National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Adverse Events (1)
- Cancer (1)
- Children/Adolescents (1)
- Chronic Conditions (1)
- Comparative Effectiveness (2)
- (-) Data (10)
- Electronic Health Records (EHRs) (5)
- Emergency Department (1)
- Healthcare Cost and Utilization Project (HCUP) (1)
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- Health Information Exchange (HIE) (1)
- (-) Health Information Technology (HIT) (10)
- Health Services Research (HSR) (1)
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- Racial and Ethnic Minorities (1)
- Surgery (1)
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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 10 of 10 Research Studies DisplayedBacon E, Budney G, Bondy J
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
This article describes attributes of regional distributed data networks using electronic health records (EHR) data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. The authors indicate that while benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.
AHRQ-funded; HS0122143.
Citation: Bacon E, Budney G, Bondy J .
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
J Public Health Manag Pract 2019 Sep/Oct;25(5):498-507. doi: 10.1097/phh.0000000000000810..
Keywords: Chronic Conditions, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Public Health
Liu L, Ni Y, Zhang N
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
The objectives of this study were: 1) to develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation. The study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The author’s approach offers the promise of targeted interventions to significantly decrease both healthcare costs and families' negative experiences.
AHRQ-funded; HS024983.
Citation: Liu L, Ni Y, Zhang N .
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Int J Med Inform 2019 Sep;129:234-41. doi: 10.1016/j.ijmedinf.2019.06.007..
Keywords: Children/Adolescents, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery
Wang E, Kang H, Gong Y
Generating a health information technology event database from FDA MAUDE reports.
This study examined using a health information technology (HIT) event database to identify patient safety events (PSEs) or medical errors. The study used the FDA Manufacturer and User Facility Device Experience (MAUDE) database to extract HIT events. Classic and CNN models were utilized on a test set. The model was capable of identifying HIT event with about a 90% accuracy.
AHRQ-funded; HS022895.
Citation: Wang E, Kang H, Gong Y .
Generating a health information technology event database from FDA MAUDE reports.
Stud Health Technol Inform 2019 Aug 21;264:883-87. doi: 10.3233/shti190350..
Keywords: Health Information Technology (HIT), Medical Devices, Adverse Events, Data, Medical Errors, Patient Safety
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)
Angier H, Gold R, Crawford C
Linkage methods for connecting children with parents in electronic health record and state public health insurance data.
The purpose of this study was to develop ways to create child-parent links in two healthcare-related data sources: Oregon clinics sharing an electronic health record (EHR) and Oregon Health Plan’s (OHP) administrative data. To create the child-parent links, researchers used the child’s emergency contact information from the EHR and household identification numbers from the OHP.
AHRQ-funded; HS018569
Citation: Angier H, Gold R, Crawford C .
Linkage methods for connecting children with parents in electronic health record and state public health insurance data.
Matern Child Health J. 2014 Nov;18(9):2025-33. doi: 10.1007/s10995-014-1453-8..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Data
Holmes JH, Elliott TE, Brown JS
Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature.
The researchers reviewed the published, peer-reviewed literature on clinical research data warehouse governance in distributed research networks (DRNs). They determined that a peer-reviewed literature on data warehouse governance is emerging but is still sparse. Understanding of DRN data governance policies and procedures is limited but expected to change as more DRN projects disseminate their governance approaches.
AHRQ-funded; HS019912
Citation: Holmes JH, Elliott TE, Brown JS .
Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature.
J Am Med Inform Assoc. 2014 Jul-Aug;21(4):730-6. doi: 10.1136/amiajnl-2013-002370..
Keywords: Comparative Effectiveness, Data, Health Information Technology (HIT)
Ji Z, Jiang X, Wang S
Differentially private distributed logistic regression using private and public data.
The purpose of this study was to develop hybrid data mining models using both public and private data sets in a differentially private and distributed manner to achieve improved utility of the disclosed data. The researchers concluded that the logistic regression models built with their new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.
AHRQ-funded; HS019913
Citation: Ji Z, Jiang X, Wang S .
Differentially private distributed logistic regression using private and public data.
BMC Med Genomics 2014;7 Suppl 1:S14. doi: 10.1186/1755-8794-7-s1-s14.
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Keywords: Comparative Effectiveness, Data, Health Information Technology (HIT)
Jiang W, Li P, Wang S
WebGLORE: a web service for Grid LOgistic Regression.
This article describes and discusses WebGLORE, a free web service enabling privacy-preserving construction of a global logistic regression model from sensitive distributed datasets using HTTP to a trusted server, where the model is synthesized.
AHRQ-funded; HS019564.
Citation: Jiang W, Li P, Wang S .
WebGLORE: a web service for Grid LOgistic Regression.
Bioinformatics. 2013 Dec 15;29(24):3238-40. doi: 10.1093/bioinformatics/btt559..
Keywords: Data, Health Information Technology (HIT), Web-Based
Shapiro JS, Johnson SA, Angiollilo J
Health information exchange improves identification of frequent emergency department users.
The goal of the project was to measure the incremental increase in the number of frequent ED users who were identified when data
from all EDs participating in an health information exchange were compared with site-specific data. When the researchers analyzed HIE-wide data instead of site-specific data, they identified 20.3 percent more frequent ED users and 16.0 percent more visits by them to the ED.
from all EDs participating in an health information exchange were compared with site-specific data. When the researchers analyzed HIE-wide data instead of site-specific data, they identified 20.3 percent more frequent ED users and 16.0 percent more visits by them to the ED.
AHRQ-funded; HS021261.
Citation: Shapiro JS, Johnson SA, Angiollilo J .
Health information exchange improves identification of frequent emergency department users.
Health Aff 2013 Dec;32(12):2193-8. doi: 10.1377/hlthaff.2013.0167..
Keywords: Data, Emergency Department, Healthcare Utilization, Health Information Exchange (HIE), Health Information Technology (HIT)
Mehrabi S, Schmidt CM, Waters JA
An efficient pancreatic cyst identification methodology using natural language processing.
Accurate identification, surveillance and treatment of pancreatic cysts represents an opportunity to prevent pancreatic cancer. Much information about pancreatic cysts can be found in free text format in various narrative medical reports. To capture this information, the researchers modified their cyst identification technique using the Unstructured Information Management Architecture (UIMA) pipeline.
AHRQ-funded; HS019818.
Citation: Mehrabi S, Schmidt CM, Waters JA .
An efficient pancreatic cyst identification methodology using natural language processing.
Stud Health Technol Inform 2013;192:822-6..
Keywords: Cancer, Electronic Health Records (EHRs), Data, Health Information Technology (HIT), Prevention