Background As a result of the 21st Century Cures Act, radiology reports are immediately released to patients. We determine if readers of radiology reports, via electronic health records (EHRs), and radiology report complexity have changed post the implementation of the 21st Century Cures Act. Methods A retrospective observational study was used to analyze 10,000 radiology reports (equal split of CT, mammogram, MRI, X-ray, and ultrasound modalities) per year between 2013 and 2023. Readability was calculated through reading grade level indices. Results Patient viewership of their radiology reports via EHRs increased from 3.3 % (95 % CI: 3.0 %-3.7 %) in 2013 to 58.2 % (95 % CI: 57.3 %-59.2 %) in 2023. Once the 21st Century Cures Act's Information Blocking Provision went into effect, there was a significant increase in viewing probability with patients having 1.71 times higher odds of viewing their reports (OR = 1.71, 95 % CI: 1.27-2.32, p < 0.001). This increase in patient viewership held for all modalities tested except CT (P < 0.01). Despite increased viewership, the reading grade level of radiologist dictated radiology reports was greater than the recommended level for health information across all years and modalities tested. Conclusions and relevance Patients are increasingly engaging with their radiology reports, but reports may be too complex for the typical patient. Solutions will be required to improve patient experience with their radiology reports.

Importance:While the number of intravenous (IV) hydration spas has grown over the past decade, information regarding their regulation and practices remains sparse, despite concerns about oversight and safety. Objective:To review state-level policies related to IV hydration spa oversight and regulation, and examine US facility practices, including product offerings, product claims, and staffing. Design, Setting, and Participants:In June 2024, a cross-sectional content analysis of state-issued, public-facing regulatory laws and statements was conducted for all 50 US states and the District of Columbia (DC). In July and August 2024, the websites of 5 IV hydration spas in each state and DC were reviewed. Finally, from August through October 2024, a secret shopper study was conducted of 2 randomly selected IV hydration spas in each state and DC from the website review. Main Outcomes and Measures:The policy analysis determined whether states explicitly addressed 4 aspects of IV hydration spa oversight: governance, prescriber credentials, dispensing practices, and compounding practices. Website review ascertained product offerings, product claims, and staffing at each site. The secret shopper script included questions on the availability of licensed health professionals, product offerings, pricing, and potential risks or waiver requirements as well as insurance coverage. Results:Although 32 states have issued some form of IV hydration spa-related guidance, policies varied widely and only 4 state policies addressed governance, prescriber credentials, dispensing practices, and compounding practices. Review of 255 facility websites found spa practices also varied with respect to product offerings, product claims, and staffing. All offered IV hydration therapy, most commonly combined with magnesium (n = 146 [57.3%]) and glutathione (n = 137 [53.7%]) and vitamin injections (n = 162 [63.5%]) were also commonly offered. All websites also included claims for beneficial uses, although only 2 (0.8%) cited sources for these beneficial health claims. The secret shopper study of 87 randomly selected facilities corroborated the website review: 24 (27.6%) required consultation with a licensed medical professional before treatment, 75 (86.2%) recommended specific therapies for proffered headache and cold symptoms, and 21 (24.4%) described potential risks. Conclusions and Relevance:This mixed-methods study found that state-level policies governing IV hydration spas and facility practices vary widely, suggesting more stringent oversight may be necessary to protect public health.

Background The complex medical terminology of radiology reports may cause confusion or anxiety for patients, especially given increased access to electronic health records. Large language models (LLMs) can potentially simplify radiology report readability. Purpose To compare the performance of four publicly available LLMs (ChatGPT-3.5 and ChatGPT-4, Bard [now known as Gemini], and Bing) in producing simplified radiology report impressions. Materials and Methods In this retrospective comparative analysis of the four LLMs (accessed July 23 to July 26, 2023), the Medical Information Mart for Intensive Care (MIMIC)-IV database was used to gather 750 anonymized radiology report impressions covering a range of imaging modalities (MRI, CT, US, radiography, mammography) and anatomic regions. Three distinct prompts were employed to assess the LLMs' ability to simplify report impressions. The first prompt (prompt 1) was "Simplify this radiology report." The second prompt (prompt 2) was "I am a patient. Simplify this radiology report." The last prompt (prompt 3) was "Simplify this radiology report at the 7th grade level." Each prompt was followed by the radiology report impression and was queried once. The primary outcome was simplification as assessed by readability score. Readability was assessed using the average of four established readability indexes. The nonparametric Wilcoxon signed-rank test was applied to compare reading grade levels across LLM output. Results All four LLMs simplified radiology report impressions across all prompts tested (P < .001). Within prompts, differences were found between LLMs. Providing the context of being a patient or requesting simplification at the seventh-grade level reduced the reading grade level of output for all models and prompts (except prompt 1 to prompt 2 for ChatGPT-4) (P < .001). Conclusion Although the success of each LLM varied depending on the specific prompt wording, all four models simplified radiology report impressions across all modalities and prompts tested. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Rahsepar in this issue.

Following the recent expansion of the Open Payments program to include advanced-practice clinicians (APCs) as covered recipients, we characterized the geographical distribution of general industry payments to nurse practitioners and physician assistants using the Open Payments database. The number and dollar value of payments, as well as the average and median payment amount earned per provider, varied by state. However, a significantly higher proportion of APCs received payments in states with more restrictive scope-of-practice laws. Understanding how and why payments to APCs vary by state can elucidate how industry-APC relationships are related to changing scope-of-practice and state-specific transparency/disclosure laws, informing future legislation.

