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Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients

BACKGROUND: Medicare claims and electronic health record data are both commonly used for research and clinical practice improvement; however, it is not known how concordant diagnoses of neurodegenerative diseases (NDD, comprising dementia and Parkinson’s disease) are in these data types. Therefore,...

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Autores principales: Lusk, Jay B., Choi, Sujung, Clark, Amy G., Johnson, Kim, Ford, Cassie B., Greiner, Melissa A., Goetz, Margarethe, Kaufman, Brystana G., O’Brien, Richard, O’Brien, Emily C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496225/
https://www.ncbi.nlm.nih.gov/pubmed/37700254
http://dx.doi.org/10.1186/s12883-023-03361-w
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author Lusk, Jay B.
Choi, Sujung
Clark, Amy G.
Johnson, Kim
Ford, Cassie B.
Greiner, Melissa A.
Goetz, Margarethe
Kaufman, Brystana G.
O’Brien, Richard
O’Brien, Emily C.
author_facet Lusk, Jay B.
Choi, Sujung
Clark, Amy G.
Johnson, Kim
Ford, Cassie B.
Greiner, Melissa A.
Goetz, Margarethe
Kaufman, Brystana G.
O’Brien, Richard
O’Brien, Emily C.
author_sort Lusk, Jay B.
collection PubMed
description BACKGROUND: Medicare claims and electronic health record data are both commonly used for research and clinical practice improvement; however, it is not known how concordant diagnoses of neurodegenerative diseases (NDD, comprising dementia and Parkinson’s disease) are in these data types. Therefore, our objective was to determine the sensitivity and specificity of neurodegenerative disease (NDD) diagnoses contained in structured electronic health record (EHR) data compared to Medicare claims data. METHODS: This was a retrospective cohort study of 101,980 unique patients seen at a large North Carolina health system between 2013–2017, which were linked to 100% North and South Carolina Medicare claims data, to evaluate the accuracy of diagnoses of neurodegenerative diseases in EHRs compared to Medicare claims data. Patients age > 50 who were enrolled in fee-for-service Medicare were included in the study. Patients were classified as having or not having NDD based on the presence of validated ICD-CM-9 or ICD-CM-10 codes associated with NDD or claims for prescription drugs used to treat NDD. EHR diagnoses were compared to Medicare claims diagnoses. RESULTS: The specificity of any EHR diagnosis of NDD was 99.0%; sensitivity was 61.3%. Positive predictive value and negative predictive value were 90.8% and 94.1% respectively. Specificity of an EHR diagnosis of dementia was 99.0%, and sensitivity was 56.1%. Specificity of an EHR diagnosis of PD was 99.7%, while sensitivity was 76.1%. CONCLUSIONS: More research is needed to investigate under-documentation of NDD in electronic health records relative to Medicare claims data, which has major implications for clinical practice (particularly patient safety) and research using real-world data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12883-023-03361-w.
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spelling pubmed-104962252023-09-13 Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients Lusk, Jay B. Choi, Sujung Clark, Amy G. Johnson, Kim Ford, Cassie B. Greiner, Melissa A. Goetz, Margarethe Kaufman, Brystana G. O’Brien, Richard O’Brien, Emily C. BMC Neurol Research BACKGROUND: Medicare claims and electronic health record data are both commonly used for research and clinical practice improvement; however, it is not known how concordant diagnoses of neurodegenerative diseases (NDD, comprising dementia and Parkinson’s disease) are in these data types. Therefore, our objective was to determine the sensitivity and specificity of neurodegenerative disease (NDD) diagnoses contained in structured electronic health record (EHR) data compared to Medicare claims data. METHODS: This was a retrospective cohort study of 101,980 unique patients seen at a large North Carolina health system between 2013–2017, which were linked to 100% North and South Carolina Medicare claims data, to evaluate the accuracy of diagnoses of neurodegenerative diseases in EHRs compared to Medicare claims data. Patients age > 50 who were enrolled in fee-for-service Medicare were included in the study. Patients were classified as having or not having NDD based on the presence of validated ICD-CM-9 or ICD-CM-10 codes associated with NDD or claims for prescription drugs used to treat NDD. EHR diagnoses were compared to Medicare claims diagnoses. RESULTS: The specificity of any EHR diagnosis of NDD was 99.0%; sensitivity was 61.3%. Positive predictive value and negative predictive value were 90.8% and 94.1% respectively. Specificity of an EHR diagnosis of dementia was 99.0%, and sensitivity was 56.1%. Specificity of an EHR diagnosis of PD was 99.7%, while sensitivity was 76.1%. CONCLUSIONS: More research is needed to investigate under-documentation of NDD in electronic health records relative to Medicare claims data, which has major implications for clinical practice (particularly patient safety) and research using real-world data. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12883-023-03361-w. BioMed Central 2023-09-12 /pmc/articles/PMC10496225/ /pubmed/37700254 http://dx.doi.org/10.1186/s12883-023-03361-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Lusk, Jay B.
Choi, Sujung
Clark, Amy G.
Johnson, Kim
Ford, Cassie B.
Greiner, Melissa A.
Goetz, Margarethe
Kaufman, Brystana G.
O’Brien, Richard
O’Brien, Emily C.
Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients
title Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients
title_full Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients
title_fullStr Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients
title_full_unstemmed Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients
title_short Dementia and Parkinson’s disease diagnoses in electronic health records vs. Medicare claims data: a study of 101,980 linked patients
title_sort dementia and parkinson’s disease diagnoses in electronic health records vs. medicare claims data: a study of 101,980 linked patients
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496225/
https://www.ncbi.nlm.nih.gov/pubmed/37700254
http://dx.doi.org/10.1186/s12883-023-03361-w
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