National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Adverse Drug Events (ADE) (1)
- Adverse Events (1)
- Brain Injury (1)
- Cancer (6)
- Cancer: Breast Cancer (2)
- Cancer: Lung Cancer (1)
- Cardiovascular Conditions (2)
- Children/Adolescents (1)
- Clinical Decision Support (CDS) (2)
- Communication (2)
- COVID-19 (1)
- Data (1)
- Decision Making (2)
- Diabetes (1)
- Diagnostic Safety and Quality (14)
- Electronic Health Records (EHRs) (11)
- Emergency Department (2)
- Emergency Medical Services (EMS) (1)
- Evidence-Based Practice (1)
- Eye Disease and Health (1)
- Guidelines (2)
- Healthcare Utilization (1)
- (-) Health Information Technology (HIT) (27)
- Heart Disease and Health (1)
- Hospital Discharge (1)
- (-) Imaging (27)
- Patient Safety (3)
- Prevention (1)
- Primary Care (1)
- Quality Improvement (1)
- Quality of Care (1)
- Risk (1)
- Stroke (1)
- Telehealth (1)
- Transitions of Care (1)
- Women (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 25 of 27 Research Studies DisplayedAbdulkadir Y, Luximon D, Morris E
Human factors in the clinical implementation of deep learning-based automated contouring of pelvic organs at risk for MRI-guided radiotherapy.
This study evaluated the performance and clinical implementation of a novel deep learning-based auto-contouring workflow for 0.35T magnetic resonance imaging (MRI)-guided pelvic radiotherapy, focusing on automation bias and objective measures of workflow savings. UNet-derived architecture was used to develop an auto-contouring model for the femoral heads, bladder, and rectum in 0.35T MR images. Training data was taken from 75 patients treated with MRI-guided radiotherapy at the authors’ institution. The model was tested against 20 retrospective cases outside the training set. It was subsequently implemented, with usability evaluated on the first 30 clinical cases by computing Dice coefficient (DSC), Hausdorff distance (HD), and the fraction of slices that were used un-modified by planners. The authors observed automation bias, but it had little deleterious effect on treatment planning.
AHRQ-funded; HS026486.
Citation: Abdulkadir Y, Luximon D, Morris E .
Human factors in the clinical implementation of deep learning-based automated contouring of pelvic organs at risk for MRI-guided radiotherapy.
Med Phys 2023 Oct; 50(10):5969-77. doi: 10.1002/mp.16676..
Keywords: Imaging, Health Information Technology (HIT)
Abbasi N, Lacson R, Kapoor N
Development and external validation of an artificial intelligence model for identifying radiology reports containing recommendations for additional imaging.
This study’s purpose was to develop and externally validate an artificial intelligence (AI)-based model for identifying radiology reports containing recommendations for additional imaging (RAIs). This retrospective study was performed at a multisite health center using a total of 6300 randomly selected radiology reports generated at one site from January 2015 to June 30, 2021. A bidirectional encoder representations from transformers (BERT)-based technique for identifying RAIS was developed using the 1260 report training set. Performance of the BERT-based model and a previously developed traditional machine learning (TML) model was assessed and compared in the test set. Among a total of 7419 unique patients, 10% of 7560 reports contained RAI. The test set using the BERT-based model had 94.4% precision, 65.4% recall, and an F1 score of 96.4%. In the test set, the TML model showed 69.0% precision, 65.4% recall, and an F1 score of 67.2%. Also in the test set, accuracy was greater for the BERT-based model than for the TML model (99.2% vs 93.1%). The external validation set showed even higher scores for the BERT-based model (99.2% precision, 91.6% recall, an F1 score of 95.2%, and 99.0% accuracy).
AHRQ-funded; HS029348.
Citation: Abbasi N, Lacson R, Kapoor N .
Development and external validation of an artificial intelligence model for identifying radiology reports containing recommendations for additional imaging.
AJR Am J Roentgenol 2023 Sep; 221(3):377-85. doi: 10.2214/ajr.23.29120..
Keywords: Imaging, Health Information Technology (HIT)
Neylon J, Luximon DC, Ritter T
Proof-of-concept study of artificial intelligence-assisted review of CBCT image guidance.
