News and Events

Chair’s Message

pth-use-this-oneWe are moving toward our vision with a number of activities. We completed the Clinical Informatics Fellowship match and filled our two open positions with two excellent candidates who started July 2018. We completed the interview process for our research focused MS and research focused PhD programs, and have a new cohort who will start in Fall 2018.  Applications are open for our applied on-line MS in Clinical Informatics and Patient Centered Technologies. We are beginning a new overall strategic planning process for all of our Departmental activities in conjunction with preparing for our every 10 year academic program review. We are still actively recruiting new faculty as part of our strategic plan to expand our core faculty by 50%, with 3-4 positions remaining to be filled over the next two years (see link).

Cordially,

Peter Tarczy-Hornoch, MD
Chair and Professor, Department of Biomedical Informatics and Medical Education

Biomedical Informatics and Medical Education Newsletter

July 15-19, 2019

UPCOMING GENERAL EXAM

Aakash Sur

Friday, August 2nd at 2pm at Brotman Auditorium, Building D, South Lake Union

Title: Data Driven Methods for Scaffolding Genomes

Abstract: Robust reference genome sequences are foundational to molecular biology, yet many organisms have incomplete genome sequences and live in the purgatory of the draft stage. Recent experimental advances in interrogating the three-dimensional structure of the genome through chromatin conformation capture (Hi-C) have provided crucial new information to the assembly problem. While several groups have leveraged Hi-C data to generate scaffolded genomes, these methods remain prone to errors. We propose to review and benchmark existing methods against known and simulated genomes to characterize their performance and accuracy. We believe that we can bring the promise of machine-learning approaches to bear on the problem to learn underlying biological patterns of data rather than relying on our assumptions about chromatin structure, which will represent a paradigm shift in the field. Here we demonstrate a proof-of-concept approach with a convolutional neural network that is able to overcome both practical and theoretical limitations of conventional methods. To demonstrate the power of data driven approaches, we propose to solve the genomes of two organisms, Crithidia fasticulata, a honeybee parasite with open questions about its genome organization, and Euglena gracilus, a photosynthetic eukaryote with a particularly fragmented and challenging genome.

FACULTY/STUDENT/ALUMNI/STAFF ACTIVITIES

Mark Phillips organized a session on ontologies at the 2019 International Conference on Computers in Radiation Therapy in Montreal. Dr. Sam Luk (post-doctoral fellow in Radiation Oncology) and Dr Phillips presented papers in the session.  This is an effort to get the radiation therapy community involved in the use of informatics in this field.

July 8-12, 2019

UPCOMING LECTURES AND SEMINARS

Janice Sabin, PhD, MSW, will be giving a series of lunch time talks, July 10, 17, 24, 12:15-1:15 PM to the Department of Biostatistics Summer Institutes: Big Data, Clinical Trials, Infectious Diseases and Statistical Genetics program.

Titles:

Update on the Science of Implicit Bias and the Implicit Revolution

Striving for Excellence: Diversity, Equity, and Inclusion (DEI)

Manifestations of Implicit Bias in Academia: Individual and Organizational Remedies

For location and description of the talks see below:

https://www.eventbrite.com/e/summer-institutes-wednesday-lunch-sessions-registration-63080438289

FACULTY/STUDENT/ALUMNI/STAFF ACTIVITIES

BIME faculty Trevor Cohen received an administrative supplement to an R01 award from the NLM for a project entitled “Robust Inference from Observational Data with Distributed Representations of Conceptual Relations” (supplement award – $400,387), with sub awards to collaborators Serguei Pakhomov at the University of Minnesota, and David S. Knopman at the Mayo Clinic. The supplement concerns the development and evaluation of computational methods through which linguistic manifestations of cognitive changes in Alzheimer’s Disease (AD) dementia can be identified in transcribed speech by adapting representational approaches developed for the parent award.

Lauren Snyder, PhD student, was recently featured on a HIMSS blog: https://www.himss.org/news/future-healthcare-qa-student-leaders

OTHER NEWS

UW Medicine is No. 8 in the world in NTU rankings.

The University of Washington is No. 7 among world universities and No. 8 in the field of medicine in the annual performance rankings by National Taiwan University (NTU).

The UW was also highly ranked in the following subjects related to clinical and basic biomedical science research:

Microbiology: No. 2

Immunology: No. 4

Clinical Medicine: No. 7

Biology & Biochemistry: No. 11

Molecular Biology & Genetics: No.11

Pharmacology & Toxicology: No. 11

The NTU ranking system evaluates and ranks the scientific paper performance of the top 800 universities worldwide. Three criteria represented by eight indicators were used to assess a university’s overall scientific paper performance: research productivity (accounting for 25% of the score), research impact (35%) and research excellence (40%).

For more information, visit the NTU Ranking website and read the UW news release.

July 1-5, 2019

UPCOMING LECTURES AND SEMINARS

Janice Sabin, PhD, MSW, will be giving a series of lunch time talks, July 10, 17, 24, 12:15-1:15 PM to the Department of Biostatistics Summer Institutes: Big Data, Clinical Trials, Infectious Diseases and Statistical Genetics program.

