Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 8 de 8
Filter
1.
J Am Med Inform Assoc ; 2022 Mar 31.
Article in English | MEDLINE | ID: covidwho-1769308

ABSTRACT

OBJECTIVE: To evaluate whether synthetic data derived from a national COVID-19 data set could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original data set (n = 1,854,968 SARS-CoV-2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip-code level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean=2.9±2.4; max=16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5,819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely-tested zip codes (which contained most of the data) were similar between original and synthetically-derived data sets. Analyses of sparsely-tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression - an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

2.
Learning Health Systems ; n/a(n/a):e10309, 2022.
Article in English | Wiley | ID: covidwho-1763262

ABSTRACT

The growing availability of multi-scale biomedical data sources that can be used to enable research and improve healthcare delivery has brought about what can be described as a healthcare ?data age.? This new era is defined by the explosive growth in bio-molecular, clinical, and population-level data that can be readily accessed by researchers, clinicians, and decision-makers, and utilized for systems-level approaches to hypothesis generation and testing as well as operational decision-making. However, taking full advantage of these unprecedented opportunities presents an opportunity to revisit the alignment between traditionally academic biomedical informatics (BMI) and operational healthcare information technology (HIT) personnel and activities in academic health systems. While the history of the academic field of BMI includes active engagement in the delivery of operational HIT platforms, in many contemporary settings these efforts have grown distinct. Recent experiences during the COVID-19 pandemic have demonstrated greater coordination of BMI and HIT activities that have allowed organizations to respond to pandemic-related changes more effectively, with demonstrable and positive impact as a result. In this position paper, we discuss the challenges and opportunities associated with driving alignment between BMI and HIT, as viewed from the perspective of a learning healthcare system. In doing so, we hope to illustrate the benefits of coordination between BMI and HIT in terms of the quality, safety, and outcomes of care provided to patients and populations, demonstrating that these two groups can be ?better together.?

3.
Contemp Clin Trials Commun ; 22: 100808, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1275235

ABSTRACT

BACKGROUND: The purpose of this paper is to describe the Automated Heart-Health Assessment (AH-HA) study protocol, which demonstrates an agile approach to cancer care delivery research. This study aims to assess the effect of a clinical decision support tool for cancer survivors on cardiovascular health (CVH) discussions, referrals, completed visits with primary care providers and cardiologists, and control of modifiable CVH factors and behaviors. The COVID-19 pandemic has caused widespread disruption to clinical trial accrual and operations. Studies conducted with potentially vulnerable populations, including cancer survivors, must shift towards virtual consent, data collection, and study visits to reduce risk for participants and study staff. Studies examining cancer care delivery innovations may also need to accommodate the increased use of virtual visits. METHODS/DESIGN: This group-randomized, mixed methods study will recruit 600 cancer survivors from 12 National Cancer Institute Community Oncology Research Program (NCORP) practices. Survivors at intervention sites will use the AH-HA tool with their oncology provider; survivors at usual care sites will complete routine survivorship visits. Outcomes will be measured immediately after the study visit, with follow-up at 6 and 12 months. The study was amended during the COVID-19 pandemic to allow for virtual consent, data collection, and intervention options, with the goal of minimizing participant-staff in-person contact and accommodating virtual survivorship visits. CONCLUSIONS: Changes to the study protocol and procedures allow important cancer care delivery research to continue safely during the COVID-19 pandemic and give sites and survivors flexibility to conduct study activities in-person or remotely.

4.
J Am Med Inform Assoc ; 28(2): 393-401, 2021 02 15.
Article in English | MEDLINE | ID: covidwho-1054313

ABSTRACT

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.


Subject(s)
COVID-19 , Electronic Health Records , Information Dissemination , Information Systems/organization & administration , Public Health Practice , Academic Medical Centers , Humans , Registries , United States
5.
BMC Med Inform Decis Mak ; 21(1): 15, 2021 01 07.
Article in English | MEDLINE | ID: covidwho-1015860

ABSTRACT

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. METHODS: In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells. RESULTS: We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. CONCLUSIONS: The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.


Subject(s)
COVID-19/drug therapy , Drug Repositioning , Pharmaceutical Preparations , Signal Transduction , Transcription, Genetic , Cells, Cultured , Epithelial Cells/virology , Gene Ontology , Humans , SARS-CoV-2/drug effects
6.
J Am Med Inform Assoc ; 28(3): 427-443, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-719257

ABSTRACT

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.


Subject(s)
COVID-19 , Data Science/organization & administration , Information Dissemination , Intersectoral Collaboration , Computer Security , Data Analysis , Ethics Committees, Research , Government Regulation , Humans , National Institutes of Health (U.S.) , United States
7.
J Am Med Inform Assoc ; 27(7): 1142-1146, 2020 07 01.
Article in English | MEDLINE | ID: covidwho-600829

ABSTRACT

Data and information technology are key to every aspect of our response to the current coronavirus disease 2019 (COVID-19) pandemic-including the diagnosis of patients and delivery of care, the development of predictive models of disease spread, and the management of personnel and equipment. The increasing engagement of informaticians at the forefront of these efforts has been a fundamental shift, from an academic to an operational role. However, the past history of informatics as a scientific domain and an area of applied practice provides little guidance or prologue for the incredible challenges that we are now tasked with performing. Building on our recent experiences, we present 4 critical lessons learned that have helped shape our scalable, data-driven response to COVID-19. We describe each of these lessons within the context of specific solutions and strategies we applied in addressing the challenges that we faced.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Electronic Health Records , Medical Informatics , Pandemics , Pneumonia, Viral/epidemiology , COVID-19 , Datasets as Topic , Humans , SARS-CoV-2
8.
Learn Health Syst ; : e10235, 2020 Jun 28.
Article in English | MEDLINE | ID: covidwho-598573

ABSTRACT

PROBLEM: The current coronavirus disease 2019 (COVID-19) pandemic underscores the need for building and sustaining public health data infrastructure to support a rapid local, regional, national, and international response. Despite a historical context of public health crises, data sharing agreements and transactional standards do not uniformly exist between institutions which hamper a foundational infrastructure to meet data sharing and integration needs for the advancement of public health. APPROACH: There is a growing need to apply population health knowledge with technological solutions to data transfer, integration, and reasoning, to improve health in a broader learning health system ecosystem. To achieve this, data must be combined from healthcare provider organizations, public health departments, and other settings. Public health entities are in a unique position to consume these data, however, most do not yet have the infrastructure required to integrate data sources and apply computable knowledge to combat this pandemic. OUTCOMES: Herein, we describe lessons learned and a framework to address these needs, which focus on: (a) identifying and filling technology "gaps"; (b) pursuing collaborative design of data sharing requirements and transmission mechanisms; (c) facilitating cross-domain discussions involving legal and research compliance; and (d) establishing or participating in multi-institutional convening or coordinating activities. NEXT STEPS: While by no means a comprehensive evaluation of such issues, we envision that many of our experiences are universal. We hope those elucidated can serve as the catalyst for a robust community-wide dialogue on what steps can and should be taken to ensure that our regional and national health care systems can truly learn, in a rapid manner, so as to respond to this and future emergent public health crises.

SELECTION OF CITATIONS
SEARCH DETAIL