Dataset Information

Clinical informatics accelerates health system adaptation to the COVID-19 pandemic: examples from Colorado.

ABSTRACT: Objective: Large health systems responding to the coronavirus disease 2019 (COVID-19) pandemic face a broad range of challenges; we describe 14 examples of innovative and effective informatics interventions.

Materials and methods: A team of 30 physician and 17 nurse informaticists with an electronic health record (EHR) and associated informatics tools.

Results: To meet the demands posed by the influx of patients with COVID-19 into the health system, the team built solutions to accomplish the following goals: 1) train physicians and nurses quickly to manage a potential surge of hospital patients; 2) build and adjust interactive visual pathways to guide decisions; 3) scale up video visits and teach best-practice communication; 4) use tablets and remote monitors to improve in-hospital and posthospital patient connections; 5) allow hundreds of physicians to build rapid consensus; 6) improve the use of advance care planning; 7) keep clinicians aware of patients' changing COVID-19 status; 8) connect nurses and families in new ways; 9) semi-automate Crisis Standards of Care; and 10) predict future hospitalizations.

Discussion: During the onset of the COVID-19 pandemic, the UCHealth Joint Informatics Group applied a strategy of "practical informatics" to rapidly translate critical leadership decisions into understandable guidance and effective tools for patient care.

Conclusion: Informatics-trained physicians and nurses drew upon their trusted relationships with multiple teams within the organization to create practical solutions for onboarding, clinical decision-making, telehealth, and predictive analytics.

SUBMITTER: Lin CT

PROVIDER: S-EPMC7454679 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Clinical informatics accelerates health system adaptation to the COVID-19 pandemic: examples from Colorado.

Lin Chen-Tan CT Bookman Kelly K Sieja Amber A Markley Katie K Altman Richard L RL Sippel Jeffrey J Perica Katharine K Reece Lori L Davis Christopher C Horowitz Edward E Pisney Larissa L Sottile Peter D PD Kao David D Adrian Bonnie B Szkil Melissa M Griffin Julie J Youngwerth Jeanie J Drew Brendan B Pell Jonathan J

Journal of the American Medical Informatics Association : JAMIA 20201201 12

<h4>Objective</h4>Large health systems responding to the coronavirus disease 2019 (COVID-19) pandemic face a broad range of challenges; we describe 14 examples of innovative and effective informatics interventions.<h4>Materials and methods</h4>A team of 30 physician and 17 nurse informaticists with an electronic health record (EHR) and associated informatics tools.<h4>Results</h4>To meet the demands posed by the influx of patients with COVID-19 into the health system, the team built solutions to ...[more]

PMID: 32687152

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Project description:BackgroundThe global outbreak of COVID-19 (coronavirus disease 2019) disease has highlighted the importance of disease monitoring, diagnosing, treating, and screening. Technology-based instruments could efficiently assist healthcare systems during pandemics by allowing rapid and widespread transfer of information, real-time tracking of data transfer, and virtualization of meetings and patient visits. Therefore, this study was conducted to investigate the applications of clinical informatics (CI) during the COVID-19 outbreak.MethodsA comprehensive search was performed on Medline and Scopus databases in September 2020. Eligible studies were selected based on the inclusion and exclusion criteria. The extracted data from the studies reviewed were about study sample, study type, objectives, clinical informatics domain, applied method, sample size, outcomes, findings, and conclusion. The risk of bias was evaluated in the studies using appropriate instruments based on the type of each study. The selected studies were then subjected to thematic synthesis.ResultsIn this review study, 72 out of 2716 retrieved articles met the inclusion criteria for full-text analysis. Most of the articles reviewed were done in China and the United States of America. The majority of the studies were conducted in the following CI domains: prediction models (60%), telehealth (36%), and mobile health (4%). Most of the studies in telehealth domain used synchronous methods, such as online and phone- or video-call consultations. Mobile applications were developed as self-triage, self-scheduling, and information delivery tools during the COVID-19 pandemic. The most common types of prediction models among the reviewed studies were neural network (49%), classification (42%), and linear models (4.5%).ConclusionThe present study showed clinical informatics applications during COVID-19 and identified current gaps in this field. Health information technology and clinical informatics seem to be useful in assisting clinicians and managers to combat COVID-19. The most common domains in clinical informatics for research on the COVID-19 crisis were prediction models and telehealth. It is suggested that future researchers conduct scoping reviews to describe and analyze other levels of medical informatics, including bioinformatics, imaging informatics, and public health informatics.

Project description:BackgroundA considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns.MethodsWe use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply.ResultsThe empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ([Formula: see text]). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When [Formula: see text] and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints.ConclusionsOur study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

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Clinical informatics accelerates health system adaptation to the COVID-19 pandemic: examples from Colorado.

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Clinical informatics accelerates health system adaptation to the COVID-19 pandemic: examples from Colorado.

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