 Titles:

Update on the Science of Implicit Bias and the Implicit Revolution

Striving for Excellence: Diversity, Equity, and Inclusion (DEI)

Manifestations of Implicit Bias in Academia: Individual and Organizational Remedies

For location and description of the talks see below:

https://www.eventbrite.com/e/summer-institutes-wednesday-lunch-sessions-registration-63080438289

PUBLICATIONS AND PRESENTATIONS

Hannah A. Burkhardt, Devika Subramanian, Justin Mower, Trevor Cohen. Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications. Paper accepted for presentation at AMIA 2019 Annual Symposium.

Jake Portanova, Nathan Murray, Justin Mower, Devika Subramanian, Trevor Cohenaer2vec: Distributed Representations of Adverse Event Reporting System Data as a Means to Identify Drug/Side-Effect Associations. Paper accepted for presentation at AMIA 2019 Annual Symposium.

Justin Mower, Trevor Cohen, Devika Subramanian. Complementing Observational Signal with Distributed Representations for Drug Side-effect Prediction. Podium abstract accepted for presentation at AMIA 2019 Annual Symposium.

William Kearns, Wilson Lau, and Jason Thomas. 2019. UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference. In Proceedings of the BioNLP 2019 workshop, Florence, Italy, August 1, 2019. Association for Computational Linguistics.

June 24-28, 2019

UPCOMING LECTURES AND SEMINARS

Speaker: Philip Payne, PhD, FACMI, Director, Institute for Informatics , Professor of Medicine, Division of General Medical Sciences, Washington University in St. Louis.

Location: Tuesday, June 25; 4:00 PM Warren G Magnuson Health Sciences Center: 1959 NE Pacific St, Seattle, WA 98195, HSB, T229

Title: The Learning Health (Record) System

Abstract: A Learning Health System (LHS) can be defined as an environment in which knowledge generation processes are embedded into daily clinical practice in order to continually improve the quality, safety, and outcomes of healthcare delivery.  While still largely an aspirational goal, the promise of the LHS is a future in which every patient encounter is an opportunity to learn and improve that patient’s care, as well as the care their family and broader community receives. The foundation for building such a LHS can and should be the Electronic Health Record (EHR), which provides the basis for the comprehensive instrumentation and measurement of healthcare services, as well as a means of delivering new evidence at the patient- and population levels.  While much has been written about the challenges associated with the use of current EHRs, the promise of these technology platforms remains vast and mostly under-realized.  In this talk, we will explore the ways in Biomedical Informatica and Data Science research are helping to realize the potential of EHR technologies in the context of an LHS, from the optimization of workflow and human factors, to the generation of reproducible and systematic clinical phenotypes, to the delivery of emergent knowledge to both providers and patients via advance clinical decision support systems.

Speaker’s Bio: Dr. Payne is the Robert J. Terry Professor and founding Director of the Institute for Informatics (I2) at Washington University in St. Louis.  He holds additional appointments as both a Professor of Medicine and Computer Science and Engineering. Dr. Payne is an internationally recognized leader in the field of translational bioinformatics (TBI) and clinical research informatics (CRI) and. He received his PhD with distinction in Biomedical Informatics from Columbia University, where his research focused on the use of knowledge engineering and human-computer interaction design principles in order to improve the efficiency of multi-site clinical and translational research programs.  Dr. Payne’s leadership in the informatics community has been recognized through his appointment to numerous national steering, scientific, editorial, and advisory committees, including efforts associated with the American Medical Informatics Association (AMIA), AcademyHealth, the Association for Computing Machinery (ACM), the National Cancer Institute (NCI), the National Library of Medicine (NLM), and the CTSA consortium, as well as his engagement as a consultant to academic health centers throughout the world.  Dr. Payne is the author of over 200 publications focusing on the intersection of biomedical informatics and the clinical and translational science domains, including several seminal reports that have served to define a new sub-domain of biomedical informatics theory and practice specifically focusing upon clinical and translational research applications.  Dr. Payne research group current focused on efforts in the following areas: 1) knowledge-based approaches to the discovery and analysis of bio-molecular and clinical phenotypes and the ensuing identification of precision diagnostic and therapeutic strategies; 2) interventional approaches to the use of electronic health records in order to address modifiable risk factors for disease and enable patient-centered decision making; 3) the study of human factors and workflow issues surrounding the optimal use of healthcare information technology; and 4) the design and evaluation of open-science platforms that enable collaborative and cumulative approaches to biomedical data analytics

UPCOMING GENERAL EXAM

Harkirat Sohi

Wednesday, June 26; 2:00 pm, UW South Lake Union, Building C, Room C123A

Title: Understanding the variation in Alzheimer’s Disease: a data science approach

Abstract: Alzheimer’s disease (AD) is a neurodegenerative condition that leads to cognitive decline and is the most common cause of dementia. AD affects many cognitive functions beyond memory. Recent work by Mukherjee & Crane  has classified individuals with AD into six subgroups based on cognitive test scores, reflective of a primary impairment in cognitive function in one of the following: executive and attention only, language only, memory only, visuospatial only, no domain and multiple domains. My dissertation work seeks to connect information from two separate domains of AD data: cognitively defined subgroups representing different behavioral phenotypes within AD and brain structural MRI data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Specifically, I propose a data science approach involving machine learning, mathematical methods and data visualization to connect the two knowledge domains (behavior and brain structural data), with the goal of shedding light on the underlying biological differences or similarities across the different AD subgroups, at the level of brain structural MRI data. I will be working with two types of data: data from a single time point (cross-sectional), specifically from the time of AD diagnosis, as well as data over several time points (longitudinal data) including the time of diagnosis. The three specific aims of my proposed work are: 1. Building classification models using cross-sectional brain structural MRI data to identify brain regions important for distinguishing between different AD subgroups, 2. Conducting mathematical analysis of longitudinal disease trajectories to characterize potential differences in AD progression in the brain across the AD subgroups, and 3. Creating data visualizations that allow for effective interpretation of the spatial and temporal trends in brain data across the AD subgroups.

