Evidence map›Paper›PMID 37700254›Full record

ArticleBMC neurology2023

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

Jay B Lusk, Sujung Choi, Amy G Clark, Kim Johnson, Cassie B Ford, Melissa A Greiner, Margarethe Goetz, Brystana G Kaufman, Richard O'Brien, Emily C O'Brien

Open access · goldAbstract read
In one paragraph

Article in BMC neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Jay B LuskDuke University School of Medicine, DUMC 3710, Durham, NC, 27710, USA. jay.lusk@duke.edu.
Sujung ChoiJanssen Scientific Affairs, Inc, Titusville, NJ, USA.
Amy G ClarkDepartment of Population Health Sciences, Duke University, Durham, NC, USA.
Kim JohnsonDepartment of Neurology, Duke University, Durham, NC, USA.
Cassie B FordDepartment of Population Health Sciences, Duke University, Durham, NC, USA.
Melissa A GreinerDepartment of Population Health Sciences, Duke University, Durham, NC, USA.
Margarethe GoetzDepartment of Neurology, Duke University, Durham, NC, USA.
Brystana G KaufmanDepartment of Population Health Sciences, Duke University, Durham, NC, USA.
Richard O'BrienDepartment of Neurology, Duke University, Durham, NC, USA.
Emily C O'BrienDepartment of Population Health Sciences, Duke University, Durham, NC, USA.
Duke University · USJanssen Scientific Affairs (United States)

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedicare 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.

methodsThis 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.

resultsThe 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%.

conclusionsMore 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.

Indexed as

DementiaNeurodegenerative DiseasesParkinson DiseaseAgedElectronic Health RecordsHumansMedicareRetrospective StudiesUnited StatesClaims dataDementiaElectronic health recordsNeurodegenerative diseaseParkinson’s disease

Identifiers

PMID37700254
PMCPMC10496225
OpenAlexW4386640069

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.