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Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Events (1)
- Cancer (1)
- Clinician-Patient Communication (1)
- Comparative Effectiveness (1)
- (-) Data (6)
- Disparities (1)
- Electronic Health Records (EHRs) (3)
- Healthcare-Associated Infections (HAIs) (1)
- Health Information Technology (HIT) (4)
- Hospitals (1)
- Injuries and Wounds (1)
- Medical Devices (1)
- Patient-Centered Outcomes Research (3)
- Quality Improvement (1)
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- Racial and Ethnic Minorities (1)
- (-) Registries (6)
- 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 6 of 6 Research Studies DisplayedOng 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
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
Cato KD, Bockting W, Larson E
Did I tell you that? Ethical issues related to using computational methods to discover non-disclosed patient characteristics.
Using the Belmont Report's principles of respect for persons, beneficence, and justice as a framework, the authors examined the ethical issues posed by electronic phenotyping. Ethical issues identified include the ability of the patient to consent for the use of their information, the ability to suppress pediatric information, and ensuring that the potential benefits justify the risks of harm to patients.
AHRQ-funded; HS022961.
Citation: Cato KD, Bockting W, Larson E .
Did I tell you that? Ethical issues related to using computational methods to discover non-disclosed patient characteristics.
J Empir Res Hum Res Ethics 2016 Jul;11(3):214-9. doi: 10.1177/1556264616661611.
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Keywords: Clinician-Patient Communication, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient-Centered Outcomes Research, Registries, Research Methodologies
Lee SJ, Grobe JE, Tiro JA
Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals.
The objective of this study was to characterize the quality of race/ethnicity data collection efforts. The authors assessed race and ethnicity data quality across cancer registries and electronic medical records in two hospitals. Their findings suggested that high-quality race/ethnicity data are attainable. Many of the "errors" in race/ethnicity data were caused by missing or "Unknown" data values.
AHRQ-funded; HS022418.
Citation: Lee SJ, Grobe JE, Tiro JA .
Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals.
J Am Med Inform Assoc 2016 May;23(3):627-34. doi: 10.1093/jamia/ocv156..
Keywords: Cancer, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Hospitals, Racial and Ethnic Minorities, Registries
Zhu Y, Chen CY, Matsuyama Y
Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal identifiers.
The researchers aimed to assess the validity of probabilistic linkage in the absence of unique personal identifiers (UPI) and the methods of cutoff weight selection. They found that probabilistic linkage without UPI generated valid linkages when an optimal cutoff was chosen and concluded that histogram inspection, the duplicate method, and the odds formula method can be used in conjunction when a gold standard is not available.
AHRQ-funded; 29020050016I.
Citation: Zhu Y, Chen CY, Matsuyama Y .
Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal identifiers.
Pharmacoepidemiol Drug Saf 2016 Apr;25(4):444-52. doi: 10.1002/pds.3832.
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Keywords: Data, Medical Devices, Registries
Bakken S, Reame N
The promise and potential perils of big data for advancing symptom management research in populations at risk for health disparities.
The purposes of this chapter are to (a) briefly summarize the current drivers for the use of big data in research; (b) describe the promise of big data and associated data science methods for advancing symptom management research; and (c) explicate the potential perils of big data and data science from the perspective of the ethical principles of autonomy, beneficence, and justice.
AHRQ-funded; HS022961.
Citation: Bakken S, Reame N .
The promise and potential perils of big data for advancing symptom management research in populations at risk for health disparities.
Annu Rev Nurs Res 2016;34:247-60. doi: 10.1891/0739-6686.34.247..
Keywords: Disparities, Data, Patient-Centered Outcomes Research, Registries