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Background Quality measurement has become a priority for national healthcare reform, and valid measures are necessary to discriminate hospital performance and support value‐based healthcare delivery. The Commission on Cancer (CoC) is the largest cancer‐specific accreditor of hospital quality in the United States and has implemented Quality of Care Measures to evaluate cancer care delivery. However, none has been formally tested as a valid metric for assessing hospital performance based on actual patient outcomes. Methods Eligibility and compliance with the Quality of Care Measures are reported within the National Cancer Database, which also captures data for robust patient‐level risk adjustment. Hospital‐level compliance was calculated for the core measures, and the association with patient survival was tested using Cox regression. Results Seven hundred sixty‐eight thousand nine hundred sixty‐nine unique cancer cases were included from 1323 facilities. Increasing hospital‐level compliance was associated with improved survival for only two measures, including a 35% reduced risk of mortality for the gastric cancer measure G15RLN (HR 0.65, 95% CI 0.58–0.72) and a 19% reduced risk of mortality for the colon cancer measure 12RLN (HR 0.81, 95% CI 0.77–0.85). For the lung cancer measure LNoSurg, increasing compliance was paradoxically associated with an increased risk of mortality (HR 1.14, 95% CI 1.08–1.20). For the remaining measures, hospital‐level compliance demonstrated no consistent association with patient survival. Conclusion Hospital‐level compliance with the CoC’s Quality of Care Measures is not uniformly aligned with patient survival. In their current form, these measures do not reliably discriminate hospital performance and are limited as a tool for value‐based healthcare delivery., The Commission on Cancer is the largest cancer‐specific accreditor of hospital quality in the United States and has implemented Quality of Care Measures to evaluate quality cancer care delivery. This study represents the first comprehensive evaluation of the Quality of Care Measures as valid metrics for assessing hospital performance based on actual patient outcomes.

EXECUTIVE SUMMARY Accountable care organizations (ACOs) need confidence in their return on investment to implement changes in care delivery that prioritize seriously ill and high-cost Medicare beneficiaries. The objective of this study was to characterize spending on seriously ill beneficiaries in ACOs with Medicare Shared Savings Program (MSSP) contracts and the association of spending with ACO shared savings. The population included Medicare fee-for-service beneficiaries identified with serious illness (N = 2,109,573) using the Medicare Master Beneficiary Summary File for 100% of ACO-attributed beneficiaries linked to MSSP beneficiary files (2014-2016). Lower spending for seriously ill Medicare beneficiaries and risk-bearing contracts in ACOs were associated with achieving ACO shared savings in the MSSP. For most ACOs, the seriously ill contribute approximately half of the spending and constitute 8%-13% of the attributed population. Patient and geographic (county) factors explained $2,329 of the observed difference in per beneficiary per year spending on seriously ill beneficiaries between high- and low-spending ACOs. The remaining $12,536 may indicate variation as a result of potentially modifiable factors. Consequently, if 10% of attributed beneficiaries were seriously ill, an ACO that moved from the worst to the best quartile of per capita serious illness spending could realize a reduction of $1,200 per beneficiary per year for the ACO population overall. Though the prevalence and case mix of seriously ill populations vary across ACOs, this association suggests that care provided for seriously ill patients is an important consideration for ACOs to achieve MSSP shared savings.

Objectives Since 2019, the Medicare Shared Savings Program (MSSP) has allowed accountable care organizations (ACOs) to choose either retrospectively or prospectively attributed ACO populations. To understand how ACOs' choice of attribution method affects incentives for care among seriously ill Medicare beneficiaries, this study compares beneficiary characteristics and Medicare per capita expenditures between prospective and retrospective ACO populations. Study design This retrospective, cross-sectional analysis describes survival, patient characteristics, and Medicare spending for Medicare fee-for-service beneficiaries identified with serious illness (n = 1,600,629) using 100% Medicare Master Beneficiary Summary and MSSP beneficiary files (2014-2016). Methods We used generalized linear models with ACO and year fixed effects to estimate the average within-ACO difference between potential retrospective and prospective ACO populations. Results Dying in the first 90 days of the performance year was associated with reduced odds of retrospective ACO attribution (odds ratio [OR], 0.24; 95% CI, 0.24-0.25) relative to beneficiaries surviving 270 days or longer. Similarly, hospice use was associated with reduced odds of retrospective assignment (OR, 0.80; 95% CI, 0.79-0.80). Among ACOs that did not achieve shared savings, average per capita Medicare expenditures (after truncation) were $2459 (95% CI, $2192-$2725) higher for prospective vs retrospective ACO populations. The difference was $834 (95% CI, $402-$1266) greater per capita among ACOs that achieved shared savings. Conclusions The difference in survival and spending for ACO populations captured by prospective vs retrospective attribution methods means that ACOs may need to employ different care management strategies to improve performance depending on their attribution method.

The Affordable Care Act played a major role in transitioning American health care away from fee-for-service payment. We explore the spread of payment reforms since the implementation of the ACA, both nationally and in North Carolina; the corresponding effects on health care costs and quality; and further steps needed to achieve greater transformation.

Care for people living with serious illness is suboptimal for many reasons, including underpayment for key services (such as care coordination and social supports) in fee-for-service reimbursement....

