PC2063: Increasing Evaluation Rates for Children at High Risk for Autism Spectrum Disorder Using QI Methodology and Ehr-based Interventions

Purpose/Objectives: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impaired of social communication with long term effects on development. ASD is often diagnosed by pediatric primary care (PC) through developmental screenings. The most widely used screening questionnaire for autism is the Modified Checklist for Autism in Toddler (M-CHAT). The original M-CHAT has a high false positive rate; therefore, the M-CHAT-R/F was developed to include follow up question if the initial screening was concerning for autism. 98% of children who screen positive on the M-CHAT-R/F are later diagnosed with developmental delays, while 54% of them are subsequently diagnosed with ASD, making it essential to appropriately refer these children for further evaluation. Various tools within the electronic health record (EHR) can help administer the M-CHAT-R/F electronically and provide decision support for medical providers to encourage appropriate referrals for these children.

Our specific aim was to increase the referral rate of patient who failed M-CHAT-R/F screening in PC to developmental-behavioral pediatrics (DBP), early intervention and speech therapy from a prior baseline rate of 10% to at least 30% within 6 months.

Design/Methods: Our team consisted of PC physicians, developmental-behavioral pediatricians (DBP), clinical informaticists, and quality improvement specialists. We used the Institute for Healthcare Improvement (IHI) quality improvement methodology.

Using a cause and effect diagram, identified three key drivers: trust in M-CHAT R/F scoring, referral process knowledge, and the need for an efficient workflow in busy PC clinics. We then implemented 2 Plan-Do-Study-Act (PDSA) cycles. First, we provided additional autism education for PC physicians and deployed an autism-specific orderset which contained default diagnoses, referral orders, follow-up and patient instructions. Second, we added rule-based guidelines and a link to the autism orderset within provider note templates for high risk patients. We also presented rule-based referral orders to the provider when a patient failed the M-CHAT R/F screening.

Results: The rate of referrals for children with failed M-CHAT R/F screening in PC increased 45% after 2 PDSA cycles over a 6 month period.

Conclusion/Discussion: Our quality improvement interventions, such as pre-selected defaults in a suggested order set, significantly improved placement of referral orders for children at high risk for autism by providing efficient and readily-available guidelines to drive clinical decision-making. By leveraging EHR functionality to place the guidelines and necessary referral orders within the existing provider workflow and documentation templates, we maintained provider autonomy while at the same time driving best outcomes for these high risk patients.