OBJECTIVE:This quality assurance study assessed the implementation of a combined artificial intelligence (AI) and natural language processing (NLP) program for pulmonary nodule detection in the emergency department setting. The program was designed to function outside of normal reading workflows to minimize radiologist interruption.MATERIALS AND METHODS:In all, 19,246 CT examinations including at least some portion of the lung anatomy performed in the emergent setting from October 1, 2021, to June 1, 2022, were processed by the combined AI-NLP program. The program used an AI algorithm trained on 6-mm to 30-mm pulmonary nodules to analyze CT images and an NLP to analyze radiological reports. Cases flagged as negative for pulmonary nodules by the NLP but positive by the AI algorithm were classified as suspected discrepancies. Discrepancies result in secondary review of examinations for possible addenda.RESULTS:Out of 19,246 CT examinations, 50 examinations (0.26%) resulted in secondary review, and 34 of 50 (68%) reviews resulted in addenda. Of the 34 addenda, 20 patients received instruction for new follow-up imaging. Median time to addendum was 11 hours. The majority of reviews and addenda resulted from missed pulmonary nodules on CT examinations of the abdomen and pelvis.CONCLUSION:A background quality assurance process using AI and NLP helped improve the detection of pulmonary nodules and resulted in increased numbers of patients receiving appropriate follow-up imaging recommendations. This was achieved without disrupting in-shift radiologist workflow or causing significant delays in patient follow for the diagnosed pulmonary nodule.

BACKGROUND. CT with CTA is widely used to exclude stroke in patients with dizziness, although MRI has higher sensitivity. OBJECTIVE. The purpose of this article was to compare patients presenting to the emergency department (ED) with dizziness who undergo CT with CTA alone versus those who undergo MRI in terms of stroke-related management and outcomes. METHODS. This retrospective study included 1917 patients (mean age, 59.5 years; 776 men, 1141 women) presenting to the ED with dizziness from January 1, 2018, to December 31, 2021. A first propensity score matching analysis incorporated demographic characteristics, medical history, findings from the review of systems, physical examination findings, and symptoms to construct matched groups of patients discharged from the ED after undergoing head CT with head and neck CTA alone and patients who underwent brain MRI (with or without CT and CTA). Outcomes were compared. A second analysis compared matched patients discharged after CT with CTA alone and patients who underwent specialized abbreviated MRI using multiplanar high-resolution DWI for increased sensitivity for posterior circulation stroke. Sensitivity analyses were performed involving MRI examinations performed as the first or only neuroimaging examination and involving alternative matching and imputation techniques. RESULTS. In the first analysis (406 patients per group), patients who underwent MRI, compared with patients who underwent CT with CTA alone, showed greater frequency of critical neuroimaging results (10.1% vs 4.7%, p = .005), change in secondary stroke prevention medication (9.6% vs 3.2%, p = .001), and subsequent echocardiography evaluation (6.4% vs 1.0%, p < .001). In the second analysis (100 patients per group), patients who underwent specialized abbreviated MRI, compared with patients who underwent CT with CTA alone, showed greater frequency of critical neuroimaging results (10.0% vs 2.0%, p = .04), change in secondary stroke prevention medication (14.0% vs 1.0%, p = .001), and subsequent echocardiography evaluation (12.0% vs 2.0%, p = .01) and lower frequency of 90-day ED readmissions (12.0% vs 28.0%, p = .008). Sensitivity analyses showed qualitatively similar findings. CONCLUSION. A proportion of patients discharged after CT with CTA alone may have benefitted from alternative or additional evaluation by MRI (including MRI using a specialized abbreviated protocol). CLINICAL IMPACT. Use of MRI may motivate clinically impactful management changes in patients presenting with dizziness.

BACKGROUND:The frequency, magnitude, and distribution of industry payments to radiologists are not well understood. RATIONALE AND OBJECTIVES:The aim of this study was to analyze the distribution of industry payments to physicians working in diagnostic radiology, interventional radiology, and radiation oncology, study the categories of payments and determine their correlation. MATERIALS AND METHODS:The Open Payments Database from the Centers for Medicare & Medicaid Services was accessed and analyzed for the period from January 1, 2016 to December 31, 2020. Payments were grouped into six categories: consulting fees, education, gifts, research, speaker fees, and royalties/ownership. The total amount and types of industry payments going to the top 5% group were determined overall and for each category of payment. RESULTS:From 2016 to 2020, a total of 513 020 payments, amounting to $370 782 608, were made to 28 739 radiologists suggesting that approximately 70% of the 41 000 radiologists in the US received at least one industry payment during the 5-year period. The median payment value was $27 (IQR: $15-$120) and the median number of payments per physician over the 5-year period was 4 (IQR: 1-13). Gifts were the most frequent payment type made (76.4%), but accounted for only 4.8% of payment value. The median total value of payments earned by members of the top 5% group over the 5-year period was $58 878 (IQR: $29 686-$162 425) ($11 776 per year) compared to $172 (IQR: $49-877) ($34 per year) in the bottom 95% group. Members of the top 5% group received a median of 67 (IQR: 26-147) individual payments (13 payments per year) while members of the bottom 95% group received a median of 3 (IQR: 1-11) (0.6 payments per year). CONCLUSION:Between 2016 and 2020, industry payments to radiologists were highly concentrated both in terms of number/frequency and value of payments.

BackgroundPatients presenting to the emergency department (ED) with dizziness may be imaged via CTA head and neck to detect acute vascular pathology including large vessel occlusion. We identify commonly documented clinical variables which could delineate dizzy patients with near zero risk of acute vascular abnormality on CTA. MethodsWe performed a cross-sectional analysis of adult ED encounters with chief complaint of dizziness and CTA head and neck imaging at three EDs between 1/1/2014-12/31/2017. A decision rule was derived to exclude acute vascular pathology tested on a separate validation cohort; sensitivity analysis was performed using dizzy "stroke code" presentations. ResultsTesting, validation, and sensitivity analysis cohorts were composed of 1072, 357, and 81 cases with 41, 6, and 12 instances of acute vascular pathology respectively. The decision rule had the following features: no past medical history of stroke, arterial dissection, or transient ischemic attack (including unexplained aphasia, incoordination, or ataxia); no history of coronary artery disease, diabetes, migraines, current/long-term smoker, and current/long-term anti-coagulation or anti-platelet medication use. In the derivation phase, the rule had a sensitivity of 100% (95% CI: 0.91-1.00), specificity of 59% (95% CI: 0.56-0.62), and negative predictive value of 100% (95% CI: 0.99-1.00). In the validation phase, the rule had a sensitivity of 100% (95% CI: 0.61-1.00), specificity of 53% (95% CI: 0.48-0.58), and negative predictive value of 100% (95% CI: 0.98-1.00). The rule performed similarly on dizzy stroke codes and was more sensitive/predictive than all NIHSS cut-offs. CTAs for dizziness might be avoidable in 52% (95% CI: 0.47-0.57) of cases. ConclusionsA collection of clinical factors may be able to "exclude" acute vascular pathology in up to half of patients imaged by CTA for dizziness. These findings require further development and prospective validation, though could improve the evaluation of dizzy patients in the ED.

Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures Act, patients have greater and quicker access to their imaging reports, but these reports are still written above the comprehension level of the average patient. Consequently, many patients have requested reports to be conveyed in language accessible to them. Numerous studies have shown that improving patient understanding of their condition results in better outcomes, so driving comprehension of imaging reports is essential. Summary statements, second reports, and the inclusion of the radiologist's phone number have been proposed, but these solutions have implications for radiologist workflow. Artificial intelligence (AI) has the potential to simplify imaging reports without significant disruptions. Many AI technologies have been applied to radiology reports in the past for various clinical and research purposes, but patient focused solutions have largely been ignored. New natural language processing technologies and large language models (LLMs) have the potential to improve patient understanding of their imaging reports. However, LLMs are a nascent technology and significant research is required before LLM-driven report simplification is used in patient care.

Follicular lymphoma (FL) is traditionally considered treatable but incurable. In March 2021, the US Food and Drug Administration approved the use of chimeric antigen receptor (CAR) T-cell therapy in patients with relapsed or refractory (R/R) FL after ≥2 lines of therapy. Priced at $373 000, CAR T-cell therapy is potentially curative, and its cost-effectiveness compared with other modern R/R FL treatment strategies is unknown. We developed a Markov model to assess the cost-effectiveness of third-line CAR T-cell vs standard of care (SOC) therapies in adults with R/R FL. We estimated progression rates for patients receiving CAR T-cell and SOC therapies from the ZUMA-5 trial and the LEO CReWE study, respectively. We calculated costs, discounted life years, quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER) of CAR T-cell vs SOC therapies with a willingness-to-pay threshold of $150 000 per QALY. Our analysis was conducted from a US payer's perspective over a lifetime horizon. In our base-case model, the cost of the CAR T-cell strategy was $731 682 compared with $458 490 for SOC therapies. However, CAR T-cell therapy was associated with incremental clinical benefit of 1.50 QALYs, resulting in an ICER of $182 127 per QALY. Our model was most sensitive to the utilities associated with CAR T-cell therapy remission and third-line SOC therapies and to the total upfront CAR T-cell therapy cost. Under current pricing, CAR T-cell therapy is unlikely to be cost-effective in unselected patients with FL in the third-line setting. Both randomized clinical trials and longer term clinical follow-up can help clarify the benefits of CAR T-cell therapy and optimal sequencing in patients with FL.