PUBLICATIONS AND PRESENTATIONS

Casanova Perez RA, Patel H, Sangameswaran S, Cronkite D, Segal C, Rosenburg D, Gore GL, Wright J, Hartzler AL. Addressing physical activity barriers among prostate cancer survivors through a peer-based digital walking program. Poster accepted for presentation at AMIA 2019 Annual Symposium.

Austin E, LeRouge C, Hartzler AL, Chung AE, Hsueh P, Petersen C, Lavallee D. Incorporating Patient Voice into Clinical Care to Advance Learning Health Systems. Workshop accepted to AMIA 2019 Annual Symposium.

OTHER NEWS

UW holds No. 4 position on global rankings among US universities

The UW trails only Harvard, Stanford and Johns Hopkins among U.S. institutions in the NTU Ranking, compiled by National Taiwan University. Overall, the UW is ranked No. 7 on the list. The ranking offers annual performance rankings of universities around the world based on their production and impact of scientific papers

June 17-21, 2019

UPCOMING GENERAL EXAM

Harkirat Sohi

Wednesday, June 26; 2:00 pm, UW South Lake Union, Building C, Room C123A

Title: Understanding the variation in Alzheimer’s Disease: a data science approach

Abstract: Alzheimer’s disease (AD) is a neurodegenerative condition that leads to cognitive decline and is the most common cause of dementia. AD affects many cognitive functions beyond memory. Recent work by Mukherjee & Crane  has classified individuals with AD into six subgroups based on cognitive test scores, reflective of a primary impairment in cognitive function in one of the following: executive and attention only, language only, memory only, visuospatial only, no domain and multiple domains. My dissertation work seeks to connect information from two separate domains of AD data: cognitively defined subgroups representing different behavioral phenotypes within AD and brain structural MRI data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Specifically, I propose a data science approach involving machine learning, mathematical methods and data visualization to connect the two knowledge domains (behavior and brain structural data), with the goal of shedding light on the underlying biological differences or similarities across the different AD subgroups, at the level of brain structural MRI data. I will be working with two types of data: data from a single time point (cross-sectional), specifically from the time of AD diagnosis, as well as data over several time points (longitudinal data) including the time of diagnosis. The three specific aims of my proposed work are: 1. Building classification models using cross-sectional brain structural MRI data to identify brain regions important for distinguishing between different AD subgroups, 2. Conducting mathematical analysis of longitudinal disease trajectories to characterize potential differences in AD progression in the brain across the AD subgroups, and 3. Creating data visualizations that allow for effective interpretation of the spatial and temporal trends in brain data across the AD subgroups.

FACULTY/STUDENT/ALUMNI/STAFF ACTIVITIES

Thomas Payne, MD, been appointed Senior Editor of the journal Applied Clinical Informatics.

June 10-14, 2019

UPCOMING GENERAL EXAM

Pascal Brandt

Tuesday, June 11; 12:00 pm, Room T473, Health Sciences Building

Title: EHR-Driven Phenotyping: Improving Standards & Methods for Secondary Use of EHR Data

Abstract: The meteoric rise of electronic health record (EHR) use over the past decade has led to the creation of large clinical databases. These databases present an un- precedented opportunity for biomedical knowledge discovery. Data may be used for any number of research purposes, such as epidemiological, operational or quality improvements studies, pragmatic trials or clinical trial recruitment, comparative effectiveness research, predictive modeling, clinical decision support, pharmacovigilance, and genome-wide association studies, to name a few. In every case, one of the first steps involved is identifying the appropriate cohort of patients matching inclusion and exclusion criteria, using only data available in the EHR. This is currently done by developing executable queries using local data models and terminologies, which is a slow, error prone process that must be repeated for each database included in a study. This process, known as EHR-driven phenotyping, is a critical rate limiting factor that prevents massive scaling of knowledge discovery, and ultimately inhibits our ability to achieve the promise of national imperatives such as the Learning Healthcare System and All of Us. This research will attempt to improve the state of the art of EHR-driven phenotyping in three specific ways. First, we will propose a framework to reason about the complexity of existing inclusion and exclusion criteria (called phenotype algorithms). Second, we will assess the potential of popular and emerging standards for their suitability as a formal phenotype algorithm representation. Finally, we will develop and evaluate a standards-based tool that can be used to author phenotype algorithms and execute them on existing EHR databases.

PUBLICATIONS AND PRESENTATIONS

Adjunct Assistant Professor Dr. Uba Backonja presented on data visualization, public health, and social determinants of health at the 2019 Nursing Knowledge Big Data Science Conference held at the University of Minnesota June 5-6.

