Objective Large health systems responding to the 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: train physicians and nurses quickly to manage a potential surge of hospital patients; build and adjust interactive visual pathways to guide decisions; scale up video visits and teach communication; use tablets and remote monitors to improve in-hospital and post-hospital patient connections; allow hundreds of physicians to build rapid consensus; improve the use of advance care planning; keep clinicians aware of patients’ changing COVID-19 status; connect nurses and families in new ways; semi-automate Crisis Standards of Care; and 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.

No abstract

Objective To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA for decision support for a Crisis Standards of Care team. Materials and Methods We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. Results The prospective cohort included 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) required intensive care unit care, 1,480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. Discussion Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction. Conclusion We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

BackgroundThe SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality.Research QuestionsTo rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA.Study Design and MethodsWe conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.ResultsWe prospectively analyzed 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) included intensive care unit (ICU)-level care, 1,480 (5.4%) included invasive mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted overall mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted overall mortality with AUROC 0.94. In the subset of patients with COVID-19, we predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.InterpretationWe developed and validated an accurate, in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model, that improved upon SOFA.Take Home PointsStudy QuestionCan we improve upon the SOFA score for real-time mortality prediction during the COVID-19 pandemic by leveraging electronic health record (EHR) data?ResultsWe rapidly developed and implemented a novel yet SOFA-anchored mortality model across 12 hospitals and conducted a prospective cohort study of 27,296 adult hospitalizations, 1,358 (5.0%) of which were positive for SARS-CoV-2. The Charlson Comorbidity Index and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94.InterpretationA novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic.