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A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers
OBJECTIVE: To provide a methodology for estimating the effect of U.S.-based Certified Electronic Health Records Technology (CEHRT) implemented by primary care physicians (PCPs) on a Healthcare Effectiveness Data and Information Set (HEDIS) measure for childhood immunization delivery. MATERIALS AND M...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486207/ https://www.ncbi.nlm.nih.gov/pubmed/32927058 http://dx.doi.org/10.1016/j.jbi.2020.103567 |
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author | Messino, Paul J. Kharrazi, Hadi Kim, Julia M. Lehmann, Harold |
author_facet | Messino, Paul J. Kharrazi, Hadi Kim, Julia M. Lehmann, Harold |
author_sort | Messino, Paul J. |
collection | PubMed |
description | OBJECTIVE: To provide a methodology for estimating the effect of U.S.-based Certified Electronic Health Records Technology (CEHRT) implemented by primary care physicians (PCPs) on a Healthcare Effectiveness Data and Information Set (HEDIS) measure for childhood immunization delivery. MATERIALS AND METHODS: This study integrates multiple health care administrative data sources from 2010 through 2014, analyzed through an interrupted time series design and a hierarchical Bayesian model. We compared managed care physicians using CEHRT to propensity-score matched comparisons from network physicians who did not adopt CEHRT. Inclusion criteria for physicians using CEHRT included attesting to the Childhood Immunization Status clinical quality measure in addition to meeting “Meaningful Use” (MU) during calendar year 2013. We used a first-presence patient attribution approach to develop provider-specific immunization scores. RESULTS: We evaluated 147 providers using CEHRT, with 147 propensity-score matched providers selected from a pool of 1253 PCPs practicing in Maryland. The estimate for change in odds of increasing immunization rates due to CEHRT was 1.2 (95% credible set, 0.88–1.73). DISCUSSION: We created a method for estimating immunization quality scores using Bayesian modeling. Our approach required linking separate administrative data sets, constructing a propensity-score matched cohort, and using first-presence, claims-based childhood visit information for patient attribution. In the absence of integrated data sets and precise and accurate patient attribution, this is a reusable method for researchers and health system administrators to estimate the impact of health information technology on individual, provider-level, process-based, though outcomes-focused, quality measures. CONCLUSION: This research has provided evidence for using Bayesian analysis of propensity-score matched provider populations to estimate the impact of CEHRT on outcomes-based quality measures such as childhood immunization delivery. |
format | Online Article Text |
id | pubmed-7486207 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74862072020-09-14 A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers Messino, Paul J. Kharrazi, Hadi Kim, Julia M. Lehmann, Harold J Biomed Inform Article OBJECTIVE: To provide a methodology for estimating the effect of U.S.-based Certified Electronic Health Records Technology (CEHRT) implemented by primary care physicians (PCPs) on a Healthcare Effectiveness Data and Information Set (HEDIS) measure for childhood immunization delivery. MATERIALS AND METHODS: This study integrates multiple health care administrative data sources from 2010 through 2014, analyzed through an interrupted time series design and a hierarchical Bayesian model. We compared managed care physicians using CEHRT to propensity-score matched comparisons from network physicians who did not adopt CEHRT. Inclusion criteria for physicians using CEHRT included attesting to the Childhood Immunization Status clinical quality measure in addition to meeting “Meaningful Use” (MU) during calendar year 2013. We used a first-presence patient attribution approach to develop provider-specific immunization scores. RESULTS: We evaluated 147 providers using CEHRT, with 147 propensity-score matched providers selected from a pool of 1253 PCPs practicing in Maryland. The estimate for change in odds of increasing immunization rates due to CEHRT was 1.2 (95% credible set, 0.88–1.73). DISCUSSION: We created a method for estimating immunization quality scores using Bayesian modeling. Our approach required linking separate administrative data sets, constructing a propensity-score matched cohort, and using first-presence, claims-based childhood visit information for patient attribution. In the absence of integrated data sets and precise and accurate patient attribution, this is a reusable method for researchers and health system administrators to estimate the impact of health information technology on individual, provider-level, process-based, though outcomes-focused, quality measures. CONCLUSION: This research has provided evidence for using Bayesian analysis of propensity-score matched provider populations to estimate the impact of CEHRT on outcomes-based quality measures such as childhood immunization delivery. Elsevier Inc. 2020-10 2020-09-12 /pmc/articles/PMC7486207/ /pubmed/32927058 http://dx.doi.org/10.1016/j.jbi.2020.103567 Text en © 2020 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Messino, Paul J. Kharrazi, Hadi Kim, Julia M. Lehmann, Harold A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers |
title | A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers |
title_full | A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers |
title_fullStr | A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers |
title_full_unstemmed | A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers |
title_short | A method for measuring the effect of certified electronic health record technology on childhood immunization status scores among Medicaid managed care network providers |
title_sort | method for measuring the effect of certified electronic health record technology on childhood immunization status scores among medicaid managed care network providers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486207/ https://www.ncbi.nlm.nih.gov/pubmed/32927058 http://dx.doi.org/10.1016/j.jbi.2020.103567 |
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