Modeling research universities: Predicting probable futures of public vs. private and large vs. small research universities.

The future of the American academic research enterprise is considered. Data are presented that characterize the resources available for the 160 best-resourced research universities, a small subset of the 2,285 4-year, nonprofit, higher education institutions. A computational model of research universities was extended and used to simulate three strategic scenarios: status quo, steady decline in foreign graduate student enrollments, and downward tuition pressures from high-quality, online professional master's programs. Four specific universities are modeled: large public and private, and small public and private. The former are at the top of the 160 in terms of resources, while the latter are at the bottom of the 160. The model's projections suggest how universities might address these competitive forces. In some situations, it would be in the economic interests of these universities to restrict research activities to avoid the inherent subsidies these activities require. The computational projections portend the need for fundamental change of approaches to business for universities without large institutional resources.

Vision for a systems architecture to integrate and transform population health.

Entities involved in population health often share a common mission while acting independently of one another and perhaps redundantly. Population health is in everybody's interest, but nobody is really in charge of promoting it. Across governments, corporations, and frontline operations, lack of coordination, lack of resources, and lack of reliable, current information have often impeded the development of situation-awareness models and thus a broad operational integration for population health. These deficiencies may also affect the technical, organizational, policy, and legal arrangements for information sharing, a desired practice of high potential value in population health. In this article, we articulate a vision for a next-generation modeling effort to create a systems architecture for broadly integrating and visualizing strategies for advancing population health. This multipurpose systems architecture would enable different views, alerts, and scenarios to better prepare for and respond to potential degradations in population health. We draw inspiration from systems engineering and visualization tools currently in other uses, including monitoring the state of the economy (market performance), security (classified intelligence), energy (power generation), transportation (global air traffic control), environment (weather monitoring), jobs (labor market dynamics), manufacturing and supply chain (tracking of components, parts, subassemblies, and products), and democratic processes (election analytics). We envision the basic ingredients for a population health systems architecture and its visualization dashboards to eventually support proactive planning and joint action among constituents. We intend our ambitious vision to encourage the work needed for progress that the population deserves.

Policy Flight Simulators: Accelerating Decisions to Adopt Evidence-Based Health Interventions.

EXECUTIVE SUMMARY: In this study, the authors used simulation to explore factors that might influence hospitals' decisions to adopt evidence-based interventions. Specifically, they developed a simulation model to examine the extent to which hospitals would benefit economically from the transitional care model (TCM). The TCM is designed to transition high-risk older adults from hospitals back to communities using interventions focused on preventing readmissions.The authors used qualitative methods to identify and validate simulation facets. Four simulation experiments explored the economic impact of the TCM on more than 3,000 U.S. hospitals: (1) magnitude of readmission penalty, (2) application to specific diagnosis-related groups, (3) level of cost sharing between payer and provider, and (4) capitated versus fee-for-service payments. The simulator projected hospital-specific economic effects. The authors used Monte Carlo methods for the simulations, which were parameterized with public data sets from the Centers for Medicare & Medicaid Services (CMS) and TCM data from randomized controlled trials and comparative effectiveness studies.Under current conditions, the simulation indicated that only 10 of more than 3,000 Medicare-certified hospitals would benefit financially from the TCM. If current readmission penalties were doubled, the number of hospitals projected to benefit would increase to 300. Targeting selected diagnosis cohorts would also increase the number of hospitals to 300. If payers reimbursed providers for 100% of the TCM costs, 2,000 hospitals would benefit financially. Under a capitated payment model, 1,500 hospitals would benefit from the TCM.Current CMS penalties-or reasonable increases-have little economic effect on the TCM. In the current environment, two strategies are likely to facilitate adoption: (1) persuading payers to reimburse TCM costs and (2) focusing on hospitals with higher bed occupancies and higher revenue patients.

Applicability of Artificial Societies to Evaluate Health Care Policies.

SUMMARY STATEMENT: We propose the use of artificial societies to support health care policymakers in understanding and forecasting the impact and adverse effects of policies. Artificial societies extend the agent-based modeling paradigm using social science research to allow integrating the human component. We simulate individuals as socially capable software agents with their individual parameters in their situated environment including social networks. We describe the application of our method to better understand policy effects on the opioid crisis in Washington, DC, as an example. We document how to initialize the agent population with a mix of empiric and synthetic data, calibrate the model, and make forecasts of possible developments. The simulation forecasts a rise in opioid-related deaths as they were observed during the pandemic. This article demonstrates how to take human aspects into account when evaluating health care policies.

Systems engineering and management.

This chapter offers a systems view of healthcare delivery and outlines a wide range of concepts, principles, models, methods and tools from systems engineering and management that can enable the transformation of the dysfunctional "as is" healthcare system to an agreed-upon "to be" system that will provide quality, affordable care for everyone. Topics discussed include systems definition, design, analysis, and control, as well as the data and information needed to support these functions. Barriers to implementation are also considered.

Prospects for change.