FACULTY/STUDENT/ALUMNI/STAFF ACTIVITIES

Fred Wolf Mentorship Award:

As most of you know, our former department Chair, Fred Wolf passed away in 2018 after a long and courageous battle. At his memorial service a recurring theme from most everyone who spoke of him was what a wonderful and inspiring mentor he was. As a way to honor Fred and keep his spirit in our midst, the department announced the establishment of the Fred Wolf Mentorship Award. We are soliciting nominations for this award again this year.

The award is open to all BIME faculty, staff, postdocs and students to both nominate and receive the award.

The process is simple, please send an email to Heidi, heidi5@uw.edu with a paragraph or two nominating a faculty member, staff member, postdoc or student to receive the award. Please briefly describe how your nominee has mentored you. A review committee will be formed to review the nominations and make the final selection.

Nomination deadline is Friday, June 7th. The award recipient will be announced at our annual end of year celebration on June 14th.

Please let Heidi know if you have any questions (heidi5@uw.edu).

Leaf Workshop

Tuesday, June 25th 1-2:30 pm in the TRAIL Space

Folks can register here: https://www.iths.org/blog/event/leaf-hands-on-workshop-june/?instance_id=626, but must have AMC System access prior to the workshop. The ITHS Leaf website provides guidance on how to do so at https://www.iths.org/investigators/services/bmi/leaf/

June 3-7, 2019

UPCOMING LECTURES AND SEMINARS

BIME 590A – Biomedical & Health Informatics Lecture Series

Thursday, June 6, 4:00pm-4:50pm, UW Medicine South Lake Union, Building C, Room C259

(livestream at tcs.slu.washington.edu)

Speakers: Reza Sadeghian and Tokunbo Akande

Title/Reza: A Brave New World” Clinical Pathway to Recognize Sentinel Injuries in the Emergency Department

Abstract/Reza: Despite good evidence on bruising as sentinel injuries of abuse, there is no standard workup among ED physicians on bruises found on children. Bruises are sometimes the only external signs of child abuse.  Nationally, bruising is missed ~44% of the time in fatal or near fatal cases of child abuse.  Children come into the SCH emergency department with bruises all the time.   Determining whether or not these bruises are abusive or not is critical.  We have several cases of children being discharged from our ED with bruises and who later came in with either more severe injuries or were found dead.  There is currently no standard approach to dealing with children who present to the ED with bruising.  There is considerable confusion about when to workup bruising as possible child abuse, who to consult and what needs to be done prior to discharging the child safely.  Providing a standard set of guidelines for how to approach a child in the ED with bruising would bring some clarity, consistency and transparency to the process and hopefully allay the fears of everyone involved in the care of the child.

There is good literature and clinical guidelines that address this issue and many hospitals and emergency departments have implemented systems to prevent discharging a child back into an abusive setting.

Develop a clinical pathway that will be triggered when a child presents to the ED with a bruise, that will inform physicians, nurses and social workers on how to assess the bruise(s), how to seek guidance in the form of a consult, what the standard workup for bruising in a child is and what outside agencies (ie. law enforcement, child protective services, etc.) need to be informed before discharging the child.

Title/Tokunbo: TBD

BIME 591C– Hands on with FHIR and other Healthcare Data Standards

Monday, June 3: 12:30pm- 1:20pm, Health Sciences Building, T530

Facilitators: Hannah Burkhardt, Jared Erwin, Piotr Mankowski, David Crosslin, Bill Lober

UPCOMING DISSERTATION DEFENSES

Karl Jablonowski

Monday, June 3; 3:00 pm; Room: CSE 403

Title: Data Mining the Electronic Medical Record with Intelligent Agents to Inform Decision Support Systems

Abstract: An intelligent agent framework is used on an ICU EMR to create prediction models for disease onset. Eleven models are created to inspect 5 diseases: acute respiratory distress syndrome (ARDS); severe acute hypoxemic respiratory failure (SAHRF); acute kidney injury (AKI); sepsis; and disseminated intravascular coagulation (DIC).

Four of the models (ARDS, AKI Stage 1, AKI Stage 2, and sepsis) are competitive or superior to the best comparable peer-reviewed models. The other seven are novel, including: SAHRF (AUC=0.952); DIC from ARDS positive patients (AUC=0.722); ARDS from DIC positive patients (AUC=0.675); AKI Stage 3 (AUC=0.983); the progression from AKI Stage 1 to Stage 2 (AUC=0.930); the progression from AKI Stage 2 to Stage 3 (AUC=0.951); and DIC (AUC=0.838).

In derivative work: a correlation between pre-DIC patients and metabolic acidosis is shown, a meta-analysis on misclassified patients is given, a disease pathway that demonstrates how ARDS and DIC can interact in a positive feedback loop is presented. DIC is shown to be implicated in 78% of all in-hospital mortality of ARDS patients.

