Addressing Housing-Related Social Needs Through Medicaid: Lessons From North Carolina's Healthy Opportunities Pilots Program.

North Carolina Medicaid's Healthy Opportunities Pilots program is the country's first comprehensive program to evaluate the impact of paying community-based organizations to provide eligible Medicaid enrollees with an array of evidence-based services to address four domains of health-related social needs, one of which is housing. Using a mixed-methods approach, we mapped the distribution of severe housing problems and then examined the design and implementation of Healthy Opportunities Pilots housing services in the three program regions. Four cross-cutting implementation and policy themes emerged: accounting for variation in housing resources and needs to address housing insecurity, defining and pricing housing services in Medicaid, engaging diverse stakeholders across sectors to facilitate successful implementation, and developing sustainable financial models for delivery. The lessons learned and actionable insights can help inform the efforts of stakeholders elsewhere, particularly other state Medicaid programs, to design and implement cross-sectoral programs that address housing-related social needs by leveraging multiple policy-based resources. These lessons can also be useful for federal policy makers developing guidance on addressing housing-related needs in Medicaid.

Analysis of North Carolina Medicaid Claims Data to Simulate a Pediatric Accountable Care Organization.

Importance: Despite momentum for pediatric value-based payment models, little is known about tailoring design elements to account for the unique needs and utilization patterns of children and young adults. Objective: To simulate attribution to a hypothetical pediatric accountable care organization (ACO) and describe baseline demographic characteristics, expenditures, and utilization patterns over the subsequent year. Design, Setting, and Participants: This retrospective cohort study used Medicaid claims data for children and young adults aged 1 to 20 years enrolled in North Carolina Medicaid at any time during 2017. Children and young adults receiving at least 50% of their primary care at a large academic medical center (AMC) in 2017 were attributed to the ACO. Data were analyzed from April 2020 to March 2021. Main Outcomes and Measures: Primary outcomes were total cost of care and care utilization during the 2018 performance year. Results: Among 930 266 children and young adults (377 233 children [40.6%] aged 6-12 years; 470 612 [50.6%] female) enrolled in Medicare in North Carolina in 2017, 27 290 children and young adults were attributed to the ACO. A total of 12 306 Black non-Hispanic children and young adults (45.1%), 6308 Hispanic or Latinx children and young adults (23.1%), and 6531 White non-Hispanic children and young adults (23.9%) were included. Most attributed individuals (23 133 individuals [84.7%]) had at least 1 claim in the performance year. The median (IQR) total cost of care in 2018 was $347 ($107-$1123); 272 individuals (1.0%) accounted for nearly half of total costs. Compared with children and young adults in the lowest-cost quartile, those in the highest-cost quartile were more likely to have complex medical conditions (399 individuals [6.9%] vs 3442 individuals [59.5%]) and to live farther from the AMC (median [IQR distance, 6.0 [4.6-20.3] miles vs 13.9 [4.6-30.9] miles). Total cost of care was accrued in home (43%), outpatient specialty (19%), inpatient (14%) and primary (8%) care. More than half of attributed children and young adults received care outside of the ACO; the median (IQR) cost for leaked care was $349 ($130-$1326). The costliest leaked encounters included inpatient, ancillary, and home health care, while the most frequently leaked encounters included behavioral health, emergency, and primary care. Conclusions and Relevance: This cohort study found that while most children attributed to the hypothetical Medicaid pediatric ACO lived locally with few health care encounters, a small group of children with medical complexity traveled long distances for care and used frequent and costly home-based and outpatient specialty care. Leaked care was substantial for all attributed children, with the cost of leaked care being higher than the total cost of care. These pediatric-specific clinical and utilization profiles have implications for future pediatric ACO design choices related to attribution, accounting for children with high costs, and strategies to address leaked care.

Hospital-level compliance with the commission on cancer's quality of care measures and the association with patient survival.

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.

Advancing Value-Based Models for Heart Failure: A Call to Action From the Value in Healthcare Initiative's Value-Based Models Learning Collaborative.

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.

Frontiers of Upstream Stroke Prevention and Reduced Stroke Inequity Through Predicting, Preventing, and Managing Hypertension and Atrial Fibrillation: A Call to Action From the Value in Healthcare Initiative's Predict & Prevent Learning Collaborative.

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.

Streamlining and Reimagining Prior Authorization Under Value-Based Contracts: A Call to Action From the Value in Healthcare Initiative's Prior Authorization Learning Collaborative.

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.

Improving Cardiovascular Drug and Device Development and Evidence Through Patient-Centered Research and Clinical Trials: A Call to Action From the Value in Healthcare Initiative's Partnering With Regulators Learning Collaborative.

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.

Can compression be reduced for breast tomosynthesis? Monte carlo study on mass and microcalcification conspicuity in tomosynthesis.

PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Why Do Accountable Care Organizations Leave The Medicare Shared Savings Program?

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.

The Need for Practical and Accurate Measures of Value for Radiology.

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.

Medicaid and CHIP Child Health Beneficiary Incentives: Program Landscape and Stakeholder Insights.

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 (<18 years old) to understand program rationale, beneficiary engagement, and program evaluation. 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.

Engaging Beneficiaries In Medicaid Programs That Incentivize Health-Promoting Behaviors.

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.

ACO Serious Illness Care: Survey And Case Studies Depict Current Challenges And Future Opportunities.

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. Accountable care organizations (ACOs) have potential to improve serious illness care because of their widespread dissemination, strong financial incentives for care coordination in downside-risk models, and flexibility in shared savings spending. Through a national survey we found that 94 percent of ACOs at least partially identify their seriously ill beneficiaries, yet only 8-21 percent have widely implemented serious illness initiatives such as advance care planning or home-based palliative care. We selected six diverse ACOs with successful programs for case studies and interviewed fifty-three leaders and front-line personnel. Cross-cutting themes include the need for up-front investment beyond shared savings to build serious illness infrastructure and workforce; supporting the business case for organizational buy-in; how ACO contract specifications affect savings for serious illness populations; and using data and health information technology to manage populations. We discuss the implications of the recent Medicare ACO regulatory overhaul and other policies related to serious illness quality measures, risk adjustment, attribution methods, supporting rural ACOs, and enhancing timely data access.

The effect of breast compression on mass conspicuity in digital mammography.

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.

A mathematical model platform for optimizing a multiprojection breast imaging system.

Multiprojection imaging is a technique in which a plurality of digital radiographic images of the same patient are acquired within a short interval of time from slightly different angles. Information from each image is combined to determine the final diagnosis. Projection data are either reconstructed into slices as in the case of tomosynthesis or analyzed directly as in the case of multiprojection correlation imaging technique, thereby avoiding reconstruction artifacts. In this study, the authors investigated the optimum geometry of acquisitions of a multiprojection breast correlation imaging system in terms of the number of projections and their total angular span that yield maximum performance in a task that models clinical decision. Twenty-five angular projections of each breast from 82 human subjects in our breast tomosynthesis database were each supplemented with a simulated 3 mm mass. An approach based on Laguerre-Gauss channelized Hotelling observer was developed to assess the detectability of the mass in terms of receiver operating characteristic (ROC) curves. Two methodologies were developed to integrate results from individual projections into one combined ROC curve as the overall figure of merit. To optimize the acquisition geometry, different components of acquisitions were changed to investigate which one of the many possible configurations maximized the area under the combined ROC curve. Optimization was investigated under two acquisition dose conditions corresponding to a fixed total dose delivered to the patient and a variable dose condition, based on the number of projections used. In either case, the detectability was dependent on the number of projections used, the total angular span of those projections, and the acquisition dose level. In the first case, the detectability approximately followed a bell curve as a function of the number of projections with the maximum between 8 and 16 projections spanning angular arcs of about 23 degrees-45 degrees, respectively. In the second case, the detectability increased with the number of projections approaching an asymptote at 11-17 projections for an angular span of about 45 degrees. These results indicate the inherent information content of the multi-projection image data reflecting the relative role of quantum and anatomical noise in multiprojection breast imaging. The optimization scheme presented here may be applied to any multiprojection imaging modalities and may be extended by including reconstruction in the case of digital breast tomosynthesis and breast computed tomography.

Does image quality matter? Impact of resolution and noise on mammographic task performance.

The purpose of this study was to examine the effects of different resolution and noise levels on task performance in digital mammography. This study created an image set with images at three different resolution levels, corresponding to three digital display devices, and three different noise levels, with noise magnitudes similar to full clinical dose, half clinical dose, and quarter clinical dose. The images were read by five experienced breast imaging radiologists. The data were then analyzed to compute two accuracy statistics (overall classification accuracy and lesion detection accuracy) and performance at four diagnostic tasks (detection of microcalcifications, benign masses, malignant masses, and discrimination of benign and malignant masses). Human observer results showed decreasing display resolution had little effect on overall classification accuracy and individual diagnostic task performance, but increasing noise caused overall classification accuracy to decrease by a statistically significant 21% as the breast dose went to one quarter of its normal clinical value. The noise effects were most prominent for the tasks of microcalcification detection and mass discrimination. When the noise changed from full clinical dose to quarter clinical dose, the microcalcification detection performance fell from 89% to 67% and the mass discrimination performance decreased from 93% to 79%, while malignant mass detection performance remained relatively constant with values of 88% and 84%, respectively. As a secondary aim, the image set was also analyzed by two observer models to examine whether their performance was similar to humans. Observer models differed from human observers and each other in their sensitivity to resolution degradation and noise. The primary conclusions of this study suggest that quantum noise appears to be the dominant image quality factor in digital mammography, affecting radiologist performance much more profoundly than display resolution.