Selecting the Best Radiology Workflow Efficiency Applications.

In the rapidly evolving digital radiology landscape, a surge in solutions has emerged including more than 500 artificial intelligence applications that have received 510 k clearance by the FDA. Moreover, there is an extensive number of non-regulated applications, specifically designed to enhance workflow efficiency within radiology departments. These efficiency applications offer tremendous opportunities to resolve operational pain points and improve efficiency for radiology practices worldwide. However, selecting the most effective workflow efficiency applications presents a major challenge due to the multitude of available solutions and unclear evaluation criteria. In this article, we share our perspective on how to structure the broad field of workflow efficiency applications and how to objectively assess individual solutions. Along the different stages of the radiology workflow, we highlight 31 key operational pain points that radiology practices face and match them with features of workflow efficiency apps aiming to address them. A framework to guide practices in assessing and curating workflow efficiency applications is introduced, addressing key dimensions, including a solution's pain point coverage, efficiency claim strength, evidence and credibility, ease of integration, and usability. We apply this framework in a large-scale analysis of workflow efficiency applications in the market, differentiating comprehensive workflow efficiency ecosystems seeking to address a multitude of pain points through a unified solution from workflow efficiency niche apps following a targeted approach to address individual pain points. Furthermore, we propose an approach to quantify the financial benefits generated by different types of applications that can be leveraged for return-on-investment calculations.

Enhancing patient outcomes: the role of clinical utility in guiding healthcare providers in curating radiology AI applications.

With advancements in artificial intelligence (AI) dominating the headlines, diagnostic imaging radiology is no exception to the accelerating role that AI is playing in today's technology landscape. The number of AI-driven radiology diagnostic imaging applications (digital diagnostics) that are both commercially available and in-development is rapidly expanding as are the potential benefits these tools can deliver for patients and providers alike. Healthcare providers seeking to harness the potential benefits of digital diagnostics may consider evaluating these tools and their corresponding use cases in a systematic and structured manner to ensure optimal capital deployment, resource utilization, and, ultimately, patient outcomes-or clinical utility. We propose several guiding themes when using clinical utility to curate digital diagnostics.

Unlocking the Value: Quantifying the Return on Investment of Hospital Artificial Intelligence.

PURPOSE: A comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)-powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI. METHODS: A calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers. RESULTS: In the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients. CONCLUSIONS: The authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management-accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.

Performance of Supplemental Imaging Modalities for Breast Cancer in Women With Dense Breasts: Findings From an Umbrella Review and Primary Studies Analysis.

Breast cancer screening performance of supplemental imaging modalities by breast density and breast cancer risk has not been widely studied, and the optimal choice of modality for women with dense breasts remains unclear in clinical practice and guidelines. This systematic review aimed to assess breast cancer screening performance of supplemental imaging modalities for women with dense breasts, by breast cancer risk. Systematic reviews (SRs) in 2000 to 2021, and primary studies in 2019 to 2021, on outcomes of supplemental screening modalities (digital breast tomography [DBT], MRI (full/abbreviated protocol), contrast enhanced mammography (CEM), ultrasound (hand-held [HHUS]/automated [ABUS]) in women with dense breasts (BI-RADS C&D) were identified. None of the SRs analyzed outcomes by cancer risk. Meta-analysis of the primary studies was not feasible due to lack of studies (MRI, CEM, DBT) or methodological heterogeneity (ultrasound); therefore, findings were summarized narratively. For average risk, a single MRI trial reported a superior screening performance (higher cancer detection rate [CDR] and lower interval cancer rate [ICR]) compared to HHUS, ABUS and DBT. For intermediate risk, ultrasound was the only modality assessed, but accuracy estimates ranged widely. For mixed risk, a single CEM study reported the highest CDR, but included a high proportion of women with intermediate risk. This systematic review does not allow a complete comparison of supplemental screening modalities for dense breast populations by breast cancer risk. However, the data suggest that MRI and CEM might generally offer superior screening performance versus other modalities. Further studies of screening modalities are urgently required.

Economic evaluation of supplemental breast cancer screening modalities to mammography or digital breast tomosynthesis in women with heterogeneously and extremely dense breasts and average or intermediate breast cancer risk in US healthcare.

OBJECTIVE: To evaluate the cost-effectiveness of supplemental breast imaging modalities for women with heterogeneously and extremely dense breasts and average or intermediate risk of breast cancer (BC) in the USA, and analyze capacity requirements for supplemental magnetic resonance imaging (MRI) and contrast-enhanced mammography (CEM). METHODS: Clinical and economic outcomes for supplemental imaging modalities including full- and abbreviated-protocol MRI (Fp-MRI, Ab-MRI), CEM, and ultrasound (U/S) as add-on to x-ray mammography (XM) or digital breast tomosynthesis (DBT), were compared to XM or DBT alone, in a decision tree linked to a Markov chain validated by comparison with a microsimulation analysis. A Delphi panel supplemented model input parameters from the literature. A capacity model evaluated the number of additional daily scans and scanners required for Fp-MRI and CEM. RESULTS: Compared to XM or DBT alone, all supplemental imaging protocols were cost-effective. Both Fp- and Ab-MRI, and to a lesser extent CEM and U/S, yielded superior clinical outcomes to XM or DBT. Compared to XM alone, U/S and Ab-MRI had the lowest incremental cost-effectiveness ratios (ICER). For U/S, the ICER was $23,394 for the average-risk population and $13,241 for the intermediate-risk population. For CEM, the ICER was $38,423 and $23,772, respectively. For the extremely dense subpopulation with intermediate risk, supplemental screening requirements could be accommodated by conducting one Fp-MRI scan per day per existing general scanner. CONCLUSIONS: While ultrasound had the lowest ICER, MRI and CEM demonstrated the best clinical outcomes, compared to XM or DBT alone for women with dense breasts and intermediate and high risk. Existing MRI scanner capacity has the potential to meet most of the supplemental screening needs of this population.