The authors presented the initial findings of a proof-of-concept clinical implementation of their AI-assisted review of cone beam computed tomography (CBCT) registrations used for patient setup. They developed an automated pipeline which utilized python scripts to interact with the clinical database through DICOM networking protocol and automated data retrieval and analysis. The AI model was validated against independent expert observers; observer scores for the stratified validation dataset showed statistically significant correlation between scores and model predictions. Case studies demonstrated potential benefits of using such a pipeline to bolster quality and safety programs in radiotherapy.
AHRQ-funded; HS026486.
Citation: Neylon J, Luximon DC, Ritter T .
Proof-of-concept study of artificial intelligence-assisted review of CBCT image guidance.
J Appl Clin Med Phys 2023 Sep; 24(9):e14016. doi: 10.1002/acm2.14016..
Keywords: Imaging, Health Information Technology (HIT)
DeSimone AK, Kapoor N, Lacson R
Impact of an automated closed-loop communication and tracking tool on the rate of recommendations for additional imaging in thoracic radiology reports.
The objectives of this retrospective, institutional review board-approved study set in academic quaternary care hospital were to assess the effects of feedback reports and the implementation of a closed-loop communication system based on rates of recommendations for additional imaging (RAIs) in thoracic radiology reports. A previously validated natural language processing tool was used to classify reports with an RAI. The results showed that feedback reports alone increased RAI rates; an IT intervention that promoted documentation of complete RAI in addition to feedback reports led to significant reductions in RAI rate, incomplete RAI, and improved overall completeness of radiology recommendations.
AHRQ-funded; HS029348.
Citation: DeSimone AK, Kapoor N, Lacson R .
Impact of an automated closed-loop communication and tracking tool on the rate of recommendations for additional imaging in thoracic radiology reports.
J Am Coll Radiol 2023 Aug; 20(8):781-88. doi: 10.1016/j.jacr.2023.05.004..
Keywords: Imaging, Health Information Technology (HIT), Communication
Su YR, Buist DSM, Lee JM
Performance of statistical and machine learning risk prediction models for surveillance benefits and failures in breast cancer survivors.
The authors compared the relative predictive performance of statistical and machine learning (ML) models to guide modeling strategy selection for surveillance mammography outcomes in women with a personal history of breast cancer. They cross-validated seven risk prediction models for two surveillance outcomes, using 9,447 mammograms. The results suggested that regularized regression outperformed other modeling approaches for predicting breast cancer surveillance mammography outcomes and balanced the trade-off between model flexibility and interpretability.
AHRQ-funded; HS018366.
Citation: Su YR, Buist DSM, Lee JM .
Performance of statistical and machine learning risk prediction models for surveillance benefits and failures in breast cancer survivors.
Cancer Epidemiol Biomarkers Prev 2023 Apr 3; 32(4):561-71. doi: 10.1158/1055-9965.Epi-22-0677..
Keywords: Cancer: Breast Cancer, Cancer, Health Information Technology (HIT), Imaging
Sun J, Peng L, Li T
Performance of a chest radiograph AI diagnostic tool for COVID-19: a prospective observational study.
The purpose of this observational study was to evaluate the real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. The researchers utilized 95,363 chest radiographs for model training, external validation, and real-time validation. There were 5,335 real-time predictions and a COVID-19 prevalence of 4.8%. The study found that participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19. Real-time model performance remained the same during the 19 weeks of implementation. Model sensitivity was higher in men than in women, but model specificity was higher in women. Sensitivity was higher for Asian and Black participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy compared with radiologist predictions. The researchers concluded that AI tools underperform when compared with radiologist results.
AHRQ-funded; HS026379.
Citation: Sun J, Peng L, Li T .
Performance of a chest radiograph AI diagnostic tool for COVID-19: a prospective observational study.
Radiol Artif Intell 2022 Jul;4(4):e210217. doi: 10.1148/ryai.210217..
Keywords: COVID-19, Imaging, Diagnostic Safety and Quality, Health Information Technology (HIT)
Zou WY, Enchakalody BE, Zhang P
Automated measurements of body composition in abdominal CT scans using artificial intelligence can predict mortality in patients with cirrhosis.
Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. I this study, the investigators sought to use artificial intelligence to develop an automated method to measure body composition and test the algorithm on a clinical cohort to predict mortality.
AHRQ-funded; HS027183.
Citation: Zou WY, Enchakalody BE, Zhang P .
Automated measurements of body composition in abdominal CT scans using artificial intelligence can predict mortality in patients with cirrhosis.
Hepatol Commun 2021 Nov;5(11):1901-10. doi: 10.1002/hep4.1768..
Keywords: Imaging, Health Information Technology (HIT)
Marin JR, Rodean J, Mannix RC
Association of clinical guidelines and decision support with CT use in pediatric mild traumatic brain injury.
The objective of this study was to examine whether the presence of clinical guidelines and clinical decision support (CDS) for mild traumatic brain injury (mTBI) were associated with lower head computed tomography (CT) use. The investigators concluded that clinical guidelines for mTBI, and particularly CDS, were associated with lower rates of head CT use without adverse clinical outcomes.
AHRQ-funded; HS026006.
Citation: Marin JR, Rodean J, Mannix RC .
Association of clinical guidelines and decision support with CT use in pediatric mild traumatic brain injury.
J Pediatr 2021 Aug;235:178-83.e1. doi: 10.1016/j.jpeds.2021.04.026..
Keywords: Children/Adolescents, Clinical Decision Support (CDS), Decision Making, Health Information Technology (HIT), Brain Injury, Guidelines, Evidence-Based Practice, Imaging
Swanson MB, Miller AC, Ward MM MM
Emergency department telemedicine consults decrease time to interpret computed tomography of the head in a multi-network cohort.
Telemedicine can improve access to emergency stroke care in rural areas, but the benefit of telemedicine across different types and models of telemedicine networks is unknown. The objectives of this study were to (1) identify the impact of telemedicine on ED stroke care, (2) identify if telemedicine impact varied by network, and (3) describe the variation in process outcomes by telemedicine across EDs.
AHRQ-funded; HS025753.
Citation: Swanson MB, Miller AC, Ward MM MM .
Emergency department telemedicine consults decrease time to interpret computed tomography of the head in a multi-network cohort.
J Telemed Telecare 2021 Jul;27(6):343-52. doi: 10.1177/1357633x19877746..
Keywords: Emergency Department, Telehealth, Health Information Technology (HIT), Imaging, Stroke, Cardiovascular Conditions
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
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)
Aneja S, Chang E, Omuro A
Applications of artificial intelligence in neuro-oncology.
This article explores the promise that artificial intelligence algorithms has for improving understanding of brain tumors and help drive future innovations in neuro-oncology.
AHRQ-funded; HS023000.
Citation: Aneja S, Chang E, Omuro A .
Applications of artificial intelligence in neuro-oncology.
Curr Opin Neurol 2019 Dec;32(6):850-56. doi: 10.1097/wco.0000000000000761.
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Keywords: Health Information Technology (HIT), Cancer, Diagnostic Safety and Quality, Imaging
Lacson R, Gujrathi I, Healey M
Closing the loop on unscheduled diagnostic imaging orders: a systems-based approach.
This study looked at the impact of implementing a tool called SCORE (System for Coordinating Orders for Radiology Exams), whose objective is to manage unscheduled orders for outpatient diagnostic imaging in an electronic health record (EHR) with embedded computerized physician order entry. The rate of unscheduled imaging orders was compared before SCORE (October 2017 to September 2018) and after (October 2018 to June 2019). There was a 49% reduction in unscheduled orders after SCORE implementation at a large academic institution.
AHRQ-funded; HS024722.
Citation: Lacson R, Gujrathi I, Healey M .
Closing the loop on unscheduled diagnostic imaging orders: a systems-based approach.
J Am Coll Radiol 2021 Jan;18(1 Pt A):60-67. doi: 10.1016/j.jacr.2020.09.031..
Keywords: Imaging, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Patient Safety
Levy AE, Shah NR, Matheny ME
Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: implications for natural language processing tools.
