National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- Adverse Drug Events (ADE) (3)
- Adverse Events (10)
- Cardiovascular Conditions (1)
- Clinical Decision Support (CDS) (1)
- Clostridium difficile Infections (1)
- Comparative Effectiveness (1)
- (-) Data (16)
- Electronic Health Records (EHRs) (2)
- Healthcare-Associated Infections (HAIs) (3)
- Health Information Technology (HIT) (5)
- Hospitals (4)
- Injuries and Wounds (1)
- Medical Devices (1)
- Medical Errors (4)
- Medication (3)
- Medication: Safety (1)
- Patient-Centered Outcomes Research (1)
- (-) Patient Safety (16)
- Public Reporting (1)
- Quality Improvement (2)
- Quality Indicators (QIs) (1)
- Quality Measures (1)
- Quality of Care (5)
- Research Methodologies (2)
- Surgery (2)
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 16 of 16 Research Studies DisplayedWang E, Kang H, Gong Y
Generating a health information technology event database from FDA MAUDE reports.
This study examined using a health information technology (HIT) event database to identify patient safety events (PSEs) or medical errors. The study used the FDA Manufacturer and User Facility Device Experience (MAUDE) database to extract HIT events. Classic and CNN models were utilized on a test set. The model was capable of identifying HIT event with about a 90% accuracy.
AHRQ-funded; HS022895.
Citation: Wang E, Kang H, Gong Y .
Generating a health information technology event database from FDA MAUDE reports.
Stud Health Technol Inform 2019 Aug 21;264:883-87. doi: 10.3233/shti190350..
Keywords: Health Information Technology (HIT), Medical Devices, Adverse Events, Data, Medical Errors, Patient Safety
Yao B, Kang H, Gong Y
Data quality assessment of narrative medication error reports.
This study examined the data quality of patient safety event (PSE) reports that are used to analyze the root causes of PSE. If the data quality is poor then the reporting and root cause analysis (RCA) will also be poor. Incomplete or missing data is the most prevalent problem in these reports. The researchers used an adapted taxonomy to assess the data quality of PSE reports, and extracted sample reports based on eight error types. The extracts were scored by experts. They found that most structured fields were ignored by reporters, but the narrative parts of the reports contained rich and valuable information. The results show that the adapted taxonomy could be a promising tool for report quality assessment and improvement.
AHRQ-funded; HS022895.
Citation: Yao B, Kang H, Gong Y .
Data quality assessment of narrative medication error reports.
Stud Health Technol Inform 2019 Aug 9;265:101-06. doi: 10.3233/shti190146..
Keywords: Adverse Drug Events (ADE), Medication, Medical Errors, Adverse Events, Data, Patient Safety
Hsu YJ, Kosinski AS, Wallace AS
Using a society database to evaluate a patient safety collaborative: the Cardiovascular Surgical Translational Study.
The authors assessed the utility of using external databases for quality improvement (QI) evaluations in the context of an innovative QI collaborative aimed to reduce three infections and improve patient safety across the cardiac surgery service line. They compared changes in each outcome between 15 intervention hospitals and 52 propensity score-matched hospitals, and found that improvement trends in several outcomes among the studied intervention hospitals were not statistically different from those in comparison hospitals. They conclude that using external databases may permit comparative effectiveness assessment by providing concurrent comparison groups, additional outcome measures, and longer follow-up.
AHRQ-funded; HS019934.
Citation: Hsu YJ, Kosinski AS, Wallace AS .
Using a society database to evaluate a patient safety collaborative: the Cardiovascular Surgical Translational Study.
J Comp Eff Res 2019 Jan;8(1):21-32. doi: 10.2217/cer-2018-0051..
Keywords: Patient Safety, Quality Improvement, Quality Indicators (QIs), Quality of Care, Surgery, Cardiovascular Conditions, Comparative Effectiveness, Data, Hospitals, Research Methodologies, Patient-Centered Outcomes Research
Wang SV, Maro JC, Baro E
Data mining for adverse drug events with a propensity score-matched tree-based scan statistic.
