National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- Adverse Events (3)
- Blood Clots (1)
- Data (1)
- Diagnostic Safety and Quality (2)
- Electronic Health Records (EHRs) (5)
- Healthcare-Associated Infections (HAIs) (3)
- (-) Health Information Technology (HIT) (8)
- Hospital Readmissions (1)
- Hospitals (1)
- Injuries and Wounds (1)
- Patient-Centered Healthcare (1)
- Quality Improvement (8)
- Quality Measures (1)
- (-) Quality of Care (8)
- Registries (1)
- (-) Surgery (8)
- Telehealth (1)
- Urinary Tract Infection (UTI) (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 8 of 8 Research Studies DisplayedShi J, Hurdle JF, Johnson SA
Natural language processing for the surveillance of postoperative venous thromboembolism.
The objective of the study was to develop a portal natural language processing approach to aid in the identification of postoperative venous thromboembolism events from free-text clinical notes. The investigators concluded that accurate surveillance of postoperative venous thromboembolism may be achieved using natural language processing on clinical notes in 2 independent health care systems. They indicated that these findings suggest natural language processing may augment manual chart abstraction for large registries such as National Surgical Quality Improvement Program.
AHRQ-funded; HS025776.
Citation: Shi J, Hurdle JF, Johnson SA .
Natural language processing for the surveillance of postoperative venous thromboembolism.
Surgery 2021 Oct;170(4):1175-82. doi: 10.1016/j.surg.2021.04.027..
Keywords: Blood Clots, Health Information Technology (HIT), Quality Improvement, Quality of Care, Surgery, Adverse Events
Zhu 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
King CR, Abraham J, Kannampallil TG
Protocol for the effectiveness of an anesthesiology control tower system in improving perioperative quality metrics and clinical outcomes: the TECTONICS randomized, pragmatic trial.
The primary objective of this trial was to determine whether an anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney injury. Clinicians in operating rooms randomized to ACT support receive decision support from clinicians in the ACT. In operating rooms randomized to no intervention, the current standard of anesthesia care is delivered. The intention-to-treat principle will be followed for all analyses.
AHRQ-funded; HS024581.
Citation: King CR, Abraham J, Kannampallil TG .
Protocol for the effectiveness of an anesthesiology control tower system in improving perioperative quality metrics and clinical outcomes: the TECTONICS randomized, pragmatic trial.
F1000Res 2019 Nov 29;8:2032. doi: 10.12688/f1000research.21016.1.
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Keywords: Quality Measures, Quality Improvement, Quality of Care, Surgery, Telehealth, Health Information Technology (HIT)
Smith AB, Mueller D, Garren B
Using qualitative research to reduce readmissions and optimize perioperative cystectomy care.
This study examined the need for qualitative research on meaningful patient-reported outcomes (PROs) to prevent complications and readmissions after cystectomy. The investigators looked at the potential use of mobile communication devices (mHealth) to capture patients’ experiences and to improve outcomes. Interviews were conducted with 15 readmitted patients and 10 of their partners over 45 semi-structured in-depth interviews. The most common perspectives were that patients and their caregivers were overloaded with cystectomy education; they need to know what are normal post-operative symptoms; and that using mHealth would help with patient and caregiver education.
AHRQ-funded; HS024134.
Citation: Smith AB, Mueller D, Garren B .
Using qualitative research to reduce readmissions and optimize perioperative cystectomy care.
Cancer 2019 Oct 15;125(20):3545-53. doi: 10.1002/cncr.32362..
Keywords: Hospital Readmissions, Surgery, Health Information Technology (HIT), Quality Improvement, Quality of Care, Hospitals, Patient-Centered Healthcare
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)
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