COVID-19 Local and Regional Informatics Innovations - Moderator: Patricia Kovatch, Icahn School of Medicine at Mount Sinai

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COVID-19 Local and Regional Informatics Innovations - Moderator: Patricia Kovatch, Icahn School of Medicine at Mount Sinai
COVID-19 Local and
      Regional Informatics
          Innovations
Moderator: Patricia Kovatch, Icahn School of Medicine at Mount Sinai

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                               clic-ctsa.org
COVID-19 Local and Regional Informatics Innovations - Moderator: Patricia Kovatch, Icahn School of Medicine at Mount Sinai
COVID-19 Local and Regional Informatics
Innovations
 Overview:
 The initial COVID surge was a once-in-a-lifetime occurrence, that lead health
 systems to reallocate all of their resources to better understand, treat and
 manage this new disease.

 Today’s objectives:
 1. To share our novel informatics solutions and experiences for addressing
    COVID-19.
 2. To identify opportunities to leverage these solutions and apply them to new
    use cases.
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COVID-19 Local and Regional Informatics Innovations - Moderator: Patricia Kovatch, Icahn School of Medicine at Mount Sinai
Themes
 We received 17 responses, representing following themes:
   1. Data-driven informatics support (1)
   2. Data sharing and harmonization (7)
   3. Clinical trial innovations (5)
   4. Free text/NLP (2)
   5. Predictive analytics (2)

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Lightning presentations
 Rules for engagement
 • 2 minutes per innovation (34 minutes)
 • Questions at the end of each theme (not to exceed 3 minutes, total 15 minutes)
 • Discuss reuse of innovations after COVID (10 minutes)

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Data-driven
decision support

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COVID-19 Informatics                                           Solution
 Innovation                             CTSI Informatics team used our clinical research IT and data
 Title: Ethics, Values, and Scarce      workflow infrastructure to extend our research database
 Resources: Implementing data-          platform to ingest inpatient SOFA sources and ventilator
                                        status every 20 min and apply the ventilator allocation
 informed standards of care using       algorithm to score patients. Allocation scores were verified
 a novel application during the         by a clinical ethics team. Patients were randomized based on
 COVID-19 crisis.                       algorithm allocation criteria to potentially receive or
                                        discontinue ventilator support in the event all ventilators
                                        were in use.
 Problem addressed with the
 innovation: How to operationalize
                                                                 Impact
 NYS data guidelines for ventilator
 allocation in the event of scarcity     Within 2 weeks data streams were implemented and the
                                         application launched, with refinements over the next 4
 during the pandemic. The goal was to    weeks. An independent clinical team continued to monitor
 provide unbiased decision support       data quality and the triage team was trained and ready to
 for scare life-saving resource using    invoke the algorithm output. Fortunately, our medical center
 real-time EHR data and an ethical       never exceeded ventilator usage capacity. The 21M rows in
 framework.                              the database along with outcome data from the EHR are
                                         providing a rich resource to study the ethical allocation of
                                         scare resources and data-driven decision support.
                                                         Future beyond COVID?
PI(s):Martin Zand, MD, PhD
      Jeanne Holden-Wiltse, MPH, MBA     Provides a data workflow and application framework for
Institution: University of Rochester     implementing algorithms for the ethical distribution of
Funding: CTSI                            medical devices for patients during times of scarcity.
Publications: pending                                                                                   6
Data sharing and
 harmonization

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COVID-19 Informatics                                                 Solution
                                           To create a de-identified COVID dataset that is
Innovation                                 enhanced regularly and refreshed daily.
Title: Making institutional                    • We began with one file with 31 data elements on
                                                 March 26, 2020 and iterated to seven files containing
near real-time COVID-19                          nearly 400 data elements by April 12, 2021.
                                               • All files are joined using a masked MRN and
data maximally available in                      encounter ID, enabling you to track a patient’s
                                                 journey over time.
the minimal amount of time                         ―   All dates are represented as elapsed time since the
                                                       start of the patient’s acute COVID encounter (t=0).
                                                   ―   For patients with multiple COVID encounters, there is
                                                       an encounter sequence #, to view the temporal
Problem addressed with the                             relationship of events
innovation: To provide researchers and
clinicians throughout the Mount Sinai                             Impact
Health System access to COVID-19 data      The de-identified COVID data set has been downloaded
in near real-time as the pandemic surged   over 6,000 times by over 300 distinct users.
in New York City.
                                           Analysis of this data set has been used in numerous
                                           NIH proposals and publications.