In this study, the investigators propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust design for investigations of drug safety. They subsequently conducted plasmode simulations to evaluate performance. The authors suggest that TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events.
AHRQ-funded; HS022193.
Citation: Wang SV, Maro JC, Baro E .
Data mining for adverse drug events with a propensity score-matched tree-based scan statistic.
Epidemiology 2018 Nov;29(6):895-903. doi: 10.1097/ede.0000000000000907..
Keywords: Adverse Drug Events (ADE), Adverse Events, Patient Safety, Medication, Medication: Safety, Data, Research Methodologies
Fong A, Adams KT, Gaunt MJ
Identifying health information technology related safety event reports from patient safety event report databases.
The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model features. A feature-constraint model was developed and evaluated to support the analysis of PSE reports. The feature-constraint model provides a method to identify HIT-related patient safety hazards using a method that is applicable across healthcare systems with variability in their PSE report structures.
AHRQ-funded; HS023701.
Citation: Fong A, Adams KT, Gaunt MJ .
Identifying health information technology related safety event reports from patient safety event report databases.
J Biomed Inform 2018 Oct;86:135-42. doi: 10.1016/j.jbi.2018.09.007..
Keywords: Health Information Technology (HIT), Patient Safety, Adverse Events, Data
Goss FR, Lai KH, Topaz M
A value set for documenting adverse reactions in electronic health records.
In this study, the investigators developed a value set for encoding adverse reactions using a large dataset from one health system, enriched by reactions from 2 large external resources. This integrated value set included clinically important severe and hypersensitivity reactions. The work contributed a value set, harmonized with existing data, to improve the consistency and accuracy of reaction documentation in electronic health records, providing the necessary building blocks for more intelligent clinical decision support for allergies and adverse reactions.
AHRQ-funded; HS022728.
Citation: Goss FR, Lai KH, Topaz M .
A value set for documenting adverse reactions in electronic health records.
J Am Med Inform Assoc 2018 Jun;25(6):661-69. doi: 10.1093/jamia/ocx139..
Keywords: Adverse Drug Events (ADE), Adverse Events, Electronic Health Records (EHRs), Medication, Data, Health Information Technology (HIT), Patient Safety
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
Couture B, Fagan M, Gershanik E
Towards analytics of the patient and family perspective: a case study and recommendations for data capture of safety and quality concerns.
Patient Family Relations (PFR) programs provide the opportunity to capture patient/family safety concerns in the hospital. This study analyzed PFR concern submissions over a 20 month period, as well as a comparison of structured data fields to those of the AHRQ Common Format. The authors identified statistically significant differences in rates of concern submissions, methods of submission, and role of submitter across patient populations.
AHRQ-funded; HS023535.
Citation: Couture B, Fagan M, Gershanik E .
Towards analytics of the patient and family perspective: a case study and recommendations for data capture of safety and quality concerns.
AMIA Annu Symp Proc 2017 Apr 16;2017:615-24..
Keywords: Data, Quality of Care, Hospitals, Patient Safety
Kang H, Gong Y
A novel schema to enhance data quality of patient safety event reports.
In this study, the researchers designed a patient safety event (PSE) similarity searching model based on semantic similarity measures, and proposed a novel schema of PSE reporting system which can effectively learn from previous experiences and timely inform the subsequent actions. Their system will not only help promote the report qualities but also serve as a knowledge base and education tool to guide healthcare providers in terms of preventing the recurrence of PSEs.
AHRQ-funded; HS022895.
Citation: Kang H, Gong Y .
A novel schema to enhance data quality of patient safety event reports.
AMIA Annu Symp Proc 2017 Feb 10;2016:1840-49.
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Keywords: Quality of Care, Patient Safety, Data, Adverse Events, Medical Errors
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.
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Keywords: Data, Health Information Technology (HIT), Electronic Health Records (EHRs), Patient Safety
Liang C, Gong Y
Enhancing patient safety event reporting by K-nearest neighbor classifier.
The debate on structured or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technical gap on text mining. The reesarchers suggested a text classification method for predicting subject categories. Their results demonstrated the feasibility of their system and indicated the advantage of such an application to raise data quality and clinical decision support in reporting patient safety events.
AHRQ-funded; HS022895.
Citation: Liang C, Gong Y .
Enhancing patient safety event reporting by K-nearest neighbor classifier.
Stud Health Technol Inform 2015;218:40603.
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Keywords: Adverse Events, Medical Errors, Patient Safety, Public Reporting, Clinical Decision Support (CDS), Health Information Technology (HIT), Data
van Mourik MS, van Duijn PJ, Moons KG
Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review.
The researchers conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI. They concluded that administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended.
AHRQ-funded; HS018414.
Citation: van Mourik MS, van Duijn PJ, Moons KG .
Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review.
BMJ Open 2015 Aug 27;5(8):e008424. doi: 10.1136/bmjopen-2015-008424..
Keywords: Healthcare-Associated Infections (HAIs), Data, Patient Safety, Quality of Care
Sundararajan V, Romano PS, Quan H
Capturing diagnosis-timing in ICD-coded hospital data: recommendations from the WHO ICD-11 topic advisory group on quality and safety.
The purpose of this project was to develop a consensus opinion regarding capturing diagnosis-timing in coded hospital data. The WHO Quality and Safety Topic Advisory Group has undertaken a narrative literature review, scanned national experiences focusing on countries currently using timing flags, and held a series of meetings to derive formal recommendations regarding diagnosis-timing reporting. This paper discusses their concerns and recommendations.
AHRQ-funded; HS020543.
Citation: Sundararajan V, Romano PS, Quan H .
Capturing diagnosis-timing in ICD-coded hospital data: recommendations from the WHO ICD-11 topic advisory group on quality and safety.
Int J Qual Health Care 2015 Aug;27(4):328-33. doi: 10.1093/intqhc/mzv037..
Keywords: Patient Safety, Quality of Care, Quality Improvement, Hospitals, Data
Pakyz AL, Patterson JA, Motzkus-Feagans C
Performance of the present-on-admission indicator for Clostridium difficile infection.
The researchers compared performance of a hospital- and community-onset Clostridium difficile infection definition using administrative data to a present on- admission indicator with definitions using clinical surveillance. For hospital-onset C. difficile infection, there was moderate sensitivity (68 percent) and high specificity (93 percent); for community-onset, sensitivity and specificity were high (both 85 percent).
AHRQ-funded; HS018578.
Citation: Pakyz AL, Patterson JA, Motzkus-Feagans C .
Performance of the present-on-admission indicator for Clostridium difficile infection.
Infect Control Hosp Epidemiol 2015 Jul;36(7):838-40. doi: 10.1017/ice.2015.63..
Keywords: Clostridium difficile Infections, Patient Safety, Healthcare-Associated Infections (HAIs), Data
Warren DK, Nickel KB, Wallace AE
Can additional information be obtained from claims data to support surgical site infection diagnosis codes?
The authors sought to confirm a claims algorithm to identify surgical site infections (SSIs) by examining the presence of clinically expected SSI treatment. They found that over 94% of patients identified by their claims algorithm as having an SSI received clinically expected treatment for infection, including antibiotics, surgical treatment, and culture, suggesting that this algorithm has very good positive predictive value. They concluded that their method may facilitate retrospective SSI surveillance and comparison of SSI rates across facilities and providers.
AHRQ-funded; HS019713.
Citation: Warren DK, Nickel KB, Wallace AE .
Can additional information be obtained from claims data to support surgical site infection diagnosis codes?
Infect Control Hosp Epidemiol 2014 Oct;35 Suppl 3:S124-32. doi: 10.1086/677830.
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Keywords: Data, Healthcare-Associated Infections (HAIs), Patient Safety, Surgery, Injuries and Wounds, Adverse Events