The ability of accountable care organizations (ACOs) to continue reducing costs and improving quality depends on understanding what affects their survival. We examined such factors for survival in the Medicare Shared Savings Program (MSSP) of 624 ACOs between performance years 2013 and 2017 (1,849 ACO-years). Overall, ACO exits from the MSSP decreased after ACOs' third year. Shared-savings bonus payment achievement, more care coordination, higher financial performance benchmarks, market-level Medicare cost growth, lower-risk patients, and contracts with upside-only risk were associated with longer survival. Quality scores, postacute care spending, organizational traits, and most market-context characteristics had no significant association with survival, which indicates that diverse organizations and markets can be successful. Put in context with the recently finalized MSSP rule from December 2018, our findings suggest that while new flexibilities for low-revenue ACOs likely reduce uncertainty for some, MSSP ACOs may need more than the new period of one to three years to prepare for downside risk. Policy makers should offer more support to ACOs (especially those with higher-risk patients) for building organizational competencies and should consider how benchmarking policy can fairly assess ACOs from regions with differing levels of cost growth.

This cross-sectional study evaluates how policy changes for Medicare Advantage plans affected supplemental benefit availability across geographic social vulnerability, membership diversity, and rebates from 2019 to 2022.

Stroke is one of the leading causes of morbidity and mortality in the United States. While age-adjusted stroke mortality was falling, it has leveled off in recent years due in part to advances in medical technology, health care options, and population health interventions. In addition to adverse trends in stroke-related morbidity and mortality across the broader population, there are sociodemographic inequities in stroke risk. These challenges can be addressed by focusing on predicting and preventing modifiable upstream risk factors associated with stroke, but there is a need to develop a practical framework that health care organizations can use to accomplish this task across diverse settings. Accordingly, this article describes the efforts and vision of the multi-stakeholder Predict & Prevent Learning Collaborative of the Value in Healthcare Initiative, a collaboration of the American Heart Association and the Robert J. Margolis, MD, Center for Health Policy at Duke University. This article presents a framework of a potential upstream stroke prevention program with evidence-based implementation strategies for predicting, preventing, and managing stroke risk factors. It is meant to complement existing primary stroke prevention guidelines by identifying frontier strategies that can address gaps in knowledge or implementation. After considering a variety of upstream medical or behavioral risk factors, the group identified 2 risk factors with substantial direct links to stroke for focusing the framework: hypertension and atrial fibrillation. This article also highlights barriers to implementing program components into clinical practice and presents implementation strategies to overcome those barriers. A particular focus was identifying those strategies that could be implemented across many settings, especially lower-resource practices and community-based enterprises representing broad social, economic, and geographic diversity. The practical framework is designed to provide clinicians and health systems with effective upstream stroke prevention strategies that encourage scalability while allowing customization for their local context.

Utilization management strategies, including prior authorization, are commonly used to facilitate safe and guideline-adherent provision of new, individualized, and potentially costly cardiovascular therapies. However, as currently deployed, these approaches encumber multiple stakeholders. Patients are discouraged by barriers to appropriate access; clinicians are frustrated by the time, money, and resources required for prior authorizations, the frequent rejections, and the perception of being excluded from the decision-making process; and payers are weary of the intensive effort to design and administer increasingly complex prior authorization systems to balance value and appropriate use of these treatments. These issues highlight an opportunity to collectively reimagine utilization management as a transparent and collaborative system. This would benefit the entire healthcare ecosystem, especially in light of the shift to value-based payment. This article describes the efforts and vision of the multistakeholder Prior Authorization Learning Collaborative of the Value in Healthcare Initiative, a partnership between the American Heart Association and the Robert J. Margolis, MD, Center for Health Policy at Duke University. We outline how healthcare organizations can take greater utilization management responsibility under value-based contracting, especially under different state policies and local contexts. Even with reduced payer-mandated prior authorization in these arrangements, payers and healthcare organizations will have a continued shared need for utilization management. We present options for streamlining these programs, such as gold carding and electronic and automated prior authorization processes. Throughout the article, we weave in examples from cardiovascular care when possible. Although reimagining prior authorization requires collective action by all stakeholders, it may significantly reduce administrative burden for clinicians and payers while improving outcomes for patients.

The pipeline of new cardiovascular drugs is relatively limited compared with many other clinical areas. Challenges causing lagging drug innovation include the duration and expense of cardiovascular clinical trials needed for regulatory evaluation and approvals, which generally must demonstrate noninferiority to existing standards of care and measure longer-term outcomes. By comparison, there has been substantial progress in cardiovascular device innovation. There has also been progress in cardiovascular trial participation equity in recent years, especially among women, due in part to important efforts by Food and Drug Administration, National Institutes of Health, American Heart Association, and others. Yet women and especially racial and ethnic minority populations remain underrepresented in cardiovascular trials, indicating much work ahead to continue recent success. Given these challenges and opportunities, the multistakeholder Partnering with Regulators Learning Collaborative of the Value in Healthcare Initiative, a collaboration of the American Heart Association and the Robert J. Margolis, MD, Center for Health Policy at Duke University, identified how to improve the evidence generation process for cardiovascular drugs and devices. Drawing on a series of meetings, literature reviews, and analyses of regulatory options, the Collaborative makes recommendations across four identified areas for improvement. First, we offer strategies to enhance patient engagement in trial design, convenient participation, and meaningful end points and outcomes to improve patient recruitment and retention (major expenses in clinical trials). Second, new digital technologies expand the potential for real-world evidence to streamline data collection and reduce cost and time of trials. However, technical challenges must be overcome to routinely leverage real-world data, including standardizing data, managing data quality, understanding data comparability, and ensuring real-world evidence does not worsen inequities. Third, as trials are driven by evidence needs of regulators and payers, we recommend ways to improve their collaboration in trial design to streamline and standardize efficient and innovative trials, reducing costs and delays. Finally, we discuss creative ways to expand the minuscule proportion of sites involved in cardiovascular evidence generation and medical product development. These actions, paired with continued policy research into better ways to pay for and equitably develop therapies, will help reduce the cost and complexity of drug and device research, development, and trials.

