National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (1)
- Adverse Events (3)
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- Clinical Decision Support (CDS) (1)
- Clinician-Patient Communication (1)
- Communication (1)
- Dementia (1)
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- Diagnostic Safety and Quality (13)
- (-) Electronic Health Records (EHRs) (13)
- Emergency Department (1)
- Healthcare-Associated Infections (HAIs) (1)
- Health Information Technology (HIT) (13)
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- Medication: Safety (1)
- Neurological Disorders (1)
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- Risk (1)
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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 13 of 13 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
Salmasian H, Blanchfield BB, Joyce K
Association of display of patient photographs in the electronic health record with wrong-patient order entry errors.
Wrong-patient order entry (WPOE) errors have a high potential for harm; these errors are particularly frequent wherever workflows are complex and multitasking and interruptions are common, such as in the emergency department (ED). The purpose of this study was to evaluate whether the use of noninterruptive display of patient photographs in the banner of the electronic health record (EHR) is associated with a decreased rate of WPOE errors.
AHRQ-funded; HS024713.
Citation: Salmasian H, Blanchfield BB, Joyce K .
Association of display of patient photographs in the electronic health record with wrong-patient order entry errors.
AMA Netw Open 2020 Nov 2;3(11):e2019652. doi: 10.1001/jamanetworkopen.2020.19652..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Errors, Adverse Drug Events (ADE), Adverse Events, Medication, Medication: Safety, Patient Safety, Diagnostic Safety and Quality
Rogith D, Satterly T, Singh H
Application of human factors methods to understand missed follow-up of abnormal test results.
This study demonstrated application of human factors methods for understanding causes for lack of timely follow-up of abnormal test results ("missed results") in outpatient settings. The investigators identified 30 cases of missed test results by querying electronic health record data, developed a critical decision method based interview guide to understand decision-making processes, and interviewed physicians who ordered these tests. They analyzed transcribed responses, developed a CI-based flow model, and conducted a fault tree analysis to identify hierarchical relationships between factors that delayed action.
AHRQ-funded; HS022087; HS022901.
Citation: Rogith D, Satterly T, Singh H .
Application of human factors methods to understand missed follow-up of abnormal test results.
Appl Clin Inform 2020 Oct;11(5):692-98. doi: 10.1055/s-0040-1716537..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Shared Decision Making, Diagnostic Safety and Quality, Communication, Clinician-Patient Communication
Shah RU, Mutharasan RK, Ahmad FS
Development of a portable tool to identify patients with atrial fibrillation using clinical notes from the electronic medical record.
The electronic medical record contains a wealth of information buried in free text. In this study, the investigators created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone. The authors concluded that this approach allowed better use of the clinical narrative and created an opportunity for precise, high-throughput cohort identification.
AHRQ-funded; HS026385.
Citation: Shah RU, Mutharasan RK, Ahmad FS .
Development of a portable tool to identify patients with atrial fibrillation using clinical notes from the electronic medical record.
Circ Cardiovasc Qual Outcomes 2020 Oct;13(10):e006516. doi: 10.1161/circoutcomes.120.006516..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality
Misra-Hebert AD, Milinovich A, Zajichek A
Natural language processing improves detection of nonsevere hypoglycemia in medical records versus coding alone in patients with type 2 diabetes but does not improve prediction of severe hypoglycemia events: an analysis using the electronic medical record
The purpose of this study was to determine if natural language processing (NLP) improves detection of non-severe hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH). The authors identified NSH events by diagnosis codes and NLP 2005 to 2017 and built an SH prediction model. Their findings showed that detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction.
AHRQ-funded; HS024128.
Citation: Misra-Hebert AD, Milinovich A, Zajichek A .
Natural language processing improves detection of nonsevere hypoglycemia in medical records versus coding alone in patients with type 2 diabetes but does not improve prediction of severe hypoglycemia events: an analysis using the electronic medical record
Diabetes Care 2020 Aug;43(8):1937-40. doi: 10.2337/dc19-1791..
Keywords: Diabetes, Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality
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
Lacson R, Healey MJ, Cochon LR
Unscheduled radiologic examination orders in the electronic health record: a novel resource for targeting ambulatory diagnostic errors in radiology.
