unique platform for close interactions among students, scholars, and industry professionals who are interested in biomedical NLP. The event will consist of three sections: 1) a graduate student consortium, where students can present their work and get feedback from experienced researchers in the field; 2) a year-in-review session, where significant NLP articles in the biomedical domain will be presented followed by a panel discussion; and 3) a NLP community challenge session.
W13: AMIA 2018 CMIO Workshop: Building Leadership Skills & Tackling Real-world Challenges
J. Hollberg, Emory University; R. Schreiber, Geisinger Holy Spirit; P. Fu, Harbor-UCLA-Medical Center; V. Reddy, Intermountain Health; N. Safdar, Emory University
There are now 1,600 physicians board certified in clinical informatics by the American Board of Preventive Medicine[i]. There are 24 Clinical Informatics Fellowship Programs accredited by the Accreditation Council for Graduate Medical Education. There are also many more non-board certified/eligible practitioners who need training in state-of-the-art applied clinical informatics. AMIA is uniquely suited to be the academic home for this community, because it provides a combination of personal experience and anecdote with firm grounding in evidence-based biomedical informatics literature, informatics theory, foundational knowledge, and proven best practices. A major part of that support is outreach to Chief Medical Information Officers (CMIOs) and those in similar roles (such as Medical Directors for Information Systems) who are charged with leading informatics change within their organizations, both large and small. More than 300 individuals have attended the CMIO workshop since 2011, some attending more than once, ranging from seasoned CMIOs of large systems to those who are just beginning their applied clinical informatics careers. During the 2017 CMIO workshop, we anonymously surveyed the 80+ participants. 91 percent responded that they desired to attend the workshop again, and the most requested topics were practical leadership skills and guidance on change management.
The 2018 CMIO Workshop will focus on leadership development, including didactic and small group exercises regarding the skills needed to be successful as an executive and potential career paths for those interested in becoming a CMIO or advancing once in that role. Additionally, we will focus on successful change management processes within a single hospital as well as for large health systems. We will then apply the leadership and change management skills to case-based discussion of current contentious yet practical topics for CMIOs including management of problem lists, whether to implement open notes, and best practices regarding EMR documentation and scribes, Didactic presentations will be integrated with structured group discussions. The CMIO workshop is a bridge between the more applied iHealth conference and the breadth of Fall Symposium offerings, with practical offerings that participants can integrate into their daily workflow and help their organizations realize the potential benefits of health IT.
Saturday, November 3, 1:00 p.m. – 4:30 p.m.
W14: Analyzing Large Drug Prescription Datasets – Principles, Tools and Examples (sponsored by Pharmacoinformatics Working Group)
O. Bodenreider, V. Huser, National Library of Medicine; C. Reich, QuintilesIMS
Large prescription datasets have become increasingly available to researchers (e.g., claims data from Medicare and private insurance companies, pharmacy data from clinical institutions, feeds from health information networks, such as Surescripts). Prescription data are generally recorded at a level that is very detailed (e.g., with National Drug Codes (NDCs) that include manufacturer and packaging information), and often need to be aggregated for meaningful clinical analysis (e.g., at the level of the ingredient or drug class).
Resources such as RxNorm, the standard terminology for drugs in the U.S. developed by the National Library of Medicine, can facilitate the mapping of NDCs to RxNorm concepts for clinical drugs. RxNorm also supports aggregation by linking clinical drug products to their ingredients, and to drug classes from ATC, MED-RT and DailyMed. The RxNorm and RxClass application programming interfaces (APIs) and companion browsers facilitate the use of RxNorm for aggregation purposes. Additionally, features have recently been added to the drug APIs to facilitate the interpretation of obsolete drug identifiers often found in clinical data warehouses.
The first part of this workshop presents basic information about drug datasets and resources for analyzing them, with emphasis on RxNorm. The audience will be invited to participate (active exploration of RxNorm through RxNav and RxClass; follow-along activities with RxMix).
In the second part, we demonstrate an application of these resources to common use cases, including the comparison of prescribed vs. defined daily doses for drugs and the identification of potentially inappropriate medications (e.g., during pregnancy, for the elderly). Finally, we present the experience of the OHDSI (Observational Health Data Sciences and Informatics) community in integrating various kinds of drug data in a large clinical data warehouse compliant with the OMOP (Observational Medical Outcomes Partnership) clinical data model, and we address issues in integrating drugs from different countries.
W16: Applying Computational Causal Discovery in Biomedicine
R. Scheines, Carnegie Mellon University; G. Cooper, University of Pittsburgh
The last 25 years have produced a revolution in statistical and computational tools for causal inference and discovery in biomedicine. In this workshop, we will focus on causal discovery in large data sets drawn from clinical and translational research.
We will explain the basics of graphical causal models using multiple examples from biomedicine, clinical research, and other fields. We will teach the rudiments of graphical causal models and several search algorithms for learning about causal structure from background knowledge and data. We will use the freely available Tetrad program to teach these ideas with hands-on exercises using simulated and real data sets. We will cover how to represent and model causal systems, and the assumptions needed to connect causal hypotheses to observable constraints that make causal discovery possible. We will discuss why multiple-regression and related techniques are unreliable for causal discovery and demonstrate superior alternatives. We will discuss the problem of causal discovery in the presence of unmeasured confounders (latent variables) and present algorithms that can reliably extract causal information even when the measured variables fail to include hidden common causes. We will spend the final third of the workshop on analyzing cancer data and electronic health record (EHR) data for causal relationships.
W17: Operational and Practical Aspects of Clinical Knowledge Management
S. Maviglia, R. Rocha, Semedy, Inc/Brigham and Women's Hospital/Harvard Medical School; A. Weitkamp, Vanderbilt University Medical Center
Healthcare institutions create increasingly large amounts of clinical knowledge assets. These assets represent a diversity of clinical data and information, such as terminologies and vocabularies, clinical guidelines, rules, order sets, nursing pathways, treatment protocols, reports, complex decision support (CDS) algorithms, policies, etc. It is the responsibility of healthcare institutions to take maintain the accuracy and transparency of the content they create.
Unfortunately, many organizations limit their knowledge management activities to a reactive and an ad-hoc approach, lack a formal content review and maintenance process, nor support an enterprise knowledge management strategy. Often, a healthcare institution does not keep track of the knowledge assets and their changes, leading to situations of multiple versions of a specific knowledge asset, such as duplicate guidelines or clinical rules with different clinical parameters. Inconsistent, incomplete, and outdated clinical knowledge assets can lead to inefficiency in care delivery and business operations, or create unnecessary patient safety risks, such as poorly functioning or malfunctioning CDS.
This workshop will provide an introduction to clinical knowledge management topics that include the cataloguing of knowledge assets, authoring and modeling of metadata, managing relationship and dependencies among data, importing and exporting knowledge assets from and to other clinical applications, guaranteeing structural and semantic integrity when knowledge assets change, and following a comprehensive asset lifecycle process. The workshop will present basic knowledge management theory and best practices, in combination with practical experiences, challenges, and lessons learned.
W18: Causal Feature Selection and its Applications in Biomedical and Health Data Science
S. Ma, University of Minnesota; A. Statnikov, New York University; C. Aliferis, University of Minnesota
Predictive modeling has become an important tool in biomedical and health science over the past few years. Applications of predictive modeling in biomedical and health science span a wide range. However, developing high quality, generalizable, and easily deployed models for biomedical and health science applications remains challenging especially when using Big Data. The workshop will address how causal feature selection methods tackle the feature selection problem, a critical component of predictive modeling. Both theoretical and practical aspects of causal feature selection will be addressed with lectures and hands-on tutorial with real biomedical data.
