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An Automated Strategy to Calculate Medication Regimen Complexity.
Lu, Yuzhi; Green, Ariel R; Quiles, Rosalphie; Taylor, Casey Overby.
Afiliación
  • Lu Y; Johns Hopkins University Whiting School of Engineering, Department of Biomedical Engineering, Baltimore, MD.
  • Green AR; Johns Hopkins University School of Medicine, Division of Geriatric Medicine and Gerontology, Baltimore, MD.
  • Quiles R; Johns Hopkins University School of Medicine, Division of Geriatric Medicine and Gerontology, Baltimore, MD.
  • Taylor CO; Johns Hopkins University Whiting School of Engineering, Department of Biomedical Engineering, Baltimore, MD.
AMIA Annu Symp Proc ; 2023: 1077-1086, 2023.
Article en En | MEDLINE | ID: mdl-38222413
ABSTRACT
Understanding medication regimen complexity is important to understand what patients may benefit from pharmacist interventions. Medication Regimen Complexity Index (MRCI), a 65-item tool to quantify the complexity by incorporating the count, dosage form, frequency, and additional administration instructions of prescription medicines, provides a more nuanced way of assessing complexity. The goal of this study was to construct and validate a computational strategy to automate the calculation of MRCI. The performance of our strategy was evaluated by comparing our calculated MRCI values with gold-standard values, using correlation coefficients and population distributions. The results revealed satisfactory performance to calculate the sub-score of MRCI that includes dosage form and frequency (76 to 80% match with gold standard), and fair performance for sub-score related to additional direction (52% match with gold standard). Our automated strategy shows potential to help reduce the effort for manually calculating MRCI and highlights areas for future development efforts.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Medicamentos bajo Prescripción Límite: Humans Idioma: En Revista: AMIA Annu Symp Proc Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Moldova

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Medicamentos bajo Prescripción Límite: Humans Idioma: En Revista: AMIA Annu Symp Proc Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Moldova
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