Medicaid programs are increasingly adopting incentive programs to improve health behaviors among beneficiaries. There is limited evidence on what incentives are being offered to Medicaid beneficiaries, how programs are engaging beneficiaries, and how programs are evaluated. In 2017-18 we synthesized available information on these programs and interviewed eighty policy stakeholders to identify the rationale behind key program design decisions and stakeholders' recommendations for beneficiary engagement and program evaluation. Key underlying program rationales included improving the use of preventive services and promoting personal responsibility. Beneficiary engagement strategies emphasized meeting members where they are and offering prizes or services customized for certain groups. Stakeholders recommended collaborating with external evaluators to design and conduct robust evaluations of incentive programs. Finally, stakeholders recommended aligning beneficiary incentives with provider incentives and other payment reforms through the use of common meaningful measures to streamline program evaluation.

Heart failure (HF) is a leading cause of hospitalizations and readmissions in the United States. Particularly among the elderly, its prevalence and costs continue to rise, making it a significant population health issue. Despite tremendous progress in improving HF care and examples of innovation in care redesign, the quality of HF care varies greatly across the country. One major challenge underpinning these issues is the current payment system, which is largely based on fee-for-service reimbursement, leads to uncoordinated, fragmented, and low-quality HF care. While the payment landscape is changing, with an increasing proportion of all healthcare dollars flowing through value-based payment models, no longitudinal models currently focus on chronic HF care. Episode-based payment models for HF hospitalization have yielded limited success and have little ability to prevent early chronic disease from progressing to later stages. The available literature suggests that primary care-based longitudinal payment models have indirectly improved HF care quality and cardiovascular care costs, but these models are not focused on addressing patients’ longitudinal chronic disease needs. This article describes the efforts and vision of the multi-stakeholder Value-Based Models Learning Collaborative of The Value in Healthcare Initiative, a collaboration of the American Heart Association and the Robert J. Margolis, MD, Center for Health Policy at Duke University. The Learning Collaborative developed a framework for a HF value-based payment model with a longitudinal focus on disease management (to reduce adverse clinical outcomes and disease progression among patients with stage C HF) and prevention (an optional track to prevent high-risk stage B pre-HF from progressing to stage C). The model is designed to be compatible with prevalent payment models and reforms being implemented today. Barriers to success and strategies for implementation to aid payers, regulators, clinicians, and others in developing a pilot are discussed.

This cross-sectional study examines the uptake of several newly allowable Medicare Advantage benefits in 2021 as well as geographic differences in benefit offerings between rural and urban areas, plan penetration, and social vulnerability.

Radiologists play a critical role in helping the health care system achieve greater value. Unfortunately, today radiology is often judged by simple “checkbox” metrics, which neither directly reflect the value radiologists provide nor the outcomes they help drive. To change this system, first, we must attempt to better define the elusive term value and, then, quantify the value of imaging through more relevant and meaningful metrics that can be more directly correlated with outcomes. This framework can further improve radiology’s value by enhancing radiologists’ integration into the care team and their engagement with patients. With these improvements, we can maximize the value of imaging in the overall care of patients.

OBJECTIVES: To describe the landscape of Medicaid and the Children’s Health Insurance Program beneficiary incentive programs for child health and garner key stakeholder insights on incentive program rationale, child and family engagement, and program evaluation. METHODS: We identified beneficiary health incentive programs from 2005 to 2018 through a search of peer-reviewed and publicly available documents and through semistructured interviews with 80 key stakeholders (Medicaid and managed-care leadership, program evaluators, patient advocates, etc). This study highlights insights from 23 of these stakeholders with expertise on programs targeting child health ( RESULTS: We identified 82 child health-targeted beneficiary incentive programs in Medicaid and the Children’s Health Insurance Program. Programs most commonly incentivized well-child checks (n = 77), preventive screenings (n = 30), and chronic disease management (n = 30). All programs included financial incentives (eg, gift cards, premium incentives); some also offered incentive material prizes (n = 12; eg, car seats). Loss-framed incentives were uncommon (n = 1; eg, lost benefits) and strongly discouraged by stakeholders. Stakeholders suggested family engagement strategies including multigenerational incentives or incentives addressing social determinants of health. Regarding evaluation, stakeholders suggested incentivizing evidence-based preventive services (eg, vaccinations) rather than well-child check attendance, and considering proximal measures of child well-being (eg, school functioning). CONCLUSIONS: As the landscape of beneficiary incentive programs for child health evolves, policy makers have unique opportunities to leverage intergenerational and social approaches for family engagement and to more effectively increase and evaluate programs’ impact.

