National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
Topics
- Adverse Drug Events (ADE) (1)
- Adverse Events (5)
- Autism (1)
- Cancer (3)
- Cardiovascular Conditions (1)
- Children/Adolescents (2)
- Chronic Conditions (1)
- Clinical Decision Support (CDS) (1)
- Clinician-Patient Communication (1)
- Communication (2)
- Comparative Effectiveness (7)
- (-) Data (43)
- Diagnostic Safety and Quality (1)
- Electronic Health Records (EHRs) (22)
- Emergency Department (2)
- Emergency Medical Services (EMS) (1)
- Family Health and History (1)
- Genetics (1)
- Healthcare-Associated Infections (HAIs) (1)
- Healthcare Cost and Utilization Project (HCUP) (1)
- Healthcare Delivery (1)
- Healthcare Utilization (1)
- Health Information Exchange (HIE) (4)
- (-) Health Information Technology (HIT) (43)
- Health Services Research (HSR) (1)
- Heart Disease and Health (1)
- Home Healthcare (1)
- Hospital Discharge (1)
- Hospital Readmissions (1)
- Hospitals (1)
- Imaging (1)
- Injuries and Wounds (1)
- Medical Devices (1)
- Medical Errors (2)
- Medication (1)
- Nursing (2)
- Patient-Centered Healthcare (2)
- Patient-Centered Outcomes Research (4)
- Patient Safety (5)
- Policy (1)
- Prevention (1)
- Public Health (1)
- Public Reporting (1)
- Quality Improvement (1)
- Quality of Care (1)
- Racial and Ethnic Minorities (3)
- Registries (5)
- Research Methodologies (5)
- Shared Decision Making (2)
- Surgery (2)
- Vitamins and Supplements (1)
- Web-Based (2)
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 25 of 43 Research Studies DisplayedByrd 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
Bacon E, Budney G, Bondy J
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
This article describes attributes of regional distributed data networks using electronic health records (EHR) data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. The authors indicate that while benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.
AHRQ-funded; HS0122143.
Citation: Bacon E, Budney G, Bondy J .
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
J Public Health Manag Pract 2019 Sep/Oct;25(5):498-507. doi: 10.1097/phh.0000000000000810..
Keywords: Chronic Conditions, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Public Health
Liu L, Ni Y, Zhang N
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
The objectives of this study were: 1) to develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation. The study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The author’s approach offers the promise of targeted interventions to significantly decrease both healthcare costs and families' negative experiences.
AHRQ-funded; HS024983.
Citation: Liu L, Ni Y, Zhang N .
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Int J Med Inform 2019 Sep;129:234-41. doi: 10.1016/j.ijmedinf.2019.06.007..
Keywords: Children/Adolescents, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery
Wang 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
Polubriaginof FCG, Ryan P, Salmasian H
Challenges with quality of race and ethnicity data in observational databases.
This study assessed the quality of race and ethnicity information in observational health databases as well as electronic health records (EHRs) and to propose patient self-recording as a way to improve accuracy. Data from the Healthcare Cost and Utilization Project (HCUP) and Optum Labs, and from a single New York City healthcare system’s EHR was compared. Among 160 million patients in the HCUP database, no race or ethnicity data was recorded for 25% of the records. Among the 2.4 million patients in the New York City HER, race or ethnicity was unknown for 57%. However, when patients were allowed to directly record their race and ethnicity, percentages rose to 86%.
AHRQ-funded; HS021816; HS023704; HS024713.
Citation: Polubriaginof FCG, Ryan P, Salmasian H .
Challenges with quality of race and ethnicity data in observational databases.
J Am Med Inform Assoc 2019 Aug;26(8-9):730-36. doi: 10.1093/jamia/ocz113..
Keywords: Healthcare Cost and Utilization Project (HCUP), Data, Racial and Ethnic Minorities, Electronic Health Records (EHRs), Health Information Technology (HIT), Health Services Research (HSR)
Yang Y, Bass EJ, Sockolow PS
Knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization.
Researchers elicit knowledge related to expert decision-making processes to inform information technology design and related interventions. In this study, the investigators examine knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization. The investigators concluded that the data collection and validation methodology showed promise for knowledge elicitation in time-constrained situations.
AHRQ-funded; HS024537.
Citation: Yang Y, Bass EJ, Sockolow PS .
Knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization.