W19: Qualitative Approaches to Design Thinking for Informatics (sponsored by Consumer and Pervasive Health Informatics, People and Organizational Issues, Public Health Informatics Working Groups)
R. Valdez, University of Virginia; B. Massoudi, RTI International
The aim of this instructional workshop is to introduce informaticians to qualitative approaches that can be used within a design thinking process, putting the user, rather than the technologist, at the center of the design effort. Design thinking is a newer iteration of a holistic engineering design process. Traditional systems development is often technologist driven, prioritizing the expertise and assumptions of the designer. Consequently, it often fails to adequately consider users’ needs and preferences. User acceptance of the resulting systems is often challenging, and redesign and development efforts can be prohibitively costly and time consuming. This workshop provides a hands-on orientation to qualitative approaches in the design thinking process, which are essential to informatics development, but not commonly taught in informatics curricula.
Using both didactic and participatory techniques, we will introduce the design thinking process; present the benefits and implications of qualitative approaches as part of the design thinking process as it relates to public health and consumer health information systems development efforts; and, introduce specific qualitative methods and techniques. This workshop will include mock data collection activities that illustrate how these tools are integrated and used in practice. Participants will be engaged in developing and presenting a design thinking approach to a system development effort they are currently working on, and will receive feedback on their approach from the instructors and their peers
W15: Frontiers in Public and Population Health: Pathways for Further Advancement of Community, Public, and Population Health Informatics and Health IT (sponsored by Public Health Informatics Working Group)
C. Stephan, Children's Mercy Hospital; R. Gamache, Population Health Informatics Solutions
Many informatics professionals’ top priority is the advancement of public, population, and community health outcomes. This year, multiple healthcare and public health factors are driving the PHI workshop to the next level including: rapidly evolving policy such as the CMS population health policies (e.g., MACRA) and patient-centered TEFCA directives. Dynamic health care market forces including public-private collaborations, as well as large organizational entrepreneurial efforts and innovative solutions provided by smaller start-up companies. Increasingly, initiatives funded are funded by venture capital investment which continue to emerge and gain momentum. Concurrently, the need for social determinants of health data and the growing opioid abuse crisis require new ways of thinking about public health data and related issues. These factors and others are driving fundamental and impactful advances as well as exposing informatics gaps in many facets of healthcare, public, and population health domains.
Timely issues to be included in the session topics that are associated with advancing population health outcomes in clinical care settings and public health such as population health management dashboards in supporting surveillance needs; connecting registries in clinical care, in Public Health Agencies and in Health Information Exchanges, as well as other organizations; using population data for the public good; public health challenges with meaningful use (MU); decision support for reporting and population health professionals workers; implementation of bi-directionaling two way“ reporting” between public health and healthcare; population health informatics needs for chronic disease management and prevention; community health interventions, and other emerging topics.
Sunday, November 04, 8:30 a.m. – 12:00 p.m.
W21: Forms and Flowsheets for FHIR Medical Records – and a Large Medical Record Test Set for Experimenting (sponsored by Clinical Information Systems Working Group)
C. McDonald, National Library of Medicine; J. Buchanan, University of Wisconsin School of Medicine and Public Health; P. Lynch, Y. Wang, S. Lu, National Library of Medicine
This workshop will present a NLM/LHCNBC set of tools for inputting and displaying FHIR medical record data. We will introduce a thumbnail overview of FHIR, and also present a large medical record test set for use in experimenting with novel applications built on FHIR medical record. We learned many lessons about efficient ways to load medical record data into FHIR while loading the 54 million records of our test set, and will share those lessons.
The forms and flowsheet tools we will present in this workshop are bookends to a FHIR medical record. In the interactive component of the workshop, participants will learn to use these tools. One tool provides machinery for entering data into FHIR EMR via input forms, and the other tool provides machinery for displaying that content in variety of flowsheet styles. All of the LHC tools are open source and based on standards. Participants will have access to the tools after the workshop.
