Medication Adherence Monitoring at the Point of Care: Pediatric Asthma as a Test Case

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Project Summary/Abstract Recent technological advances have brought pharmacy dispensing data to the bedside in near real time for a large number of practicing providers. Ready access to patients' pharmacy dispensing data has the potential to transform the way we monitor medication adherence and how we react to this information. Because this technology is relatively new, providers have yet to design systematic approaches to incorporating these data into their clinical practice. Children with asthma are among those most likely to benefit from proactive monitoring of prescription dispensing data because of the availability of effective controller medications, historically poor adherence, and costly yet preventable exacerbations. Additionally, the asthma medication ratio (AMR) (# of controller medication fills/(# of controller fills +# of rescue fills)) is a pharmacy claims based asthma risk predictor that identifies children at high risk for subsequent exacerbation. The AMR has been used as a quality of care metric but has not been translated into a bedside risk prediction tool, owing in part to the previous lack of available, timely pharmacy dispensing data. Pharmacy dispensing reports available through Surescripts have increased the likelihood that the AMR could be successfully translated into a bedside risk prediction tool. This proactive approach could ultimately prevent costly ED visits and hospitalizations for asthma. In order for a risk predictor to be utilized as a population management or bedside risk prediction tool, it must be precise (based on accurate data and with optimized sensitivity and specificity), convenient (readily available to clinicians at the point of care, measureable on large populations of children), and timely (determined using the most proximal and fewest number of data points possible). Surescripts has improved the convenience and timeliness of this potentially effective risk prediction tool but before it can be used at the bedside we must determine its precision. First we must determine and compare the precision of the AMR against previously utilized risk prediction tools. Additionally, we must examine the accuracy of the pharmacy dispensing data available in Surescripts by comparing it to the gold standard of payer supplied claims data. By attempting to independently verify the accuracy and completeness of bedside dispensing data and determining if the AMR is the most accurate asthma risk predictor available we will take an important step towards developing a systematic approach for using pharmacy dispensing data to improve patient care.