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Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality

The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequat...

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Autores principales: Schorer, Anna E., Moldwin, Richard, Koskimaki, Jacob, Bernstam, Elmer V., Venepalli, Neeta K., Miller, Robert S., Chen, James L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9848533/
https://www.ncbi.nlm.nih.gov/pubmed/34985912
http://dx.doi.org/10.1200/CCI.21.00128
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author Schorer, Anna E.
Moldwin, Richard
Koskimaki, Jacob
Bernstam, Elmer V.
Venepalli, Neeta K.
Miller, Robert S.
Chen, James L.
author_facet Schorer, Anna E.
Moldwin, Richard
Koskimaki, Jacob
Bernstam, Elmer V.
Venepalli, Neeta K.
Miller, Robert S.
Chen, James L.
author_sort Schorer, Anna E.
collection PubMed
description The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. MATERIALS AND METHODS: Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases). RESULTS: Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients. CONCLUSION: Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.
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spelling pubmed-98485332023-01-19 Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality Schorer, Anna E. Moldwin, Richard Koskimaki, Jacob Bernstam, Elmer V. Venepalli, Neeta K. Miller, Robert S. Chen, James L. JCO Clin Cancer Inform ORIGINAL REPORTS The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. MATERIALS AND METHODS: Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases). RESULTS: Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients. CONCLUSION: Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical. Wolters Kluwer Health 2022-01-05 /pmc/articles/PMC9848533/ /pubmed/34985912 http://dx.doi.org/10.1200/CCI.21.00128 Text en © 2022 by American Society of Clinical Oncology https://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: https://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle ORIGINAL REPORTS
Schorer, Anna E.
Moldwin, Richard
Koskimaki, Jacob
Bernstam, Elmer V.
Venepalli, Neeta K.
Miller, Robert S.
Chen, James L.
Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality
title Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality
title_full Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality
title_fullStr Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality
title_full_unstemmed Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality
title_short Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality
title_sort chasm between cancer quality measures and electronic health record data quality
topic ORIGINAL REPORTS
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9848533/
https://www.ncbi.nlm.nih.gov/pubmed/34985912
http://dx.doi.org/10.1200/CCI.21.00128
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