Evidence map›Paper›PMID 33354323›Full record

ArticleHealth systems (Basingstoke, England)2019

Key considerations when using health insurance claims data in advanced data analyses: an experience report.

Renata Konrad, Wenchang Zhang, Margrét Bjarndóttir, Ruben Proaño

Abstract read
In one paragraph

Article in Health systems (Basingstoke, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–field-weighted citation impact
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

36 citing papers in PubMed.

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  8. Data Classification Methodology: The Way Forward to Data-Enabled Healthcare in Egypt.Global journal on quality and safety in healthcare · 2025
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  15. Review
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  17. Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data.IEEE access : practical innovations, open solutions · 2025
    Article
  18. Article
  19. Article
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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

4 authors.

Renata KonradFosie School of Business, Worcester Polytechnic Institute, Worcester, MA, USA.
Wenchang ZhangRobert H. Smith School of Business, University of Maryland College Park, College Park, MD, USA.
Margrét BjarndóttirRobert H. Smith School of Business, University of Maryland College Park, College Park, MD, USA.
Ruben ProañoIndustrial Systems Engineering, Rochester Institute of Technology, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health claims have become a popular source of data for healthcare analytics, with numerous applications ranging from disease burden estimation and policy evaluation to drug event detection and advanced predictive analytics. Independent of the application, a researcher utilising claims information will likely encounter challenges in using the data, which include dealing with several coding systems and coding irregularities. We highlight some of these challenges and approaches for successful analysis that may reduce implementation time and help in avoiding common pitfalls. We describe the experiences of a group of academic researchers in using an extensive seven-year repository of US medical and pharmaceutical claims data in a research study, and provide an overview of the challenges encountered with handling claims records for data analysis while sharing suggestions on how to address these challenges. To illustrate our experiences, we use the example of defining episodes of care for a bundled payment reimbursement system in the US context.

Indexed as

analyticsbundled payment systemClaims data

Identifiers

PMID33354323
PMCPMC7738306

What OpenQuestion holds

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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.