                                                          Future beyond COVID?
PI(s): Sharon Nirenberg, Timothy           To continue to build upon the existing data set in order
Quinn, Patricia Kovatch                    to support research on COVID-19 including long-term
                                           sequelae of COVID-19 infection.
Institution: Icahn School of Medicine at
Mount Sinai                                To utilize this agile framework to track a cohort while
                                           still providing detail on an individual patient’s journey.          8
COVID-19 Informatics                                                                   Solution
Innovation                                Mayo Clinic’s Clinical Data Warehouse Solution, the Unified Data Platform (UDP), adopts transformative
Title: A Unified Data and Analytics       technology based on open architecture, common core services, and ensures alignment with both clinical and
                                          research priorities. It emphasizes a data-centric design, bringing disparate data types together for varied
Platform Enabling Rapid COVID             purposes such as patient care, education, research and administration leveraging data-as-a-service and
                                          analytics-as-a-service. Managing data in this way enables the enterprise to consume data independent of the
Response across the Mayo Clinic           system of record and protects against changes in technology.

Enterprise                                In late March 2020, we created UDP COVID-19 Datamart to monitor Mayo’s COVID-19 situation in the hospital
                                          along with positive tests and other important metrics. Over time, the Datamart grew from monitoring hospital
                                          census and lab tests to enabling real-time surveillance and predictive modeling for COVID-19 cases,
Problem addressed with the                hospitalizations, and staff absences. We also implemented a COVID-19 surveillance system

innovation:                               In April 2020, we implemented a COVID-19 visualization dashboard with a predictive modeling algorithm to
                                          support institutional decision making regarding clinical operation and resource allocation.
An unified data and analytics platform                                                     Impact
has enabled us to quickly implement       Our work has been a vital part of Mayo Clinic’s ability to care for our patients, ensure a safe environment,
near real time informatics and            and manage its resources during the pandemic.
analytics solutions in response to        The COVID-19 Datamart has become the source for leveraging data-driven solutions for Mayo’s COVID-
COVID-19                                  19 response. A total of 52,000 queries were executed from Jan 1, 2021 to March 21, 2021. Over 100
                                          projects at Mayo Clinic have leveraged the COVID-19 Datamart.

PI(s): Hongfang Liu, Daryl J. Kor, Curt   A retrospective analysis demonstrated that we accurately predicted the timing and extent of the
                                          case/hospitalization surges that took place across our Mayo Clinic sites. This gave us time to prepare and
Storlie                                   ensure we could continue to provide optimal care while keeping our patients and staff safe.

Institution: Mayo Clinic                  Our predictive modeling work received honorable mention at XPRIZE Pandemic Response Challenge.
Funding: UL1TR002377, U01TR002062                                          Future beyond COVID?
and R01EB019403                           Our rapid response to COVID-19, leveraged data enabled by our innovative data warehouse solution. It
Publications: PMID: 3257770               confirms that our strategy of data-as-a-service and analytics-as-a-service across the enterprise facilitates
                                          rapid discovery, translation, and application.
https://evolution.ml/pdf/xprize/Advance
                                          Our innovative data warehouse solutions can be leveraged for broad impact across the CTSA-wide
4Covid.pdf                                community, as demonstrated by our text analytics efforts to enhance the community capacity of using
                                          unstructured data in clinical data warehouses.                                                                 9
Solution
COVID-19 Informatics Innovation
                                                            •       Increased refresh rate for i2b2 (incremental update twice a week
i2b2 Enhancements to Support Access
                                                            •       Expanded inclusion of EHR data: COVID-19-specific documentation and clinical
to EHR Data for UAB Enterprise                                      observations (e.g., ventilator-related)
COVID-19 Cohort Protocol                                    •       Inclusion of COVID Enterprise Protocol enrollment data, research data, and biospecimens

Problems addressed with the innovation:                     •       Application of the ACT Ontologies in the ACT Test Network and local instance
The CTSA program developed an IRB-approved                  •       Concepts added ontology (e.g., critical care, course of illness, other variables of interest)
COVID-19 cohort for recruitment into clinical trials.       •       Implementation of intermediary "Data Transformation" process for COVID data requests
Researchers need to:
                                                            •       Standing “limited data” datasets (OMOP and raw formats) to download on demand
1) Estimate phenotype cohort sizes
                                                            •       Expanded data set exports: regular (ACT, All of Us, TriNetX) and new (N3C)
2)    Identify eligible subjects
                                                            •       Supporting "Post Acute Sequalae of COVID-19" proposals
3)    Download limited data sets

