National Healthcare Quality and Disparities Report
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Topics
- Adverse Drug Events (ADE) (1)
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- (-) Data (36)
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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 25 of 36 Research Studies DisplayedSangal RB, Fodeh S, Taylor A
Identification of patients with nontraumatic intracranial hemorrhage using administrative claims data.
Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. In this study, the investigators aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.
AHRQ-funded; HS023554.
Citation: Sangal RB, Fodeh S, Taylor A .
Identification of patients with nontraumatic intracranial hemorrhage using administrative claims data.
J Stroke Cerebrovasc Dis 2020 Dec;29(12):105306. doi: 10.1016/j.jstrokecerebrovasdis.2020.105306..
Keywords: Cardiovascular Conditions, Neurological Disorders, Diagnostic Safety and Quality, Data
Byrd TF, Ahmad FS, Liebovitz DM
Defragmenting heart failure care: medical records integration.
This article discusses the need to improve interoperability of software systems so that so that providers and patients can access clinical information needed to help coordinate care of heart failure patients. New data standards currently being proposed in legislation would make it possible to guide clinical decision-making.
AHRQ-funded; HS026385.
Citation: Byrd TF, Ahmad FS, Liebovitz DM .
Defragmenting heart failure care: medical records integration.
Heart Fail Clin 2020 Oct;16(4):467-77. doi: 10.1016/j.hfc.2020.06.007..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Heart Disease and Health, Cardiovascular Conditions, Data
Lin JS, Murad MH, Leas B
A narrative review and proposed framework for using health system data with systematic reviews to support decision-making.
This paper addresses when and how the use of health system data might make systematic reviews more useful to decisionmakers. The authors have developed a framework to guide the use of health system data alongside systematic reviews based on a narrative review of the literature and empirical experience. They recommend future methodological work on how best to handle internal and external validity concerns of health system data in the context of systematically reviewed data and work on developing infrastructure to do this type of work.
AHRQ-funded; 290201500007I; 29032001T05; 290201500005I; 290201500009I.
Citation: Lin JS, Murad MH, Leas B .
A narrative review and proposed framework for using health system data with systematic reviews to support decision-making.
J Gen Intern Med 2020 Jun;35(6):1830-35. doi: 10.1007/s11606-020-05783-5..
Keywords: Learning Health Systems, Health Systems, Evidence-Based Practice, Data, Shared Decision Making
Dixon BE, Wen C, French T
Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI).
The authors extended the open-source software Observational Health Data Sciences and Informatics (OHDSI) to incorporate new functions useful for population health. They developed and tested methods to measure the completeness, timeliness and entropy of information; timeliness was not adopted as its context did not fit with the existing OHDSI domains. The case report examined the process and reasons for acceptance and rejection of ideas proposed to an open-source community like OHDSI.
AHRQ-funded; HS025502.
Citation: Dixon BE, Wen C, French T .
Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI).
BMJ Health Care Inform 2020 Mar;27(1). doi: 10.1136/bmjhci-2019-100054..
Keywords: Public Health, Data
Jarrin OF, Nyandege AN, Grafova IB
Validity of race and ethnicity codes in Medicare administrative data compared with gold-standard self-reported race collected during routine home health care visits.
The authors compared the validity of two race/ethnicity variables found in Medicare administrative data against a gold-standard source also available in the Medicare data warehouse. They found that the race/ethnicity variables contained in Medicare administrative data for minority health disparities research can be improved through the use of self-reported race/ethnicity data. They conclude that future work to improve the accuracy of Medicare beneficiaries' race/ethnicity data should incorporate and augment the self-reported race/ethnicity data contained in assessment and survey data, available within the Medicare data warehouse.
AHRQ-funded; HS022406.
Citation: Jarrin OF, Nyandege AN, Grafova IB .
Validity of race and ethnicity codes in Medicare administrative data compared with gold-standard self-reported race collected during routine home health care visits.
Med Care 2020 Jan;58(1):e1-e8. doi: 10.1097/mlr.0000000000001216..
Keywords: Racial and Ethnic Minorities, Home Healthcare, Medicare, Data, Disparities, Research Methodologies
Cohen GR, Jones DJ, Heeringa J
AHRQ Author: Furukawa MF, Miller D
Leveraging diverse data sources to identify and describe U.S. health care delivery systems.
Health care delivery systems are a growing presence in the U.S., yet research is hindered by the lack of universally agreed-upon criteria to denote formal systems. This study assesses available data sources to identify and describe systems, including system members and relationships among the members.
AHRQ-authored.
Citation: Cohen GR, Jones DJ, Heeringa J .