Background: The emergency department (ED) is a primary access point for hospital admissions/ readmissions. Recent analyses have found that state-level Medicaid expansion is associated with higher ED volumes and longer ED length of stay (LOS). Increased ED congestion has been proposed to lead to higher readmission rates, though this has not been empirically evaluated. We examined the association of Medicaid expansion with readmission rates for AMI and HF specifically given that these readmissions are prevalent, publicly reported, and subject to financial penalties. Methods: We utilized the Hospital Compare database to construct a panel of US acute care hospitals offering ED services over three time intervals: 10/01/2012-09/30/2013, before any Medicaid expansion effective coverage dates (“Period 1”); 10/1/2013-9/30/2014 and 10/1/2014-9/30/2015, after Medicaid expansion effective coverage dates (“Periods 2 and 3”). We used multivariate linear fixed-effects regression to evaluate the independent effect of Medicaid expansion on absolute unplanned 30-day case-mix adjusted readmission rates for AMI and HF. Fixed effects regression adjusts for time-invariant confounders by design and potential time-variant confounders were included as covariates, including year fixed effects to control for temporal variation. Standard errors were clustered on states. Sensitivity analysis was performed to evaluate the interaction between Medicaid expansion and ED LOS with respect to the primary outcome. Results: Our model examined 2,257 hospitals for AMI and 2,947 hospitals for HF. Overall, within-hospital 30-day unplanned readmission rates for AMI and HF decreased over the three defined periods. AMI readmission rates decreased by 1.37% (95% CI: -1.46% to -1.27%) from Period 1 to Period 3. HF readmission rates decreased by 1.07% (95% CI: -1.21% to -0.93%) from Period 1 to Period 3. All p-values were Medicaid expansion effective coverage dates did not affect within-hospital readmissions rates for AMI (ARR -0.09%; 95% CI: -0.22% to 0.04%; p-value 0.162) or HF (ARR -0.03%; 95% CI: -0.20 to 0.15%; p-value 0.770). A sensitivity analysis showed no interaction between Medicaid expansion and ED LOS with respect to the primary outcome (p value = 0.190 for AMI, p-value = 0.255 for HF). Conclusion: Among hospitals with publicly reported outcomes in the Hospital Compare database, readmission rates for AMI and HF decreased from 2012-2015. Medicaid expansion was not associated with significant within-hospital differences in 30-day readmission rates for AMI and HF. There was no interaction by ED LOS. Our results offer evidence to quell concerns that Medicaid expansion, by increasing ED LOS, may adversely impact unplanned readmission rates. Further studies are needed to evaluate factors affecting within-hospital readmission rates.

Rationale and Objectives Optimization studies for x-ray−based breast imaging systems using computer simulation can greatly benefit from a phantom capable of modeling varying anatomical variability across different patients. This study aimed to develop a three-dimensional phantom model with realistic and randomizable anatomical features. Materials and Methods A voxelized breast model was developed consisting of an outer layer of skin and subcutaneous fat, a mixture of glandular and adipose, stochastically generated ductal trees, masses, and microcalcifications. Randomized realization of the breast morphology provided a range of patient models. Compression models were included to represent the breast under various compression levels along different orientations. A Monte Carlo (MC) simulation code was adapted to simulate x-ray based imaging systems for the breast phantom. Simulated projections of the phantom at different angles were generated and reconstructed with iterative methods, simulating mammography, breast tomosynthesis, and computed tomography (CT) systems. Phantom dose maps were further generated for dosimetric evaluation. Results Region of interest comparisons of simulated and real mammograms showed strong similarities in terms of appearance and features. Noise-power spectra of simulated mammographic images demonstrated that the phantom provided target properties for anatomical backgrounds. Reconstructed tomosynthesis and CT images and dose maps provided corresponding data from a single breast enabling optimization studies. Dosimetry result provided insight into the dose distribution difference between modalities and compression levels. Conclusion The anthropomorphic breast phantom, combined with the MC simulation platform, generated a realistic model for a breast imaging system. The developed platform is expected to provide a versatile and powerful framework for optimizing volumetric breast imaging systems.

Ehsan Samei1,2,3,4, Robert S Saunders1, Cristian T Badea2,5, Ketan B Ghaghada2,5†, Laurence W Hedlund2,5, Yi Qi2,5, Hong Yuan6, RexC Bentley7, Srinivasan Mukundan Jr2,3,5*1CarlE Ravin Advanced Imaging Laboratories, 2Department of Radiology, 3Department of Biomedical Engineering, 4Department of Physics, 5Center for In Vivo Microscopy, 6Department of Radiation Oncology, 7Department of Pathology, Duke University, Durham, NC, USA; †Current address: School of Health Information Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA; *Current address: Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USAAbstract: A long circulating liposomal, nanoscale blood pool agent encapsulating traditional iodinated contrast agent (65 mg I/mL) was used for micro-computed tomography (CT) imaging of rats implanted with R3230AC mammary carcinoma. Three-dimensional vascular architecture of tumors was imaged at 100-micron isotropic resolution. The image data showed good qualitative correlation with pathologic findings. The approach holds promise for studying tumor angiogenesis and for evaluating anti-angiogenesis therapies.Keywords: molecular imaging, liposome, angiogenesis, breast cancer, small animal imaging, micro-CT

