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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 12 of 12 Research Studies DisplayedBoudreaux M, Gangopadhyaya A, Long SK
AHRQ Author: Karaca Z
Using data from the Healthcare Cost and Utilization Project for state health policy research.
Investigators describe the opportunities and challenges of using HCUP data to conduct state health policy research and to provide empirical examples of what can go wrong when using the national HCUP data inappropriately. Analyzing cesarean delivery rates, discharges per capita, and discharges by the payer, they found that state-level estimates are volatile and often provide misleading policy conclusions. They conclude that the Nationwide Inpatient Sample should not be used for state-level research and specified that AHRQ provides resources to assist analysts with state-specific studies using State Inpatient Database files.
AHRQ-authored.
Citation: Boudreaux M, Gangopadhyaya A, Long SK .
Using data from the Healthcare Cost and Utilization Project for state health policy research.
Med Care 2019 Nov;57(11):855-60. doi: 10.1097/mlr.0000000000001196..
Keywords: Healthcare Cost and Utilization Project (HCUP), Policy, Health Services Research (HSR), Healthcare Costs, Data, Research Methodologies
Polubriaginof FCG, Ryan P, Salmasian H
Challenges with quality of race and ethnicity data in observational databases.
This study assessed the quality of race and ethnicity information in observational health databases as well as electronic health records (EHRs) and to propose patient self-recording as a way to improve accuracy. Data from the Healthcare Cost and Utilization Project (HCUP) and Optum Labs, and from a single New York City healthcare system’s EHR was compared. Among 160 million patients in the HCUP database, no race or ethnicity data was recorded for 25% of the records. Among the 2.4 million patients in the New York City HER, race or ethnicity was unknown for 57%. However, when patients were allowed to directly record their race and ethnicity, percentages rose to 86%.
AHRQ-funded; HS021816; HS023704; HS024713.
Citation: Polubriaginof FCG, Ryan P, Salmasian H .
Challenges with quality of race and ethnicity data in observational databases.
J Am Med Inform Assoc 2019 Aug;26(8-9):730-36. doi: 10.1093/jamia/ocz113..
Keywords: Healthcare Cost and Utilization Project (HCUP), Data, Racial and Ethnic Minorities, Electronic Health Records (EHRs), Health Information Technology (HIT), Health Services Research (HSR)
Wiehe SE, Rosenman MB, Chartash D
A solutions-based approach to building data-sharing partnerships.
This paper aims to enhance the van Panhuis et al. framework of barriers to data sharing; the authors present a complementary solutions-based data-sharing process in order to encourage both emerging and established researchers, whether or not in academia, to engage in data-sharing partnerships.
AHRQ-funded; HS023318; HS024296.
Citation: Wiehe SE, Rosenman MB, Chartash D .
A solutions-based approach to building data-sharing partnerships.
eGEMS 2018 Aug 22;6(1):20. doi: 10.5334/egems.236..
Keywords: Data, Health Services Research (HSR), Research Methodologies
Lu B, Cai D, Tong X
Testing causal effects in observational survival data using propensity score matching design.
The researchers proposed a strategy to test for survival function differences based on the matching design and explored sensitivity of the P-values to assumptions about unmeasured confounding. Next, they applied their method to an observational cohort of chronic liver disease patients from a Mayo Clinic study. Results showed evidence of a significant treatment effect. They recommended caution, however, as the sensitivity analysis reveals that the P-value becomes non-significant if there exists an unmeasured confounder with a small impact.
AHRQ-funded; HS024263.
Citation: Lu B, Cai D, Tong X .
Testing causal effects in observational survival data using propensity score matching design.
Stat Med 2018 May 20;37(11):1846-58. doi: 10.1002/sim.7599.
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Keywords: Data, Health Services Research (HSR), Research Methodologies
Sun B, Perkins NJ, Cole SR
AHRQ Author: Mitchell EM
Inverse-probability-weighted estimation for monotone and nonmonotone missing data.
The goal of this study was to examine the issue of missing data in epidemiologic research by estimating the association of maternal smoking behavior with spontaneous abortion. Three data sets with induced missing values from the Collaborative Perinatal Project are provided in the article as examples of prototypical epidemiologic studies with missing data. The article also describes a proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random that can be used in constructing the weights in inverse probability weighting complete-case estimation.
AHRQ-authored.
Citation: Sun B, Perkins NJ, Cole SR .
Inverse-probability-weighted estimation for monotone and nonmonotone missing data.
Am J Epidemiol 2018 Mar;187(3):585-91. doi: 10.1093/aje/kwx350..
Keywords: Data, Health Services Research (HSR), Pregnancy, Research Methodologies
Cohen GR, Jones DJ, Heeringa J
AHRQ Author: Furukawa MF, Miller D
Leveraging diverse data sources to identify and describe U.S. health care delivery systems.