Ryan James

Tuesday, June 4; 9:00 am, 4545 Roosevelt Way NE, #300

Title: A New Perspective On Minimally Invasive Procedures: Exploring the Utility of a Novel Virtual Reality Endovascular Navigation System

Abstract: TBD

UPCOMING MASTER’S DEFENSE

Sandeep Napa

Monday, June 3; 11:00 am, Room: Trail Room, Health Sciences Library

Title: An evaluation of the insidious consequences of clinical computing infrastructure failures at a large academic medical center

Abstract: Electronic Health Records (EHRs) are intended to make healthcare delivery safer, more effective and accountable. EHRs are complex socio-technical systems that are dependent on the proper functioning of many individual components that comprise the clinical computing infrastructure (CCI), such as networking equipment, message routing systems, and departmental clinical computing systems and many others. However, on occasion these CCI components fail or need maintenance, causing clinical workflow and data flow disruptions. Considering the inherently disruptive nature of EHR downtimes, organizations typically have mitigating procedures in place. However, many other small hardware or software CCI components also fail, causing loss of EHR functionality, insidiously. A systematic analysis of CCI failures has not been undertaken so far. A dataset of CCI system failure logs gathered at one health care system was classified and categorized to shed light on the nature, frequency and user impact of such CCI failures. By number of records, the top 3 components that had the highest frequency of failure are: Network (393 incidents, 59.5% of which were unscheduled) the inpatient EHR (ORCA) (372 incidents, 49.5% unscheduled) the outpatient EHR (Epic) (228 incidents, 12.3% unscheduled). In terms of user impact, components that accumulated the most failures are: the inpatient EHR (ORCA) (284.8 hours among under 5 users), Cloverleaf (interface engine) (263.5 hours among under 200 users), imaging (205.8 hours among under 50 users), and network (193.9 hours among under 50 users, and 193.4 hours among under 10 users). It is interesting to note that 4 of the 5 aforementioned components affected under 50 users. So, it is possible that cumulatively these small-impact but more frequent CCI component failures may approach or exceed the clinical impact of EHR downtimes. Although the data used in this work have important limitations in in their accuracy and completeness, this exploratory analysis is the first step towards a better understanding how to build a safe, resilient CCI that more reliably serves the needs of patients and providers.

UPCOMING GENERAL EXAM

Pascal Brandt

Tuesday, June 11; 12:00 pm, Room T473, Health Sciences Building

Title: EHR-Driven Phenotyping: Improving Standards & Methods for Secondary Use of EHR Data

Abstract: The meteoric rise of electronic health record (EHR) use over the past decade has led to the creation of large clinical databases. These databases present an un- precedented opportunity for biomedical knowledge discovery. Data may be used for any number of research purposes, such as epidemiological, operational or quality improvements studies, pragmatic trials or clinical trial recruitment, comparative effectiveness research, predictive modeling, clinical decision support, pharmacovigilance, and genome-wide association studies, to name a few. In every case, one of the first steps involved is identifying the appropriate cohort of patients matching inclusion and exclusion criteria, using only data available in the EHR. This is currently done by developing executable queries using local data models and terminologies, which is a slow, error prone process that must be repeated for each database included in a study. This process, known as EHR-driven phenotyping, is a critical rate limiting factor that prevents massive scaling of knowledge discovery, and ultimately inhibits our ability to achieve the promise of national imperatives such as the Learning Healthcare System and All of Us. This research will attempt to improve the state of the art of EHR-driven phenotyping in three specific ways. First, we will propose a framework to reason about the complexity of existing inclusion and exclusion criteria (called phenotype algorithms). Second, we will assess the potential of popular and emerging standards for their suitability as a formal phenotype algorithm representation. Finally, we will develop and evaluate a standards-based tool that can be used to author phenotype algorithms and execute them on existing EHR databases.

PUBLICATIONS AND PRESENTATIONS

Pollack, A.H., Simon, T.D., Snyder, J., Pratt, W. “Creating synthetic patient data to support the design and evaluation of novel health information technology.” Journal of Biomedical Informatics. 2019. July, Volume 95. PMID: 31078659  DOI: 10.1016/j.jbi.2019.103201

FACULTY/STUDENT/ALUMNI/STAFF ACTIVITIES

Fred Wolf Mentorship Award:

As most of you know, our former department Chair, Fred Wolf passed away in 2018 after a long and courageous battle. At his memorial service a recurring theme from most everyone who spoke of him was what a wonderful and inspiring mentor he was. As a way to honor Fred and keep his spirit in our midst, the department announced the establishment of the Fred Wolf Mentorship Award. We are soliciting nominations for this award again this year.

The award is open to all BIME faculty, staff, postdocs and students to both nominate and receive the award.

The process is simple, please send an email to Heidi, heidi5@uw.edu with a paragraph or two nominating a faculty member, staff member, postdoc or student to receive the award. Please briefly describe how your nominee has mentored you. A review committee will be formed to review the nominations and make the final selection.

Nomination deadline is Friday, June 7th. The award recipient will be announced at our annual end of year celebration on June 14th.

Please let Heidi know if you have any questions (heidi5@uw.edu).

