Physician Self-Referral and Imaging Use Appropriateness: Negative Cervical Spine MRI Frequency as an Assessment Metric

Amrhein, Timothy J.; Paxton, Ben E.; Lungren, Matthew P.; Befera, Nicholas T.; Collins, Heather; Yurko, C.; Eastwood, James D.; Kilani, Ramsey K.

doi:10.3174/ajnr.a4076

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…Moreover, the neural networks used for NLP tasks have tremendous value in many applications such as generating systems for radiology case prioritization based on report analysis, patient cohort generation, eligibility screening for clinical trials, and in radiology clinical decision support to manage imaging utilization or use yield as a metric. For example, understanding the rate of negative studies could serve as an indirect marker of utilization appropriateness and guide clinical decision making [50,51,8,52,53,54,55].…”

Section: Discussionmentioning

confidence: 99%

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

Banerjee

Yuan

Chen

et al. 2019

Artificial Intelligence in Medicine

188

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This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system -PEFinder and traditional machine learning methods -SVM and Adaboost. We proposed two distinct deep learning models -(i) CNN Word -Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7,370 clin-ical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers. Our proposed DPA-HNN model encodes domain-dependent phrases into an attention mechanism and represents a radiology report through a hierarchical RNN structure composed of word-level, sentence-level and document-level representations. Experimental results suggest that the performance of the deep learning models that are trained on a single institutional dataset, are better than rule-based PEFinder on our multi-institutional test sets. The best F1 score for the presence of PE in an adult patient population was 0.99 (DPA-HNN) and for a pediatrics population was 0.99 (HNN) which shows that the deep learning models being trained on adult data, demonstrated generalizability to pediatrics population with comparable accuracy. Our work suggests feasibility of broader usage of neural network models in automated classification of multi-institutional imaging text reports for a variety of applications including evaluation of imaging utilization, g

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Section: Discussionmentioning

confidence: 99%

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

Banerjee

Yuan

Chen

et al. 2019

Artificial Intelligence in Medicine

188

View full text Add to dashboard Cite

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“…We identified self-referred imaging from patterns in the imaging claims including the medical specialty and practice affiliations of the physicians submitting the claims. 13,33 We identified two primary forms of self-referral arrangements. One was when the same non-radiologist physician who ordered the procedure also performed it.…”

Section: Variables and Measuresmentioning

confidence: 99%

“…Similarly, physician specialty has been linked to higher rates of service utilization 10 and some small-scale investigations suggest that physicians are less likely to order inappropriate imaging for clinical conditions in which they specialize. 11,12 Regarding self-referral imaging, which is generally defined as physicians referring patients for imaging to facilities in which they or their partners have financial interests, 13 this type of arrangement has been linked to higher rates of imaging utilization [13][14][15][16][17][18] and also higher frequencies of negative findings from imaging studies. 19,20 However, little current research has examined the central question of whether self-referral is, in fact, associated with a higher likelihood of inappropriate imaging.…”

Section: Introductionmentioning

confidence: 99%

Effects of Physician Experience, Specialty Training, and Self-referral on Inappropriate Diagnostic Imaging

et al. 2020

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BACKGROUND: Although previous research has demonstrated high rates of inappropriate diagnostic imaging, the potential influence of several physician-level characteristics is not well established. OBJECTIVE: To examine the influence of three types of physician characteristics on inappropriate imaging: experience, specialty training, and self-referral. DESIGN: A retrospective analysis of over 70,000 MRI claims submitted for commercially insured individuals. Physician characteristics were identified through a combination of administrative records and primary data collection. Multi-level modeling was used to assess relationships between physician characteristics and inappropriate MRIs. SETTING: Massachusetts PARTICIPANTS: Commercially insured individuals who received an MRI between 2010 and 2013 for one of three conditions: low back pain, knee pain, and shoulder pain. MEASUREMENTS: Guidelines from the American College of Radiology were used to classify MRI referrals as appropriate/inappropriate. Experience was measured from the date of medical school graduation. Specialty training comprised three principal groups: general internal medicine, family medicine, and orthopedics. Two forms of self-referral were examined: (a) the same physician who ordered the procedure also performed it, and (b) the physicians who ordered and performed the procedure were members of the same group practice and the procedure was performed outside the hospital setting. RESULTS: Approximately 23% of claims were classified as inappropriate. Physicians with 10 or less years of experience had significantly higher odds of ordering inappropriate MRIs. Primary care physicians were almost twice as likely to order an inappropriate MRI as orthopedists. Self-referral was not associated with higher rates of inappropriate MRIs. LIMITATIONS: Classification of MRIs was conducted with claims data. Not all self-referred MRIs could be detected. CONCLUSIONS: Inappropriate imaging continues to be a driver of wasteful health care spending. Both physician experience and specialty training were highly associated with inappropriate imaging.

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“…7 We used CT imaging reports for precise, temporally specific cohort generation to avoid reliance on error-prone claims data that lack information about the exact time of diagnosis. 32,33,34,35 All patients included in our study underwent CT imaging, even those for whom an age-adjusted d -dimer level would have precluded imaging in most scoring systems. In addition, patients not referred for imaging were excluded because the design of the study was not to displace existing rule-out criteria, but instead to reduce unneeded PE imaging for patients referred for CT imaging for PE.…”

Section: Discussionmentioning

confidence: 99%

Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support

et al. 2019

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Key Points Question Can machine-learning approaches achieve an objective pulmonary embolism risk score by analyzing temporal patient data to accurately inform computed tomographic imaging decisions? Findings In this multi-institutional diagnostic study of 3214 patients, a machine learning model was designed to achieve an accurate patient-specific risk score for pulmonary embolism diagnosis. The model was successfully evaluated in both multi-institutional inpatient and outpatient settings. Meaning Machine learning algorithms using retrospective temporal patient data appear to be a valuable and feasible tool for accurate computation of patient-specific risk score to better inform clinical decision-making for computed tomographic pulmonary embolism imaging.

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Physician Self-Referral and Imaging Use Appropriateness: Negative Cervical Spine MRI Frequency as an Assessment Metric

Cited by 9 publications

References 22 publications

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

Effects of Physician Experience, Specialty Training, and Self-referral on Inappropriate Diagnostic Imaging

Development and Performance of the Pulmonary Embolism Result Forecast Model (PERFORM) for Computed Tomography Clinical Decision Support

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