Rapid Adjustment of Clinical Decision Support in Response to Updated Recommendations for Palivizumab Eligibility

Background: Palivizumab is effective at reducing hospitalizations due to respiratory syncytial virus among high-risk children, but is indicated for a small population. Identification of patients eligible to receive palivizumab is labor-intensive and error-prone. To support patient identification we developed Clinical Decision Support (CDS) based on published recommendations in 2012. This CDS was developed using a systematic process, which directly linked computer code to a recommendation’s narrative text. In 2014, updated recommendations were published, which changed several key criteria used to determine eligible patients. Objective: Assess the effort required to update CDS in response to new palivizumab recommendations and identify factors that impacted these efforts. Methods: We reviewed the updated American Academy of Pediatrics (AAP) policy statement from Aug 2014 and identified areas of divergence from the prior publication. We modified the CDS to account for each difference. We recorded time spent on each activity to approximate the total effort required to update the CDS. Results: Of the 15 recommendations in the initial policy statement, 7 required updating. The CDS update was completed in 11 person-hours. Comparison of old and new recommendations was facilitated by the AAP policy statement structure and required 3 hours. Validation of the revised logic required 2 hours by a clinical domain expert. An informaticist required 3 hours to update and test the CDS. This included adding 24 lines and deleting 37 lines of code. Updating relevant data queries took an additional 3 hours and involved 10 edits. Conclusion: We quickly adapted CDS in response to changes in recommendations for palivizumab administration. The consistent AAP policy statement structure and the link we developed between these statements and the CDS rules facilitated our efforts. We recommend that CDS implementers establish linkages between published narrative recommendations and their executable rules to facilitate maintenance efforts.