Leveraging diverse data sources to identify and describe U.S. health care delivery systems.
eGEMS 2017 Dec 15;5(3):9. doi: 10.5334/egems.200..
Keywords: Healthcare Delivery, Data, Health Services Research (HSR), System Design
Liang C, Gong Y
Automated classification of multi-labeled patient safety reports: a shift from quantity to quality measure.
The capacity for extracting useful information from patient safety reports remains limited. This study investigated the multi-labeled nature of patient safety reports as a key to disclose the complex relations between many components during the courses and development of medical errors. The authors developed automated multi-label text classifiers to process patient safety reports. The experiments demonstrated feasibility and efficiency of a combination of multi-label algorithms in the benchmark comparison.
AHRQ-funded; HS022895.
Citation: Liang C, Gong Y .
Automated classification of multi-labeled patient safety reports: a shift from quantity to quality measure.
Stud Health Technol Inform 2017;245:1070-74..
Keywords: Adverse Events, Data, Patient Safety, Quality Measures
Liang C, Gong Y
Predicting harm scores from patient safety event reports.
The Harm Scale developed by the AHRQ is widely used in the US hospitals. However, recent studies have indicated a moderate to poor inter-rater reliability of the scale across a number of US hospitals. This study proposed that key information to identify and refine the severity of harm is contained in the narrative data in patient safety reports. The researchers found that using automated text classification to categorize harm score provided reduced subjective judgments and improved efficiency.
AHRQ-funded; HS022895.
Citation: Liang C, Gong Y .
Predicting harm scores from patient safety event reports.
Stud Health Technol Inform 2017;245:1075-79..
Keywords: Adverse Events, Data, Hospitals, Patient Safety
Cohen KB, Goss FR, Zweigenbaum P
Translational morphosyntax: distribution of negation in clinical records and biomedical journal articles.
This paper describes the distribution of negation in two types of biomedical texts: scientific journal articles and progress notes. Two types of negation are examined: explicit negation at the syntactic level and affixal negation at the sub-word level. The data show that the distribution of negation is significantly different in the two document types.
AHRQ-funded; HS024541.
Citation: Cohen KB, Goss FR, Zweigenbaum P .
Translational morphosyntax: distribution of negation in clinical records and biomedical journal articles.
Stud Health Technol Inform 2017;245:346-50.
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Keywords: Data, Health Information Technology (HIT), Research Methodologies
Ji X, Machiraju R, Ritter A
Visualizing article similarities via sparsified article network and map projection for systematic reviews.
In this study, the authors visualized article similarities to extend its utilization in practical settings for SR researchers, aiming to promote human comprehension of article distributions and hidden patterns. To prompt an effective visualization in an interpretable, intuitive, and scalable way, they implemented a graph-based network visualization with three network sparsification approaches and a distance-based map projection via dimensionality reduction.
AHRQ-funded; HS025047.
Citation: Ji X, Machiraju R, Ritter A .
Visualizing article similarities via sparsified article network and map projection for systematic reviews.
Stud Health Technol Inform 2017;245:422-26.
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Keywords: Data, Evidence-Based Practice, Health Services Research (HSR), Research Methodologies
Wallace BC, Noel-Storr A, Marshall IJ
Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.
Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. In this study, the investigators aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML.
AHRQ-funded; HS025024.
Citation: Wallace BC, Noel-Storr A, Marshall IJ .
Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.
J Am Med Inform Assoc 2017 Nov 1;24(6):1165-68. doi: 10.1093/jamia/ocx053..
Keywords: Data, Research Methodologies
Rhee C, Dantes R, Epstein L
Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014.
The researchers estimated the US national incidence of sepsis and trends using detailed clinical data from the electronic health record (EHR) systems of diverse hospitals. In clinical data from 409 hospitals, sepsis was present in 6 percent of adult hospitalizations, and in contrast to claims-based analyses, neither the incidence of sepsis nor the combined outcome of death or discharge to hospice changed significantly between 2009-2014.
AHRQ-funded; HS025008.
Citation: Rhee C, Dantes R, Epstein L .
Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014.
JAMA 2017 Oct 3;318(13):1241-49. doi: 10.1001/jama.2017.13836.
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Keywords: Data, Electronic Health Records (EHRs), Hospitals, Mortality, Sepsis
Angraal S, Ross JS, Dhruva SS
Merits of data sharing: The Digitalis Investigation Group Trial.
This letter discusses the merits of data sharing, such as its importance in maximizing what can be learned from clinical trials. The letter describes The DIG (Digitalis Investigation Group) trial as an ideal o assess the effects of data sharing.
AHRQ-funded; HS023000.
Citation: Angraal S, Ross JS, Dhruva SS .
Merits of data sharing: The Digitalis Investigation Group Trial.
