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Drugs & Aging

, Volume 30, Issue 11, pp 893–900 | Cite as

The Medication Appropriateness Index at 20: Where It Started, Where It Has Been, and Where It May Be Going

  • Joseph T. HanlonEmail author
  • Kenneth E. Schmader
Review Article

Abstract

Potentially inappropriate prescribing for older adults is a major public health concern. While there are multiple measures of potentially inappropriate prescribing, the medication appropriateness index (MAI) is one of the most common implicit approaches published in the scientific literature. The objective of this narrative review is to describe findings regarding the MAI’s reliability, comparison of the MAI with other quality measures of potentially inappropriate prescribing, its predictive validity with important health outcomes, and its responsiveness to change within the framework of randomized controlled trials. A search restricted to English-language literature involving humans aged 65+ years from January 1992 to June 2013 was conducted using MEDLINE and EMBASE databases using the search term ‘medication appropriateness index’. A manual search of the reference lists from identified articles and the authors’ article files, book chapters, and recent reviews was conducted to identify additional articles. A total of 26 articles were identified for inclusion in this narrative review. The main findings were that the MAI has acceptable inter- and intra-rater reliability, it more frequently detects potentially inappropriate prescribing than a commonly used set of explicit criteria, it predicts adverse health outcomes, and it is able to demonstrate the positive impact of interventions to improve this public health problem. We conclude that the MAI may serve as a valuable tool for measuring potentially inappropriate prescribing in older adults.

Keywords

Narrative Review Inappropriate Prescribe Explicit Criterion Nursing Home Patient Beers Criterion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Supported in part by the National Institute on Aging Grants (R01-AG 027017, P30-AG024827, K07-AG033174, P30-AG028716), and an Agency for Healthcare Research and Quality Grant (R01-HS018721). The authors report no potential conflicts of interest.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Division of Geriatrics, Department of MedicineUniversity of PittsburghPittsburghUSA
  2. 2.Geriatric Pharmaceutical Outcomes and Gero-Informatics Research and Training Program, School of MedicineUniversity of PittsburghPittsburghUSA
  3. 3.Center for Health Equity Research and PromotionVeterans Affairs Pittsburgh Healthcare SystemPittsburghUSA
  4. 4.Geriatric Research Education and Clinical CenterVeterans Affairs Pittsburgh Healthcare SystemPittsburghUSA
  5. 5.Division of Geriatrics, Department of Medicine, School of MedicineDuke University Medical CenterDurhamUSA
  6. 6.Geriatric Research Education and Clinical CenterDurham Veterans Affairs Medical CenterDurhamUSA
  7. 7.Department of Medicine (Geriatrics)University of PittsburghPittsburghUSA

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