HomeRadiologyVol. 309, No. 2 PreviousNext Original ResearchFree AccessComputer ApplicationsAccuracy of ChatGPT, Google Bard, and Microsoft Bing for Simplifying Radiology ReportsKanhai S. Amin, Melissa A. Davis, Rushabh Doshi, Andrew H. Haims, Pavan Khosla, Howard P. Forman Kanhai S. Amin, Melissa A. Davis, Rushabh Doshi, Andrew H. Haims, Pavan Khosla, Howard P. Forman Author AffiliationsFrom the Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT 06520.Address correspondence to H.P.F. (email: [email protected]).Kanhai S. AminMelissa A. DavisRushabh DoshiAndrew H. HaimsPavan KhoslaHoward P. Forman Published Online:Nov 21 2023https://doi.org/10.1148/radiol.232561MoreSectionsPDF ToolsAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In AbstractDownload as PowerPointIntroductionWith the advent of the Office of the National Coordinator for Health Information Technology’s Cures Act Final Rule and its information blocking provision, radiology reports have become increasingly accessible to patients (1). However, patients may not be able to understand their reports due to many factors, including radiology-specific jargon. This can lead to increased patient anxiety and call volume to providers (2). Many solutions, such as providing a lay summary or second report in lay language, have been proposed (2). Emerging technologies such as large language models (LLMs) powered by natural language processing (NLP) can generate these additional lay language materials without significantly hindering the radiologist workflow. While these technologies may soon be used on the provider side, patients are already engaging with publicly available LLMs: ChatGPT alone has more than 100 million users (3).One study (4) has demonstrated that four publicly available LLMs—ChatGPT-3.5 (5), GPT-4 (6), Google Bard (7), and Microsoft Bing (8)—can significantly simplify radiology reports. The present work assesses the accuracy of the four LLMs when asked the basic prompt “Simplify this radiology report.”Materials and MethodsFrom 750 radiology report impressions—gathered from the de-identified, publicly available, and Health Insurance Portability and Accountability Act–compliant MIMIC-IV database (9)—assessed in a previous article (4), we randomly selected 150 impressions (30 from CT, 30 from mammography, 30 from MRI, 30 from US, and 30 from radiography) and their simplified output. The average reading grade level (aRGL) was reassessed for this subset of reports by averaging grade-level scores calculated with the Gunning Fog Index, Flesch-Kincaid Grade Level Readability Calculator, Automated Readability Index, and Coleman-Liau Readability Index (4).Two radiology attending physicians (M.A.D. and A.H.H., with 9 and 24 years of experience, respectively)—blinded to the specific model—compared the LLM-simplified output to the radiologist-dictated impression. The radiologists were asked to rate four statements (Table) via a five-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). The statements were as follows: statement 1, “The simplified version does not contain any inaccurate or misleading information;” statement 2, “The simplified version includes all relevant/actionable information present in the original impression;” statement 3, “The simplified version offers beneficial supplementary information not found in the original impression;” and statement 4, “I feel comfortable giving the simplifieded output to patients without any supervision.” For each specific model and output, the radiologists scores were averaged together.Table 1: Reading Grade Level, Word Count, and Survey Scores for Each Model and ModalityPython version 3.11 (2022) was used to gather readability scores and word count. R (R Core Team, 2022) was used for data visualization and to conduct Wilcoxon signed-rank tests.ResultsAll models significantly simplified the impression aRGL across all modalities (P < .0001) (Table). Both radiologists strongly agreed that 86% (129 of 150), 83.3% (125 of 150), 75.3% (113 of 150), and 83.3% (125 of 150) of the simplified output contained both no inaccurate information (statement) and all the relevant and/or actionable information (statement 2) for ChatGPT-3.5, GPT-4, Google Bard, and Bing, respectively. Furthermore, there were 0, one, two, and 0 instances where the average reviewer score was neutral or worse for statement 1 for ChatGPT-3.5, GPT-4, Google Bard, and Bing, respectively. There were 0, 0, two, and 0 instances where the average reviewer score was neutral or worse for statement 2 for ChatGPT-3.5, GPT-4, Google Bard, and Bing, respectively.Overall, both ChatGPT-3.5 and Bing were significantly more accurate (statement 1) than Bard, while both ChatGPT models and Bing contained the relevant/actionable information (statement 2) significantly more often than Bard (P < .05) (Figure, Table). Bard’s output contained the most supplemental information (statement 3) and the greatest word count, followed by Bing, GPT-4, and ChatGPT-3.5, with each sequential difference in supplementary information and word count statistically significant (P < .01) (Figure, Table). The reviewers felt significantly more comfortable providing output (statement 4) from both ChatGPT models to patients compared with output from Bing and Google Bard (P < .01) (Figure, Table).Survey responses for each model and modality. Survey statements were as follows: accurate—the simplified version does not contain any inaccurate or misleading information; relevant—the simplified version includes all relevant/actionable information present in the original impression; supplemental information (info)—the simplified version offers beneficial supplementary information not found in the original impression; and release—I feel comfortable giving the simplified output to patients without any supervision. All reviews were conducted with use of a five-point Likert scale with whole numbers (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree); 0.5 values arise due to the averaging of reviewer scores. (A, B) Bar charts show survey responses for (A) all reports and (B) US reports. Bar charts show survey responses for (C) mammography and (D) CT. Bar charts show survey responses for (E) MRI and (F) radiography.Download as PowerPointDiscussionAll models evaluated, particularly ChatGPT-3.5 and Bing, were accurate when simplifying radiology reports with a basic prompt. The outputs of both ChatGPT-3.5 and Bing were at a higher aRGL, which may contribute to their greater accuracy. Our findings suggest LLMs may help patients simplify radiologist-dictated impressions. At the same time, the relatively high accuracy and/or relevance of simplified impressions and low word count suggests providers could readily provide—with an accuracy check—simplified output to patients (within locally hosted and Health Insurance Portability and Accountability Act–compliant LLMs). However, future workflow studies are required, particularly to ensure that the added value to patients does not come at an onerous cost to radiologists.Disclosures of conflicts of interest: K.S.A. No relevant relationships. M.A.D. Honorarium for grand rounds at Massachusetts General Hospital; board member, Joint Review Committee on Education in Radiologic Technology. R.D. Patents planned, issued or pending with Yale School of Medicine. A.H.H. Payment for expert testimony from various law firms for counsel in malpractice cases. P.K. No relevant relationships. H.P.F. Associate editor for Radiology.AcknowledgmentThe authors used large language models to generate the simplified radiology reports.Author ContributionsAuthor contributions: Guarantors of integrity of entire study, K.S.A., M.A.D., R.D., P.K., H.P.F.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, K.S.A., R.D., A.H.H., P.K.; clinical studies, M.A.D., R.D., A.H.H.; experimental studies, K.S.A., R.D., H.P.F.; statistical analysis, K.S.A., M.A.D., R.D., P.K.; and manuscript editing, K.S.A., M.A.D., R.D., P.K., H.P.F.References1. ONC’s Cures Act Final Rule. The Office of the National Coordinator for Health Information Technology (ONC). https://www.healthit.gov/topic/oncs-cures-act-final-rule. Accessed September 17, 2023. Google Scholar2. Amin K, Khosla P, Doshi R, Chheang S, Forman HP. Artificial Intelligence to Improve Patient Understanding of Radiology Reports. Yale J Biol Med 2023;96(3):407–417. Crossref, Medline, Google Scholar3. Ward E, Gross C. Evolving Methods to Assess Chatbot Performance in Health Sciences Research. JAMA Intern Med 2023;183(9):1030–1031. Crossref, Medline, Google Scholar4. Doshi R, Amin K, Khosla P, Bajaj S, Chheang S, Forman HP. Utilizing Large Language Models to Simplify Radiology Reports: a comparative analysis of ChatGPT3. 5, ChatGPT4. 0, Google Bard, and Microsoft Bing. medRxiv [preprint] 2023.06.04.23290786. https://doi.org/10.1101/2023.06.04.23290786. Published June 7, 2023. Accessed September 17, 2023. Google Scholar5. ChatGPT-3.5. (July 20, 2023 version). OpenAI. https://openai.com/blog/chatgpt. Accessed Juy 23–26, 2023. Google Scholar6. ChatGPT-4. (July 20, 2023 version). OpenAI. https://openai.com/blog/chatgpt. Accessed Juy 23–26, 2023. Google Scholar7. Google Bard. (July 13, 2023 version). https://bard.google.com. Google Scholar8. Microsoft Corporation. Microsoft Bing Chat. (July 21, 2023 version). https://www.microsoft.com/en-us/edge/features/bing-chat?form=MT00D8. Accessed Juy 23–26, 2023. Google Scholar9. Johnson A, Bulgarelli L, Pollard T, Horng S, Celi LA, Mark R. Mimic-iv PhysioNet. https://physionet.org/content/mimiciv/0.4/. Published August 13, 2020. Accessed July 18, 2023. Google ScholarArticle HistoryReceived: Sept 23 2023Revision requested: Oct 17 2023Revision received: Oct 25 2023Accepted: Oct 30 2023Published online: Nov 21 2023 FiguresReferencesRelatedDetailsRecommended Articles Natural Language Processing in Radiology: A Systematic ReviewRadiology2016Volume: 279Issue: 2pp. 329-343Evaluating GPT-4 on Impressions Generation in Radiology ReportsRadiology2023Volume: 307Issue: 5Deep Learning–based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology ReportsRadiology: Artificial Intelligence2022Volume: 4Issue: 5Application of a Domain-specific BERT for Detection of Speech Recognition Errors in Radiology ReportsRadiology: Artificial Intelligence2022Volume: 4Issue: 4Effect of Shift, Schedule, and Volume on Interpretive Accuracy: A Retrospective Analysis of 2.9 Million Radiologic ExaminationsRadiology2017Volume: 287Issue: 1pp. 205-212See More RSNA Education Exhibits Pandemic Preparedness: Streamlining the Breast Imaging Patient Care and Teaching Experience During COVID-19Digital Posters2020Certifications, Audits, And National Benchmarks: Breaking Down The Basics For The New Mammography AttendingDigital Posters2021Easy Introduction Of The Photon Counting Detector CT (PCD-CT) For RadiologistsDigital Posters2021 RSNA Case Collection Fitz-Hugh-Curtis syndromeRSNA Case Collection2021Secondary Angiosarcoma of the BreastRSNA Case Collection2021Sternalis muscle - normal variantRSNA Case Collection2020 Vol. 309, No. 2 Metrics Altmetric Score PDF download