The input side (https://lhncbc.nlm.nih.gov/project/lhc-forms) of this pair of bookends includes tools for defining input forms, generating forms from these definitions, entering EMR data through them, validating and converting units of measure. These LHC-Forms include skip logic, calculated values, survey instrument scoring, repeating groups of questions, extensive error checks, responsive design and many other capabilities. The attendees will learn to use and modify such forms. All LOINC panels also serve as LHC-Forms. There are more than 2000 such LHC-Forms to play with.
The output side is a flowsheet. Users can choose sets (panels) of terms to include in the flowsheet, show each panel member in a timeline tailored to the panel, and alternatively, one can insert all results in one common timeline. Each variable shows a spark graph at the beginning of its timeline. Yet another display presents the flowsheet in a problem-oriented fashion, including tests, drugs, and imaging studies.
W22: Developing an i2b2 Cell and Client Plugin
M. Mendis, S. Murphy, Partners Healthcare Inc.
Informatics for Integrating Biology and the Bedside (i2b2), is an open source software suite to construct and manage the clinical research chart in the genomic age. With it, query tool become generally available to researchers to search and work with pretention populations. This workshop will focus on the mechanics of setting up and populating an i2b2 database, and the more advanced topic of extending i2b2 software for custom uses needed at a site.
W23: Analysis of Human Interactive Behavior for Improving Health IT Usability and Clinical Workflow
T. Kannampallil, University of Illinois; K. Zheng, University of California at Irvine; V. Patel, The New York Academy of Medicine
Most clinical environments resemble a paradigmatic complex system with its dynamic and interactive collaborative work, non-linear and interdependent activities, and uncertainty. Addition of new organizational and systemic interventions, such as health IT, can cause considerable cascading effects in the clinical processes, workflow, and consequently, on throughput and efficiency. A 2011 IOM report  called for a socio-technical approach for designing and incorporating health IT in clinical settings. One of the critical aspects of a socio-technical approach is to understand the progression and evolution of human interactions within a socio-technical context. In this instructional workshop, we will discuss a set of convergent methodologies for analyzing human interactive behavior both with technology and with other humans or artifacts. These methodologies can help in capturing underlying patterns of human interactive behavior, and provide a mechanism to develop integrative, longitudinal metrics for clinical activities for sustained interactive episodes that evolve over time (for e.g., metrics related to performance, or errors). Such analysis of interactive behavior can also provide significant input to patient safety outcomes through the design of safe and efficient health IT. Specifically, we will (a) identify challenges to capturing and analyzing human interaction from complex clinical contexts; (b) discuss new approaches for capturing and analyzing sequences of human interaction in clinical settings using sequential analysis and network-theoretic, time-series based and probabilistic methods; (c) utilize one or more of these techniques to demonstrate their effectiveness as a viable mechanism for developing insights on clinical work activities through hands-on sessions; (d) provide participants hands-on experience in using data collection and data analysis tools; and (e) discuss the implications of these techniques for the design of health IT and patient safety initiatives.
W24: Guidelines and Resources for Extracting ONC Standards Datasets into your i2b2 Data Warehouse; Analysis for Two Vendors
J. Campbell, University of Nebraska; W. Campbell, J. Pedersen, University of Nebraska Medical Center; K. Wilkinson, University of Missouri
This workshop will be presented in two parts. In the first, the semantic interoperability (S&I) framework of the Office of the National Coordinator for Health Information Technology (ONCHIT) will be presented. Details of the semantic structures of the ONC standard terminology set and a maintenance framework for scheduled release and update of i2b2 community metadata for deployment of ONC standards will be presented. In the second phase, two i2b2 research datamart managers will discuss the analytical, programmatic and data management issues involved in the extraction of structured, coded datasets from the Electronic Health Record into i2b2 Emphasis will be given to deployment and support of ONC standard terminologies of SNOMED CT, RXNORM, LOINC and NDC as well as HIPAA transaction sets including ICD-10-CM, ICD-10-PCS, CPT-4 and HCPCS type 2. The two organizations have Epic® and Cerner® respectively as their EHRs, and will provide a library of software resources pertinent to their platforms to the attendees who employ those EHRs in their enterprise.
