National Healthcare Quality and Disparities Report
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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
176 to 180 of 180 Research Studies DisplayedAnderson HD, Pace WD, Brandt E
Monitoring suicidal patients in primary care using electronic health records.
The objective of this study was to estimate the use of diagnostic codes in EHRs to document suicidal ideation and attempt among patients seen in primary care. It found that few cases of suicidal ideation and suicide attempt as documented in a primary care setting using a clinician’s notes field or a patient-reported PHQ-9 were also documented in the patient’s EHR using diagnostic codes.
AHRQ-funded; HS019464.
Citation: Anderson HD, Pace WD, Brandt E .
Monitoring suicidal patients in primary care using electronic health records.
J Am Board Fam Med 2015 Jan-Feb;28(1):65-71. doi: 10.3122/jabfm.2015.01.140181..
Keywords: Behavioral Health, Primary Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Green LA, Potworowski G, Day A
Sustaining "meaningful use" of health information technology in low-resource practices.
The objective of this paper was to identify potential barriers to maintenance of meaningful use of EHRs in priority primary care practices using a qualitative observational study for federally qualified health centers and priority practices in Michigan. The authors concluded that priority practices, especially in rural areas, are at high risk for falling on the wrong side of a digital divide as payers and regulators enact increasing expectations for EHR use and information management.
AHRQ-funded; HS018170.
Citation: Green LA, Potworowski G, Day A .
Sustaining "meaningful use" of health information technology in low-resource practices.
Ann Fam Med 2015 Jan-Feb;13(1):17-22. doi: 10.1370/afm.1740.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Primary Care, Rural Health
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
Turner A, Osterhage K, Joe J
Use of patient portals: personal health information management in older adults.
This paper described initial results from the SOARING (Studying Older Adults & Researching Information Needs and Goals) study at the University of Washington, a participatory design investigation of personal health information management (PHIM) in older adults. Its findings indicate that patient portals designed to target the specific needs for older adults can support PHIM.
AHRQ-funded; HS022106.
Citation: Turner A, Osterhage K, Joe J .
Use of patient portals: personal health information management in older adults.
Stud Health Technol Inform 2015;216:978..
Keywords: Elderly, Electronic Health Records (EHRs), Health Information Technology (HIT), Web-Based
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