National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Adverse Events (2)
- Children/Adolescents (1)
- Chronic Conditions (1)
- Clinical Decision Support (CDS) (1)
- Communication (2)
- Comparative Effectiveness (4)
- (-) Data (19)
- Electronic Health Records (EHRs) (8)
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- Health Information Exchange (HIE) (3)
- (-) Health Information Technology (HIT) (19)
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- Medical Errors (2)
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- Patient-Centered Healthcare (1)
- Patient Safety (2)
- Policy (1)
- Public Health (1)
- Public Reporting (1)
- Racial and Ethnic Minorities (2)
- Registries (1)
- Research Methodologies (1)
- Surgery (1)
- Vitamins and Supplements (1)
- Web-Based (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 19 of 19 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)
Liang C, Gong Y
Enhancing patient safety event reporting by K-nearest neighbor classifier.
The debate on structured or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technical gap on text mining. The reesarchers suggested a text classification method for predicting subject categories. Their results demonstrated the feasibility of their system and indicated the advantage of such an application to raise data quality and clinical decision support in reporting patient safety events.
AHRQ-funded; HS022895.
Citation: Liang C, Gong Y .
Enhancing patient safety event reporting by K-nearest neighbor classifier.
Stud Health Technol Inform 2015;218:40603.
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Keywords: Adverse Events, Medical Errors, Patient Safety, Public Reporting, Clinical Decision Support (CDS), Health Information Technology (HIT), Data
Swain MJ, Kharrazi H
Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data.
The researchers conducted a semi-systematic review of readmission predictive factors published prior to March 2013. They found that mapping of these variables with common HL7 segments resulted in an 89.2 percent total coverage, with the DG1 (diagnosis) segment having the highest coverage of 39.4 percent. The PID (patient identification) and OBX (observation results) segments cover 13.9 percent and 9.1 percent of the variables.
AHRQ-funded; HS022578.
Citation: Swain MJ, Kharrazi H .
Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data.
Int J Med Inform 2015 Dec;84(12):1048-56. doi: 10.1016/j.ijmedinf.2015.09.003.
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Keywords: Health Information Exchange (HIE), Hospital Readmissions, Health Information Technology (HIT), Data
Meeker D, Jiang X, Matheny ME
A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.
The authors’ objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features. They were able to implement massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared.
AHRQ-funded; HS019913.
Citation: Meeker D, Jiang X, Matheny ME .
A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.
J Am Med Inform Assoc 2015 Nov;22(6):1187-95. doi: 10.1093/jamia/ocv017..
Keywords: Communication, Comparative Effectiveness, Data, Health Information Technology (HIT), Policy, Research Methodologies
Hazlehurst BL, Kurtz SE, Masica A
CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data.
The authors describe the CER Hub, a web-based informatics platform for developing and conducting research studies that combine comprehensive electronic clinical data from multiple health care organizations. They conclude that CER requires coordinated and scalable methods for extracting, aggregating, and analyzing complex, multi-institutional clinical data.
AHRQ-funded; HS019828.
Citation: Hazlehurst BL, Kurtz SE, Masica A .
CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data.
Int J Med Inform 2015 Oct;84(10):763-73. doi: 10.1016/j.ijmedinf.2015.06.002..
Keywords: Comparative Effectiveness, Health Information Technology (HIT), Data, Web-Based
Price LE, Shea K, Gephart S
The Veterans Affairs's Corporate Data Warehouse: uses and implications for nursing research and practice.
This article described the developments in research associated with the VHA's transition into the world of Big Data analytics through Corporate Data Warehouse (CDW) utilization. The authors found that the most commonly-occurring research topics are pharmacy/medications, systems issues, and weight management/obesity. They concluded that, despite the potential benefit of data mining techniques to improve patient care and services, the CDW and alternative analytical approaches are underutilized by researchers and clinicians.
AHRQ-funded; HS022908.
Citation: Price LE, Shea K, Gephart S .
The Veterans Affairs's Corporate Data Warehouse: uses and implications for nursing research and practice.
Nurs Adm Q 2015 Oct-Dec;39(4):311-8. doi: 10.1097/naq.0000000000000118.
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Keywords: Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Nursing
Brennan PF, Bakken S
Nursing needs big data and big data needs nursing.
Nursing science and nursing practice has much to gain from the data science initiatives. Existing approaches to large data set analysis provide a necessary but not sufficient foundation for nursing to participate in the big data revolution. Nursing’s Social Policy Statement provides a principled, ethical perspective on big data and data science.
AHRQ-funded; HS022961; HS02254.
Citation: Brennan PF, Bakken S .
Nursing needs big data and big data needs nursing.
J Nurs Scholarsh 2015 Sep;47(5):477-84. doi: 10.1111/jnu.12159..