                                                                                                          Impact
Problems:
                                                                •    Rapid response to local data needs
1) Matching patients to phenotype criteria
                                                                •    Rapid response to national efforts (first 4of N3C)
2)    Navigating i2b2 ontology
                                                                •    Preliminary experience relevant to responses to funding opportunities
3)    Identifying relevant data for filters and downloads            (e.g., PASC)

                PI:       James Cimino                                                       Future beyond COVID?
       Institution:       University of Alabama
                                                                     Three words: ontology based approach
                            at Birmingham
        Funding:          N3C                                        Creating concepts in the ontology, organizing them into
     Publications:        None                                       usable, useful hierarchies, and mapping concepts to
                                                                                                                                                              10
                                                                     patients as “facts” is domain-independent
COVID-19 Informatics                                                      Solution
Innovation                              The      Weill    Cornell    Medicine
                                        Institutional Data Repository (IDR)
Title: Weill Cornell                    integrates electronic patient data
                                        from multiple electronic health
                                        record (EHR) and research systems,
Medicine COVID                          transforming raw data into a format
                                        accessible to biostatisticians and
Institutional Data                      clinicians     without    informatics
                                        training.    Also, the IDR enables
Repository                              investigators to browse and request
                                        biospecimens linked with EHR data.

Problem addressed with the                                        Impact
innovation: Clinicians and scientists    The IDR has supported more than 30+ publications
needed patient and biospecimen data      while also informing clinical response activities in New
from multiple disparate electronic       York City. In addition to providing a technical solution,
systems to support pandemic              the IDR has extended existing investigator engagement,
response efforts.                        including requirements gathering and regulatory
                                         oversight, provided by the institutional Research
                                         Informatics team.
                                                                Future beyond COVID?
PI(s): Thomas R. Campion, Jr., Ph.D.     Positive feedback from investigators suggests that the
Institution: Weill Cornell Medicine      IDR can extend to other disease areas in support of
Funding: UL1TR000457                     retrospective and prospective studies.
Publications: 30+                                                                                    11
Solution
COVID-COHD (Columbia                        We released large-scale privacy-preserving concept co-
                                            occurrence information derived from the electronic
Open Health Data)                           health records data of COVID-19 patients.

Problem addressed: A lot of
translational investigators need
access to clinical data but often do
not have such access or lack the
knowledge to process complex                                        Impact
clinical data. Patient privacy is a big
barrier for making clinical data            COVID-COHD is available at http://covid.cohd.io It is
available to the broad translational        being actively used by the NCATS-funded Biomedical
science community.                          Data Translator consortium. Smart APIs for data access
                                            is provided.

PI(s): Chunhua Weng
Institution: Columbia University                            Future beyond COVID?
Funding: R01LM009886, UL1TR001873
Publications: COHD-COVID: Columbia          We will expand COVID-COHD to cover more data types
Open Health Data for COVID-19               such as concepts extracted from notes.
Research, Scientific Data, Under revision                                                            12
COVID-19 Informatics                                               Solution
Innovation                                  To obtain input from a diverse group of ACT members
Title: COVID-19 Application                 we communicated through weekly online meetings, a
                                            shared GitHub repository, and an i2b2 server dedicated
Ontology for ACT Network                    for viewing the ontology as it was developed. We
Problem addressed with the                  established a detailed process to develop, validate, and
  innovation: We developed a COVID-         deploy the ontology.
  19 application ontology in the
  national Accrual to Clinical Trials
  (ACT) network to enable                                            Impact
  harmonization and querying of data
  elements that that are critical to        Since the beginning of the pandemic, we developed,
  COVID-19 research.                        released and deployed three versions of the ontology.
                                            We included terms and their associated codes from
                                            commonly used terminologies that include ICD-10-CM,
                                            CPT-4, HCPCS, LOINC, and SNOMED-CT. We crafted
                                            computable phenotypes, derived concepts and
                                            harmonized value sets that are pertinent to COVID-19.
                                                            Future beyond COVID?
PI(s): Shyam Visweswaran                    The processes and pipelines we created for computable
Institution: University of Pittsburgh       phenotypes, derived concepts and harmonized value
Funding: UL1 TR001857                       sets will be useful for development of other ontologies.
Publications: medRxiv 2021.03.15.21253596                                                              14
Clinical trial innovations

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Solution
COVID-19 Informatics                       We developed a multidisciplinary team to vet and prioritize
Innovation                                 clinical trials and a cross functional implementation team
                                           to design and implement standardized EHR-based tools
Clinical Trial Prioritization              and processes to support clinical trials. We developed
                                           tools, including a cohort identification list with automation
Problem:                                   and cross study communication capabilities, a contactless
                                           consent process, and a dynamic order set that provided
With a large population of COVID-19        front-line clinicians information on study specifics.
patients, the call for therapeutic
clinical trials grew to an almost                                     Impact
unmanageable number. Individual
researchers were unable to gauge           This approach allowed us to limit duplicative clinical
what was sustainable by the                trials, minimize research waste, speed up time to
institutional infrastructure and how       implement trials and provide communication pathways
much they would need to take on with       between primary investigators on distinct trials and with
their own resources. It was difficult to   front line clinicians to optimize study recruitment. Thus
know which trials would be in              15 out of 80 offered (8 ambulatory, 7 in-patient) clinical
competition with which and to reach        trials were selected with 10 PIs and ~230 patients.
consensus on the potential
significance of each trial.                                  Future beyond COVID?

                                           The NorthShore Outcomes Research Network, with six
PI(s): Nirav Shah, MD MPH                  Core Program Directors and a further 8 Affiliate Program
Institution: NorthShore                    Directors, is poised ready to expand this program
Funding: Institutional/Philanthropic       beyond COVID. Regulatory differences will have to be
Publications: in preparation / tbc         addressed for work beyond COVID.                                16
Solution
   VICTR COVID-19                                 •       As an Opt-In institution we document Consent to Contact for COVID
   Recruitment Data Mart                                  research opportunities.
                                                  •       Harness REDCap-Epic interoperability to find the right patient for the
                                                          right trial at the right time using study-specific logic computable in
   Problem addressed with the                             the EHR.
   innovation: Multiple COVID-19 trials           •       Use of an honest broker to share potential matches with active
   competing for the same patient                         studies daily using a rotating schedule. Primary study gets first right
   population                                             of refusal and secondary studies get access around mid-day.
                                                  •       Study teams required to use Epic enrollment statuses to increase
                                                          transparency across studies and minimize participant fatigue.

                                                                                      Impact
                                                      •   Feedback from study teams has been very positive in terms of
                                                          saving time with screening.
                                                      •   [For interventional trials] Enrollment through the Data Mart account
                                                          for about 40% or more of their overall enrollment.
                                                      •   A technical solution alone would not be sufficient; Project
                                                          Management support is critical to success.

                                                                           Future beyond COVID?
PI(s):     Paul Harris, PhD
           Vanderbilt University Medical Center       •   The technical framework and overall model could be applied to other
Funding: CTSA (UL1TR002243), RIC(U24TR001579),            biomedical research domains where multiple trials should be
           NLM FHIR Contract (#75N97019P00279)            considered for each patient (e.g., diabetes, movement disorders, HIV,
Publication: Accepted to JBI                              cardiovascular disease).
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Solution
                                             The COVID-19 Trial Finder was designed to facilitate
The COVID-19 Trial Finder                    patient-centered search of COVID-19 trials, first by
                                             location and radius distance from trial sites, and then by
                                             brief, dynamically generated questions to allow users to
                                             prescreen their eligibility for nearby COVID-19 trials with
Problem addressed: Existing clinical         minimum human computer interaction. A simulation
trial search engines including               study using 20 publicly available patient case reports
ClinicalTrials.gov presents significant      demonstrates its precision and effectiveness.
information overload. With over 1000
coronavirus disease 2019 (COVID-19)                                    Impact
trials conducted in the United States,
it is imperative to provide a user-           The system is accessible online
friendly and efficient search engine          (https://covidtrialx.dbmi.columbia.edu), as well as its
for COVID-19 trials to enable rapid           source code (https://github.com/WengLab-
recruitment to these studies.                 InformaticsResearch/COVID19-TrialFinder).

