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 1 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. 2 clic-ctsa.org
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) 3 clic-ctsa.org
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) 4 clic-ctsa.org
Data-driven decision support 5 clic-ctsa.org
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 7 clic-ctsa.org
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 15 clic-ctsa.org
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). 17
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 21 clic-ctsa.org
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 22
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 24 clic-ctsa.org
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 28 clic-ctsa.org
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