Keywords: Nursing, Data, Health Information Technology (HIT)
Naessens JM, Visscher SL, Peterson SM
Incorporating the last four digits of social security numbers substantially improves linking patient data from de-identified hospital claims databases.
The study objective was to assess algorithms for linking patients across de-identified databases without compromising confidentiality. It found that addition of SSNL4 to administrative data, accompanied by appropriate data use and data release policies, can enable trusted repositories to link data with nearly perfect accuracy.
AHRQ-funded; HS020043.
Citation: Naessens JM, Visscher SL, Peterson SM .
Incorporating the last four digits of social security numbers substantially improves linking patient data from de-identified hospital claims databases.
Health Serv Res 2015 Aug;50 Suppl 1:1339-50. doi: 10.1111/1475-6773.12323..
Keywords: Data, Registries, Hospital Discharge, Health Information Technology (HIT)
Kim KK, Joseph JG, Ohno-Machado L
Comparison of consumers' views on electronic data sharing for healthcare and research.
The researchers surveyed California consumers to learn their views of privacy, security, and consent in electronic data sharing for healthcare and research together. They found considerable concern that health information exchanges will worsen privacy (40.3 percent) and security (42.5 percent). Consumers are in favor of electronic data sharing but elements of transparency are important: individual control, who has access, and the purpose for use of data.
AHRQ-funded; HS019913.
Citation: Kim KK, Joseph JG, Ohno-Machado L .
Comparison of consumers' views on electronic data sharing for healthcare and research.
J Am Med Inform Assoc 2015 Jul;22(4):821-30. doi: 10.1093/jamia/ocv014..
Keywords: Communication, Data, Electronic Health Records (EHRs), Health Information Exchange (HIE), Health Information Technology (HIT), Patient-Centered Healthcare
Del Fiol G, Crouch BI, Cummins MR
Data standards to support health information exchange between poison control centers and emergency departments.
The researchers identified and assessed a set of data standards to enable a standards-based health information exchange process between emergency departments (EDs) and poison control centers (PCCs). They determined that four Consolidated Clinical Document Architecture document types were necessary to support the PCC–ED information exchange process: History & Physical Note, Consultation Note, Progress Note, and Discharge Summary.
AHRQ-funded; HS021472.
Citation: Del Fiol G, Crouch BI, Cummins MR .
Data standards to support health information exchange between poison control centers and emergency departments.
J Am Med Inform Assoc 2015 May;22(3):519-28. doi: 10.1136/amiajnl-2014-003127..
Keywords: Data, Emergency Department, Emergency Medical Services (EMS), Health Information Exchange (HIE), Health Information Technology (HIT)
Manohar N, Adam TJ, Pakhomov SV
Evaluation of herbal and dietary supplement resource term coverage.
This pilot study investigated coverage of supplement databases to one another as well as coverage by the Unified Medical Language System (UMLS) and RxNorm for supplement terms. It found that none of the supplement databases completely covers supplement terms.
AHRQ-funded; HS022085.
Citation: Manohar N, Adam TJ, Pakhomov SV .
Evaluation of herbal and dietary supplement resource term coverage.
Stud Health Technol Inform 2015;216:785-9..
Keywords: Health Information Technology (HIT), Vitamins and Supplements, Data
Bakken SN, Hill JN, Guihan M
Factors influencing consent for electronic data linkage in urban Latinos.
Within the context of patient participation in a Learning Health System, this study examined consent rates and factors associated with consent for linking survey data with electronic clinical data in a sample of 2,271 Latinos. Consent rate was 96.3%. Government insurance status and health literacy significantly influenced the odds of consent.
AHRQ-funded; HS022961.
Citation: Bakken SN, Hill JN, Guihan M .
Factors influencing consent for electronic data linkage in urban Latinos.
Stud Health Technol Inform 2015;216:984..
Keywords: Racial and Ethnic Minorities, Health Information Technology (HIT), Electronic Health Records (EHRs), Data, Racial and Ethnic Minorities
Shenvi EC, Meeker D, Boxwala AA
Understanding data requirements of retrospective studies.
This study seeks to characterize the types and patterns of data usage from EHRs for clinical research. It found that studies used an average of 4.46 (range 1–12) data element types in the selection criteria and 6.44 (range 1–15) in the study variables. The most frequently used items (e.g., procedure, condition, medication) are often available in coded form in EHRs.
AHRQ-funded; HS019913.
Citation: Shenvi EC, Meeker D, Boxwala AA .
Understanding data requirements of retrospective studies.
Int J Med Inform 2015 Jan;84(1):76-84. doi: 10.1016/j.ijmedinf.2014.10.004..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Data, Healthcare Delivery
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)