Exploring Patterns and Disparities in E-Consult Referrals: An Analysis of Patient and Community Factors in Colorado Health Care.

Objective: Electronic consultations (e-consults) provide a strategic solution to address challenges in health care systems related to cost management and access to care. This study aims to investigate the multilevel patient characteristics associated with higher frequency of receiving e-consults and increased likelihood of completion. Materials and Methods: University of Colorado's electronic medical record were analyzed to study factors influencing referral types (e-consult vs. standard) and their completion rates from April 2018 to September 2023. Multivariate probit regression assessed the impact of patient-level and community-level factors (urban-rural classification, Social Vulnerability Index, and technology accessibility) on e-consult referrals and completion. Results: In 263,882 records, 92.5% were standard referrals, and 7.4% were e-consult referrals. Analysis showed that females were less likely than males (OR = 0.95, 95%CI[0.93, 0.96]), and Blacks were more likely than Whites (OR = 1.03, 95%CI[1.01,1.06]) to receive e-consult referrals. Medicaid patients had lower odds compared to those with Medicare only (OR = 1.04, 95%CI[1.00,1.07]), and rural residency was associated with lower odds (OR = 0.80, 95%CI[0.73,0.88]) of e-consult referral. Factors such as areas with higher population without internet subscription (OR = 1.03, 95%CI[1.01,1.04]) and higher social vulnerabilities (OR = 1.26, 95%CI[1.16,1.37]) increased e-consult odds. Black patients were less likely to have their referrals completed compared to Whites. Patients who resided in regions with limited computer and smartphone access, as well as higher social vulnerabilities, showed decreased odds of referral completion. Discussions and Conclusion: This study highlights the need for partnering with a variety of health care organizations, especially those serving low-income and disadvantaged populations, to enhance health care access equity through the use of e-consults.

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.