Cargando…
Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims
BACKGROUND: Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs. OBJECTIVES: To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medica...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Academy of Managed Care Pharmacy
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391244/ https://www.ncbi.nlm.nih.gov/pubmed/32584680 http://dx.doi.org/10.18553/jmcp.2020.26.7.860 |
_version_ | 1785082661485674496 |
---|---|
author | Ma, Xiaomeng Jung, Changmi Chang, Hsien-Yen Richards, Thomas M. Kharrazi, Hadi |
author_facet | Ma, Xiaomeng Jung, Changmi Chang, Hsien-Yen Richards, Thomas M. Kharrazi, Hadi |
author_sort | Ma, Xiaomeng |
collection | PubMed |
description | BACKGROUND: Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs. OBJECTIVES: To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims. METHODS: We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman’s coefficient (SC) after adjusting for age and sex. RESULTS: The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.3 [37.9], P < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], P < 0.001; 30-day PFR = 75.7 [23.6%], P < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, P < 0.001) or with 30-day PFR (SC = -0.17, P < 0.001) at significant levels. CONCLUSIONS: Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts. |
format | Online Article Text |
id | pubmed-10391244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Academy of Managed Care Pharmacy |
record_format | MEDLINE/PubMed |
spelling | pubmed-103912442023-08-02 Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims Ma, Xiaomeng Jung, Changmi Chang, Hsien-Yen Richards, Thomas M. Kharrazi, Hadi J Manag Care Spec Pharm Research BACKGROUND: Nonadherence to medication regimens can lead to adverse health care outcomes and increasing costs. OBJECTIVES: To (a) assess the level of medication complexity at an outpatient setting using population-level electronic health record (EHR) data and (b) evaluate its association with medication adherence measures derived from medication-dispensing claims. METHODS: We linked EHR data with insurance claims of 70,054 patients who had an encounter with a U.S. midwestern health system between 2012 and 2013. We constructed 3 medication-derived indices: medication regimen complexity index (MRCI) using EHR data; medication possession ratio (MPR) using insurance pharmacy claims; and prescription fill rates (PFR; 7 and 30 days) using both data sources. We estimated the partial correlation between indices using Spearman’s coefficient (SC) after adjusting for age and sex. RESULTS: The mean age (SD) of 70,054 patients was 37.9 (18.0) years, with an average Charlson Comorbidity Index of 0.308 (0.778). The 2012 data showed mean (SD) MRCI, MPR, and 30-day PFR of 14.6 (17.8), 0.624 (0.310), and 81.0 (27.0), respectively. Patients with previous inpatient stays were likely to have high MRCI scores (36.3 [37.9], P < 0.001) and were less adherent to outpatient prescriptions (MPR = 50.3 [27.6%], P < 0.001; 30-day PFR = 75.7 [23.6%], P < 0.001). However, MRCI did not show a negative correlation with MPR (SC = -0.31, P < 0.001) or with 30-day PFR (SC = -0.17, P < 0.001) at significant levels. CONCLUSIONS: Medication complexity and adherence indices can be calculated on a population level using linked EHR and claims data. Regimen complexity affects patient adherence to outpatient medication, and strength of correlations vary modestly across populations. Future studies should assess the added values of MRCI, MPR, and PFR to population health management efforts. Academy of Managed Care Pharmacy 2020-07 /pmc/articles/PMC10391244/ /pubmed/32584680 http://dx.doi.org/10.18553/jmcp.2020.26.7.860 Text en Copyright © 2020, Academy of Managed Care Pharmacy. All rights reserved. https://creativecommons.org/licenses/by/4.0/This article is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Research Ma, Xiaomeng Jung, Changmi Chang, Hsien-Yen Richards, Thomas M. Kharrazi, Hadi Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims |
title | Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims |
title_full | Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims |
title_fullStr | Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims |
title_full_unstemmed | Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims |
title_short | Assessing the Population-Level Correlation of Medication Regimen Complexity and Adherence Indices Using Electronic Health Records and Insurance Claims |
title_sort | assessing the population-level correlation of medication regimen complexity and adherence indices using electronic health records and insurance claims |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391244/ https://www.ncbi.nlm.nih.gov/pubmed/32584680 http://dx.doi.org/10.18553/jmcp.2020.26.7.860 |
work_keys_str_mv | AT maxiaomeng assessingthepopulationlevelcorrelationofmedicationregimencomplexityandadherenceindicesusingelectronichealthrecordsandinsuranceclaims AT jungchangmi assessingthepopulationlevelcorrelationofmedicationregimencomplexityandadherenceindicesusingelectronichealthrecordsandinsuranceclaims AT changhsienyen assessingthepopulationlevelcorrelationofmedicationregimencomplexityandadherenceindicesusingelectronichealthrecordsandinsuranceclaims AT richardsthomasm assessingthepopulationlevelcorrelationofmedicationregimencomplexityandadherenceindicesusingelectronichealthrecordsandinsuranceclaims AT kharrazihadi assessingthepopulationlevelcorrelationofmedicationregimencomplexityandadherenceindicesusingelectronichealthrecordsandinsuranceclaims |