Joy in Work for Clinicians and Staff: Identifying Remedial Predictors of Burnout from the Mini Z Survey.

PURPOSE: The CMS Transforming Clinical Practice Initiative (TCPI) provided coaching and learning support to practices during transition to new models of value-based care. Maryland ambulatory practices participated in the Garden Practice Transformation Network (GPTN) as a part of the TCPI. During practices assessment, we measured prevalence of burnout and identified its remediable predictors among GPTN-Maryland practices. METHODS: A modified Mini Z tool survey was distributed among clinicians and staff in November 2018 - July 2019. Association between presence of burnout and burnout drivers was assessed using a Generalized Estimating Equation regression model for ordinal outcome. RESULTS: Data from 166 responses were analyzed. Prevalence of burnout symptoms was 22%, with 35% enjoying their work. A 100-point Time Constraints/Teamwork (T/T) score was constructed using factors significantly associated with burnout symptoms. T/T score increase by 1 unit was associated with 10% increase in the odds of moving from the group experiencing burnout or stress to the group who found 'joy in work' (OR = 1.1, 95% CI 1.07, 1.13, p < 0.0001). CONCLUSIONS: The Mini Z-derived T/T score could be useful for quick assessment of the degree of burnout and identifying burnout drivers related to effective organizational structure and supportive teamwork in practice personnel.

Hypertension and Diabetes Quality Improvement in a Practice Transformation Network.

The Garden Practice Transformation Network in Maryland brought together primary and specialty practices working toward value-based models and efficient care delivery. Practices were provided with coaching and live and multimedia education regarding practice transformation and quality improvement. Coaches supported practices in multipronged approaches to quality improvement. Practice champions and clinical staff were trained on appropriate documentation of blood pressure (BP) and diabetes measures, and new workflows to optimize care delivery. Quality improvement staff were trained in extracting data from electronic health records, providing feedback to practice clinicians, and reporting to Practice Transformation Network staff. Final measurement of BP control was 66%, and final measurement of blood glucose control was 28%. Quality improvement activities in a practice transformation network led to the delivery of high-quality care and quality improvement.

Practice Transformation Analytics Dashboard for Clinician Engagement.

PURPOSE: Practice transformation in primary care is a movement toward data-driven redesign of care, patient-centered care delivery, and practitioner activation. A critical requirement for achieving practice transformation is availability of tools to engage practices. METHODS: A total of 48 practices with 109 practice sites participate in the Garden Practice Transformation Network in Maryland (GPTN-Maryland) to work together toward practice transformation and readiness for the Quality Payment Program implemented by the Centers for Medicare & Medicaid Services. Practice-specific data are collected in GPTN-Maryland by practices themselves and by practice transformation coaches, and are provided by the Centers for Medicare & Medicaid Services. These data are overwhelming to practices when presented piecemeal or together, a barrier to practices taking action to ensure progress on the transformation spectrum. The GPTN-Maryland team therefore created a practice transformation analytics dashboard as a tool to present data that are actionable in care redesign. RESULTS: When practices reviewed their data provided by the Centers for Medicare & Medicaid Services using the dashboard, they were often seeing, for the first time, cost data on their patients, trends in their key performance indicator data, and their practice transformation phase. Overall, 72% of practices found the dashboard engaging, and 48% found the data as presented to be actionable. CONCLUSIONS: The practice transformation analytics dashboard encourages practices to advance in practice transformation and improvement of patient care delivery. This tool engaged practices in discussions about data, care redesign, and costs of care, and about how to develop sustainable change within their practices. Research is needed to study the impact of the dashboard on costs and quality of care delivery.

Data Needs for a Serious Illness Care Accountability System: A Framework and Recommendations.

BACKGROUND: Successful implementation of a comprehensive accountability system for community-based serious illness care will require a robust data infrastructure. Data will be needed to support care delivery, quality measurement, value-based payment, and evaluation and monitoring. OBJECTIVE: The specific data needs in these areas need to be identified and understood, so that gaps in currently available data may be addressed. DESIGN: We developed a framework that includes the needed data and data infrastructure to support the features and characteristics of a serious illness care accountability system. Based on this framework, we analyze the current data landscape to identify gaps in available data resources and capacities. This analysis was informed by conducting Internet-based research, interviews with key informants, and a survey of key informants. RESULTS: Based on the identified gaps, we present a series of priority recommendations for advancing the data infrastructure to support community-based serious illness care. These recommendations include additional measurement of patient-reported outcomes, increasing interoperability among various data sources, increasing development and exchange of patient care plans, leveraging newly standardized data on patient functional and cognitive status, and using patient-reported information for clinical decision support at the point of care. CONCLUSION: There are significant unmet data needs for a comprehensive accountability system in serious illness care, but these gaps can be prioritized and addressed through alignment and collaboration across stakeholders.

Accountability for Community-Based Programs for the Seriously Ill.

Innovation is needed to improve care of the seriously ill, and there are important opportunities as we transition from a volume- to value-based payment system. Not all seriously ill are dying; some recover, while others are persistently functionally impaired. While we innovate in service delivery and payment models for the seriously ill, it is important that we concurrently develop accountability that ensures a focus on high-quality care rather than narrowly focusing on cost containment. The Gordon and Betty Moore Foundation convened a meeting of 45 experts to arrive at guiding principles for measurement, create a starter measurement set, specify a proposed definition of the denominator and its refinement, and identify research priorities for future implementation of the accountability system. A series of articles written by experts provided the basis for debate and guidance in formulating a path forward to develop an accountability system for community-based programs for the seriously ill, outlined in this article. As we innovate in existing population-based payment programs such as Medicare Advantage and develop new alternative payment models, it is important and urgent that we develop the foundation for accountability along with actionable measures so that the healthcare system ensures high-quality person- and family-centered care for persons who are seriously ill.

A Population Health Measurement Framework: Evidence-Based Metrics for Assessing Community-Level Population Health in the Global Budget Context.

Population health is one of the pillars of the Triple Aim to improve US health care. The authors developed a framework for population health measurement and a proposed set of measures for further exploration to guide the population health efforts in Maryland. The authors searched peer-reviewed, expert-authored literature and current public health measures. Using a semi-structured analysis, a framework was proposed, which consisted of a conceptual model of several domains and identified population health measures addressing them. Stakeholders were convened to review the framework and identified the most feasible population health measures considering the underlying health information technology (IT) infrastructure in Maryland. The framework was organized based on health system factors, determinants of health, and population-based and clinical outcomes. Measurement specifications were developed that addressed different aspects of selected measures and assessed various national and local data sources for selected measures. Data sources were identified based on their key characteristics, challenges, opportunities, and potential applicability to the proposed measures, as well as the issue of interoperability of data sources among different organizations. The proposed framework and measures can act as a platform to quantify the determinants of health and the state overall population health goals. Key considerations for developing a population health measures framework include health IT infrastructure, data denominators, feasibility, health system environment, and policy factors. Measurement development and progression using the framework will largely depend on the users' focus areas and availability of data. The authors believe that the proposed framework and road map can serve as a model for communities elsewhere.

Recommendations for clinical trials of off-label drugs used to treat advanced-stage cancer.

PURPOSE: To provide recommendations to trialists and sponsors that guide the design and implementation of prospective postapproval clinical trials for oncology drugs used outside US Food and Drug Administration-labeled indications for treatment of late-stage cancers. METHODS: A meeting was hosted by the Center for Medical Technology Policy in Baltimore, MD, on November 12, 2009. Discussions during the meeting and key informant interviews were conducted before and after this stakeholder meeting. Peer review by multidisciplinary stakeholders was followed by a public comment period. Input was received from patient advocacy groups, medical oncologists, pharmaceutical companies, the US Food and Drug Administration, Centers for Medicare and Medicaid Services, the National Cancer Institute, foreign government agencies involved in health technology assessment, public and private payers, drug compendia, clinical research entities, statisticians, academics, and the American Society of Clinical Oncology. RESULTS: To address the needs of patients and their clinical providers, compendia, payers, and policy makers, recommendations are proposed to guide the design of future prospective trials for off-label use of oncology drugs across four areas: trial design and data analysis, patient and site recruitment, comparators, and outcomes. CONCLUSION: The US Food and Drug Administration provides guidance to the pharmaceutical industry and others designing randomized clinical trials for regulatory approval. However, a gap exists for postregulatory decision makers, including patients, prescribers, and payers, because regulatory trials do not answer the questions most relevant to them. Therefore, guidance is needed for trials performed in the postapproval environment for these postapproval decision makers.

How best to engage patients, doctors, and other stakeholders in designing comparative effectiveness studies.

Having patients, doctors, health plan managers, hospital executives, and other stakeholders participate in the design of comparative effectiveness studies can ensure that this vital research focuses on the evidence gaps most relevant to health care decision makers. Through a qualitative assessment of case studies, we identify five key principles for the effective engagement of a broad coalition of participants in research intended to improve health care and control costs. Those principles are to ensure balance among the participating stakeholders; get participants to "buy in" to the process and understand their roles; provide neutral and expert facilitators for research discussions; establish connections among the participants; and keep the participants engaged throughout the research process.

Uncertainty in assessing value of oncology treatments.

Patients, clinicians, payers, and policymakers face an environment of significant evidentiary uncertainty as they attempt to achieve maximum value, or the greatest level of benefit possible at a given level of cost in their respective health care decisions. This is particularly true in the area of oncology, for which published evidence from clinical trials is often incongruent with real-world patient care, and a substantial portion of clinical use is for off-label indications that have not been systematically evaluated. It is this uncertainty in the knowledge of the clinical harms and benefits associated with oncology treatments that prevents postregulatory decision makers from making accurate assessments of the value of these treatments. Because of the incentives inherent in the clinical research enterprise, randomized control trials (RCTs) are designed for the specific purpose of regulatory approval and maximizing market penetration. The pursuit of these goals results in RCT study designs that achieve maximal internal validity at the expense of generalizability to diverse real-world patient populations that may have significant comorbidities and other clinically mitigating factors. As such, systematic reviews for the purposes of coverage and treatment decisions often find relevant and high-quality evidence to be limited or nonexistent. For a number of reasons, including frequent off-label use of medications and the expedited approval process for cancer drugs by the U.S. Food and Drug Administration, this situation is exacerbated in the area of oncology. This paper investigates the convergence of incentives and circumstances that lead to widespread uncertainty in oncology and proposes new paradigms for clinical research, including pragmatic clinical trials, methodological guidance, and coverage with evidence development. Each of these initiatives would support the design of clinical research that is more informative for postregulatory decision makers, and would therefore reduce uncertainty and provide greater confidence in conclusions about the value of these treatments.