National Healthcare Quality and Disparities Report
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Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Events (1)
- Cancer (1)
- (-) Diagnostic Safety and Quality (4)
- (-) Electronic Health Records (EHRs) (4)
- Emergency Department (1)
- Healthcare-Associated Infections (HAIs) (1)
- Health Information Technology (HIT) (4)
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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 4 of 4 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
Enayati M, Sir M, Zhang X
Monitoring diagnostic safety risks in emergency departments: protocol for a machine learning study.
This study’s objective will be to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. It will use trigger algorithms with electronic health record (EHR) data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on if they meet certain criteria. This study will be conducted by 2 academic medical centers with affiliated community hospitals.
AHRQ-funded; HS027363; HS026622.
Citation: Enayati M, Sir M, Zhang X .
Monitoring diagnostic safety risks in emergency departments: protocol for a machine learning study.
JMIR Res Protoc 2021 Jun 14;10(6):e24642. doi: 10.2196/24642..
Keywords: Emergency Department, Diagnostic Safety and Quality, Patient Safety, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Jones OT, Calanzani N, Saji S
Artificial intelligence techniques that may be applied to primary care data to facilitate earlier diagnosis of cancer: systematic review.
This study’s objective was a systematic review of artificial intelligence (AI) techniques that might facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. Findings showed that AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.
AHRQ-funded; HS027363.
Citation: Jones OT, Calanzani N, Saji S .
Artificial intelligence techniques that may be applied to primary care data to facilitate earlier diagnosis of cancer: systematic review.
J Med Internet Res 2021 Mar 3;23(3):e23483. doi: 10.2196/23483..
Keywords: Cancer, Diagnostic Safety and Quality, Primary Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Lacson R, Cochon L, Ching PR
Integrity of clinical information in radiology reports documenting pulmonary nodules.
Researchers sought to quantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports, and to assess impact on making follow-up recommendations. Their retrospective cohort study was performed at an academic medical center and natural language processing was used on radiology reports of CT scans of chest, abdomen, or spine to assess presence of pulmonary nodules. They found that essential pulmonary nodule characteristics were under-reported, potentially impacting recommendations for pulmonary nodule follow-up. They concluded that the lack of documentation of pulmonary nodule characteristics in radiology reports was common, with the potential for compromising patient care and clinical decision support tools.
AHRQ-funded; HS024722.
Citation: Lacson R, Cochon L, Ching PR .
Integrity of clinical information in radiology reports documenting pulmonary nodules.
J Am Med Inform Assoc 2021 Jan 15;28(1):80-85. doi: 10.1093/jamia/ocaa209..
Keywords: Imaging, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety