National Healthcare Quality and Disparities Report
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Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (1)
- Adverse Events (1)
- Autism (1)
- Children/Adolescents (1)
- Comparative Effectiveness (1)
- (-) Data (7)
- (-) Electronic Health Records (EHRs) (7)
- Healthcare-Associated Infections (HAIs) (1)
- Health Information Technology (HIT) (4)
- Hospitals (1)
- Injuries and Wounds (1)
- Medication (2)
- Medication: Safety (1)
- Mortality (1)
- Patient-Centered Outcomes Research (1)
- Provider: Pharmacist (1)
- Quality Improvement (1)
- Quality of Care (1)
- Registries (1)
- Sepsis (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 7 of 7 Research Studies DisplayedRhee 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
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)
Lybarger K, Ostendorf M, Yetisgen M
Automatically detecting likely edits in clinical notes created using automatic speech recognition.
Aiming to reduce the time required to edit automatic speech recognition (ASR) transcripts, this paper investigates novel methods for automatic detection of edit regions within the transcripts, including both putative ASR errors but also regions that are targets for cleanup or rephrasing.
AHRQ-funded; HS023631.
Citation: Lybarger K, Ostendorf M, Yetisgen M .
Automatically detecting likely edits in clinical notes created using automatic speech recognition.
AMIA Annu Symp Proc 2017 Apr 16;2017:1186-95.
Keywords: Health Information Technology (HIT), Electronic Health Records (EHRs), Data
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
Blumenthal KG, Acker WW, Li Y
Allergy entry and deletion in the electronic health record.
The researchers aimed to assess drug allergy entry, deletion, and accumulation, to identify health care professional types recording allergy data, and to assess the reaction types that lead to allergy entry and deletion. They found that of all allergies, 38.2 percent were immune mediated and 29.6 percent included only adverse effect reactions. Unavailable or unknown reactions comprised 32.2 percent of all allergies entered or deleted.
AHRQ-funded; HS022728.
Citation: Blumenthal KG, Acker WW, Li Y .
Allergy entry and deletion in the electronic health record.
Ann Allergy Asthma Immunol 2017 Mar;118(3):380-81. doi: 10.1016/j.anai.2016.12.020.
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Keywords: Data, Electronic Health Records (EHRs), Medication: Safety, Medication, Adverse Drug Events (ADE)