Search all AHRQ sites
Menu
SHARE:
AHRQ Research Studies
Sign up: 
AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 9 of 9 Research Studies DisplayedRoosan D, Samore M, Jones M
Big-data based decision-support systems to improve clinicians' cognition.
This study focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. It found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records.
AHRQ-funded; HS023349.
Citation: Roosan D, Samore M, Jones M .
Big-data based decision-support systems to improve clinicians' cognition.
IEEE Int Conf Healthc Inform 2016;2016:285-88. doi: 10.1109/ichi.2016.39.
.
.
Keywords: Clinical Decision Support (CDS), Decision Making, Data, Electronic Health Records (EHRs)
Murphy DR, Meyer AN, Bhise V
Computerized triggers of big data to detect delays in follow-up of chest imaging results.
A "trigger" algorithm was used to identify delays in follow-up of abnormal chest imaging results in a large national clinical data warehouse of electronic health record (EHR) data. In this study, the authors applied a trigger in a repository hosting EHR data from all Department of Veterans Affairs health-care facilities and analyzed data from seven facilities. The investigators concluded that application of triggers on "big" EHR data may aid in identifying patients experiencing delays in diagnostic evaluation of chest imaging results suspicious for malignancy.
Citation: Murphy DR, Meyer AN, Bhise V .
Computerized triggers of big data to detect delays in follow-up of chest imaging results.
Chest 2016 Sep;150(3):613-20. doi: 10.1016/j.chest.2016.05.001..
Keywords: Imaging, Electronic Health Records (EHRs), Health Information Technology (HIT), Data, Diagnostic Safety and Quality, Cancer
Richesson RL, Sun J, Pathak J
Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
The authors sought to use electronic health records data to advance understanding of disease risk and drug response, and to support the practice of precision medicine on a national scale. They found that machine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, as it comes from data rather than experts. They suggested that research networks and phenotype developers cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and modernize biomedical research.
AHRQ-funded; HS023921; HS023077.
Citation: Richesson RL, Sun J, Pathak J .
Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
Artif Intell Med 2016 Jul;71:57-61. doi: 10.1016/j.artmed.2016.05.005.
.
.
Keywords: Data, Electronic Health Records (EHRs), Genetics, Patient-Centered Healthcare
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.
.
.
Keywords: Clinician-Patient Communication, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient-Centered Outcomes Research, Registries, Research Methodologies
Vock DM, Wolfson J, Bandyopadhyay S
Adapting machine learning techniques to censored time-to-event health record data: a general-purpose approach using inverse probability of censoring weighting.
In this paper, the authors present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). They illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models.
AHRQ-funded; HS017622.
Citation: Vock DM, Wolfson J, Bandyopadhyay S .
Adapting machine learning techniques to censored time-to-event health record data: a general-purpose approach using inverse probability of censoring weighting.
J Biomed Inform 2016 Jun;61:119-31. doi: 10.1016/j.jbi.2016.03.009.
.
.
Keywords: Data, Electronic Health Records (EHRs), Health Information Technology (HIT)
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
Plasek JM, Goss FR, Lai KH
Food entries in a large allergy data repository.
This study examined, encoded, and grouped foods that caused any adverse sensitivity in a large allergy repository using natural language processing and standard terminologies. It identified 158,552 food allergen records (2,140 unique terms) in the Partners repository, corresponding to 672 food allergen concepts. High-frequency groups included shellfish (19.3 percent), fruits or vegetables (18.4 percent), dairy (9.0 percent), and peanuts (8.5 percent).
AHRQ-funded; HS022728.
Citation: Plasek JM, Goss FR, Lai KH .
Food entries in a large allergy data repository.
J Am Med Inform Assoc 2016 Apr;23(e1):e79-87. doi: 10.1093/jamia/ocv128.
.
.
Keywords: Data, Health Information Technology (HIT), Electronic Health Records (EHRs), Patient Safety
Hsu D, Brieva J, Nardone B
Validation of database search strategies for the epidemiological study of pemphigus and pemphigoid.
The authors hypothesized that the assigned ICD-9-CM codes of 694.4 (pemphigus) and 694.5 (pemphigoid) would demonstrate a high predictive value for the confirmed diagnosis of their respective diseases. Their results did not support the hypothesis that a single ICD-9-CM code for pemphigus and pemphigoid is sufficient to identify these disorders in largescale epidemiological studies.
AHRQ-funded; HS023011.
Citation: Hsu D, Brieva J, Nardone B .
Validation of database search strategies for the epidemiological study of pemphigus and pemphigoid.
Br J Dermatol 2016 Mar;174(3):645-8. doi: 10.1111/bjd.14172.
.
.
Keywords: Data, Electronic Health Records (EHRs), 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
