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Record W3020046452 · doi:10.2196/17174

Evaluating the Representativeness of US Centricity Electronic Medical Records With Reports From the Centers for Disease Control and Prevention: Comparative Study on Office Visits and Cardiometabolic Conditions

2020· article· en· W3020046452 on OpenAlexvenueno aff
Olga Montvida, John Dibato, Sanjoy K. Paul

Bibliographic record

VenueJMIR Medical Informatics · 2020
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilTherapeutic Innovation AustraliaMedical Research CouncilAustralian Government
KeywordsGeneralizability theoryRepresentativeness heuristicMedicineAmbulatoryPopulationEpidemiologyMedical recordAmbulatory careHealth careEthnic groupEnvironmental healthFamily medicineMedical emergencySurgeryStatisticsPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Electronic medical record (EMR)-based clinical and epidemiological research has dramatically increased over the last decade, although establishing the generalizability of such big databases for conducting epidemiological studies has been an ongoing challenge. To draw meaningful inferences from such studies, it is essential to fully understand the characteristics of the underlying population and potential biases in EMRs. OBJECTIVE: This study aimed to assess the generalizability and representativity of the widely used US Centricity Electronic Medical Record (CEMR), a primary and ambulatory care EMR for population health research, using data from the National Ambulatory Medical Care Surveys (NAMCS) and the National Health and Nutrition Examination Surveys (NHANES). METHODS: The number of office visits reported in the NAMCS, designed to meet the need for objective and reliable information about the provision and the use of ambulatory medical care services, was compared with similar data from the CEMR. The distribution of major cardiometabolic diseases in the NHANES, designed to assess the health and nutritional status of adults and children in the United States, was compared with similar data from the CEMR. RESULTS: Gender and ethnicity distributions were similar between the NAMCS and the CEMR. Younger patients (aged <15 years) were underrepresented in the CEMR compared with the NAMCS. The number of office visits per 100 persons per year was similar: 277.9 (95% CI 259.3-296.5) in the NAMCS and 284.6 (95% CI 284.4-284.7) in the CEMR. However, the number of visits for males was significantly higher in the CEMR (CEMR: 270.8 and NAMCS: 239.0). West and South regions were underrepresented and overrepresented, respectively, in the CEMR. The overall prevalence of diabetes along with age and gender distribution was similar in the CEMR and the NHANES: overall prevalence, 10.1% and 9.7%; male, 11.5% and 10.8%; female, 9.1% and 8.8%; age 20 to 40 years, 2.5% and 1.8%; and age 40 to 60 years, 9.4% and 11.1%, respectively. The prevalence of obesity was similar: 42.1% and 39.6%, with similar age and female distribution (41.5% and 41.1%) but different male distribution (42.7% and 37.9%). The overall prevalence of high cholesterol along with age and female distribution was similar in the CEMR and the NHANES: overall prevalence, 12.4% and 12.4%; and female, 14.8% and 13.2%, respectively. The overall prevalence of hypertension was significantly higher in the CEMR (33.5%) than in the NHANES (95% CI: 27.0%-31.0%). CONCLUSIONS: The distribution of major cardiometabolic diseases in the CEMR is comparable with the national survey results. The CEMR represents the general US population well in terms of office visits and major chronic conditions, whereas the potential subgroup differences in terms of age and gender distribution and prevalence may differ and, therefore, should be carefully taken care of in future studies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.111
GPT teacher head0.506
Teacher spread0.395 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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