ObjectiveThe aim of this study is to assess the trends in industry payments to radiologists and the impact of the COVID-19 pandemic, including trends in different categories of payments.MethodsThe Open Payments Database from CMS was accessed and analyzed for the period from January 1, 2016, to December 31, 2021. Payments were grouped into six categories: consulting fees, education, gifts, research, speaker fees, and royalties or ownership. The total number, value, and types of industry payments to radiologists were subsequently determined and compared pre- and postpandemic from 2016 to 2021.ResultsThe total number of industry payments and the number of radiologists receiving these payments dropped by 50% and 32%, respectively, between 2019 and 2020, with only partial recovery in 2021. However, the mean payment value and total payment value increased by 177% and 37%, respectively, between 2019 and 2020. Gifts and speaker fees experienced the largest decreases between 2019 and 2020 (54% and 63%, respectively). Research and education grants were also disrupted, with the number of payments decreasing by 37% and 36% and payment value decreasing by 37% and 25%, respectively. However, royalty or ownership increased during the first year of the pandemic (8% for number of payments and 345% for value of payments).ConclusionsThere was significant decline in overall industry payments coinciding with the COVID-19 pandemic, with biggest declines in gifts and speaker fees. The impact on the different categories of payments and recovery in the last 2 years has been heterogeneous.

Understanding costs associated with breastfeeding is critical to developing maximally effective policy to support breastfeeding by addressing financial barriers. Breastfeeding is not without cost; direct costs include those of equipment, modified nutritional intake, and time (opportunity cost). Breastfeeding need not require more equipment than formula feeding, though maternal equipment use varies by maternal preference. Meeting increased nutritional demands requires increased spending on food and potentially dietary supplementation, the marginal cost of which depends on a mother’s baseline diet. The opportunity cost of the three to four hours per day breastfeeding demands may be prohibitively high, particularly to low-income workers. These costs are relatively highest for low-income individuals, a group disproportionately comprising racial and ethnic minorities, and who demonstrate lower rates of breastfeeding than their white and higher-income peers. Acknowledging and addressing these costs and their regressive nature represents a critical component of effective breastfeeding policy and promotion.