To assess, in a voxelized anthropomorphic breast phantom, how the conspicuity of breast masses and microcalcifications may be affected by applying reduced breast compression in tomosynthesis.A breast tomosynthesis system was modeled by using a Monte Carlo program and a voxelized anthropomorphic breast phantom. The Monte Carlo program created simulated tomosynthesis projection images, which were reconstructed by using filtered back-projection software. Reconstructed images were analyzed for mass and microcalcification conspicuity, or the ratio of the lesion contrast to the anatomic and quantum noise surrounding the lesion. This analysis was performed at two compression levels (standard and 12.5% reduction) and for two breast compression thicknesses (4 and 6 cm). The change in conspicuity was analyzed for significance by using a bootstrap method and a paired Student t test.While keeping the glandular radiation dose constant with respective standard and reduced compression levels, the mean mass conspicuities were 1.39 +/- 0.15 (standard error of the mean) and 1.46 +/- 0.22 for a 4-cm breast compression phantom and 1.26 +/- 0.15 and 1.22 +/- 0.20 for a 6-cm breast phantom, and the mean microcalcification conspicuities were 16.2 +/- 2.87 and 18.6 +/- 2.63 for a 4-cm breast phantom and 11.4 +/- 1.11 and 10.6 +/- 1.18 for a 6-cm breast compression phantom.For constant glandular dose, mass and microcalcification conspicuity remained approximately constant with decreased compression. Constant conspicuity implies that reduced compression would have a minimal effect on radiologists' performance, which suggests that there is justification for a measured reduction of breast compression for breast tomosynthesis, increasing the comfort of women undergoing the examination.

Rationale and Objectives Correlation imaging (CI) is a form of multiprojection imaging in which multiple images of a patient are acquired from slightly different angles. Information from these images is combined to make the final diagnosis. A critical factor affecting the performance of CI is its data acquisition scheme, because nonoptimized acquisition may distort pathologic indicators. The authors describe a computer-aided detection (CADe) methodology to optimize the acquisition scheme of CI for superior diagnostic accuracy. Materials and Methods Images from 106 subjects were used. For each subject, 25 angular projections of a single breast were acquired. Projection images were supplemented with a simulated 3-mm three-dimensional lesion. Each projection was then processed using a traditional CADe algorithm at high sensitivity, followed by the reduction of false-positives by combining the geometric correlation information available from the multiple images. The performance of the CI system was determined in terms of free-response receiver-operating characteristic curves and the areas under receiver-operating characteristic curves. For optimization, the components of acquisition, such as the number of projections and their angular span, were systematically changed to investigate which of the many possible combinations maximized the obtainable CADe sensitivity and specificity. Results The performance of the CI system was improved by increasing the angular span. Increasing the number of angular projections beyond a certain number did not improve performance. Maximum performance was obtained between 7 and 10 projections spanning a maximum angular arc of 45°. Conclusion The findings suggest the existence of an optimum acquisition scheme for CI of the breast. CADe results confirmed earlier predictions on the basis of observer models. An optimized CI system may be an important diagnostic tool for improved breast cancer detection.

This study analyzed how the inherent quality of diagnostic information in digital mammography could be affected by breast compression. A digital mammography system was modeled using a Monte Carlo algorithm based on the Penelope program, which has been successfully used to model several medical imaging systems. First, the Monte Carlo program was validated against previous measurements and simulations. Once validated, the Monte Carlo software modeled a digital mammography system by tracking photons through a voxelized software breast phantom, containing anatomical structures and breast masses, and following photons until they were absorbed by a selenium-based flat-panel detector. Simulations were performed for two compression conditions (standard compression and 12.5% reduced compression) and three photon flux conditions (constant flux, constant detector signal, and constant glandular dose). The results showed that reduced compression led to higher scatter fractions, as expected. For the constant photon flux condition, decreased compression also reduced glandular dose. For constant glandular dose, the SdNR for a 4 cm breast was 0.60 +/- 0.11 and 0.62 +/- 0.11 under standard and reduced compressions, respectively. For the 6 cm case with constant glandular dose, the SdNR was 0.50 +/- 0.11 and 0.49 +/- 0.10 under standard and reduced compressions, respectively. The results suggest that if a particular imaging system can handle an approximately 10% increase in total tube output and 10% decrease in detector signal, breast compression can be reduced by about 12% in terms of breast thickness with little impact on image quality or dose.

The effect of reduction in dose levels normally used in mammographic screening procedures on the detection of breast lesions were analyzed. Four types of breast lesions were simulated and inserted into clinically-acquired digital mammograms. Dose reduction by 50% and 75% of the original clinically-relevant exposure levels were simulated by adding corresponding simulated noise into the original mammograms. The mammograms were converted into luminance values corresponding to those displayed on a clinical soft-copy display station and subsequently analyzed by Laguerre-Gauss and Gabor channelized Hotelling observer models for differences in detectability performance with reduction in radiation dose. Performance was measured under a signal known exactly but variable detection task paradigm in terms of receiver operating characteristics (ROC) curves and area under the ROC curves. The results suggested that luminance mapping of digital mammograms affects performance of model observers. Reduction in dose levels by 50% lowered the detectability of masses with borderline statistical significance. Dose reduction did not have a statistically significant effect on detection of microcalcifications. The model results indicate that there is room for optimization of dose level in mammographic screening procedures.

