The Future of Population-Based Reimbursement

David C. Chin, MD, MBA is a Distinguished Scholar at the Johns Hopkins Bloomberg School of Public Health and Johns Hopkins School of Medicine, where he focuses on novel industry/academic research partnerships and preparing health systems and academic medical centers for success under accountable care. Dr. Chin will describe the Maryland-All payer system as a model of innovative population-based reimbursement. He will discuss early adaptive strategies, including ways in which health care systems will need to change. Dr. Chin will also provide a comparison of the new incentives in the model vs. fee-for-service. Prior to joining Hopkins Dr. Chin has held positions with PwC, Novalis, and Harvard Community Health Plan. Chin holds a BA from Harvard College, an MD from Harvard Medical School, and an MBA from Stanford Business School, as a Robert Wood Johnson Clinical Scholar. PowerPoint slides at bottom of page. Presentation: 51 minute

The Economics of Policing Research

In 2012, provincial, territorial and federal governments of Canada reached consensus on an important policy issue: public policing costs were escalating and something needed to be done about ‘the economics of policing’. They also discovered that, as a result of the federal government’s chronic defunding of policing research, they had very little Canadian knowledge upon which to draw. The focus of the present paper is on how both the ‘economics of policing’ crisis, and policy-makers’ inability to utilize domestic research to resolve it, were generated by successive governments sharing an ideologically-informed view of the relative importance of criminal justice research

Open Data for Global Science

The global science system stands at a critical juncture. On the one hand, it is overwhelmed by a hidden avalanche of ephemeral bits that are central components of modern research and of the emerging ‘cyberinfrastructure’4 for e-Science.5 The rational management and exploitation of this cascade of digital assets offers boundless opportunities for research and applications. On the other hand, the ability to access and use this rising flood of data seems to lag behind, despite the rapidly growing capabilities of information and communication technologies (ICTs) to make much more effective use of those data. As long as the attention for data policies and data management by researchers, their organisations and their funders does not catch up with the rapidly changing research environment, the research policy and funding entities in many cases will perpetuate the systemic inefficiencies, and the resulting loss or underutilisation of valuable data resources derived from public investments. There is thus an urgent need for rationalised national strategies and more coherent international arrangements for sustainable access to public research data, both to data produced directly by government entities and to data generated in academic and not-for-profit institutions with public funding. In this chapter, we examine some of the implications of the ‘data driven’ research and possible ways to overcome existing barriers to accessibility of public research data. Our perspective is framed in the context of the predominantly publicly funded global science system. We begin by reviewing the growing role of digital data in research and outlining the roles of stakeholders in the research community in developing data access regimes. We then discuss the hidden costs of closed data systems, the benefits and limitations of openness as the default principle for data access, and the emerging open access models that are beginning to form digitally networked commons. We conclude by examining the rationale and requirements for developing overarching international principles from the top down, as well as flexible, common-use contractual templates from the bottom up, to establish data access regimes founded on a presumption of openness, with the goal of better capturing the benefits from the existing and future scientific data assets. The ‘Principles and Guidelines for Access to Research Data from Public Funding’ from the Organisation for Economic Cooperation and Development (OECD), reported on in another article by Pilat and Fukasaku,6 are the most important recent example of the high-level (inter)governmental approach. The common-use licenses promoted by the Science Commons are a leading example of flexible arrangements originating within the community. Finally, we should emphasise that we focus almost exclusively on the policy—the institutional, socioeconomic, and legal aspects of data access—rather than on the technical and management practicalities that are also important, but beyond the scope of this article

The Right to Benefit from Big Data as a Public Resource

The information that we reveal from interactions online and with electronic devices has massive value—for both private profit and public benefit, such as improving health, safety, and even commute times. Who owns the lucrative big data that we generate through the everyday necessity of interacting with technology? Calls for legal regulation regarding how companies use our data have spurred laws and proposals framed by the predominant lens of individual privacy and the right to control and delete data about oneself. By focusing on individual control over droplets of personal data, the major consumer privacy regimes overlook the important question of rights in the big data ocean. This Article is the first to frame a right of the public to benefit from our consumer big data. Drawing on insights from property theory, regulatory advances, and open innovation, the Article introduces a model that permits controlled access and the use of big data for public interest purposes while protecting against privacy harms, among others. I propose defining a right of access to pooled personal data for public purposes, with sensitive information safeguarded by a controlled-access procedure akin to that used by institutional review boards in medical research today. To encourage companies to voluntarily share data for public interest purposes, the Article also proposes regulatory sandboxes and safe harbors akin to those successfully deployed in other domains, such as antitrust, financial technology, and intellectual property law

