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Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (1)
- Children/Adolescents (1)
- Chronic Conditions (1)
- Clinical Decision Support (CDS) (1)
- Communication (1)
- (-) Data (8)
- (-) Electronic Health Records (EHRs) (8)
- Healthcare Delivery (1)
- Health Information Exchange (HIE) (1)
- Health Information Technology (HIT) (4)
- Heart Disease and Health (2)
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- Racial and Ethnic Minorities (1)
- Risk (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 8 of 8 Research Studies DisplayedPanahiazar M, Taslimitehrani V, Pereira NL
Using EHRs for heart failure therapy recommendation using multidimensional patient similarity analytics.
The authors developed a multidimensional patient similarity assessment technique that leverages multiple types of information from the electronic health records and predicts a medication plan for each new patient based on prior knowledge and data from similar patients.Their findings suggest that it is feasible to harness population-based information for an individual patient-specific assessment.
AHRQ-funded; HS023077.
Citation: Panahiazar M, Taslimitehrani V, Pereira NL .
Using EHRs for heart failure therapy recommendation using multidimensional patient similarity analytics.
Stud Health Technol Inform 2015;210:369-73.
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Keywords: Clinical Decision Support (CDS), Data, Electronic Health Records (EHRs), Heart Disease and Health, Patient-Centered Healthcare
Zhang R, Manohar N, Arsoniadis E
Evaluating term coverage of herbal and dietary supplements in electronic health records.
Some supplements can interact with prescription medications, potentially leading to clinically important and potentially preventable adverse reactions. Clinical notes and corresponding medication lists from an integrated healthcare system were extracted and compared with online databases. The authors found that, overall, about 40% of listed medications are supplements, most of which are included in medication lists as nutritional or miscellaneous products. They found gaps between supplement and standard medication terminologies and identified supplements which were not mentioned in the medication lists.
AHRQ-funded; HS022085.
Citation: Zhang R, Manohar N, Arsoniadis E .
Evaluating term coverage of herbal and dietary supplements in electronic health records.
AMIA Annu Symp Proc 2015 Nov 5;2015:1361-70.
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Keywords: Adverse Drug Events (ADE), Data, Electronic Health Records (EHRs), Medication, Vitamins and Supplements
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
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
Neff JM, Clifton H, Popalisky J
Stratification of children by medical complexity.
The investigators stratified children using the software, Clinical Risk Groups (CRGs), in a tertiary children's hospital and a state's Medicaid claims data into 3 condition groups: complex chronic disease; noncomplex chronic disease, and nonchronic disease. They concluded that CRGs can be used to stratify children receiving care at a tertiary care hospital according to complexity in both hospital and Medicaid administrative data.
AHRQ-funded; HS020506.
Citation: Neff JM, Clifton H, Popalisky J .
Stratification of children by medical complexity.
Acad Pediatr 2015 Mar-Apr;15(2):191-6. doi: 10.1016/j.acap.2014.10.007.
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Keywords: Children/Adolescents, Chronic Conditions, Data, Electronic Health Records (EHRs), Children/Adolescents
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
Panahiazar M, Taslimitehrani V, Pereira N
Using EHRs and machine learning for heart failure survival analysis.
This study assessed the performance of the Seattle Heart Failure Model using EHRs at Mayo Clinic, and sought to develop a risk prediction model using machine learning techniques that applied routine clinical care data. Its results showed the models which were built using EHR data are more accurate (11 percent improvement in AUC) with the convenience of being more readily applicable in routine clinical care.
AHRQ-funded; HS023077.
Citation: Panahiazar M, Taslimitehrani V, Pereira N .
Using EHRs and machine learning for heart failure survival analysis.
Stud Health Technol Inform 2015;216:40-4..
Keywords: Electronic Health Records (EHRs), Heart Disease and Health, Risk, Data