HomeRadiologyVol. 302, No. 3 PreviousNext Reviews and CommentaryFree AccessEditorialPrice Transparency Comes to Radiology: Are We Ready for the Attention?Howard P. Forman Howard P. Forman Author AffiliationsFrom the Department of Diagnostic Radiology, Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510; Yale School of Management, New Haven, Conn; Department of Economics, Yale College, New Haven, Conn; and Yale School of Public Health, New Haven, Conn.Address correspondence to the author (e-mail: [email protected]).Howard P. Forman Published Online:Nov 30 2021https://doi.org/10.1148/radiol.2021212464MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by Jiang et al in this issue.Dr Forman is professor of diagnostic radiology, public health (health policy), economics, and management at Yale University. A practicing emergency and trauma radiologist, he is actively involved in patient care and issues related to financial administration, health care compliance, and contracting. His research has been focused on improving imaging services delivery through better access to information. He has worked as a health policy fellow in the U.S. Senate, on Medicare legislation.Download as PowerPointOpen in Image Viewer To have a perfectly competitive market, several key factors should be true—there must be a large number of buyers and sellers, the consumer must be rational, the goods are homogeneous, the providers maximize profit, and both the buyer and seller have full information available about price and quality. Health care, assuredly, is not a truly competitive market. The seemingly easiest among these factors—price—is as clear as mud, opaque to even the most expert viewer. In this issue of Radiology, Jiang and colleagues (1), in a tightly written research letter, look at the latest government effort to ensure transparency regarding price.Following through on a statute from the Affordable Care Act, on November 27, 2019, the U.S. Centers for Medicare and Medicaid Services laid out the final rule, the Hospital Price Transparency rule, for implementation on January 1, 2021. Hospitals must "establish, update, and make public a list of their standard charges for the items and services that they provide" (2). "Standard charges" was defined to include both list prices (ie, from the chargemaster) and rates negotiated with payers. Thirteen of the codes required for reporting were radiology codes. In their article, Jiang and colleagues (1) look at both the level of compliance as well as the findings among those who complied.The results are at once unsurprising and startling. Price transparency has always been a challenge for health care. However, one would expect that the force of federal law would induce compliance. The authors found that only 36% of hospitals (2053 of the 5700 hospitals reviewed) were in at least partial compliance. Furthermore, they learned that the ranges for negotiated, or median, prices ran from just more than two times Medicare reimbursement for three mammography Current Procedural Terminology [CPT] codes to almost six times Medicare reimbursement for brain CT. For similar studies, there was vast spread in prices paid by different private payers for the same CPT code, often approaching or exceeding 10 times the Medicare rates.The lack of compliance is not limited to radiology. In a study of the largest 100 hospitals, Henderson and Mouslim (3), searching 7 months earlier than the current investigation, found that 65 hospitals (65%) were "unambiguously noncompliant." Their findings were all the more shocking given that they had limited their search to leading hospitals. The current note, even 7 months later, would indicate that compliance is still not the norm.When it comes to prices, Jiang et al (1) confirm findings offered by many scholars and journalists, as published over the past few years (4,5). There has been enormous variation in costs passed on to individuals, employers, and insurers, and it persists to the present day. The current investigation addresses neurologic, musculoskeletal, breast, obstetric, gynecologic, and abdominal imaging. It covers multiple modalities, including conventional radiography, CT, MRI, and US. The median negotiated price is always at least twice Medicare reimbursement, and the variation around this median is, as noted, enormous.Radiologists can do little to encourage or to force compliance by hospitals. There are some radiologists who play leadership roles in their health systems and can advocate for transparency. Still, this area of compliance is firmly in the hands of the hospitals and health systems, and not within the control of individual radiologists or department or group leadership. However, we all should understand the motivation for transparency and why it is important.With price transparency comes a more informed consumer. Consumers, increasingly, are being asked to bear cost-sharing (eg, copays, deductibles, and coinsurance) for their medical imaging and may want to use price as a factor in choosing an imaging provider. Insurance companies and large employers, who bear much of the risk for their employees' health care costs, deserve to consider cost in their decision making about choice and considering options. Outpatient radiology practices can more effectively compete in a marketplace, perhaps opening more centers where there is sufficient demand, when they have clearer price discovery. Although it remains to be seen if this effort will help lower health care costs, there is reason to believe that it should (6).Now that we have the beginnings of more widespread transparency—even if still murky—what should we make of the wide variation in prices, negotiated and otherwise, for identical CPT codes? Why should the performance, not the interpretation, of a single brain CT examination vary by 10-fold between the lowest and highest negotiated rates? These questions beget more questions, and radiologists and hospital leadership would do well to consider them seriously.If there is more value in one imaging study than another identical one, then ask yourself why. Is it newer equipment with additional features? Are you providing additional sequences or added value in the delivery of the image set? Is the patient more challenging to image and, therefore, more resource intensive, thus imposing additional cost? Similarly, ask yourself whether the additional costs that are imposed by the examination you are overseeing are appropriate to this specific patient.If transparency eventually rules the day, then payers will demand to know why they should pay more to one hospital to obtain the same examination that could be obtained for far less money elsewhere. Because patients are often sharing in costs, how will consumers respond when they are freely able to choose among different cost providers? These questions and their answers, along with meaningful transparency, have the capacity to upend markets. Academic practices that may have long depended on higher reimbursements may be forced to identify where their "value add" lies and where it might not lie.This is not just theoretical. We have seen examples where payers have exerted pressure on markets to steer patients to imaging equipment that both meets their needs as well as minimizes costs (7). An academic practice, or private practice, for that matter, may have the best equipment and want to use it, but why would they need to use it on all patients? Perhaps the latest MRI sequences are of great value to image a patient with suspected stroke, but do they add meaningful value for a spine study? Must every patient pay a premium for features that are not meaningfully used?There may also be hard questions asked by radiologists themselves. The imaging revenue received by large institutions for the technical services rendered have hitherto been unknown to most group leaders and chairs. As radiologists are asked to do more and more noninterpretive work on behalf of hospitals and health systems, one might question whether financial remuneration is appropriate. Knowledge of pricing and technical service margins can inform negotiations covering an array of such services.Jiang et al (1) are to be commended for shining a light on this issue. Turning off the light is not an option. Patients benefit from more transparency around imaging services. This will not stop at price discovery. We will likely be required to report on quality, too. We would do well to be prepared.Disclosures of conflicts of interest: H.P.F. No relevant relationships.References1. Jiang JX, Makary MA, Ge B. Commercial Negotiated Prices for CMS-specified Shoppable Radiology Services in U.S Hospitals. Radiology 2022;302(3):622–624. Abstract, Google Scholar2. Medicare and Medicaid Programs: CY 2020 Hospital Outpatient PPS Policy Changes and Payment Rates and Ambulatory Surgical Center Payment System Policy Changes and Payment Rates. Price Transparency Requirements for Hospitals To Make Standard Charges Public. Federal Register. https://www.federalregister.gov/documents/2019/11/27/2019-24931/medicare-and-medicaid-programs-cy-2020-hospital-outpatient-pps-policy-changes-and-payment-rates-and#p-984. Published November 27, 2019. Accessed November 2, 2021. Google Scholar3. Henderson M, Mouslim MC. Low Compliance From Big Hospitals on CMS's Hospital Price Transparency Rule.Health Affairs Blog.https://www.healthaffairs.org/do/10.1377/hblog20210311.899634/full/. Published March 16, 2021. Accessed November 2, 2021. Google Scholar4. Kliff S. How much does an MRI cost? In D.C., anywhere from $400 to $1,861.Washington Post. https://www.washingtonpost.com/news/wonk/wp/2013/03/13/how-much-does-an-mri-cost-in-d-c-anywhere-from-400-to-1861/. Published March 13, 2013. Accessed November 2, 2021. Google Scholar5. Kurani N, Rae M, Pollitz K, Amin K, Cox C. Price transparency and variation in U.S. health services. Health System Tracker. https://www.healthsystemtracker.org/brief/price-transparency-and-variation-in-u-s-health-services/. Published January 13, 2021. Accessed September 25, 2021. Google Scholar6. Brown ZY. Equilibrium Effects of Health Care Price Information. Rev Econ Stat 2019;101(4):699–712. Crossref, Google Scholar7. Wu SJ, Sylwestrzak G, Shah C, DeVries A. Price transparency for MRIs increased use of less costly providers and triggered provider competition. Health Aff (Millwood) 2014;33(8):1391–1398. Crossref, Medline, Google ScholarArticle HistoryReceived: Sept 28 2021Revision requested: Oct 12 2021Revision received: Oct 12 2021Accepted: Oct 14 2021Published online: Nov 30 2021Published in print: Mar 2022 FiguresReferencesRelatedDetailsCited ByPrice Variability for Common Radiology Services within U.S. HospitalsJohn (Xuefeng) Jiang, Howard P. Forman, Sanjay Gupta, Ge Bai, 18 October 2022 | Radiology, Vol. 306, No. 3Accompanying This ArticleCommercial Negotiated Prices for CMS-specified Shoppable Radiology Services in U.S. HospitalsNov 30 2021RadiologyShoppable Radiology ServicesMar 1 2022Default Digital Object SeriesRecommended Articles Mentorship in Academic Radiology: A Review from a Trainee's Perspective—Radiology In TrainingRadiology2022Volume: 303Issue: 1pp. E17-E19Discovering New Imaging Biomarkers of Stroke EtiologyRadiology2020Volume: 298Issue: 2pp. 382-383When "Deep Faking" Results Means "Improving Diagnosis"Radiology2022Volume: 303Issue: 1pp. 160-161Use of Triaging Algorithms to Decrease CT Use Following Blunt Head and Neck TraumaRadiology2021Volume: 298Issue: 3pp. 630-631Additional Value of Dual-Energy CT for Patients with Wrist TraumaRadiology2020Volume: 296Issue: 3pp. 603-604See More RSNA Education Exhibits The Hitchhiker's Guide to Idiopathic Normal Pressure Hydrocephalus: What the Radiologist Needs to KnowDigital Posters2018Multimodality Imaging of Skeletal Dysplasias: What the Radiologist Needs to KnowDigital Posters2018Superficial Siderosis: Where to LookDigital Posters2019 RSNA Case Collection Cytotoxic lesion of the corpus callosum RSNA Case Collection2021Mineralising vasculopathy with infarct RSNA Case Collection2021Intracranial hypotension syndromeRSNA Case Collection2021 Vol. 302, No. 3 PodcastMetrics Altmetric Score PDF download