Universities, Community Engagement, and Democratic Social Science

The purpose of this dissertation is to identify and compare differences between institutional conceptualizations of community engagement with the understanding and practices of faculty engaged in community-based research (CBR), and analyze the implications of these differences. The study contrasts the model of community engagement that is being promoted by universities and the granting agencies (specifically the Social Sciences and Humanities Research Council of Canada) with what community-engaged researchers experience it to be, with a view to developing an analysis of the relationship between individuals and the political economy of research in which they work. In Canada, universities are being encouraged by the federal government to assume greater responsibility for economic development and to translate knowledge into products and services for the market—while at the same time being tasked to work with communities in alleviating the social and economic excesses of the market. Drawing upon a qualitative, interview-based research design, my main line of argument is that there is a contradiction regarding the democratization of knowledge production between universities and communities that the institutionalization of community engagement promises—and the aligning of this process of knowledge production with market-driven forces and outcomes. The concern addressed in the dissertation is that the emancipatory intentions of community-based research are being co-opted by the entrepreneurial and managerial ethos influencing and structuring the "doing" of research. Such developments necessitate an interrogation of the institutional contexts in which participatory and community-engaged research are becoming positioned within the market-driven and performance-based governance of university research

Aging-related technologies: A multiple case study of innovation processes

Introduction: As part of a Canadian research network focused on aging and technology – Aging Gracefully across Environments using technology to support Wellness, Engagement, and Long Life (AGE-WELL) – this thesis explored how technologies currently being developed to support older adults and their caregivers fare through the processes of innovation. This included an exploration of the factors that might facilitate or constrain these new technologies from their initial development to implementation, as well as any policy, regulatory and/or health system issues that may be relevant. Methods: A multiple case study was conducted of four AGE-WELL technology projects. For each, data were collected through: interviews with project members and key stakeholders (n=20); surveys (n=4); ethnographic observations at each project site (n=4); and document reviews. Data were analyzed using directed coding, guided by the ADOPT (Accelerating Diffusion of Proven Technologies for Older Adults) framework (Wang et al., 2010). The results were compared across sites using a cross-case analysis. Results: Challenges related to the initial stages of the work included obtaining ethics clearance, recruitment of study participants, and getting small-scale studies completed. Challenges were also experienced in creating business models – including uncertainties around who might benefit from or pay for the technologies. Facilitators included collaboration among stakeholders (e.g. clinicians, industry, end-users) and support from the AGE-WELL network to form partnerships. Conclusions: Technologies have the potential to help older adults maintain their independence, health and quality of life. Understanding the factors that facilitate or constrain the development and implementation of these types of technologies can help promote their diffusion and adoption

Information-intensive innovation: the changing role of the private firm in the research ecosystem through the study of biosensed data

In a world instrumented with smart sensors and digital platforms, some of our most intimate and information-rich data are being collected and curated by private companies. The opportunities and risks derived from potential knowledge carried within these data streams are undeniable, and the clustering of data within the private sector is challenging traditional data infrastructures and sites of research. The role of private industry in research and development (R&D) has traditionally been limited—especially for earlier stage research—given the high risk, long time horizons, and uncertain returns on investment. However, the information economy has changed the way Silicon Valley and other technology firms operate their business models, which has vast implications for how they respectively innovate. Information drives competitive advantage, and builds upon the emergence of technical infrastructure for collecting, storing, and analyzing data at scale. Basic research and fundamental inquiry are becoming important innovation priorities for private firms as they tailor algorithms and customize services, and these changes have vast implications for individual privacy and research ethics. This information-intensive innovation does not simply introduce a new source of inquiry, but a shift in the possibilities and boundaries that enable market edge. This shift challenges prior models of innovation and reconsiders the role of the private firm within the research ecosystem—specifically in regards to Vannevar Bush’s Linear Model of Innovation and Donald Stokes’ Quadrant Model of Scientific Research. This change builds upon prior Silicon Valley innovation models outlined by AnnaLee Saxenian and Henry Chesbrough, but features additional key changes within industry R&D that are fundamentally reshaping the role of the firm within the broader ecosystem. No longer can industry be cast as a place only equipped to grapple exclusively with narrowly applied or developmental research and fully separated or agnostic from users, customers, and citizens. Within this information and data abundant moment, the research and innovation ecosystem is at an inflection point that could alter decades of embedded beliefs and assumptions on who should conduct research and ask fundamental questions, not to mention who should govern and grant access to research data. This dissertation studies how the rise of data science infrastructure is changing the role of the private firm in the R&D ecosystem. This research works to understand how and under what conditions private sector firms are synthesizing user data (e.g., those picked up by sensors) internally and/or shared externally for research purposes. This dissertation specifically looks at applications of biosensed data for the purposes of social, behavioral, health, or public health research applications. Qualitative and mixed methods are used to research, document, and examine practices within the lens of existing research and innovation theoretical models. Historical frameworks are used to ground and place contemporary practices within broader context. This research presents three illustrative cases on firms that exemplify different aspects of strategies to adapt to the competitive pressures of information-intensive innovation. The firms include the Lioness smart vibrator, Kinsa smart thermometer, and Basis smart watch. This research establishes findings about how firms are working within the data and R&D landscape, and how new pressures are influencing emerging practices and strategies. Findings outline the changing definitional boundaries of research within the private firm, and evolving practices relating to knowledge sharing and research activities within the firms. This analysis also points to two key emerging challenges firms are coping with, including how to grapple with research ethics and the rise of secrecy practices that may impede collaboration and research strategies implicit with information-intensive innovation. Research is occurring at many levels within firms, breaking free of any traditional laboratory structure. