Evidence map›Paper›PMID 35043402›Full record

ArticleHealth services research2022

A national assessment of legacy versus new generation Medicaid data.

Jessy K Nguyen, Prachi Sanghavi

Abstract read
In one paragraph

Article in Health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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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

15 citing papers in PubMed.

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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

2 authors.

Jessy K NguyenDepartment of Public Health Sciences, Biological Sciences Division, The University of Chicago, Chicago, Illinois, USA.
Prachi SanghaviDepartment of Public Health Sciences, Biological Sciences Division, The University of Chicago, Chicago, Illinois, USA.ORCID 0000-0002-0738-3193

Funding

AHRQ HHS R01 HS025720
6 · The paper itself

Abstract

objectiveTo compare the performance of Medicaid legacy, Medicaid new generation, and Medicare claims on data analytic tasks. DATA SOURCES: Medicaid Analytic eXtract (MAX) claims (legacy) of 100% beneficiaries in 2011 (all states except Idaho), 2012 (all states), 2013 (28 states), and 2014 (17 states); 2016 Transformed Medicaid Statistical Information System Analytic Files (TAF) claims (new generation) of 100% beneficiaries from all states; Medicare claims of 20% beneficiaries in 2011-2014, 2016. STUDY

designWe focused on the chain of events that starts with an out-of-hospital medical emergency and ends with hospital death or survival to discharge. We developed six data quality indicators to assess ambulance variables; linkage between claims; external cause of injury code reporting; and death reporting on hospital discharge status codes. For the latter, we estimated injury severity and modeled its association with death in the Medicare population. We used the model to compare reported versus expected deaths by injury severity in the Medicaid population. Datasets were compared by state and fee-for-service versus managed care. DATA EXTRACTION

methodsMedicare and Medicaid beneficiaries with emergency ambulance transports. PRINCIPAL

findingsMedicare claims had high performance across indicators and states; MAX claims substantially underperformed on multiple indicators in most states. For example, most states reported external cause codes for over 90% of Medicare but less than 15% of Medicaid injury cases. Medicaid fee-for-service did not consistently perform better than Medicaid managed care. Compared with MAX, TAF claims performed significantly better on some indicators but continued to have poor external cause code reporting. Finally, MAX and TAF managed care records reported deaths at discharge in the range of expected deaths; however, fee-for-service claims might have underreported high-severity injury deaths.

conclusionsNew generation Medicaid claims performed better than legacy claims on some indicators, but much more improvement is needed to allow high-quality policy analysis.

Indexed as

MedicaidMedicareAgedFee-for-Service PlansHumansManaged Care ProgramsPatient DischargeUnited Statesdata qualitydeath reportinginjuriesMedicaidmedical emergenciesMedicare

Identifiers

PMID35043402
PMCPMC9264472

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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.