AMIA Annu Symp Proc 2018 Dec 5;2018:1127-36..
Keywords: Home Healthcare, Shared Decision Making, Health Information Technology (HIT), Data
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
Polubriaginof FCG, Vanguri R, Quinnies K
Disease heritability inferred from familial relationships reported in medical records.
Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. This study used EHR data to compute heritability estimates for 500 disease phenotypes. These analyses provided a validation of the use of EHRs for genetics and disease research.
AHRQ-funded; HS021816; HS022961.
Citation: Polubriaginof FCG, Vanguri R, Quinnies K .
Disease heritability inferred from familial relationships reported in medical records.
Cell 2018 Jun 14;173(7):1692-704.e11. doi: 10.1016/j.cell.2018.04.032..
Keywords: Data, Family Health and History, Genetics, Health Information Technology (HIT), Electronic Health Records (EHRs)
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
Hultman G, McEwan R, Pakhomov S
Usability evaluation of an unstructured clinical document query tool for researchers.
This study aimed to conduct a user-centered analysis with clinical researchers to gain insight into Natural Language Processing - Patient Information Extraction for Researchers (NLP-PIER) usability and to gain an understanding of the needs of clinical researchers when using an application for searching clinical notes.
AHRQ-funded; HS022085.
Citation: Hultman G, McEwan R, Pakhomov S .
Usability evaluation of an unstructured clinical document query tool for researchers.
AMIA Jt Summits Transl Sci Proc 2018 May 18;2018:84-93..
Keywords: Data, Health Information Technology (HIT), Research Methodologies
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.
.
.
Keywords: Data, Health Information Technology (HIT), 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.
.
.
Keywords: Comparative Effectiveness, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient-Centered Outcomes Research
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)
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
LeRouge C, Hasselquist MB, Kellogg L
Using heuristic evaluation to enhance the visual display of a provider dashboard for patient-reported outcomes.
A human-centered design (HCD) approach to understanding the data visualization needs for patient-reported outcomes (PRO) in clinical practice can optimize the visual design of an interactive PRO system. Beyond iterative methods, the authors explored the additive value of other HCD methods such as heuristic evaluation. Their evaluation led to several recommendations to improve the display, accessibility, and interpretability of the dashboard’s data.
AHRQ-funded; HS023785.
Citation: LeRouge C, Hasselquist MB, Kellogg L .
Using heuristic evaluation to enhance the visual display of a provider dashboard for patient-reported outcomes.
eGEMS 2017 Apr 20;5(2):Article 6. doi: 10.13063/2327-9214.1283.
.
.
Keywords: Patient-Centered Healthcare, Patient-Centered Outcomes Research, Health Information Technology (HIT), Data, Shared Decision Making
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.
.
.
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
Hettinger AZ, Roth EM, Bisantz AM
Cognitive engineering and health informatics: applications and intersections.
This article provides an overview of relevant cognitive engineering methods, and illustrates how they have been applied to the design of health information technology (HIT) systems. Additionally, although cognitive engineering methods have been applied in the design of user-centered informatics systems, methods drawn from informatics are not typically incorporated into a cognitive engineering analysis. This article presents a discussion regarding ways in which data-rich methods can inform cognitive engineering.
AHRQ-funded; HS022542.
Citation: Hettinger AZ, Roth EM, Bisantz AM .
Cognitive engineering and health informatics: applications and intersections.
J Biomed Inform 2017 Mar;67:21-33. doi: 10.1016/j.jbi.2017.01.010.
.
.
Keywords: Data, Health Information Technology (HIT), Health Information Technology (HIT)
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
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
Wang SV, Verpillat P, Rassen JA
Transparency and reproducibility of observational cohort studies using large healthcare databases.
The researchers explored the extent to which published pharmacoepidemiologic studies using commercially available databases could be reproduced by other investigators. Based on a nonsystematic sample of 38 descriptive or comparative safety/effectiveness cohort studies, they concludedc that an essential component of transparent and reproducible databases is more complete reporting of study implementation.
AHRQ-funded; HS022193.
Citation: Wang SV, Verpillat P, Rassen JA .
Transparency and reproducibility of observational cohort studies using large healthcare databases.
Clin Pharmacol Ther 2016 Mar;99(3):325-32. doi: 10.1002/cpt.329..
Keywords: Health Information Technology (HIT), Data, Research Methodologies, Comparative Effectiveness
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)