Methods for Quantifying Medical and Financial Benefits of Acute Behavioral Response during Inpatient Hospitalizations for Children with ASD

Thursday, May 12, 2016: 5:30 PM-7:00 PM
Hall A (Baltimore Convention Center)
S. Marler1, J. E. Staubitz2, P. Juarez3, Z. Warren1, L. L. Altstein4, E. A. Macklin5 and K. Sanders6, (1)Vanderbilt University, Nashville, TN, (2)Pediatrics, Vanderbilt University Medical Center, Nashville, TN, (3)Vanderbilt University Medical Center, Nashville, TN, (4)Massachusetts General Hospital Biostatistics Center, Boston, MA, (5)Biostatistics Center, Massachusetts General Hospital, Boston, MA, (6)Vanderbilt, Nashville, TN
Background: With an estimated prevalence of 1 in 68 (CDC, 2014), the inpatient care of children with Autism Spectrum Disorder (ASD) represents a critical public health issue.  Although there have been advances in awareness and treatment of ASD in medical settings, children with ASD still have higher rates of acute healthcare utilization, an increased burden of unmet needs, and decreased satisfaction with care received (Lajonchere et al., 2012; Croen et al., 2006; Siegal et al., 2014). These patients frequently present with more severe behaviors (i.e. aggression, self-injury, etc.) than children without ASD and due to this, often remain hospitalized beyond medical clearance. While hospitalized, these children typically require increased levels of staffing and intensive interventions (e.g. physical/chemical restraint).

Hospitalization beyond medical clearance holds a negative financial return for hospitals. The cost of hospitalizing this population is much higher due to prolonged stay and increased staffing, with many charges remaining unreimbursed. There has been little study of inpatient care or interventions designed to improve hospitalizations in ASD. Innovative programs to reduce problematic behaviors of children with ASD in inpatient settings have the potential to improve outcomes on both child and system. While methods for quantifying change on the behavioral level are widely available, the availability of relevant financial data is far less common.  

Objectives:  As part of a larger study investigating the effect of brief Analogue Functional Analysis (AFA) on hospitalization for children with ASD, we studied the ability to draw relevant financial data from existing electronic medical records (EMR) to quantify care and potential cost of hospitalization.

Methods:  This single site, pilot study involved a sample of 36 children with ASD and aggressive behaviors, ages 6-18, admitted to either a medical or psychiatric inpatient unit. Participants were randomized into a treatment group (receiving brief AFA and behavioral intervention) or a control group.  We evaluated which primary data on medical, treatment, and financial factors were available from the EMR and administrative financial data to better understand additional methods/metrics necessary for quantifying impact of intervention.

Results:  There were several relevant cost-related variables available via EMR.  We were able to quantify length/total cost of hospitalization, use of physical/chemical restraint, and staffing ratios for all patients.  However, it was not possible to extract reliable data with the EMR indexing the amount of time spent by ABA interventionists while implementing intervention.  To assess this resource/cost we required specific datalogs from behavioral providers.  Other challenges replicated known findings in previous work (e.g., quantifying “human” costs, staff costs; separating costs of behavior challenges from medical need).  We were able to quantify re-hospitalization for all patients within our facility; however, determining the medical vs. behavioral necessity of hospitalization was not feasible.

Conclusions:  It is becoming increasingly apparent that bolstering behavioral services for children with ASD in hospital settings will require demonstrating both clinical and cost efficacy.  This pilot work documented that (1) novel metrics/methods are needed to ensure resources around behavioral interventions are captured and (2) separating medical and behavioral expenditures within financial data also require innovative methods.