Background: The COVID-19 pandemic demanded rapid development of telemedicine services for pediatric care and highlighted disparities for marginalized communities. Objective: To understand the demographic characteristics of patients with completed and incomplete telemedicine visits at Ann and Robert H. Lurie Children's Hospital of Chicago. Methods: This was a cross-sectional retrospective analysis of telemedicine visits for patients <25 years old scheduled between March 21, 2020, and March 17, 2021. We examined visit outcomes and compared outcomes by race/ethnicity, language, and payer using logistic regression. Geographic information system mapping and linear regression were used to examine the relationship between incomplete visits and broadband access within Cook County. Results: A total of 13,655 eligible video visits were scheduled for children within 147 ZIP codes during the study time frame. Patient characteristics included median age 9 years, 53% female, 42% non-Latinx White, 31% Latinx, 13% non-Latinx Black, 11% non-Latinx other, and 3% declined/unknown. Preferred language was 89% English, 10% Spanish, and 1% other. Payer was 56% private, 43% public, and <1% other/self-pay. Overall, 86% video visits were completed, 7% cancelled, and 7% no-show with significant variation by patient demographic. Odds of incomplete visits were higher for Latinx patients (odds ratio [OR] 1.93) and non-Latinx Black patients (OR 2.33) than for non-Latinx White patients, patients with preferred language other than English (OR 1.53), and patients not privately insured (OR 1.89). Incomplete visit rates and broadband access were inversely related. Conclusion: System and policy solutions are needed to ensure equitable access and address disparities in incomplete telemedicine visits for marginalized populations in urban areas with lower broadband.
ImportanceReadmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children.ObjectiveTo develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages.Design, Setting, and ParticipantsThis population-based prognostic analysis used electronic health record–derived data from a freestanding children’s hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021.Main Outcomes and MeasuresThree models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges.ResultsThe derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort.Conclusions and RelevanceIn this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.
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