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
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clic-ctsa.org
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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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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clic-ctsa.orgLightning 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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clic-ctsa.orgData-driven
decision support
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clic-ctsa.orgCOVID-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 6Data sharing and
harmonization
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clic-ctsa.orgCOVID-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. 8COVID-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. 9Solution
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
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patients as “facts” is domain-independentCOVID-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+ 11Solution
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 12COVID-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 14Clinical trial innovations
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clic-ctsa.orgSolution
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. 16Solution
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).
17Solution
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
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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. 19Solution
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. 20Free text/NLP
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clic-ctsa.orgCOVID-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
22COVID-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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clic-ctsa.orgSolution
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 25COVID-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.
26Discuss reuse of innovations
after COVIDRejoin Session I
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