W25: Biomedical and Healthcare Blockchain
T. Kuo, L. Ohno-Machado, University of California San Diego, VA San Diego Healthcare System
In this workshop, we propose to deliver a tutorial about the origins and characteristics of blockchain technology and its applications in biomedical/healthcare domain. The target audiences include informatics researchers, clinicians, IT professionals, and leaders in healthcare or other organizations. The two instructors, Dr. Lucila Ohno-Machado and Dr. Tsung-Ting Kuo, formed the team that was a winner at the Use of Blockchain in Health IT and Health-related Research Challenge held by The Office of the National Coordinator for Health Information Technology. They published a recent, highly-read biomedical/healthcare blockchain review paper in the Journal of the American Medical Informatics Association (JAMIA). Dr. Kuo was awarded an NIH Pathway for Independence Award (K99/R00) award for biomedical/healthcare research based on blockchain technology. We plan to first provide an overview of biomedical/healthcare blockchain, followed by an introduction to features of different blockchain platforms, and finally present a use case of privacy-preserving predictive modeling as an exemplar application of biomedical/healthcare blockchain. The didactic contents will be delivered within 90 minutes. Additionally, we plan to enrich the blockchain experience of participants through hands-on interaction for 60 minutes in which they construct a basic blockchain network for use as a distributed ledger. Our tutorial is platform independent, while we will use one of the introduced platforms in the hands-on part of the course. After attending the course, we expect participants to have basic knowledge of blockchain technologies, compare features of popular blockchain platforms, understand a potential biomedical/healthcare blockchain application through a use case, and acquire basic skills to be an active participant in a team that builds a blockchain network for a biomedical/healthcare application.
W26: Happy Bayes are Here Again!
M. Weiner, Temple University School of Medicine; H. Lehmann, Johns Hopkins University School of Medicine
In an ideal word, all diagnostic tests would make perfect predictions, all therapies would be completely effective and without harm, and available resources would be limitless. However, even with ongoing innovation in sophisticated machine learning algorithms that underlie the current era of precision medicine, for many clinical areas, prediction of outcomes remains imperfect, therapies are not completely effective and resources are most certainly limited. Given the imperfect environment in which sophisticated predictive algorithms are applied, the output of these algorithms must be placed in a realistic clinical context to support clinical decision making. Through incorporation of prior evidence-based knowledge and from the clinical valuation of outcomes and decision points Bayesian analysis and synthesis plays a key role in translating results of machine learning algorithms into actionable information for clinicians at the point of care.
Bayesian analysis has been around since the 18th century, and became inexorably tied to clinical decision making with the work on Ledley and Lusted in their 1959 seminal paper “Reasoning Foundations of Medical Diagnosis,” which applied Bayes theorem to predict the likelihood of disease on the basis of presenting symptoms. Over the ensuing years, the number and granularity of the disease predictors has increased, the principles were applied to a range of decision support systems, and algorithms that deal with these large data sets have become more sophisticated. Bayesian analysis helps define how accurate a new diagnostic test needs to be in the context of the effectiveness and the expense of existing therapies. Bayesian fundamentals are used to model the impact of uncertainty in developing a computable phenotype for enrollment in a clinical trial, or for inclusion in a cohort for retrospective analysis. These techniques can also be used to predict the impact of a new clinical decision support system on producing desired outcomes balanced against alert fatigue.
A follow up to last year’s successful “Bayes’d and Confused” workshop, this year’s instructional workshop will provide attendees with an overview of Bayesian fundamentals, of decision-analytic fundamentals, and with experience in using those fundamentals applied to an updated set of real-world use cases.