PI(s): Chunhua Weng                                           Future beyond COVID?
Institution: Columbia University
Funding: R01LM009886, UL1TR001873             The structured COVID-19 trial summary will be released
Publications: The COVID-19 Trial Finder, J    to the community shortly.
Am Med Inform Assoc. 2021 Mar
                                                                                                           18
1;28(3):616-621.
Solution
COVID-19 Trial
                                          We transformed COVID-19 trial summaries into
Collaboration Opportunity                 structured representations and developed methods to
Recommendation                            identify similar or related COVID-19 trials. A user-
                                          friendly web application is also created to allow flexible
Problem addressed: As many                parameter configuration for recommending
institutions rush to design clinical      collaboration opportunities for COVID-19 trial designers.
trials in search of effective treatment
for COVID-19, lot of trials are created
rapidly in a short time without                                   Impact
coordination, causing redundancy
and competition. There is a need for       A prototype is developed and made available at
better coordination and collaboration      http://apex.dbmi.columbia.edu/collaboration/

                                                           Future beyond COVID?
PI(s): Chunhua Weng
Institution: Columbia University           We will continue to improve the usability of the software
Funding: R01LM009886, UL1TR001873          prototype and test it with more clinical trial researchers
Publications: Under prep                   in order to better understand how clinical trial
                                           collaborations can be facilitated by informatics.            19
Solution
Data-Driven Eligibility
                                                This research evaluated the impact of eligibility criteria
Criteria Optimization for                       on recruitment and observable clinical outcomes of
COVID-19 Clinical Trials                        COVID-19 clinical trials using electronic health record
                                                (EHR) data.
Problem addressed: It is increasingly
recognized that clinical trials need to
be more inclusive. However, making
eligibility criteria inclusive in a
clinically meaningful way is hard due                                     Impact
to the lack of evidence-based
                                                 By adjusting the thresholds of common eligibility criteria
approaches for criteria design.                  based on the characteristics of COVID-19 patients, we
                                                 could observe more composite events from fewer patients.
                                                 This research demonstrated the potential of using the EHR
                                                 data of COVID-19 patients to inform the selection of
                                                 eligibility criteria and their thresholds, supporting data-
                                                 driven optimization of participant selection towards
                                                 improved statistical power of COVID-19 trials.
PI(s): Chunhua Weng
Institution: Columbia University                                 Future beyond COVID?
Funding: R01LM009886, UL1TR001873
Publications: Towards clinical data-driven       We will extend the methods to clinical trials in other
eligibility criteria optimization for            disease domains.
interventional COVID-19 clinical trials, J Am
Med Inform Assoc. 2021 Jan 15;28(1):14-22.                                                                     20
Free text/NLP

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COVID-19 Informatics                                             Solution
Innovation                                The Electronic Medical Record Search Engine
Title: A free text search                 (EMERSE) was supported throughout the pandemic to
                                          include clinical notes that contained important clinical
engine to study COVID                     details on all COVID-19 patients at the U of Michigan.
Problem addressed with the                The University of California – San Francisco also
innovation: Researchers are looking       implemented an instance of EMERSE containing only
for simple ways to access the             COVID-19 patients to support research there.
unstructured clinical data in the
medical record. A secure, self-service,                             Impact
free text search engine enables rapid
identification of clinical cohorts and     Having software tools like EMERSE in place is important
clinical concepts based on mentions        to support research efforts that need to get completed
in the notes. These notes often            rapidly. Free text continues to be important to support
contain details that are not present in    research efforts for which structured data are not
the structured data.                       sufficient to provide the necessary clinical details.

