National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Adverse Events (2)
- Autism (1)
- Children/Adolescents (2)
- Chronic Conditions (1)
- Comparative Effectiveness (4)
- (-) Data (15)
- Decision Making (1)
- Electronic Health Records (EHRs) (8)
- Healthcare-Associated Infections (HAIs) (1)
- Healthcare Cost and Utilization Project (HCUP) (1)
- (-) Health Information Technology (HIT) (15)
- Health Services Research (HSR) (1)
- Injuries and Wounds (1)
- Medical Devices (1)
- Medical Errors (1)
- Patient-Centered Healthcare (1)
- Patient-Centered Outcomes Research (3)
- Patient Safety (1)
- Public Health (1)
- Quality Improvement (1)
- Quality of Care (1)
- Racial and Ethnic Minorities (1)
- Registries (2)
- Research Methodologies (1)
- Surgery (2)
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 15 of 15 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)
Cohen KB, Goss FR, Zweigenbaum P
Translational morphosyntax: distribution of negation in clinical records and biomedical journal articles.
This paper describes the distribution of negation in two types of biomedical texts: scientific journal articles and progress notes. Two types of negation are examined: explicit negation at the syntactic level and affixal negation at the sub-word level. The data show that the distribution of negation is significantly different in the two document types.
AHRQ-funded; HS024541.
Citation: Cohen KB, Goss FR, Zweigenbaum P .
Translational morphosyntax: distribution of negation in clinical records and biomedical journal articles.
Stud Health Technol Inform 2017;245:346-50.
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Keywords: Data, Health Information Technology (HIT), Research Methodologies
Ong TC, Kahn MG, Kwan BM
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
The researchers designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code. Their results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets.
AHRQ-funded; HS019908; HS022956.
Citation: Ong TC, Kahn MG, Kwan BM .
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
BMC Med Inform Decis Mak 2017 Sep 13;17(1):134. doi: 10.1186/s12911-017-0532-3.
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Keywords: Comparative Effectiveness, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient-Centered Outcomes Research
Bush RA, Connelly CD, Perez A
Extracting autism spectrum disorder data from the electronic health record.
This study uses electronic health record (EHR) data to examine medical utilization and track outcomes among children with Autism Spectrum Disorder (ASD). The study also identifies challenges inherent in designing inclusive algorithms for identifying individuals with ASD and demonstrates the utility of employing multiple extractions to improve the completeness and quality of EHR data when conducting research.
AHRQ-funded; HS022404.
Citation: Bush RA, Connelly CD, Perez A .
Extracting autism spectrum disorder data from the electronic health record.
Appl Clin Inform 2017 Jul 19;8(3):731-41. doi: 10.4338/aci-2017-02-ra-0029..
Keywords: Autism, Children/Adolescents, Data, Health Information Technology (HIT), Electronic Health Records (EHRs)
Ong T, Pradhananga R, Holve E
A framework for classification of electronic health data extraction-transformation-loading challenges in data network participation.
The researchers conducted key-informant interviews with data partner representatives to survey the Extract, Transform, Load (ETL) process challenges faced in clinical data research networks (CDRNs) and registries. The paper concluded that overcoming ETL technical challenges requires significant investments in a broad array of information technologies and human resources. Identifying these technical obstacles can inform optimal resource allocation to minimize the barriers and cost of entry for new data partners into extant networks, which in turn can expand data networks' inclusiveness and diversity.
AHRQ-funded; HS019564.
Citation: Ong T, Pradhananga R, Holve E .
A framework for classification of electronic health data extraction-transformation-loading challenges in data network participation.
eGEMS 2017 Jun 13;5(1):10. doi: 10.5334/egems.222..
Keywords: Comparative Effectiveness, Data, Health Information Technology (HIT), Patient-Centered Outcomes Research, Registries
LeRouge C, Hasselquist MB, Kellogg L
Using heuristic evaluation to enhance the visual display of a provider dashboard for patient-reported outcomes.
A human-centered design (HCD) approach to understanding the data visualization needs for patient-reported outcomes (PRO) in clinical practice can optimize the visual design of an interactive PRO system. Beyond iterative methods, the authors explored the additive value of other HCD methods such as heuristic evaluation. Their evaluation led to several recommendations to improve the display, accessibility, and interpretability of the dashboard’s data.
AHRQ-funded; HS023785.
Citation: LeRouge C, Hasselquist MB, Kellogg L .
Using heuristic evaluation to enhance the visual display of a provider dashboard for patient-reported outcomes.
eGEMS 2017 Apr 20;5(2):Article 6. doi: 10.13063/2327-9214.1283.
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Keywords: Patient-Centered Healthcare, Patient-Centered Outcomes Research, Health Information Technology (HIT), Data, Decision Making
Lybarger K, Ostendorf M, Yetisgen M
Automatically detecting likely edits in clinical notes created using automatic speech recognition.
Aiming to reduce the time required to edit automatic speech recognition (ASR) transcripts, this paper investigates novel methods for automatic detection of edit regions within the transcripts, including both putative ASR errors but also regions that are targets for cleanup or rephrasing.
AHRQ-funded; HS023631.
Citation: Lybarger K, Ostendorf M, Yetisgen M .
Automatically detecting likely edits in clinical notes created using automatic speech recognition.
AMIA Annu Symp Proc 2017 Apr 16;2017:1186-95.
Keywords: Health Information Technology (HIT), Electronic Health Records (EHRs), Data
Hu Z, Melton GB, Arsoniadis EG
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for postoperative complication detection is unclear. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values.
AHRQ-funded; HS024532.
Citation: Hu Z, Melton GB, Arsoniadis EG .
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
J Biomed Inform 2017 Apr;68:112-20. doi: 10.1016/j.jbi.2017.03.009.
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Keywords: Data, Electronic Health Records (EHRs), Healthcare-Associated Infections (HAIs), Registries, Surgery, Injuries and Wounds, Health Information Technology (HIT), Quality Improvement, Quality of Care, Adverse Events
Hettinger AZ, Roth EM, Bisantz AM
Cognitive engineering and health informatics: applications and intersections.
This article provides an overview of relevant cognitive engineering methods, and illustrates how they have been applied to the design of health information technology (HIT) systems. Additionally, although cognitive engineering methods have been applied in the design of user-centered informatics systems, methods drawn from informatics are not typically incorporated into a cognitive engineering analysis. This article presents a discussion regarding ways in which data-rich methods can inform cognitive engineering.
AHRQ-funded; HS022542.
Citation: Hettinger AZ, Roth EM, Bisantz AM .
Cognitive engineering and health informatics: applications and intersections.
J Biomed Inform 2017 Mar;67:21-33. doi: 10.1016/j.jbi.2017.01.010.
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Keywords: Data, Health Information Technology (HIT), Health Information Technology (HIT)
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)