W20: Community Strengthening and Knowledge Sharing Towards Systematic and Scalable Clinical Data Quality Assessment (sponsored by Clinical Research Informatics Working Group)
C. Weng, Columbia University; A. Mosa, University of Missouri, Columbia; M. Ahuja, Geisinger; A. Szarfman, FDA; A. Solomonides, NorthShore University HealthSystem; V. Huser, NIH; S. Gold, University of Maryland; M. Zozus, UAMS
Given the increasing need for a rapid learning health system based on clinical data and the emerging culture of data-driven clinical research, unreliable clinical data quality can undermine the evidence base for biomedicine. To address this need, the AMIA CRI working group workshop aims to provide a unique opportunity for community building for clinical data quality assessment and for facilitating close interactions among trainees, scholars, stakeholders, and industry professionals interested in scalable and standards-based approaches to clinical data quality improvement. The event will consist of four sections: 1) a panel presenting the state-of-art clinical data quality checking methods, tools and best practices from experts; 2) a journal club-style highlight session, where significant data quality articles will be presented followed by a panel discussion; 3) a tutorial introducing endorsed data quality assessment resources followed by Q&A with the participants; and 4) a moderated brainstorming session aiming to develop a research agenda for clinical data quality improvement with identified knowledge gaps and prioritized research tasks.
W27: Patient and Consumer Engagement in Health Information Technologies
J. Wald, RTI; D. Sands, Harvard Medical School/Society for Participatory Medicine, Beth Israel Deaconess Medical Center
Patient engagement has been called the “blockbuster drug of the century.”1 While patient and consumer engagement concepts vary among different stakeholders, health care professionals and patients intrinsically understand its power. Over the last decade, patients and caregivers, along with their clinicians have employed connected technologies to enhance self-care and manage their conditions to better engage in their health and health care. Innovative use of technologies such as patient portals, mobile applications, and digital health services are enhancing communication, access to clinical records, use of medical reference information, participation in online communities, self-diagnosis, self-management, and tracking/sharing of biometric and other health information. However, the impact and adoption of these technologies for patient engagement have been limited by challenges including weak or conflicting drivers, technology silos, device and data incompatibility, information gaps, workflow obstacles, and policy or cultural conflicts.
This workshop, building on a popular tutorial given in the past by these presenters, will offer clinicians, system administrators, IT developers, policymakers, and patients (we are all patients, eventually!) examples of how these tools are used successfully to enhance patient engagement during a didactic portion of the workshop. Next, we will break into groups to discuss and problem-solve around two in-depth topics in participatory health: patient portals and patient-generated health data. After small groups discuss their own experiences, they will share insights with the assembled group. Workshop instructors will them summarize key learnings.
W28: FHIR® -- Implementing the HL7 Interoperability Platform: A Community of Implementers for Research, Patient Care, and Value-based Care
C. Jaffe, Health Level 7 International; J. Mandel, Verily (Google Life Sciences); R. Bloomfield, Apple; S. Huff, Intermountain Healthcare; W. Hammond, Duke Medical School; M. Tripathi, Massachusetts eHealth Collaborative
The simple unambiguous sharing of healthcare data is insufficient to meet the needs of our delivery systems if we are to improve quality and reduce costs. Traditional standards development processes are too slow and inefficient. Moreover, the means for exchanging data has not facilitated data reuse for a broad range of purposes, including quality evaluation, decision support, clinical research, primary medical science applications, and population health. Moreover, the standards innovation process must evolve more rapidly, despite an environment constrained by the limited availability of resources, by government regulation and by the soaring knowledge base. The report of the JASON Task Force provided a clear and achievable path to that goal, beginning with the enablement of Application Programming Interfaces (APIs). Last year, the use of open APIs was mandated in the 21st Century Cures legislation. Ever increasingly, the FHIR (Fast Healthcare Interoperability Resources) platform is fulfilling these needs in mobile health, Precision Medicine, and value-based care. The Argonaut Project, a private-sector initiative, is helping to drive FHIR implementation. The implementation community has grown exponentially in the last year to include payers (Da Vinci Project), BioPharma (TransCelerate), government (VA Lighthouse Gateway), and quality measurement leaders (NCQA on FHIR). The FHIR Foundation provides much needed public resources for the implementation community.