This chapter addresses the prospects for change in health care delivery. The focus is on value - high quality, affordable care for everyone. We consider three domains that participate in the flow of value and the nature of the interfaces among these domains. We also discuss strategic priorities that should align in various ways with these domains. Finally, we address the business transformations needed to enable the provision of value by enterprises that are viable and successful.

Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine.

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice; the role of -omics technologies in Health Care; the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases; and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances.

Service supply chains for population health: Overcoming fragmentation of service delivery ecosystems.

INTRODUCTION: Population health involves integration of health, education, and social services to keep a defined population healthy, to address health challenges holistically, and to assist with the realities of being mortal. The fragmentation of the US population health delivery system is addressed. The impacts of this fragmentation on the treatment of substance abuse in the United States are considered. Innovations needed to overcome this fragmentation are proposed. APPROACH: Treatment capacity issues, including scheduling practices, are discussed. Costs of treatment and lack of treatment are considered. Models of integrated care delivery are reviewed. Potential innovations from systems science, behavioral economics, and social networks are considered. The implications of these innovations are discussed in terms of information technology (IT) systems and governance. CONCLUSIONS: Enormous savings are possible with more integrated treatment. Based on a range of empirical findings, it is argued that investments of these resources in integrated delivery of care have the potential to dramatically improve health outcomes, thereby significantly reducing the costs of population health.

Developing a Policy Flight Simulator to Facilitate the Adoption of an Evidence-Based Intervention.

While the use of evidence-based interventions (EBIs) has been advocated by the medical research community for quite some time, uptake of these interventions by healthcare providers has been slow. One possible explanation is that it is challenging for providers to estimate impacts of a specific EBI on their particular organization. To address that concern, we developed and evaluated a type of simulation called a policy flight simulator to determine if it could improve the adoption decision about a specific EBI, the transitional care model (TCM). The TCM uses an advanced practice nurse-led model of care to transition older adults with multiple chronic conditions from a hospitalization to home. An evaluation by a National Advisory Committee, made up of senior representatives from various stakeholders in the U.S. healthcare system, found the policy flight simulator to be a useful tool that has the potential to better inform adoption decisions. This paper describes the simulation development effort and documents lessons learned that may be useful to the healthcare modeling community and those interested in using simulation to support decisions based on EBIs.

Interactive visualizations for decision support: Application of Rasmussen's abstraction-aggregation hierarchy.

Data visualization has of late received an enormous amount of attention from both researchers and practitioners. Even the popular press often includes impressive visualizations of various data sets. Interactive visualizations frequently include data visualizations, but they differ in that users employ the visualizations to make inferences, reach conclusions, and make decisions that result in changed and/or new visualizations. Data visualizations emphasize "what is," but interactive visualizations address "what if." In this way, interactive visualizations are often intended for decision support. This article addresses the design of interactive visualizations for decision support. An overall methodology is presented; central to this methodology is Jens Rasmussen's abstraction-aggregation hierarchy. The results of two applications and evaluations of the outcomes of using this methodology are discussed. The first application focused on interactive visualizations for helicopter maintenance. The second application addressed "enterprise diagnostics" in the automobile industry where subjects were asked to diagnose the cause of failed automobile brands. The results of these two applications are used to assess the efficacy of the proposed methodology.

Learning in the health care enterprise.

INTRODUCTION: The overall enterprise of health care delivery is considered. The 4 levels of the enterprise include clinical practices, processes that provide capabilities and information, structure that includes the business entities involved, and ecosystem that includes Centers for Medicare and Medicaid Services and Congress, as well as societal values and norms. It is argued that the enterprise of health care delivery needs to be transformed to enable high-quality, affordable care for everyone. DISCUSSION: The constructs of enterprise transformation and organizational learning are reviewed. The distinction of single-loop versus double-loop learning is discussed and illustrated for all levels of the health care delivery enterprise. Three health care examples are used to elaborate this distinction-cancer, population health, and health IT. Four strategies are outlined that the health care delivery enterprise can use to more effectively learn at all levels of the enterprise. CONCLUSIONS: This overall line of reasoning suggests several important research issues. The health care delivery enterprise involves much more than treating disease and paying for it. We need to improve our methods and tools for addressing the overall enterprise. Research is also needed on better means for portraying consequences of decisions to the full range of stakeholders in the enterprise. In general, the overall goal should be a healthy, educated, and productive population that is competitive in the global marketplace. We need to better understand the available levers for achieving this goal and how to best portray the intricacies of the overall enterprise to motivate those who can pull these levers to do so.

Human interaction with policy flight simulators.

Policy flight simulators are designed for the purpose of exploring alternative management policies at levels ranging from individual organizations to national strategy. This article focuses on how such simulators are developed and on the nature of how people interact with these simulators. These interactions almost always involve groups of people rather than individuals, often with different stakeholders in conflict about priorities and courses of action. The ways in which these interactions are framed and conducted are discussed, as well as the nature of typical results.