Comparing Prescribing and Dispensing Data of the PCORnet Common Data Model Within PCORnet Antibiotics and Childhood Growth Study

Abstract

Researchers often use prescribing data from electronic health records (EHR) or dispensing data from medication or medical claims to determine medication utilization. However, neither source has complete information on medication use. We compared antibiotic prescribing and dispensing records for 200,395 patients in the National Patient-Centered Clinical Research Network (PCORnet) Antibiotics and Childhood Growth Study. We stratified analyses by delivery system type [closed integrated (cIDS) and non-cIDS]; 90.5 percent and 39.4 percent of prescribing records had matching dispensing records, and 92.7 percent and 64.0 percent of dispensing records had matching prescribing records at cIDS and non-cIDS, respectively. Most of the dispensings without a matching prescription did not have same-day encounters in the EHR, suggesting they were medications given outside the institution providing data, such as those from urgent care or retail clinics. The sensitivity of prescriptions in the EHR, using dispensings as a gold standard, was 99.1 percent and 89.9 percent for cIDS and non-cIDS, respectively. Only 0.7 percent and 6.1 percent of patients at cIDS and non-cIDS, respectively, were classified as false-negative, i.e. entirely unexposed to antibiotics when they in fact had dispensings. These patients were more likely to have a complex chronic condition or asthma. Overall, prescription records worked well to identify exposure to antibiotics. EHR data, such as the data available in PCORnet, is a unique and vital resource for clinical research. Closing data gaps by understanding why prescriptions may not be captured can improve this type of data, making it more robust for observational research.

National Trends in Use of Sodium‐Glucose Cotransporter‐2 Inhibitors and Glucagon‐like Peptide‐1 Receptor Agonists by Cardiologists and Other Specialties, 2015 to 2020

“…This registry captures data on drug dispensing by pharmacies, rather than clinician prescribing, and the 2 metrics may be slightly discordant. 52 , 53 Additionally, the NPA does not include any demographic or diagnostic information about individuals, nor does it include CIs for its projections, although the audit provides a near census of the US retail market. Furthermore, because the NPA categorizes both newly initiated therapy as well as a newly written prescription to continue existing therapy after a patient is completely out of refills as new prescriptions, it was not possible to fully limit our analysis to only those prescriptions that are confirmed new initiations.…”

Association of Early Antibiotic Exposure With Childhood Body Mass Index Trajectory Milestones

“…One limitation, however, is that we captured antibiotic prescribing data electronically and not based on pharmacy dispensing or claims, which might have led to misclassification of the exposure. However, previous analyses among a subset of participating institutions from PCORnet 24 showed the prescribing data had good sensitivity in identifying patients who filled the antibiotic prescription. Further, we accounted for mostly oral antibiotic prescriptions, although we did capture intramuscular ceftriaxone and penicillin use.…”

“…We accounted for duplicated same-day prescriptions and created antibiotic treatment episodes by joining antibiotic prescriptions within 10 days, giving priority to the broadest-spectrum antibiotic prescribed. The PCORnet study group 24 previously compared antibiotic prescription data from electronic health records with pharmacy dispensing records for antibiotics, which showed that misclassification of antibiotic exposure was low. We defined narrow-spectrum antibiotics as penicillin, amoxicillin, and dicloxacillin sodium and broad-spectrum antibiotics as all other antibacterials.…”

Review of Clinical Research Informatics

“…With an eye to what matters to stakeholders-parents and primary physicians-the study also considered whether these findings would influence prescribing patterns and parental expectations; the answer was unambiguously "no" [38]. They were also able to examine data quality by comparing prescriptions and dispensing in 200,395 records and identified gaps in these data, although prescription data were adequate for the question at hand [39]. Finally, in a technical proof of principle, they showed that a form of distributed regression analysis, avoiding the aggregation of patient-level data, generated results comparable to those of the main study [40].…”