IMPORTANCE Historically marginalized racial and ethnic groups are generally more likely to experience sleep deficiencies. It is unclear how these sleep duration disparities have changed during recent years. OBJECTIVE To evaluate 15-year trends in racial and ethnic differences in self-reported sleep duration among adults in the US. DESIGN, SETTING, AND PARTICIPANTS This serial cross-sectional study used US population-based National Health Interview Survey data collected from 2004 to 2018. A total of 429 195 noninstitutionalized adults were included in the analysis, which was performed from July 26, 2021. to February 10, 2022. EXPOSURES Self-reported race, ethnicity, household income, and sex. MAIN OUTCOMES AND MEASURES Temporal trends and racial and ethnic differences in short (<7 hours in 24 hours) and long (>9 hours in 24 hours) sleep duration and racial and ethnic differences in the association between sleep duration and age. RESULTS The study sample consisted of 429 195 individuals (median [IQR] age, 46 [31-60] years; 51.7% women), of whom 5.1% identified as Asian. 11.8% identified as Black, 14.7% identified as Hispanic or Latino, and 68.5% identified as White. In 2004, the adjusted estimated prevalence of short and long sleep duration were 31.4% and 2.5%. respectively, among Asian individuals; 35.3% and 6.4%, respectively, among Black individuals; 27.0% and 4.6%, respectively, among Hispanic or Latino individuals; and 27.8% and 3.5%, respectively, among White individuals. During the study period, there was a significant increase in short sleep prevalence among Black (6.39 [95% CI, 3.329.46] percentage points), Hispanic or Latino (6.61[95% Cl, 4.03-9.20] percentage points), and White (3.22 [95% CI, 2.06-4.38] percentage points) individuals (P < .001 for each), whereas prevalence of long sleep changed significantly only among Hispanic or Latino individuals (-1.42 [95% CI, -2.52 to -0.32] percentage points; P = .01). In 2018, compared with White individuals, short sleep prevalence among Black and Hispanic or Latino individuals was higher by 10.68 (95% CI, 8.12-13.24; P < .001) and 2.44 (95% CI, 0.23-4.65; P = .03) percentage points, respectively, and long sleep prevalence was higher only among Black individuals (1.44 [95% CI, 0.39-2.48] percentage points; P = .007). The short sleep disparities were greatest among women and among those with middle or high household income. In addition, across age groups, Black individuals had a higher short and long sleep duration prevalence compared with White individuals of the same age. CONCLUSIONS AND RELEVANCE The findings of this cross-sectional study suggest that from 2004 to 2018, the prevalence of short and long sleep duration was persistently higher among Black individuals in the US. The disparities in short sleep duration appear to be highest among women, individuals who had middle or high income, and young or middle-aged adults, which may be associated with health disparities.

