Reduction of Environmental Pollutants and Travel Burden Through an Academic Medical Center-based Electronic Consultation Program.

Background: We evaluated the impact of electronic consultation (eConsult) in reducing the environmental pollutants associated with health care delivery. Methods: A retrospective analysis of the eConsult data between July 2018 and December 2022 was extracted from the electronic health record (Epic). Travel time and mileage from the patient home to the academic medical center (AMC) were calculated along with fuel expenditure and greenhouses gas savings. Projected savings through the end of the decade were forecast using a random walk model. Results: A total of 15,499 eConsults were submitted to AMC specialist providers from community primary care providers. Completed eConsults (n = 11,590) eliminated the need for a face-to-face visit with a specialist provider, eliminating mileage, fuel, time, and pollutants associated with face to face visits. In-state travel distance saved was 310,858 miles, travel time saved was 5,491 h, with an associated fuel reduction of 13,575 gallons and $56,893 savings. This reduced greenhouse gas emissions by 128 metric tons of carbon dioxide, 0.022 tons of nitrogen oxide, 0.005 tons of methane, and 0.001 tons of nitrous oxide. Out of state travel distance saved was 188,346 miles with 2,842 h reduced travel time, and associated fuel reduction of 8,225 gallons and of $34,118. Reduced greenhouse gas emissions were equivalent to 77 metric tons of carbon dioxide, 0.0132 tons of nitrogen oxide, 0.0033 tons of methane, and 0.0007 tons of nitrous oxide. Conclusion: This study indicates that medical care provided through telehealth modalities reduces the environmental impact of pollutants associated with face to face visits.

Exploring Inpatient Medication Patterns: A Big Data and Multilevel Approach.

PURPOSE: Exploratory study to examine inpatient medication administration patterns. METHODS: Data from multiple sources were utilized for this study. The outcome was time difference between medication schedule and administration. A 3-level hierarchical linear regression approach, both unadjusted and adjusted, was considered for this study where medication administration events are nested within patients nested within nurses or units. Intraclass correlation coefficients (ICCs) were calculated and compared. RESULTS: On average, medications were delayed by 12 (SD, 48.8) minutes. From the full model, patient ICCs decreased when "unit" replaced "nurse" as the 3rd level (0.541 vs 0.444). Patients who spoke Spanish had a significant 2.3- to 4.2-minute delay in medication administration. Certified nurses significantly give medications earlier compared with noncertified nurses by 1.6 minutes. DISCUSSION: Optimal medication administration is a multifactorial concern with nurses playing a role. Nursing leaders should also consider patient demographics and unit conditions, such as culture, for medication administration optimization.