The authors investigated whether Natural Language Processing (NLP) tools could potentially help estimate myocardial perfusion imaging (MPI) risk. Subjects were VA patients who underwent stress MPI and coronary angiography 2009-11; stress test reports were randomly selected for analysis. The authors found that post-test ischemic risk was determinable but rarely reported in this sample of stress MPI reports. They conclude that this supports the potential use of NLP to help clarify risk and recommend further study of NLP in this context.
AHRQ-funded; HS022998.
Citation: Levy AE, Shah NR, Matheny ME .
Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: implications for natural language processing tools.
J Nucl Cardiol 2019 Dec;26(6):1878-85. doi: 10.1007/s12350-018-1275-y..
Keywords: Imaging, Risk, Clinical Decision Support (CDS), Health Information Technology (HIT), Diagnostic Safety and Quality, Cardiovascular Conditions, Heart Disease and Health
Kang SK, Garry K, Chung R
Natural language processing for identification of incidental pulmonary nodules in radiology reports.
The authors developed natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations using the electronic health records for patients who underwent chest CT before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. They concluded that NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.
AHRQ-funded; HS024376.
Citation: Kang SK, Garry K, Chung R .
Natural language processing for identification of incidental pulmonary nodules in radiology reports.
J Am Coll Radiol 2019 Nov;16(11):1587-94. doi: 10.1016/j.jacr.2019.04.026..
Keywords: Imaging, Diagnostic Safety and Quality, Health Information Technology (HIT), Electronic Health Records (EHRs), Quality Improvement, Quality of Care
Deng F, Li MD, Wong A
Quality of documentation of contrast agent allergies in electronic health records.
The purpose of this study was to describe and appraise contrast agent allergy documentation in the electronic health record (EHR). The investigators concluded that contrast allergy records in EHRs were diverse and commonly low quality. They suggest that continued EHR enhancements and training are needed to support contrast allergy documentation to facilitate improved patient care and medical research.
AHRQ-funded; HS025375.
Citation: Deng F, Li MD, Wong A .
Quality of documentation of contrast agent allergies in electronic health records.
J Am Coll Radiol 2019 Aug;16(8):1027-35. doi: 10.1016/j.jacr.2019.01.027..
Keywords: Adverse Drug Events (ADE), Adverse Events, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Imaging
Lacson R, Laroya R, Wang A
Integrity of clinical information in computerized order requisitions for diagnostic imaging.
This study compared the integrity of electronic health record (EHR) imaging order requisitions with EHR provider notes and their potential impact on order inaccuracies and interpretation of results. This retrospective study was conducted at a tertiary academic medical center using MRI lumbar spine and CT abdomen/pelvis orders performed from April 1 to May 31, 2016. Results showed that requisition indications were more likely to be incomplete than provider notes. Researchers recommended that relevant documentation be more readily available in EHRs.
Citation: Lacson R, Laroya R, Wang A .
Integrity of clinical information in computerized order requisitions for diagnostic imaging.
J Am Med Inform Assoc 2018 Dec;25(12):1651-56. doi: 10.1093/jamia/ocy133..
Keywords: Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Imaging
Gupta A, Lacson R, Balthazar PC
Assessing documentation of critical imaging result follow-up recommendations in emergency department discharge instructions.
The purpose of this study was to facilitate follow-up of critical test results across transitions in patient care settings, the investigators implemented an electronic discharge module that enabled care providers to include follow-up recommendations in the discharge instructions. Implementation of a discharge module was associated with increased documentation of critical imaging finding follow-up recommendations in ED discharge instructions. However, one in four patients still did not receive adequate follow-up recommendations, suggesting further opportunities for performance improvement exist.
AHRQ-funded; HS022586.
Citation: Gupta A, Lacson R, Balthazar PC .
Assessing documentation of critical imaging result follow-up recommendations in emergency department discharge instructions.
J Digit Imaging 2018 Aug;31(4):562-67. doi: 10.1007/s10278-017-0039-6..
Keywords: Emergency Department, Health Information Technology (HIT), Hospital Discharge, Imaging, Transitions of Care
Murphy DR, Meyer AND, Vaghani V
Electronic triggers to identify delays in follow-up of mammography: harnessing the power of big data in health care.
