Literature DB >> 26760428

Using aggregated pharmacy claims to identify primary nonadherence.

Dominique Comer, Joseph Couto, Ruth Aguiar, Pan Wu, Daniel Elliott1.   

Abstract

OBJECTIVES: Aggregate pharmacy claims available within an electronic health record (EHR) provide an opportunity to understand primary nonadherence in real time. The objective of this study was to use pharmacy claims data available within the EHR to identify the prevalence and predictors of primary nonadherence to antihypertensive drug therapy in a multi-payer primary care network. STUDY
DESIGN: We conducted a retrospective cohort study of patients prescribed a new antihypertensive medication in a large primary care practice network between January 2011 and September 2012.
METHODS: We matched prescriptions for the new antihypertensive to pharmacy claims listed in the EHR. The primary outcome was the presence of a fill for the new medication within 30 days of the prescription.
RESULTS: Of 791 patients in our study cohort, two-thirds (522; 66%) filled their prescription within 30 days. The majority (409; 78.4%) of that group filled the prescription on the day it was issued. Lower diastolic blood pressure and Medicare coverage increased the probability of nonadherence.
CONCLUSIONS: Medication fill data within the provider EHR can identify primary nonadherence in clinical practice. As adoption of this technology increases, it provides an opportunity to identify nonadherence, allowing for the effective design of interventions to improve adherence to therapy.

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Year:  2015        PMID: 26760428

Source DB:  PubMed          Journal:  Am J Manag Care        ISSN: 1088-0224            Impact factor:   2.229


  5 in total

1.  Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims.

Authors:  Megan Hoopes; Heather Angier; Lewis A Raynor; Andrew Suchocki; John Muench; Miguel Marino; Pedro Rivera; Nathalie Huguet
Journal:  J Am Med Inform Assoc       Date:  2018-10-01       Impact factor: 4.497

2.  Validation of EHR medication fill data obtained through electronic linkage with pharmacies.

Authors:  Saul Blecker; Samrachana Adhikari; Hanchao Zhang; John A Dodson; Sunita M Desai; Lisa Anzisi; Lily Pazand; Antoinette M Schoenthaler; Devin M Mann
Journal:  J Manag Care Spec Pharm       Date:  2021-10

Review 3.  Data Science for Child Health.

Authors:  Tellen D Bennett; Tiffany J Callahan; James A Feinstein; Debashis Ghosh; Saquib A Lakhani; Michael C Spaeder; Stanley J Szefler; Michael G Kahn
Journal:  J Pediatr       Date:  2019-01-25       Impact factor: 4.406

Review 4.  Initial non-adherence to antihypertensive medications in the United States: a systematic literature review.

Authors:  Catherine E Cooke; Shan Xing; Stormi E Gale; Sadie Peters
Journal:  J Hum Hypertens       Date:  2021-05-14       Impact factor: 3.012

Review 5.  The Challenges of Electronic Health Records and Diabetes Electronic Prescribing: Implications for Safety Net Care for Diverse Populations.

Authors:  Neda Ratanawongsa; Lenny L S Chan; Michelle M Fouts; Elizabeth J Murphy
Journal:  J Diabetes Res       Date:  2017-01-18       Impact factor: 4.011

  5 in total

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