81 - Identifying risk factors for readmission in children with complex medical conditions

Poster Number: 81
Publication Number: 81.305

Alyx Paul, University of Florida College of Medicine, St Johns, FL, United States; Alexandria Timmer, University of Florida College of Medicine, Jacksonville, FL, United States; Stephanie Carlin, University of Florida College of Medicine, Jacksonville, FL, United States; Matthew Garber, FCAAP/UF-Jacksonville, JACKSONVILLE, FL, United States

Background: Children with medical complexities (CMC) represent 1% of the pediatric population but account for over a third of pediatric health care costs. The Bower Lyman Center for Medically Complex Children is a medical home with 580 patients aged 0-21. We noticed a high readmission rate within our patient population with an average of 40% of our patients readmitted within 30 days of hospital discharge.

Objective: We hoped to identify patients most at risk of readmission and target future interventions in hope of reducing the number of readmissions. We hypothesized that certain factors like length of hospital admission, having a new diagnosis or having a preferred language other than English may lead to an increase in hospital readmissions.

Design/Methods: We conducted a retrospective chart review of all patients from January to December 2019 and July 2021 to June 2022 who came to hospital follow up appointments. We collected data on patient demographics and preferred language, length of stay (LOS), technology dependence, primary service of admission, new diagnoses during the stay, whether the child received intensive care, 30 day readmissions and emergency department (ED) revisits, and whether the child died on a subsequent admission. We dichotomized LOS into < = 3 days and > 3 days. We also analyzed factors associated with LOS > 3 days. We used Fisher’s exact test for all analyses with a p value of 0.05 considered significant.

Results: We analyzed 288 patient charts. We found that patients with technology (p = .021) and race identified as other (p = .011) were more likely to be readmitted, while there was no association with dichotomized LOS, receiving intensive care, new diagnosis, gender, preferred language, or subsequent death.  Patients with technology (p = 0.031), race identified as other (p = 0.046), and ethnicity identified as Hispanic (p = 0.013) had more ED revisits. Patients with technology (p = .001), who received intensive care (p = .000), and who had a new diagnosis (p = .009) were more likely to have LOS  >3 days.

Conclusion(s): Technology dependence was proven to increase risks of readmission as well as ED visits. Parents often disclose that they are not fully prepared to troubleshoot all of their child's technology on discharge. Younger patients were more likely to be admitted in general, likely due to a combination of frequent respiratory infections and parental learning curve. Future interventions will target these patients with more comprehensive follow-up visits. We plan to dive deeper into the race and ethnicity discrepancies by reaching out to families to get more qualitative data.