Leaf Workshop

Tuesday, June 25th 1-2:30 pm in the TRAIL Space

Folks can register here: https://www.iths.org/blog/event/leaf-hands-on-workshop-june/?instance_id=626, but must have AMC System access prior to the workshop. The ITHS Leaf website provides guidance on how to do so at https://www.iths.org/investigators/services/bmi/leaf/

May 27-May 31, 2019

UPCOMING LECTURES AND SEMINARS

BIME 590A – Biomedical & Health Informatics Lecture Series

Thursday, May 30, 4:00pm-4:50pm, UW Medicine South Lake Union, Building C, Room C259 (livestream at tcs.slu.washington.edu)

Speaker: Milen Nokolov

Title: TBD

BIME 591C– Hands on with FHIR and other Healthcare Data Standards

Monday, May 27: No Class, Memorial Day Holiday

UPCOMING DISSERTATION DEFENSE

Karl Jablonowski

Monday, June 3; 3:00 pm; Room: CSE 403

Title: Data Mining the Electronic Medical Record with Intelligent Agents to Inform Decision Support Systems

Abstract: An intelligent agent framework is used on an ICU EMR to create prediction models for disease onset. Eleven models are created to inspect 5 diseases: acute respiratory distress syndrome (ARDS); severe acute hypoxemic respiratory failure (SAHRF); acute kidney injury (AKI); sepsis; and disseminated intravascular coagulation (DIC).

Four of the models (ARDS, AKI Stage 1, AKI Stage 2, and sepsis) are competitive or superior to the best comparable peer-reviewed models. The other seven are novel, including: SAHRF (AUC=0.952); DIC from ARDS positive patients (AUC=0.722); ARDS from DIC positive patients (AUC=0.675); AKI Stage 3 (AUC=0.983); the progression from AKI Stage 1 to Stage 2 (AUC=0.930); the progression from AKI Stage 2 to Stage 3 (AUC=0.951); and DIC (AUC=0.838).

In derivative work: a correlation between pre-DIC patients and metabolic acidosis is shown, a meta-analysis on misclassified patients is given, a disease pathway that demonstrates how ARDS and DIC can interact in a positive feedback loop is presented. DIC is shown to be implicated in 78% of all in-hospital mortality of ARDS patients.

Ryan James

Tuesday, June 4; 9:00 am, 4545 Roosevelt Way NE, #300

Title: A New Perspective On Minimally Invasive Procedures: Exploring the Utility of a Novel Virtual Reality Endovascular Navigation System

Abstract: TBD

UPCOMING MASTER’S DEFENSE

Sandeep Napa

Monday, June 3; 11:00 am, Room: Trail Room, Health Sciences Library

Title: An evaluation of the insidious consequences of clinical computing infrastructure failures at a large academic medical center

Abstract: Electronic Health Records (EHRs) are intended to make healthcare delivery safer, more effective and accountable. EHRs are complex socio-technical systems that are dependent on the proper functioning of many individual components that comprise the clinical computing infrastructure (CCI), such as networking equipment, message routing systems, and departmental clinical computing systems and many others. However, on occasion these CCI components fail or need maintenance, causing clinical workflow and data flow disruptions. Considering the inherently disruptive nature of EHR downtimes, organizations typically have mitigating procedures in place. However, many other small hardware or software CCI components also fail, causing loss of EHR functionality, insidiously. A systematic analysis of CCI failures has not been undertaken so far. A dataset of CCI system failure logs gathered at one health care system was classified and categorized to shed light on the nature, frequency and user impact of such CCI failures. By number of records, the top 3 components that had the highest frequency of failure are: Network (393 incidents, 59.5% of which were unscheduled) the inpatient EHR (ORCA) (372 incidents, 49.5% unscheduled) the outpatient EHR (Epic) (228 incidents, 12.3% unscheduled). In terms of user impact, components that accumulated the most failures are: the inpatient EHR (ORCA) (284.8 hours among under 5 users), Cloverleaf (interface engine) (263.5 hours among under 200 users), imaging (205.8 hours among under 50 users), and network (193.9 hours among under 50 users, and 193.4 hours among under 10 users). It is interesting to note that 4 of the 5 aforementioned components affected under 50 users. So, it is possible that cumulatively these small-impact but more frequent CCI component failures may approach or exceed the clinical impact of EHR downtimes. Although the data used in this work have important limitations in in their accuracy and completeness, this exploratory analysis is the first step towards a better understanding how to build a safe, resilient CCI that more reliably serves the needs of patients and providers.

UPCOMING GENERAL EXAM

Pascal Brandt

Tuesday, June 11; 12:00 pm, Room T473, Health Sciences Building

Title: EHR-Driven Phenotyping: Improving Standards & Methods for Secondary Use of EHR Data

Abstract: The meteoric rise of electronic health record (EHR) use over the past decade has led to the creation of large clinical databases. These databases present an un- precedented opportunity for biomedical knowledge discovery. Data may be used for any number of research purposes, such as epidemiological, operational or quality improvements studies, pragmatic trials or clinical trial recruitment, comparative effectiveness research, predictive modeling, clinical decision support, pharmacovigilance, and genome-wide association studies, to name a few. In every case, one of the first steps involved is identifying the appropriate cohort of patients matching inclusion and exclusion criteria, using only data available in the EHR. This is currently done by developing executable queries using local data models and terminologies, which is a slow, error prone process that must be repeated for each database included in a study. This process, known as EHR-driven phenotyping, is a critical rate limiting factor that prevents massive scaling of knowledge discovery, and ultimately inhibits our ability to achieve the promise of national imperatives such as the Learning Healthcare System and All of Us. This research will attempt to improve the state of the art of EHR-driven phenotyping in three specific ways. First, we will propose a framework to reason about the complexity of existing inclusion and exclusion criteria (called phenotype algorithms). Second, we will assess the potential of popular and emerging standards for their suitability as a formal phenotype algorithm representation. Finally, we will develop and evaluate a standards-based tool that can be used to author phenotype algorithms and execute them on existing EHR databases.

