Use of path modeling to inform a clinical decision support application to encourage osteoporosis medication use.

Autor: Miller MJ; Mid-Atlantic Permanente Research Institute (MAPRI), 2101 East Jefferson Street, Rockville, MD, 20852, USA. Electronic address: michael.j1.miller@kp.org., Jou T; PGY1 Pharmacy Resident, Memorial Hermann Southwest Hospital, Houston, TX, 77074, USA. Electronic address: tzuchenj@gmail.com., Danila MI; Medicine - Immunology and Rheumatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294-2182, USA. Electronic address: mdanila@uabmc.edu., Mudano AS; School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294-2182, USA. Electronic address: amymudano@uabmc.edu., Rahn EJ; Department of Medical Education, University of Alabama at Birmingham, Birmingham, AL, 35294-2182, USA. Electronic address: rahneli@uab.edu., Outman RC; Division of Hematology and Oncology, UAB Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294-2182, USA. Electronic address: routman@uabmc.edu., Saag KG; Medicine - Immunology and Rheumatology, Vice Chair, Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294-2182, USA. Electronic address: ksaag@uabmc.edu.
Jazyk: angličtina
Zdroj: Research in social & administrative pharmacy : RSAP [Res Social Adm Pharm] 2021 Jul; Vol. 17 (7), pp. 1267-1275. Date of Electronic Publication: 2020 Sep 20.
DOI: 10.1016/j.sapharm.2020.09.010
Abstrakt: Background: Osteoporosis medication use is suboptimal. Simple interventions personalized to a patients' stage of readiness are needed to encourage osteoporosis medication use.
Objectives: To estimate interrelationships of sociodemographic factors, perceived fracture risk, health literacy, receipt of medication information, medication trust and readiness to use osteoporosis medication; and apply observed relationships to inform design specifications for a clinical decision support application that can be used for personalized patient counseling.
Methods: Data from a national sample of older women (n = 1759) with self-reported history of fractures and no current use of osteoporosis medication treatment were used to estimate an acceptable path model that describes associations among key sociodemographic characteristics, health literacy, perceived fracture risk, receipt of osteoporosis medication information within the past year, trust in osteoporosis medications, and readiness to use osteoporosis medication. Path model results were used to inform an application for personalized patient counseling that can be easily integrated into clinical decision support systems.
Results: Increased age (β = 0.13), trust for medications (β = 0.12), higher perceived fracture risk (β = 0.21), and having received medication information within the past year (β = 0.21) were all positively associated with readiness to use osteoporosis medication (p < 0.0001). Whereas, health literacy (β = -0.09) was inversely associated with readiness to use osteoporosis medication (p < 0.0001). Using these results, a brief 6-item question set was constructed for simple integration into clinical decision support applications. Patient responses were used to inform a provider dashboard that integrates a patient's stage of readiness for osteoporosis medication use, predictors of readiness, and personalized counseling points appropriate to their stage of readiness.
Conclusion: Content of counseling strategies must be aligned with a patient's stage of readiness to use treatment. Path modeling can be effectively used to identify factors for inclusion in an evidenced-based clinical decision support application designed to assist providers with personalized patient counseling and osteoporosis medication use decisions.
(Copyright © 2020 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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