The purpose of this study was to evaluate the effect of dose reduction in digital mammography on the detection of two lesion types-malignant masses and clusters of microcalcifications. Two free-response observer studies were performed-one for each lesion type. Ninety screening images were retrospectively selected; each image was originally acquired under automatic exposure conditions, corresponding to an average glandular dose of 1.3 mGy for a standard breast (50 mm compressed breast thickness with 50% glandularity). For each study, one to three simulated lesions were added to each of 40 images (abnormals) while 50 were kept without lesions (normals). Two levels of simulated system noise were added to the images yielding two new image sets, corresponding to simulated dose levels of 50% and 30% of the original images (100%). The manufacturer's standard display processing was subsequently applied to all images. Four radiologists experienced in mammography evaluated the images by searching for lesions and marking and assigning confidence levels to suspicious regions. The search data were analyzed using jackknife free-response (JAFROC) methodology. For the detection of masses, the mean figure-of-merit (FOM) averaged over all readers was 0.74, 0.71, and 0.68 corresponding to dose levels of 100%, 50%, and 30%, respectively. These values were not statistically different from each other (F = 1.67, p = 0.19) but showed a decreasing trend. In contrast, in the microcalcification study the mean FOM was 0.93, 0.67, and 0.38 for the same dose levels and these values were all significantly different from each other (F = 109.84, p < 0.000 1). The results indicate that lowering the present dose level by a factor of two compromised the detection of microcalcifications but had a weaker effect on mass detection.

Rationale and Objectives To compare two display technologies, cathode ray tube (CRT) and liquid crystal display (LCD), in terms of diagnostic accuracy for several common clinical tasks in digital mammography. Materials and Methods Simulated masses and microcalcifications were inserted into normal digital mammograms to produce an image set of 400 images. Images were viewed on one CRT and one LCD medical-quality display device by five experienced breast-imaging radiologists who rated the images using a categorical rating paradigm. The observer data were analyzed to determine overall classification accuracy, overall lesion detection accuracy, and accuracy for four specific diagnostic tasks: detection of benign masses, malignant masses, and microcalcifications, and discrimination of benign and malignant masses. Results Radiologists had similar overall classification accuracy (LCD: 0.83 ± 0.01, CRT: 0.82 ± 0.01) and lesion detection accuracy (LCD: 0.87 ± 0.01, CRT: 0.85 ± 0.01) on both displays. The difference in accuracy between LCD and CRT for the detection of benign masses, malignant masses, and microcalcifications, and discrimination of benign and malignant masses was –0.019 ± 0.009, 0.020 ± 0.008, 0.012 ± 0.013, and 0.0094 ± 0.011, respectively. Overall, the two displays did not exhibit any statistically significant difference ( P > .05). Conclusion This study explored the suitability of two different soft-copy displays for the viewing of mammographic images. It found that LCD and CRT displays offer similar clinical utility for mammographic tasks.

Rationale and Objectives This study presents a method for generating breast masses and microcalcifications in mammography via simulation. This simulation method allows for the creation of large image datasets with particular lesions, which may serve as a useful tool for perception studies measuring imaging system performance. Materials and Methods The study first characterized the radiographic appearance of both masses and microcalcifications, examining the following five properties: contrast, edge gradient profile of masses, edge characteristics of masses, shapes of individual microcalcifications, and shapes of microcalcification distributions. The characterization results then guided the development of routines that created simulated masses and microcalcifications. The quality of the simulations was verified by experienced breast imaging radiologists who evaluated simulated and real lesions and rated whether a given lesion had a realistic appearance. Results The radiologists rated real and simulated lesions to have similarly realistic appearances. Using receiver operating characteristic analysis to characterize the degree of similarity, the results showed an Az of 0.68 ± 0.07 for benign masses, 0.65 ± 0.07 for malignant masses, and 0.62 ± 0.07 for microcalcifications, thus showing notable overlap in the simulated and real lesion ratings. Conclusion This research introduced a new approach for simulating breast masses and microcalcifications that relied on anatomic characteristics measured from real lesions. Results from an observer performance experiment indicate that our simulation routine produced realistic simulations of masses and microcalcifications as judged by expert radiologists.

The purpose of this study was to measure experimentally the physical performance of a prototype mammographicimager based on a direct detection, flat-panel array design employing an amorphous selenium converter with 70 μm pixels. The system was characterized for two different anode types, a molybdenum target with molybdenum filtration ( Mo ∕ Mo ) and a tungsten target with rhodium filtration ( W ∕ Rh ) , at two different energies, 28 and 35 kVp, with approximately 2 mm added aluminum filtration. To measure the resolution, the presampled modulation transfer function(MTF) was measured using an edge method. The normalized noise power spectrum (NNPS) was measured by two-dimensional Fourier analysis of uniformly exposed mammograms. The detective quantum efficiencies (DQEs) were computed from the MTFs, the NNPSs, and theoretical ideal signal to noise ratios. The MTF was found to be close to its ideal limit and reached 0.2 at 11.8 mm − 1 and 0.1 at 14.1 mm − 1 for images acquired at an RQA-M2 technique ( Mo ∕ Mo anode, 28 kVp, 2 mm Al). Using a tungsten technique (MW2; W ∕ Rh anode, 28 kVp, 2 mm Al), the MTF went to 0.2 at 11.2 mm − 1 and to 0.1 at 13.3 mm − 1 . The DQE reached a maximum value of 54% at 1.35 mm − 1 for the RQA-M2 technique at 1.6 μ C ∕ kg and achieved a peak value of 64% at 1.75 mm − 1 for the tungsten technique (MW2) at 1.9 μ C ∕ kg . Nevertheless, the DQE showed strong exposure and frequency dependencies. The results indicated that the detector offered high MTFs and DQEs, but structured noise effects may require improved calibration before clinical implementation.