Because of the unique clinical, logistic, and legal aspects of mammography, this study was conducted to evaluate the effectiveness of a trigger to flag delayed follow-up on mammography. The investigators found that care delays appeared to continue despite federal laws requiring patient notification of mammographic results within 30 days. They suggest that clinical application of mammography-related triggers could help detect these delays.
AHRQ-funded; HS022901.
Citation: Murphy DR, Meyer AND, Vaghani V .
Electronic triggers to identify delays in follow-up of mammography: harnessing the power of big data in health care.
J Am Coll Radiol 2018 Feb;15(2):287-95. doi: 10.1016/j.jacr.2017.10.001..
Keywords: Cancer: Breast Cancer, Cancer, Electronic Health Records (EHRs), Health Information Technology (HIT), Imaging, Diagnostic Safety and Quality, Prevention, Women
Huhdanpaa HT, Tan WK, Rundell SD
Using natural language processing of free-text radiology reports to identify type 1 modic endplate changes.
This study’s goal was to determine the feasibility of using natural language processing (NLP) to convert text from electronic medical records reports of magnetic resonance (MR) imaging results into variables to identify patients with Type 1 Modic endplate changes in the spine. Those patients with Type 1 Modic endplace changes may be eligible for clinical trials. Using a rule-based algorithm in Java they found the prevalence was 10%. The results showed that specificity was higher than recall due to the high number of keywords used in lumbar spine reporting.
AHRQ-funded; HS022972.
Citation: Huhdanpaa HT, Tan WK, Rundell SD .
Using natural language processing of free-text radiology reports to identify type 1 modic endplate changes.
J Digit Imaging 2018 Feb;31(1):84-90. doi: 10.1007/s10278-017-0013-3..
Keywords: Electronic Health Records (EHRs), Imaging, Health Information Technology (HIT)
Chandakkar PS, Venkatesan R, Li B
MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images.
Computer-aided diagnosis has the potential of improving diabetic retinopathy (DR) screening or diagnosis. The researchers developed a feature space of a modified color correlogram appended with statistics of steerable Gaussian filter responses selected by fast radial symmetric transform points. Experiments with real DR images collected from five different datasets demonstrate that the proposed approach is able to outperform existing methods.
AHRQ-funded; HS019792.
Citation: Chandakkar PS, Venkatesan R, Li B .
MIRank-KNN: multiple-instance retrieval of clinically relevant diabetic retinopathy images.
J Med Imaging 2017 Jul;4(3):034003. doi: 10.1117/1.jmi.4.3.034003.
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Keywords: Diabetes, Imaging, Eye Disease and Health, Health Information Technology (HIT), Diagnostic Safety and Quality
Kessler R, Stowell JR, Vogel JA
Effect of interventional program on the utilization of PACS in point-of-care ultrasound.
The study’s objective was to determine if a simple interventional program would influence the utilization of Picture Archiving and Communication Systems (PACS) in point-of-care ultrasound. It concluded that a simple interventional program for emergency physicians can significantly increase and sustain the utilization of PACS for point-of-care ultrasound.
AHRQ-funded; HS023901.
Citation: Kessler R, Stowell JR, Vogel JA .
Effect of interventional program on the utilization of PACS in point-of-care ultrasound.
J Digit Imaging 2016 Dec;29(6):701-05. doi: 10.1007/s10278-016-9893-x.
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Keywords: Emergency Medical Services (EMS), Imaging, Healthcare Utilization, 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
Liu S, Wu Y, Wooten HO
Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning.
A software tool is developed to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image-guided radiation therapy (MR-IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in a clinic. After modeling and quantitative analysis, the researchers proposed an algorithm that is able to predict the ViewRay treatment delivery time with the average prediction error 0.22min or 1.82 percent.
AHRQ-funded; HS022888.
Citation: Liu S, Wu Y, Wooten HO .
Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning.
J Appl Clin Med Phys 2016 Mar 8;17(2):5907.
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Keywords: Cancer, Health Information Technology (HIT), Imaging