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 Events (2)
- Data (1)
- Diagnostic Safety and Quality (2)
- (-) Electronic Health Records (EHRs) (6)
- Healthcare-Associated Infections (HAIs) (3)
- Health Information Technology (HIT) (5)
- Injuries and Wounds (1)
- Patient-Centered Outcomes Research (1)
- (-) Quality Improvement (6)
- Quality of Care (5)
- Registries (2)
- (-) Surgery (6)
- Urinary Tract Infection (UTI) (1)
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 6 of 6 Research Studies DisplayedZhu Y, Simon GJ, Wick EC
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. The purpose of the study was to understand the generalizability of a machine learning algorithm between sites; automated surgical site infection (SSI) detection algorithms developed at one center were tested at another distinct center.
AHRQ-funded; HS024532.
Citation: Zhu Y, Simon GJ, Wick EC .
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
J Am Coll Surg 2021 Jun;232(6):963-71.e1. doi: 10.1016/j.jamcollsurg.2021.03.026..
Keywords: Healthcare-Associated Infections (HAIs), Surgery, Adverse Events, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Bucher BT, Shi J, Ferraro JP
Portable automated surveillance of surgical site infections using natural language processing: development and validation.
The authors presented the development and validation of a portable natural language processing (NLP) approach for automated surveillance of surgical site infections (SSIs). Patient clinical text notes from EHRs following surgical procedures from two independent healthcare systems were abstracted. The authors found that automated surveillance of SSIs can be achieved using NLP of clinical notes with high sensitivity and specificity.
AHRQ-funded; HS025776.
Citation: Bucher BT, Shi J, Ferraro JP .
Portable automated surveillance of surgical site infections using natural language processing: development and validation.
Ann Surg 2020 Oct;272(4):629-36. doi: 10.1097/sla.0000000000004133..
Keywords: Surgery, Healthcare-Associated Infections (HAIs), Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Bronsert M, Singh AB, Henderson WG
Identification of postoperative complications using electronic health record data and machine learning.
Investigators developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR). They concluded that using machine learning on EHR postoperative data linked to American College of Surgeons National Surgical Quality Improvement Program outcomes data, a model with 163 predictors from the EHR identified complications well at their institution.
AHRQ-funded; HS026019.
Citation: Bronsert M, Singh AB, Henderson WG .
Identification of postoperative complications using electronic health record data and machine learning.
Am J Surg 2020 Jul;220(1):114-19. doi: 10.1016/j.amjsurg.2019.10.009..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery, Quality Improvement, Quality of Care, Diagnostic Safety and Quality
Colborn KL, Bronsert M, Hammermeister K
Identification of urinary tract infections using electronic health record data.
Using the American College of Surgeons National Surgical Quality Improvement Program UTI status of patients who underwent an operation at the University of Colorado Hospital, the investigators sought to develop an algorithm for identifying UTIs using data from the electronic health record. The investigators concluded that a model with 14 predictors from the electronic health record identifies UTIs well, and it could be used to scale up UTI surveillance or to estimate the impact of large-scale interventions on UTI rates.
AHRQ-funded; HS026019.
Citation: Colborn KL, Bronsert M, Hammermeister K .
Identification of urinary tract infections using electronic health record data.
Am J Infect Control 2019 Apr;47(4):371-75. doi: 10.1016/j.ajic.2018.10.009..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Quality of Care, Quality Improvement, Surgery, Urinary Tract Infection (UTI)
Devine EB, Van Eaton E, Zadworny ME
Automating electronic clinical data capture for quality improvement and research: The CERTAIN Validation Project of Real World Evidence.
Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) is a network of hospitals participating in quality improvement (QI) registries wherein data are manually abstracted from EHRs. To create the Comparative Effectiveness Research and Translation Network (CERTAIN), researchers semi-automated SCOAP data abstraction using a centralized federated data model, created a central data repository (CDR), and assessed whether these data could be used as real world evidence for QI and research. They concluded that semi-automated data abstraction may be useful, although raw data collected as a byproduct of health care delivery is not immediately available for use as real world evidence. New approaches to gathering and analyzing extant data are required.
AHRQ-funded; HS020025.
Citation: Devine EB, Van Eaton E, Zadworny ME .
Automating electronic clinical data capture for quality improvement and research: The CERTAIN Validation Project of Real World Evidence.
eGEMS 2018 May 22;6(1):8. doi: 10.5334/egems.211..
Keywords: Patient-Centered Outcomes Research, Quality Improvement, Registries, Surgery, Electronic Health Records (EHRs)
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