Health care delivery systems are a growing presence in the U.S., yet research is hindered by the lack of universally agreed-upon criteria to denote formal systems. This study assesses available data sources to identify and describe systems, including system members and relationships among the members.
AHRQ-authored.
Citation: Cohen GR, Jones DJ, Heeringa J .
Leveraging diverse data sources to identify and describe U.S. health care delivery systems.
eGEMS 2017 Dec 15;5(3):9. doi: 10.5334/egems.200..
Keywords: Healthcare Delivery, Data, Health Services Research (HSR), System Design
Ji X, Machiraju R, Ritter A
Visualizing article similarities via sparsified article network and map projection for systematic reviews.
In this study, the authors visualized article similarities to extend its utilization in practical settings for SR researchers, aiming to promote human comprehension of article distributions and hidden patterns. To prompt an effective visualization in an interpretable, intuitive, and scalable way, they implemented a graph-based network visualization with three network sparsification approaches and a distance-based map projection via dimensionality reduction.
AHRQ-funded; HS025047.
Citation: Ji X, Machiraju R, Ritter A .
Visualizing article similarities via sparsified article network and map projection for systematic reviews.
Stud Health Technol Inform 2017;245:422-26.
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Keywords: Data, Evidence-Based Practice, Health Services Research (HSR), Research Methodologies
Groeneveld PW, Rumsfeld JS
Can big data fulfill its promise?
This article discusses the pros and cons of using big data analytics in healthcare. The authors note that the rise of big data analytics in health care settings has promise. However, they assert that it is critical to recognize that the fundamental pitfalls of observational data analysis cannot be ignored, and in fact the risks of such pitfalls demand rigorous scientific testing and novel methods for peer review of big data analytic models.
AHRQ-funded; HS023615.
Citation: Groeneveld PW, Rumsfeld JS .
Can big data fulfill its promise?
Circ Cardiovasc Qual Outcomes 2016 Nov;9(6):679-82. doi: 10.1161/circoutcomes.116.003097..
Keywords: Data, Health Services Research (HSR)
Terza JV
Inference using sample means of parametric nonlinear data transformations.
AHRQ-funded; HS017434.
Citation: Terza JV .
Inference using sample means of parametric nonlinear data transformations.
Health Serv Res 2016 Jun;51(3):1109-13. doi: 10.1111/1475-6773.12494.
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Keywords: Research Methodologies, Health Services Research (HSR), Data
Kuehl DR, Berdahl CT, Jackson TD
Advancing the use of administrative data for emergency department diagnostic imaging research.
This article summarizes the discussions of the breakout session on the use of administrative data for emergency imaging research at the May 2015 Academic Emergency Medicine consensus conference, "Diagnostic Imaging in the Emergency Department: A Research Agenda to Optimize Utilization." The authors describe the areas where administrative data have been applied to research evaluating the use of diagnostic imaging in the ED, the common sources for these data, and the strengths and limitations of administrative data.
AHRQ-funded; HS023498.
Citation: Kuehl DR, Berdahl CT, Jackson TD .
Advancing the use of administrative data for emergency department diagnostic imaging research.
Acad Emerg Med 2015 Dec;22(12):1417-26. doi: 10.1111/acem.12827.
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Keywords: Data, Emergency Department, Health Services Research (HSR), Imaging
Neprash HT, Wallace J, Chernew ME
Measuring prices in health care markets using commercial claims data.
The objective of this study was to compare methods of price measurement in health care markets. It concluded that market-level price measures reflecting broad sets of services are likely to rank markets similarly. Price indices relying on individual sentinel services may be more appropriate for examining specialty- or service-specific drivers of prices.
AHRQ-funded; HS000055.
Citation: Neprash HT, Wallace J, Chernew ME .
Measuring prices in health care markets using commercial claims data.
Health Serv Res 2015 Dec;50(6):2037-47. doi: 10.1111/1475-6773.12304..
Keywords: Data, Healthcare Costs, Health Insurance, Health Services Research (HSR)
Zingmond DS, Parikh P, Louie R
Improving hospital reporting of patient race and ethnicity--approaches to data auditing.
This study investigated new metrics to improve the reporting of patient race and ethnicity (R/E) by hospitals. It examined agreement between hospital reported R/E versus self-report among mothers delivering babies and a cancer cohort in California. It concluded that comparison between reported R/E and R/E estimates using zip code level data may be a reasonable first approach to evaluate and track hospital R/E reporting.
AHRQ-funded; HS019963.
Citation: Zingmond DS, Parikh P, Louie R .
Improving hospital reporting of patient race and ethnicity--approaches to data auditing.
Health Serv Res 2015 Aug;50 Suppl 1:1372-89. doi: 10.1111/1475-6773.12324..
Keywords: Racial and Ethnic Minorities, Hospitals, Hospital Discharge, Health Services Research (HSR), Registries, Quality Improvement, Quality of Care, Data