Evidence map›Paper›PMID 28765864›Full record

ArticleApplied clinical informatics2017

Comparison of EHR-based diagnosis documentation locations to a gold standard for risk stratification in patients with multiple chronic conditions.

Shelby Martin, Jesse Wagner, Nicoleta Lupulescu-Mann, Katrina Ramsey, Aaron Cohen, Peter Graven, Nicole G Weiskopf, David A Dorr

Open access · bronzeAbstract readComparative Study
In one paragraph

Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 28% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
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  9. Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.Journal of the American Medical Informatics Association : JAMIA · 2023
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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

8 authors at 1 institution in 1 country.

Shelby Martin
Jesse Wagner
Nicoleta Lupulescu-Mann
Katrina Ramsey
Aaron Cohen
Peter Graven
Nicole G Weiskopf
David A DorrDavid A. Dorr, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd., MDYMICE, Portland, OR 97239-3098, USA, Email: dorrd@ohsu.edu.
Oregon Health & Science University · US

Funding

AHRQ HHS R21 HS023091
6 · The paper itself

Abstract

objectiveTo measure variation among four different Electronic Health Record (EHR) system documentation locations versus 'gold standard' manual chart review for risk stratification in patients with multiple chronic illnesses.

methodsAdults seen in primary care with EHR evidence of at least one of 13 conditions were included. EHRs were manually reviewed to determine presence of active diagnoses, and risk scores were calculated using three different methodologies and five EHR documentation locations. Claims data were used to assess cost and utilization for the following year. Descriptive and diagnostic statistics were calculated for each EHR location. Criterion validity testing compared the gold standard verified diagnoses versus other EHR locations and risk scores in predicting future cost and utilization.

resultsNine hundred patients had 2,179 probable diagnoses. About 70% of the diagnoses from the EHR were verified by gold standard. For a subset of patients having baseline and prediction year data (n=750), modeling showed that the gold standard was the best predictor of outcomes on average for a subset of patients that had these data. However, combining all data sources together had nearly equivalent performance for prediction as the gold standard.

conclusionsEHR data locations were inaccurate 30% of the time, leading to improvement in overall modeling from a gold standard from chart review for individual diagnoses. However, the impact on identification of the highest risk patients was minor, and combining data from different EHR locations was equivalent to gold standard performance. The reviewer's ability to identify a diagnosis as correct was influenced by a variety of factors, including completeness, temporality, and perceived accuracy of chart data.

Indexed as

DocumentationElectronic Health RecordsMultiple Chronic ConditionsFalse Positive ReactionsHumansReference StandardsRisk AssessmentMultiple Chronic Conditions; Health Information Systems; Risk Stratification; Forecasting; Data Quality

Identifiers

PMID28765864
PMCPMC6220706
OpenAlexW2740020869

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.