The purpose of this study was to assess the prevalence of unscheduled radiologic examination orders in an electronic health record and to assess the proportion of unscheduled orders that are clinically necessary. Unscheduled radiologic examination orders were retrieved for seven modalities: computed tomography, magnetic resonance imaging, ultrasound, obstetric ultrasound, bone densitometry, mammography, and fluoroscopy. Findings showed that large numbers of radiologic examination orders remain unscheduled in the electronic health record. Identifying and performing clinically necessary unscheduled radiologic examination orders may help reduce diagnostic errors related to diagnosis and treatment delays and enhance patient safety.
AHRQ-funded; HS024722.
Citation: Lacson R, Healey MJ, Cochon LR .
Unscheduled radiologic examination orders in the electronic health record: a novel resource for targeting ambulatory diagnostic errors in radiology.
J Am Coll Radiol 2020 Jun;17(6):765-72. doi: 10.1016/j.jacr.2019.12.021..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality, Imaging, Patient Safety
Zhou Y, Abel GA, Hamilton W
Imaging activity possibly signalling missed diagnostic opportunities in bladder and kidney cancer: a longitudinal data-linkage study using primary care electronic health records.
Sub-optimal use or interpretation of imaging investigations prior to diagnosis of certain cancers may be associated with less timely diagnosis, but pre-diagnostic imaging activity for urological cancer is unknown. In this study, the investigators analysed linked data derived from primary and secondary care records and cancer registration to evaluate the use of clinically relevant imaging tests pre-diagnosis, in patients with bladder and kidney cancer diagnosed in 2012-15 in England.
AHRQ-funded; HS022087.
Citation: Zhou Y, Abel GA, Hamilton W .
Imaging activity possibly signalling missed diagnostic opportunities in bladder and kidney cancer: a longitudinal data-linkage study using primary care electronic health records.
Cancer Epidemiol 2020 Jun;66:101703. doi: 10.1016/j.canep.2020.101703..
Keywords: Cancer, Diagnostic Safety and Quality, Imaging, Primary Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Soleimani J, Pinevich Y, Barwise AK
Feasibility and reliability testing of manual electronic health record reviews as a tool for timely identification of diagnostic error in patients at risk.
Although diagnostic error (DE) is a significant problem, it remains challenging for clinicians to identify it reliably and to recognize its contribution to the clinical trajectory of their patients. The purpose of this work was to evaluate the reliability of real-time electronic health record (EHR) reviews using a search strategy for the identification of DE as a contributor to the rapid response team (RRT) activation. Early and accurate recognition of critical illness is of paramount importance.
AHRQ-funded; HS026609.
Citation: Soleimani J, Pinevich Y, Barwise AK .
Feasibility and reliability testing of manual electronic health record reviews as a tool for timely identification of diagnostic error in patients at risk.
Appl Clin Inform 2020 May;11(3):474-82. doi: 10.1055/s-0040-1713750..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality, Medical Errors, Adverse Events, Patient Safety
Barnes DE, Zhou J, Walker RL
Development and validation of eRADAR: a tool using EHR Data to detect unrecognized dementia.
The goal of this retrospective cohort study was to develop and validate an electronic health record (EHR)-based tool to help detect patients with unrecognized dementia. The tool was named EHR Risk of Alzheimer’s and Dementia Assessment Rule (eRADAR). This study was conducted at Kaiser Permanente Washington (KPWA) using participants in the Adult Changes in Thought (ACT) study who undergo comprehensive testing every 2 years to detect and diagnose dementia and have linked KPWA EHR data. Overall, 1015 ACT visits resulted in a diagnosis of incident dementia, of which 49% were previously unrecognized in the EHR. The final 31-predictor model included markers of dementia-related symptoms, healthcare utilization patterns, and dementia risk factors. The study showed good discrimination in the development interval and validation samples.
AHRQ-funded; HS022982.
Citation: Barnes DE, Zhou J, Walker RL .
Development and validation of eRADAR: a tool using EHR Data to detect unrecognized dementia.
J Am Geriatr Soc 2020 Jan;68(1):103-11. doi: 10.1111/jgs.16182..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Dementia, Neurological Disorders, Diagnostic Safety and Quality, Clinical Decision Support (CDS), Shared Decision Making