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

> **NIH AHRQ R03** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2020 · $46,810

## Abstract

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.

## Key facts

- **NIH application ID:** 10005343
- **Project number:** 5R03HS026783-02
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Anne L Andrews
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $46,810
- **Award type:** 5
- **Project period:** 2019-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005343

## Citation

> US National Institutes of Health, RePORTER application 10005343, Medication Adherence Monitoring at the Point of Care: Pediatric Asthma as a Test Case (5R03HS026783-02). Retrieved via MORVS 2026-10-27 from https://api.morvs.ai/grant/nih/10005343. Licensed CC0.

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