National Healthcare Quality and Disparities Report
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Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (1)
- Adverse Events (3)
- Children/Adolescents (1)
- Chronic Conditions (1)
- Comparative Effectiveness (2)
- (-) Data (12)
- Decision Making (1)
- Electronic Health Records (EHRs) (6)
- Family Health and History (1)
- Genetics (1)
- Healthcare Cost and Utilization Project (HCUP) (1)
- (-) Health Information Technology (HIT) (12)
- Health Services Research (HSR) (1)
- Home Healthcare (1)
- Medical Devices (1)
- Medical Errors (1)
- Medication (1)
- Patient Safety (3)
- Public Health (1)
- Racial and Ethnic Minorities (1)
- Research Methodologies (1)
- Surgery (1)
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 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)
Yang Y, Bass EJ, Sockolow PS
Knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization.
Researchers elicit knowledge related to expert decision-making processes to inform information technology design and related interventions. In this study, the investigators examine knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization. The investigators concluded that the data collection and validation methodology showed promise for knowledge elicitation in time-constrained situations.
AHRQ-funded; HS024537.
Citation: Yang Y, Bass EJ, Sockolow PS .
Knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization.
AMIA Annu Symp Proc 2018 Dec 5;2018:1127-36..
Keywords: Home Healthcare, Decision Making, Health Information Technology (HIT), Data
Fong A, Adams KT, Gaunt MJ
Identifying health information technology related safety event reports from patient safety event report databases.
The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model features. A feature-constraint model was developed and evaluated to support the analysis of PSE reports. The feature-constraint model provides a method to identify HIT-related patient safety hazards using a method that is applicable across healthcare systems with variability in their PSE report structures.
AHRQ-funded; HS023701.
Citation: Fong A, Adams KT, Gaunt MJ .
Identifying health information technology related safety event reports from patient safety event report databases.
J Biomed Inform 2018 Oct;86:135-42. doi: 10.1016/j.jbi.2018.09.007..
Keywords: Health Information Technology (HIT), Patient Safety, Adverse Events, Data
Polubriaginof FCG, Vanguri R, Quinnies K
Disease heritability inferred from familial relationships reported in medical records.
Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. This study used EHR data to compute heritability estimates for 500 disease phenotypes. These analyses provided a validation of the use of EHRs for genetics and disease research.
AHRQ-funded; HS021816; HS022961.
Citation: Polubriaginof FCG, Vanguri R, Quinnies K .
Disease heritability inferred from familial relationships reported in medical records.
Cell 2018 Jun 14;173(7):1692-704.e11. doi: 10.1016/j.cell.2018.04.032..
Keywords: Data, Family Health and History, Genetics, Health Information Technology (HIT), Electronic Health Records (EHRs)
Goss FR, Lai KH, Topaz M
A value set for documenting adverse reactions in electronic health records.
In this study, the investigators developed a value set for encoding adverse reactions using a large dataset from one health system, enriched by reactions from 2 large external resources. This integrated value set included clinically important severe and hypersensitivity reactions. The work contributed a value set, harmonized with existing data, to improve the consistency and accuracy of reaction documentation in electronic health records, providing the necessary building blocks for more intelligent clinical decision support for allergies and adverse reactions.
AHRQ-funded; HS022728.
Citation: Goss FR, Lai KH, Topaz M .
A value set for documenting adverse reactions in electronic health records.
J Am Med Inform Assoc 2018 Jun;25(6):661-69. doi: 10.1093/jamia/ocx139..
Keywords: Adverse Drug Events (ADE), Adverse Events, Electronic Health Records (EHRs), Medication, Data, Health Information Technology (HIT), Patient Safety
Hultman G, McEwan R, Pakhomov S
Usability evaluation of an unstructured clinical document query tool for researchers.
This study aimed to conduct a user-centered analysis with clinical researchers to gain insight into Natural Language Processing - Patient Information Extraction for Researchers (NLP-PIER) usability and to gain an understanding of the needs of clinical researchers when using an application for searching clinical notes.
AHRQ-funded; HS022085.
Citation: Hultman G, McEwan R, Pakhomov S .
Usability evaluation of an unstructured clinical document query tool for researchers.
AMIA Jt Summits Transl Sci Proc 2018 May 18;2018:84-93..
Keywords: Data, Health Information Technology (HIT), Research Methodologies
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)