                                                           Future beyond COVID?
PI(s): David Hanauer
Institution: University of Michigan
                                           EMERSE can be used for any type of clinical research
Funding: NCI (U24CA204863) & NCATS         involving cohort discovery or data abstraction. It is
(UL1TR000433)                              currently being installed at multiple CTSA sites and
Publications: PMIDs: 32949274, 33046294    cancer centers nationwide. Details about the free, open-
                                           source tool can be found at https://project-emerse.org
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COVID-19 Informatics                                                                                             Solution
  Innovation                                                                        • We created a High Definition – Natural Language Processing
  Title: HD-NLP, unlocking the                                                        HD-NLP Pipeline
  Information in Free Text Notes                                                    • The pipeline can code in SNOMED CT, LOINC, RxNorm,
                                                                                      Gene Ontology, HPO and Solor
  and Reports
  Problem addressed with the innovation:                                            • We pre-trained models with general medical knowledge to
  Eighty-three percent of healthcare data is                                          improve the accuracy and sensitivity to incomplete or bad
  in free text notes and reports. Without that                                        data and to decrease bias within these algorithms and to
                                                                                      improve generalizability across healthcare organizations.
  data our ability to do real world EHR based
  research is limited.                                                                                               Impact
 This slide shows an NLP pipeline that                                                  •    We tested the pipeline on 170,000 patients with opioid exposure
 allows all hubs to codify their free text                                                   and looked at the rates of Opioid use disorder and opioid
                                                                                             overdoses.
 notes and reports so that the data is ready
 for insertion into machine learning                                                    •    The models were more accurate and less sensitive to the
 algorithms / predictive analytics that have                                                 removal of data or to adding in bad data than those models built
                                                                                             on the same data but without the pre-trained embeddings.
 the potential to improve our research and
 to quickly translate those improvements                                                •    We used the same system to discover new treatments for
                                                                                             COVID 19 and for stage I NSCLC
 into clinical practice.
PI(s): Peter L. Elkin, MD                                                                                   Future beyond COVID?
Institution: University at Buffalo                                                  •       These principals can be used to improve all real world
Funding: NLM T15LM012495, NIAAA R21AA026954, R33AA0226954 and NCATS                         evidence based research and for recruitment to clinical trials.
UL1TR001412. This study was funded in part by the Department of Veterans Affairs.
                                                                                    •       Biosurveillance for the next pandemic can be put in place
Publications: Schlegel DR, Crowner C, Lehoullier F, Elkin PL. HTP-NLP: A                    using this method to improve the rapidity of our public health
New NLP System for High Throughput Phenotyping. Stud Health Technol Inform.                                                                                   23
                                                                                            response when we are faced with the next pandemic.
2017;235:276-280. PMID: 28423797; PMCID: PMC7767581         .
Predictive analytics

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Solution
Severity Prediction for                        We proposed a Recurrent neural network (RNN) model
                                               to predict severity for COVID-19 patients.
COVID-19 Patients
                                               Input: a COVID-19 patient with all historical EHR data
                                               and basic demographic information (sex and age)
Problem addressed: To develop a
model to predict the risk of                   Output: risk score scaled 0-1 indicating the likelihood of
developing severe status for a COVID           the patient developing into one of severe outcomes
patient using only the patient’s               (mechanical ventilation, tracheostomy, and death)
electronic health records data.                                         Impact
                                                The model achieved high AUC (0.864) utilizing only
                                                historical medical record data. The severity scores
                                                showed advantages over basic characteristics.

PI(s): Chunhua Weng                                             Future beyond COVID?
Institution: Columbia University
Funding: R01LM012895, UL1TR001873               We will test the generalizability of this model to data
Publications: Severity Prediction for COVID-    from other EHR systems, hopefully using the N3C data,
19 Patients Via Recurrent Neural Networks,      and implement this model at clinical practice.
Proc of AMIA Summits 2021, in press                                                                         25
COVID-19 Informatics                                                           Solution
 Innovation                                            We extended the agent-based model, SpatioTemporal
 Title: Human Activity Pattern                         Human Activity Model (STHAM), for simulating SARS-CoV-2
 Implications for Modeling SARS-                       transmission dynamics. See Lund, A.M., Gouripeddi, R. &
 CoV-2 Transmission                                    Facelli, J.C. STHAM: an agent based model for simulating
                                                       human exposure across high resolution spatiotemporal
                                                       domains. J Expo Sci Environ Epidemiol 30, 459–468 (2020).
Problem addressed with the                             https://doi.org/10.1038/s41370-020-0216-4
innovation: How to model human
activity patterns to provide less
invasive non pharmaceutical                                                        Impact
interventions.                                          We presented preliminary STHAM simulation results that
                                                        reproduce the overall trends observed in the Wasatch Front
                                                        (Utah, United States of America) for the general population.
                                                        The results presented here clearly indicate that human
PI(s): Julio C. Facelli                                 activity patterns are important in predicting the rate of
                                                        infection for different demographic groups in the population..
Institution: University of Utah
Funding: University of Utah Seed Grant
Publications: Yulan Wang, Bernard Li, Ramkiran                           Future beyond COVID?
Gouripeddi, Julio C. Facelli, Human activity pattern
implications for modeling SARS-CoV-2 transmission,
Computer Methods and Programs in Biomedicine, Volume    Future work in pandemic simulations should use empirical
199, 2021, 05896,                                       human activity data for agent-based techniques
https://doi.org/10.1016/j.cmpb.2020.105896.
                                                                                                                         26
Discuss reuse of innovations
        after COVID
Rejoin Session I

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