The potential cost-effectiveness of novel cord blood therapies in children with autism spectrum disorder.

OBJECTIVE: To model the long-term clinical and economic outcomes of potential cord blood therapy in autism spectrum disorder (ASD). STUDY DESIGN: Markov microsimulation of ASD over the lifespan was used to compare two strategies: 1) standard of care (SOC), including behavioral and educational interventions, and 2) novel cord blood (CB) intervention in addition to SOC. Input data reflecting behavioral outcomes included baseline Vineland Adaptive Behavior Scale (VABS-3), monthly VABS-3 changes, and CB intervention efficacy on adaptive behavior based on a randomized, placebo-controlled trial (DukeACT). Quality-adjusted life-years (QALYs) were correlated to VABS-3. Costs for children with ASD ($15,791, ages 2-17 years) and adults with ASD ($56,559, ages 18+ years), and the CB intervention (range $15,000-45,000) were incorporated. Alternative CB efficacy and costs were explored. RESULTS: We compared model-projected results to published data on life-expectancy, mean VABS-3 changes, and lifetime costs. Undiscounted lifetime QALYs in the SOC and CB strategies were 40.75 and 40.91. Discounted lifetime costs in the SOC strategy were $1,014,000, and for CB ranged from $1,021,000-$1,058,000 with CB intervention cost ($8,000-$45,000). At $15,000 cost, CB was borderline cost-effective (ICER = $105,000/QALY). In one-way sensitivity analysis, CB cost and efficacy were the most influential parameters on CB ICER. CB intervention was cost-effective at costs<$15,000 and efficacies ≥2.0. Five-year healthcare payer projected budgetary outlays at a $15,000 CB cost were $3.847B. CONCLUSIONS: A modestly effective intervention designed to improve adaptive behavior in autism can be cost-effective under certain circumstances. Intervention cost and efficacy most affected the cost-effectiveness results and should be targeted to increase economic efficiency.

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.

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.

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data.

OBJECTIVE: Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is 'fit' for specific uses. MATERIALS AND METHODS: DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework's inclusiveness was evaluated against ten published DQ terminologies. RESULTS: Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies. DISCUSSION: Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data. CONCLUSION: A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.