Our purpose in this study was to evaluate the fundamental image quality characteristics of a new slot-scan digital chest radiographysystem (ThoraScan, Delft Imaging Systems/Nucletron, Veenendaal, The Netherlands). The linearity of the system was measured over a wide exposure range at 90, 117, and 140 kVp with added Al filtration. System uniformity and reproducibility were established with an analysis of images from repeated exposures. The modulation transfer function(MTF) was evaluated using an established edge method. The noise power spectrum (NPS) and the detective quantum efficiency (DQE) of the system were evaluated at the three kilo-voltages over a range of exposures. Scatter fraction (SF) measurements were made using a posterior beam stop method and a geometrical chest phantom. The system demonstrated excellent linearity, but some structured nonuniformities. The 0.1 MTF values occurred between 3.3–3.5 mm −1 . The DQE(0.15) and DQE(2.5) were 0.21 and 0.07 at 90 kVp, 0.18 and 0.05 at 117 kVp, and 0.16 and 0.03 at 140 kVp, respectively. The system exhibited remarkably lower SFs compared to conventional full-field systems with anti-scatter grid, measuring 0.13 in the lungs and 0.43 in the mediastinum. The findings indicated that the slot-scan design provides marked scatter reduction leading to high effective DQE ( DQE eff ) of the system and reduced patient dose required to achieve high image quality.

A new computer simulation approach is presented that is capable of modeling several varieties of digital radiographic systems by their image quality characteristics. In this approach, the resolution and noise characteristics of ideal supersampled input images are modified according to input modulation transfer functions (MTFs) and noise power spectra (NPS). The modification process is separated into two routines-one for modification of the resolution and another for modification of the noise characteristics of the input image. The resolution modification routine blurs the input image by applying a frequency filter described by the input MTF. The resulting blurred image is then reduced to its final size to account for the sampling process of the digital system. The noise modification routine creates colored noise by filtering the frequency components of a white noise spectrum according to the input noise power. This noise is then applied to the image by a moving region of interest to account for variations in noise due to differences in attenuation. In order to evaluate the efficacy of the modification routines, additional routines were developed to assess the resolution and noise of digital images. The MTFs measured from the output images of the resolution modification routine were within 3% of the input MTF The NPS measured from the output images of the noise modification routine were within 2% of the input NPS. The findings indicate that the developed modification routines provide a good means of simulating the resolution and noise characteristics of digital radiographic systems for optimization or processing purposes.

Objective: Overlapping anatomy in projection mammography can degrade the detection of mammographic lesions and further create mass-like features that can confound computer-aided detection (CADe) systems and degrade their performance. Tri-plane correlation imaging (TCI) aims to reduce this impact by incorporating three projection images into CADe using a geometric correlation scheme. This study aimed to assess the efficacy of TCI for breast cancer detection. Materials and Methods: An image set of 198 human subjects was used from a breast tomosynthesis database. Each case included 25 projections acquired within a 45 degree angular span at an approximate total glandular dose equal to that of two-view mammography. Triplet images were derived from each case (the central projection along with two symmetrical images) and analyzed by two independent CADe programs. The CADe results from the three projections from each program were combined using two TCI correlation rules based on unanimous vote and majority vote. The findings were analyzed in terms of true positives (TP), sensitivity, false positives per breast volume (FP), and a positive predictive index combining the two figures (i.e., TP/(TP+FP)). Results: For the first CADe program, the TCI scheme using the majority voting rule improved sensitivity by 40% while maintaining specificity, leading to a 40% improvement in the PPI performance. For the second, a higher-sensitivity/lower-specificity CADe program, the TCI using the majority voting rule improved sensitivity by 10% while increasing false positives per breast volume by 3%, leading to an improvement of 8% in the PPI performance. The unanimous voting rule led to notably lower performance for both programs. For both CADe programs, an angular separation of six degrees (± 3 degrees) proved optimal. Conclusions: TCI was able to improve sensitivity over single projection imaging while maintaining specificity for both CADe programs, which suggests its potential as a supplement to standard mammography and as a complementary module to existing CADe algorithms.

Ab-1 SUMMARY S-1 The Imperatives S-3 The Vision S-11 The Path S-13 Actions for Continuous Learning, Best Care, and Lower Costs S-19 Achieving the Vision S-27 References S-29 PART I: THE IMPERATIVES

While considerable uncertainty exists about the future of health care reform in the United States, interest continues to grow in benefit and payment reforms that support models of care that control costs and improve quality. Despite the large number of reforms being implemented, such as bundled episode payments and per-beneficiary payments to primary care groups and integrated health care organizations, evidence on the effectiveness of these “value-based” financing reforms is mixed and limited,1 leading to ongoing questions about how such reforms affect outcomes and costs in various contexts. This makes it difficult to know which initiatives should be scaled up and which need further refinement or replacement to support a more efficient, high-value health care system.

To explore the issues and opportunities central to lowering health-care expenditures in the United States, the IOM Roundtable on Value & Science-Driven Health Care convened the four-part series The Healthcare Imperative: Lowering Costs and Improving Outcomes in May, July, September, and December of 2009 at the National Academies in Washington, DC. These meetings were part of the Roundtable’s Learning Health System series. The series aimed to gather stakeholders in a trusted venue to engage the issues and concerns needed to facilitate the development of a health-care system that not only delivers best practices and adds value with each clinical encounter, but adds seamlessly to the knowledge base for health improvement. Motivated by the proposition noted above of reducing per capita health spending in the country by 10 percent within 10 years without compromising health status, quality of care, or innovation, the meeting objectives included: characterizing and discussing the major causes of excess healthcare spending, waste, and inefficiency in the United States; considering the strategies that might reduce per capita health spending in the United States while improving health outcomes; and exploring policy options relevant to those strategies.

Ehsan Samei1,2,3,4, Robert S Saunders1, Cristian T Badea2,5, Ketan B Ghaghada2,5†, Laurence W Hedlund2,5, Yi Qi2,5, Hong Yuan6, RexC Bentley7, Srinivasan Mukundan Jr2,3,5*1CarlE Ravin Advanced Imaging Laboratories, 2Department of Radiology, 3Department of Biomedical Engineering, 4Department of Physics, 5Center for In Vivo Microscopy, 6Department of Radiation Oncology, 7Department of Pathology, Duke University, Durham, NC, USA; †Current address: School of Health Information Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA; *Current address: Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USAAbstract: A long circulating liposomal, nanoscale blood pool agent encapsulating traditional iodinated contrast agent (65 mg I/mL) was used for micro-computed tomography (CT) imaging of rats implanted with R3230AC mammary carcinoma. Three-dimensional vascular architecture of tumors was imaged at 100-micron isotropic resolution. The image data showed good qualitative correlation with pathologic findings. The approach holds promise for studying tumor angiogenesis and for evaluating anti-angiogenesis therapies.Keywords: molecular imaging, liposome, angiogenesis, breast cancer, small animal imaging, micro-CT

We studied the influence of signal variability on human and model observers for detection tasks with realistic simulated masses superimposed on real patient mammographic backgrounds and synthesized mammographic backgrounds (clustered lumpy backgrounds, CLB). Results under the signal-known-exactly (SKE) paradigm were compared with signal-known-statistically (SKS) tasks for which the observers did not have prior knowledge of the shape or size of the signal. Human observers' performance did not vary significantly when benign masses were superimposed on real images or on CLB. Uncertainty and variability in signal shape did not degrade human performance significantly compared with the SKE task, while variability in signal size did. Implementation of appropriate internal noise components allowed the fit of model observers to human performance.

Dual-energy contrast-enhanced breast tomosynthesis has been proposed as a technique to improve the detection of early-stage cancer in young, high-risk women. This study focused on optimizing this technique using computer simulations. The computer simulation used analytical calculations to optimize the signal difference to noise ratio (SdNR) of resulting images from such a technique at constant dose. The optimization included the optimal radiographic technique, optimal distribution of dose between the two single-energy projection images, and the optimal weighting factor for the dual energy subtraction. Importantly, the SdNR included both anatomical and quantum noise sources, as dual energy imaging reduces anatomical noise at the expense of increases in quantum noise. Assuming a tungsten anode, the maximum SdNR at constant dose was achieved for a high energy beam at 49 kVp with 92.5 μm copper filtration and a low energy beam at 49 kVp with 95 μm tin filtration. These analytical calculations were followed by Monte Carlo simulations that included the effects of scattered radiation and detector properties. Finally, the feasibility of this technique was tested in a small animal imaging experiment using a novel iodinated liposomal contrast agent. The results illustrated the utility of dual energy imaging and determined the optimal acquisition parameters for this technique. This work was supported in part by grants from the Komen Foundation (PDF55806), the Cancer Research and Prevention Foundation, and the NIH (NCI R21 CA124584-01). CIVM is a NCRR/NCI National Resource under P41-05959/U24-CA092656.

Due to the high prevalence of breast cancer among women, much is being done to detect breast cancer earlier and more accurately. In current clinical practice, the most widely-used mode of breast imaging is mammography. Its main advantages are high sensitivity and low patient dose, although it is still merely a two-dimensional projection of a three-dimensional object. In digital breast tomosynthesis, a three-dimensional image of the breast can be reconstructed, but x-ray projection images of the breast are taken over a limited angular span. However, the breast tomosynthesis device itself is more similar to a digital mammography system and thus is a feasible replacement for mammography. Because of the angular undersampling in breast tomosynthesis, the reconstructed images are not considered quantitative, so a worthwhile question to answer would be whether the voxel values (VVs) in breast tomosynthesis images can be made to indicate tissue type as Hounsfield units do in CT. through some image processing scheme. To investigate this, simple phantoms were imaged consisting of layers of uniform, tissue-equivalent plastic for the background sandwiching a layer of interest containing multiple, small cuboids of tissue-equivalent plastic. After analyzing the reconstructed tomosynthesis images, it was found that the VV in each lesion increases linearly with tissue glandularity. However, for the two different x-ray tube energies and for the two different beam exposure levels tested, the trend-lines all have different slopes and y-intercepts. Thus, breast tomosynthesis has a definite potential to be quantitative, and it would be worthwhile to study other possible dependent parameters (phantom thickness, overall density, etc.) as well as alternative reconstruction algorithms.