ABSTRACT
BACKGROUND: There is limited understanding of the characteristics and operational burden of persons under investigation (PUIs) and those testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presenting to emergency departments (EDs). METHODS: We reviewed all adult ED visits to 5 Johns Hopkins Health System hospitals in the Maryland/District of Columbia (DC) region during the initial coronavirus disease 2019 (COVID-19) surge, analyzing SARS-CoV-2 polymerase chain reaction test eligibility, results, demographics, acuity, clinical conditions, and dispositions. RESULTS: Of 27,335 visits, 11,402 (41.7%) were tested and 2484 (21.8%) were SARS-CoV-2 positive. Test-positive rates among Hispanics, Asians, African Americans/Blacks, and Whites were 51.6%, 23.7%, 19.8%, and 12.7% respectively. African American/Blacks infection rates (25.5%-33.8%) were approximately double those of Whites (11.1%-21.1%) in the 3 southern Maryland/DC EDs. Conditions with high test-positive rates were fever (41.9%), constitutional (36.4%), upper respiratory (36.9%), and lower respiratory (31.2%) symptoms. Test-positive rates were similar in all age groups (19.9% to 25.8%), although rates of hospitalization increased successively with age. Almost half, 1103 (44.4%), of test-positive patients required admission, of which 206 (18.7%) were to an ICU. CONCLUSION: The initial surge of SARS-CoV-2 test-positive patients experienced in a regional hospital system had ≈ 42% of patients meeting testing criteria and nearly one-fifth of those testing positive. The operational burden on ED practice, including intense adherence to infection control precautions, cannot be understated. Disproportionately high rates of infection among underrepresented minorities underscores the vulnerability in this population. The high rate of infection among self-identified Asians was unexpected.
ABSTRACT
A growing number of hospitals are turning to predictive analytics to anticipate and manage volume better. The approach, which involves using sophisticated simulation and modeling techniques, enables administrators to get ahead of patient surges and to focus on pressure points. For example, Johns Hopkins Hospital in Baltimore has made significant progress on a range of measures, using a centralized command center to monitor the hospital's data streams. The approach enables the hospital to accelerate decision-making and optimize hospital resources. Investigators at Columbia University believe similar modeling techniques can be used to avert ED congestion when used in conjunction with proactive diversion strategies. The 5,000-square-foot command center at Johns Hopkins Hospital monitors 14 IT systems on a 24/7 basis so that all relevant inputs can factor into decision-making about beds, transfers, consults, admissions, discharges, and other aspects of care. Administrators say they have been able to achieve 96% accuracy in their predictions. In just 10 months of operation, the data-driven command center has achieved dramatic improvements, including a 30% reduction in the number of emergency patients who must wait for an inpatient bed and a one-hour reduction in the time it takes to get out the door to retrieve a patient identified for transfer to the Hopkins facility. In addition, the hospital has all but eliminated procedure cancellations due to OR holds. Investigators at Columbia University contend that by using predictive analytics to guide proactive diversion strategies, ED delays can be reduced by as much as 15%.