PUBLICATIONS AND PRESENTATIONS

Lucy Lu Wang; G. Thomas Hayman; Jennifer R. Smith; Monika Tutaj; Mary E. Shimoyama; John H. Gennari; Predicting instances of Pathway Ontology classes for pathway integration
Journal of Biomedical Semantics JBSM-D-18-00054R2

FACULTY/STUDENT/ALUMNI/STAFF ACTIVITIES

Jason Thomas, BS, University of Washington; Congratulations to the 2019-2020 JAMIA Student Editorial Board! The AMIA student members will contribute to the peer-review process for JAMIA by attending Editorial Board meetings and reviewing manuscripts.

Fred Wolf Mentorship Award:

As most of you know, our former department Chair, Fred Wolf passed away in 2018 after a long and courageous battle. At his memorial service a recurring theme from most everyone who spoke of him was what a wonderful and inspiring mentor he was. As a way to honor Fred and keep his spirit in our midst, the department announced the establishment of the Fred Wolf Mentorship Award. We are soliciting nominations for this award again this year.

The award is open to all BIME faculty, staff, postdocs and students to both nominate and receive the award.

The process is simple, please send an email to Heidi, heidi5@uw.edu with a paragraph or two nominating a faculty member, staff member, postdoc or student to receive the award. Please briefly describe how your nominee has mentored you. A review committee will be formed to review the nominations and make the final selection.

Nomination deadline is Friday, June 7th. The award recipient will be announced at our annual end of year celebration on June 14th.

Please let Heidi know if you have any questions (heidi5@uw.edu).

May 20-May 24, 2019

UPCOMING LECTURES AND SEMINARS

BIME 591C– Hands on with FHIR and other Healthcare Data Standards

Monday, May 13: 12:30pm- 1:20pm, Health Sciences Building, T530

Facilitators: Hannah Burkhardt, Jared Erwin, Piotr Mankowski, David Crosslin, Bill Lober

UPCOMING DISSERTATION DEFENSE

Karl Jablonowski

Monday, June 3; 3:00 pm; Room: CSE 403

Title: Data Mining the Electronic Medical Record with Intelligent Agents to Inform Decision Support Systems

Abstract: An intelligent agent framework is used on an ICU EMR to create prediction models for disease onset. Eleven models are created to inspect 5 diseases: acute respiratory distress syndrome (ARDS); severe acute hypoxemic respiratory failure (SAHRF); acute kidney injury (AKI); sepsis; and disseminated intravascular coagulation (DIC).

Four of the models (ARDS, AKI Stage 1, AKI Stage 2, and sepsis) are competitive or superior to the best comparable peer-reviewed models. The other seven are novel, including: SAHRF (AUC=0.952); DIC from ARDS positive patients (AUC=0.722); ARDS from DIC positive patients (AUC=0.675); AKI Stage 3 (AUC=0.983); the progression from AKI Stage 1 to Stage 2 (AUC=0.930); the progression from AKI Stage 2 to Stage 3 (AUC=0.951); and DIC (AUC=0.838).

In derivative work: a correlation between pre-DIC patients and metabolic acidosis is shown, a meta-analysis on misclassified patients is given, a disease pathway that demonstrates how ARDS and DIC can interact in a positive feedback loop is presented. DIC is shown to be implicated in 78% of all in-hospital mortality of ARDS patients.

UPCOMING MASTER’S DEFENSE

Sandeep Napa

Monday, June 3; 11:00 am, Room: Trail Room, Health Sciences Library

Title: An evaluation of the insidious consequences of clinical computing infrastructure failures at a large academic medical center

Abstract: Electronic Health Records (EHRs) are intended to make healthcare delivery safer, more effective and accountable. EHRs are complex socio-technical systems that are dependent on the proper functioning of many individual components that comprise the clinical computing infrastructure (CCI), such as networking equipment, message routing systems, and departmental clinical computing systems and many others. However, on occasion these CCI components fail or need maintenance, causing clinical workflow and data flow disruptions. Considering the inherently disruptive nature of EHR downtimes, organizations typically have mitigating procedures in place. However, many other small hardware or software CCI components also fail, causing loss of EHR functionality, insidiously. A systematic analysis of CCI failures has not been undertaken so far. A dataset of CCI system failure logs gathered at one health care system was classified and categorized to shed light on the nature, frequency and user impact of such CCI failures. By number of records, the top 3 components that had the highest frequency of failure are: Network (393 incidents, 59.5% of which were unscheduled) the inpatient EHR (ORCA) (372 incidents, 49.5% unscheduled) the outpatient EHR (Epic) (228 incidents, 12.3% unscheduled). In terms of user impact, components that accumulated the most failures are: the inpatient EHR (ORCA) (284.8 hours among under 5 users), Cloverleaf (interface engine) (263.5 hours among under 200 users), imaging (205.8 hours among under 50 users), and network (193.9 hours among under 50 users, and 193.4 hours among under 10 users). It is interesting to note that 4 of the 5 aforementioned components affected under 50 users. So, it is possible that cumulatively these small-impact but more frequent CCI component failures may approach or exceed the clinical impact of EHR downtimes. Although the data used in this work have important limitations in in their accuracy and completeness, this exploratory analysis is the first step towards a better understanding how to build a safe, resilient CCI that more reliably serves the needs of patients and providers.

PUBLICATIONS AND PRESENTATIONS

Hartzler AL, Ralston JD, Hannan T, Kelleher K, Penfold RB. Designing safer use of antipsychotics in youth: A human-centered approach. Psychiatric Services, in Press.

Chen, A. T., Wu, S., Tomasino, K. N., Lattie, E. G., & Mohr, D. C. (2019). A multi-faceted approach to characterizing user behavior and experience in a digital mental health intervention. Journal of Biomedical Informatics, 94, 103187. https://doi.org/10.1016/j.jbi.2019.103187

Swaminathan, A., Shirts, B. H., & Chen, A. T. (2019). Incorporating user feedback in the design of a genetics analysis tool: A two-part approach. Journal of Biomedical Informatics, 103204. https://doi.org/10.1016/j.jbi.2019.103204

Luo, B.L. Stone, C. Koebnick, S. He, D.H. Au, X. Sheng, M.A. Murtaugh, K.A. Sward, M. Schatz, R.S. Zeiger, G.H. Davidson, and F.L. Nkoy. Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for Secondary Analysis. JMIR Research Protocols, 2019

FACULTY/STUDENT/ALUMNI/STAFF ACTIVITIES

Mike Leu, MD, MS, MHS, FAAP, has been recognized as part of the inaugural class of Fellows of the American Medical Informatics Association at the AMIA Clinical Informatics Conference in Atlanta in April. https://www.amia.org/fellows-amia

Abdul Alshammari, PhD Candidate, and his wife Sana are proud parents to a new baby girl. Alia was born on April 17th at 2:08 AM, weighing about 7 pounds and measuring about 20 inches. All are doing well!

May 13-May 17, 2019

UPCOMING LECTURES AND SEMINARS

BIME 590A – Biomedical & Health Informatics Lecture Series

Thursday, May 16, 4:00pm-4:50pm, UW Medicine South Lake Union, Building C, Room C123AB

(livestream at tcs.slu.washington.edu)

 Speaker: Joey Mukherjee

Title: TBD

BIME 591C– Hands on with FHIR and other Healthcare Data Standards

Monday, May 13: 12:30pm- 1:20pm, Health Sciences Building, T530

Facilitators: Hannah Burkhardt, Jared Erwin, Piotr Mankowski, David Crosslin, Bill Lober

UPCOMING MASTER’S DEFENSE

Sandeep Napa

Monday, June 3; 11:00 am, Room: TBD

Title: An evaluation of the insidious consequences of clinical computing infrastructure failures at a large academic medical center

Abstract: Electronic Health Records (EHRs) are intended to make healthcare delivery safer, more effective and accountable. EHRs are complex socio-technical systems that are dependent on the proper functioning of many individual components that comprise the clinical computing infrastructure (CCI), such as networking equipment, message routing systems, and departmental clinical computing systems and many others. However, on occasion these CCI components fail or need maintenance, causing clinical workflow and data flow disruptions. Considering the inherently disruptive nature of EHR downtimes, organizations typically have mitigating procedures in place. However, many other small hardware or software CCI components also fail, causing loss of EHR functionality, insidiously. A systematic analysis of CCI failures has not been undertaken so far. A dataset of CCI system failure logs gathered at one health care system was classified and categorized to shed light on the nature, frequency and user impact of such CCI failures. By number of records, the top 3 components that had the highest frequency of failure are: Network (393 incidents, 59.5% of which were unscheduled) the inpatient EHR (ORCA) (372 incidents, 49.5% unscheduled) the outpatient EHR (Epic) (228 incidents, 12.3% unscheduled). In terms of user impact, components that accumulated the most failures are: the inpatient EHR (ORCA) (284.8 hours among under 5 users), Cloverleaf (interface engine) (263.5 hours among under 200 users), imaging (205.8 hours among under 50 users), and network (193.9 hours among under 50 users, and 193.4 hours among under 10 users). It is interesting to note that 4 of the 5 aforementioned components affected under 50 users. So, it is possible that cumulatively these small-impact but more frequent CCI component failures may approach or exceed the clinical impact of EHR downtimes. Although the data used in this work have important limitations in in their accuracy and completeness, this exploratory analysis is the first step towards a better understanding how to build a safe, resilient CCI that more reliably serves the needs of patients and providers.

PUBLICATIONS AND PRESENTATIONS

Accepted for MedInfo 2019: Backonja U, Velez O, Cato K, Hardiker NR. Addressing Health Disparities Through Informatics (panel).

Misirli, Goksel; Taylor, Renee; Goñi-Moreno, Angel; Mclaughlin, James; Myers, Chris; Gennari, John; Lord, Phillip; Wipat, Anil. SBOL-OWL: An ontological approach for formal and semantic representation of synthetic biology information. sb-2018-00532g.R1 ACS Synthetic Biology

FACULTY/STUDENT/ALUMNI/STAFF ACTIVITIES

BIME Happy Hour
Thursday, May 16, 5:00 p.m.,
South Lake Union, Reception Lounge (immediately following the BIME 590 Seminar)

Please join us for our monthly departmental BYOB Happy Hour. As always, please bring your own beverage; snacks will be provided!