BACKGROUND:Triage for suspected acute stroke has two main options: (1) transport to the closest primary stroke center (PSC) and then to the nearest comprehensive stroke center (CSC) (Drip-and-Ship) or (2) transport the patient to the nearest CSC, bypassing a closer PSC (mothership). The purpose was to evaluate the effectiveness of drip-and-ship versus mothership models for acute stroke patients.METHODS:A Markov decision-analytic model was constructed. All model parameters were derived from recent medical literature. Our target population was adult patient with sudden onset of acute stroke within 8 h of onset over a one-year horizon. The primary outcome was quantified in terms of quality-adjusted-life-years (QALYs).RESULTS:The base case scenario show that the drip-and-ship strategy has a slightly higher expected health benefit, 0.591 QALY, as compared to 0.586 QALY in the mothership strategy when the time to PSC is 30 min and to CSC is 65 min, although the difference in health benefit becomes minimal as the time to PSC increases towards 60 min. Multiple sensitivity analyses show that when both PSC and CSC are far from place of onset (>1.5 h away), drip-and-ship becomes the better strategy. Mothership strategy is favored by smaller difference between distances to PSC and CSC, shorter transfer time from PSC to CSC, and longer delay in reperfusion in CSC for transferred patients. Drip-and-ship is favored by the reverse.CONCLUSION:Drip-and-ship has a slightly higher utility than mothership. This study assesses the complex issue of prehospital triage of acute stroke patients and can provide a framework for real-world data input.

HomeRadiologyVol. 306, No. 3 PreviousNext Original ResearchHealth Policy and PracticePrice Variability for Common Radiology Services within U.S. HospitalsJohn (Xuefeng) Jiang, Howard P. Forman, Sanjay Gupta, Ge Bai John (Xuefeng) Jiang, Howard P. Forman, Sanjay Gupta, Ge Bai Author AffiliationsFrom the Department of Accounting and Information Systems, Broad College of Business, Michigan State University, East Lansing, Mich (J.X.J., S.G.); Department of Diagnostic Radiology, Yale University School of Medicine, New Haven, Conn (H.P.F.); Johns Hopkins Carey Business School, Baltimore, Md (G.B.); and Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, 100 International Drive, Baltimore, MD 21202 (G.B.).Address correspondence to G.B. (email: [email protected]).John (Xuefeng) JiangHoward P. FormanSanjay GuptaGe Bai Published Online:Oct 18 2022https://doi.org/10.1148/radiol.221815See editorial byRonald L. ArensonMoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In AbstractCommercial negotiated price for common shoppable radiology services varied substantially within the same hospital and hospital-insurance-company pair. Services involving high-cost equipment had wider variations and higher prices (relative to Medicare).Download as PowerPointReferences1. Department of Health and Human Services. Medicare and Medicaid Programs: CY 2020 hospital outpatient PPS policy changes and payment rates and ambulatory surgical center payment system policy changes and payment rates. Price transparency requirements for hospitals to make standard charges public. https://www.federalregister.gov/documents/2019/11/27/2019-24931/medicare-and-medicaid-programscy-2020-hospital-outpatient-pps-policy-changes-and-payment-ratesand#p-531. Published November 27, 2019. Accessed June 15, 2022. Google Scholar2. Jiang JX, Makary MA, Bai G. Commercial Negotiated Prices for CMS-specified Shoppable Radiology Services in U.S. Hospitals. Radiology 2022;302(3):622–624. Link, Google Scholar3. Forman HP. Price Transparency Comes to Radiology: Are We Ready for the Attention? Radiology 2022;302(3):625–626. Link, Google Scholar4. Liao JM, Bai G, Forman HP, White AA, Lee CI. JACR health policy expert panel: hospital price transparency. J Am Coll Radiol 2022;19(6):792–794. Crossref, Medline, Google Scholar5. Henderson MA, Mouslim MC. Hospital and regional characteristics associated with emergency department facility fee cash pricing. Health Aff (Millwood) 2022;41(7):1029–1035. Crossref, Medline, Google Scholar6. Jiang JX, Makary MA, Bai G. Comparison of US hospital cash prices and commercial negotiated prices for 70 services. JAMA Netw Open 2021;4(12):e2140526. Crossref, Medline, Google ScholarArticle HistoryReceived: July 21 2022Revision requested: Aug 15 2022Revision received: Aug 25 2022Accepted: Sept 1 2022Published online: Oct 18 2022 FiguresReferencesRelatedDetailsCited ByImaging from Hospital Profit Center to Cost CenterRonald L. Arenson, 18 October 2022 | Radiology, Vol. 306, No. 3Price Transparency in Hospitals—Current Research and Future DirectionsJohn XuefengJiang, RanjaniKrishnan, GeBai2023 | JAMA Network Open, Vol. 6, No. 1Commercial COVID-19 PCR Test Price in US HospitalsJohn XuefengJiang, SanjayGupta, GerardAnderson, GeBai2023 | Journal of General Internal MedicineAccompanying This ArticleImaging from Hospital Profit Center to Cost CenterOct 18 2022RadiologyPrice Variability for Common Radiology Services within U.S. HospitalsApr 11 2023Default Digital Object SeriesRecommended Articles Financial Forecasting and Stochastic Modeling: Predicting the Impact of Business DecisionsRadiology2017Volume: 283Issue: 2pp. 342-358Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade GliomaRadiology2021Volume: 300Issue: 2pp. 410-420Diffusion-weighted Imaging of Invasive Breast Cancer: Relationship to Distant Metastasis–free SurvivalRadiology2019Volume: 291Issue: 2pp. 300-307Brain MRI with Quantitative Susceptibility Mapping: Relationship to CT Attenuation ValuesRadiology2020Volume: 294Issue: 3pp. 600-609Fully Automated 3D Vestibular Schwannoma Segmentation with and without Gadolinium-based Contrast Material: A Multicenter, Multivendor StudyRadiology: Artificial Intelligence2022Volume: 4Issue: 4See More RSNA Education Exhibits Magnetoencephalography (MEG): Taking Functional Brain Imaging to New HeightsDigital Posters2020Radiomics In Breast Imaging For NoobDigital Posters2021Equality, Equity, Disparity: Why Radiology Community Should Care?Digital Posters2021 RSNA Case Collection Transcranial Cerebral HerniationRSNA Case Collection2020Primary GlioblastomaRSNA Case Collection2021Leptomeningeal siderosisRSNA Case Collection2021 Vol. 306, No. 3 PodcastMetrics Altmetric Score PDF download