J Am Coll Cardiol 2017 Oct 3;70(14):1825-27. doi: 10.1016/j.jacc.2017.07.786..
Keywords: Data, Patient-Centered Outcomes Research, Research Methodologies
Guise JM, Chang C, Butler M
AHRQ Author: Chang C
AHRQ series on complex intervention systematic reviews-paper 1: an introduction to a series of articles that provide guidance and tools for reviews of complex interventions.
The seven articles in this series reflect and distill the discussions from the in-person meeting and follow-up workgroups on tools and approaches to systematic reviews of complex interventions. The first three articles address how systematic reviews for complex interventions are conceptualized and operationalized for the protocol. The next two articles discuss how to choose appropriate analytic methods to implement analyses of complex interventions. The final two articles describe proposed reporting elements for systematic reviews of complex interventions.
AHRQ-authored; AHRQ-funded; 290201200004C; 290201200016I; 290201500011I.
Citation: Guise JM, Chang C, Butler M .
AHRQ series on complex intervention systematic reviews-paper 1: an introduction to a series of articles that provide guidance and tools for reviews of complex interventions.
J Clin Epidemiol 2017 Oct;90:6-10. doi: 10.1016/j.jclinepi.2017.06.011.
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Keywords: Data, Evidence-Based Practice, Guidelines, Research Methodologies
Kelly MP, Noyes J, Kane RL
AHRQ Author: Chang C
AHRQ series on complex intervention systematic reviews-paper 2: defining complexity, formulating scope, and questions.
This paper builds on concepts introduced in paper 1 of this series. It describes the methodological, practical, and philosophical challenges and potential approaches for formulating the questions and scope of systematic reviews of complex interventions. Furthermore, it discusses the use of theory to help organize reviews of complex interventions.
AHRQ-authored; AHRQ-funded; 290-2012-00004-C; 290-2015-00008I; 290-2015-00011I.
Citation: Kelly MP, Noyes J, Kane RL .
AHRQ series on complex intervention systematic reviews-paper 2: defining complexity, formulating scope, and questions.
J Clin Epidemiol 2017 Oct;90:11-18. doi: 10.1016/j.jclinepi.2017.06.012.
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Keywords: Data, Evidence-Based Practice, Guidelines, Research Methodologies
Viswanathan M, McPheeters ML, Murad MH
AHRQ series on complex intervention systematic reviews-paper 4: selecting analytic approaches.
This article addresses the uncertainty that systematic reviewers face in selecting methods for reviews of complex interventions. Specifically, it lays out parameters for systematic reviewers to consider when selecting analytic approaches that best answer the questions at hand and suggests analytic techniques that may be appropriate in different circumstances.
AHRQ-funded; 290201200004C.
Citation: Viswanathan M, McPheeters ML, Murad MH .
AHRQ series on complex intervention systematic reviews-paper 4: selecting analytic approaches.
J Clin Epidemiol 2017 Oct;90:28-36. doi: 10.1016/j.jclinepi.2017.06.014.
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Keywords: Data, Evidence-Based Practice, Guidelines, Research Methodologies
Guise JM, Butler M, Chang C
AHRQ Author: Chang C
AHRQ series on complex intervention systematic reviews-paper 7: PRISMA-CI elaboration and explanation.
The Complex Interventions Methods Workgroup developed an extension to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Complex Interventions (PRISMA-CI). Following the guidance for Preferred Reporting Items for Systematic Reviews and Meta-Analysis extensions, this Explanation and Elaboration (EE) document accompanies the PRISMA-CI checklist to promote consistency in reporting of systematic reviews of complex interventions.
AHRQ-authored; AHRQ-funded; 290201200004C; 290201200016I; 290201500011I.
Citation: Guise JM, Butler M, Chang C .
AHRQ series on complex intervention systematic reviews-paper 7: PRISMA-CI elaboration and explanation.
J Clin Epidemiol 2017 Oct;90:51-58. doi: 10.1016/j.jclinepi.2017.06.017.
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Keywords: Data, Evidence-Based Practice, Guidelines, Research Methodologies
Ong TC, Kahn MG, Kwan BM
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
The researchers designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code. Their results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets.
AHRQ-funded; HS019908; HS022956.
Citation: Ong TC, Kahn MG, Kwan BM .
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
BMC Med Inform Decis Mak 2017 Sep 13;17(1):134. doi: 10.1186/s12911-017-0532-3.
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Keywords: Comparative Effectiveness, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient-Centered Outcomes Research
Rowan CG, Flory J, Gerhard T
Agreement and validity of electronic health record prescribing data relative to pharmacy claims data: a validation study from a US electronic health record database.
The researchers conducted a retrospective cohort study among patients with linked claims and EHR data in OptumLabs Data Warehouse. Their aim was to evaluate the validity of classifying medication exposure using EHR prescribing (EHR-Rx) data. They concluded that, despite substantial variability among different medications, there was very good agreement between EHR-Rx data and PC-Rx data.
AHRQ-funded; HS023898.
Citation: Rowan CG, Flory J, Gerhard T .
Agreement and validity of electronic health record prescribing data relative to pharmacy claims data: a validation study from a US electronic health record database.
Pharmacoepidemiol Drug Saf 2017 Aug;26(8):963-72. doi: 10.1002/pds.4234.
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Keywords: Data, Electronic Health Records (EHRs), Medication, Provider: Pharmacist
Bush RA, Connelly CD, Perez A
Extracting autism spectrum disorder data from the electronic health record.
This study uses electronic health record (EHR) data to examine medical utilization and track outcomes among children with Autism Spectrum Disorder (ASD). The study also identifies challenges inherent in designing inclusive algorithms for identifying individuals with ASD and demonstrates the utility of employing multiple extractions to improve the completeness and quality of EHR data when conducting research.
AHRQ-funded; HS022404.
Citation: Bush RA, Connelly CD, Perez A .
Extracting autism spectrum disorder data from the electronic health record.
Appl Clin Inform 2017 Jul 19;8(3):731-41. doi: 10.4338/aci-2017-02-ra-0029..
Keywords: Autism, Children/Adolescents, Data, Health Information Technology (HIT), Electronic Health Records (EHRs)
Bashir R, Bourgeois FT, Dunn AG
A systematic review of the processes used to link clinical trial registrations to their published results.
Studies measuring the completeness and consistency of trial registration and reporting rely on linking registries with bibliographic databases. In this systematic review, the researchers quantified the processes used to identify these links. In 43 studies that examined cohorts of registry entries, 24 used automatic and manual processes to find articles; 3 only automatic; and 11 only manual (5 did not specify).
AHRQ-funded; HS024798.
Citation: Bashir R, Bourgeois FT, Dunn AG .
A systematic review of the processes used to link clinical trial registrations to their published results.
Syst Rev 2017 Jul 3;6(1):123. doi: 10.1186/s13643-017-0518-3.
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Keywords: Evidence-Based Practice, Research Methodologies, Data
Hsu DY, Dalal P, Sable KA
Validation of International Classification of Disease Ninth Revision codes for atopic dermatitis.
This study sought to validate the use of ICD-9-CM codes for identifying atopic dermatitis. It found that in the outpatient setting, the ICD-9-CM codes 691.8 and 692.9 alone have poor positive predictive value (PPV). Incorporation of diagnoses of asthma, hay fever, and food allergy improves PPV and specificity. In the inpatient setting, a primary discharge diagnosis of 691.8 had excellent PPV.
AHRQ-funded; HS023011.
Citation: Hsu DY, Dalal P, Sable KA .
Validation of International Classification of Disease Ninth Revision codes for atopic dermatitis.
Allergy 2017 Jul;72(7):1091-95. doi: 10.1111/all.13113.
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Keywords: Data, Diagnostic Safety and Quality, Skin Conditions
Ong 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
Cefalu M, Dominici F, Arvold N
Model averaged double robust estimation.
Researchers estimating causal effects are increasingly challenged with decisions on how to best control for a potentially high-dimensional set of confounders. Typically, a single propensity score model is chosen and used to adjust for confounding. The researchers propose a practical and generalizable approach that overcomes limitations through the use of model averaging. They develop and evaluate this approach in the context of double robust estimation.
AHRQ-funded; HS021991.
Citation: Cefalu M, Dominici F, Arvold N .
Model averaged double robust estimation.
Biometrics 2017 Jun;73(2):410-21. doi: 10.1111/biom.12622.
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Keywords: Data, Research Methodologies
Mirel LB, Chowdhury SR
AHRQ Author: Chowdhury SR
Using linked survey paradata to improve sampling strategies in the Medical Expenditure Panel Survey.
The main objective of this article is to examine how paradata from a prior survey can be used in developing a sampling scheme in a subsequent survey. A framework for optimal allocation of the sample in substrata formed for this purpose is presented and evaluated for the relative effectiveness of alternative substratification schemes.
AHRQ-authored.
Citation: Mirel LB, Chowdhury SR .
Using linked survey paradata to improve sampling strategies in the Medical Expenditure Panel Survey.
J Off Stat 2017 Jun;33(2):367–83.
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Keywords: Data, Medical Expenditure Panel Survey (MEPS), Research Methodologies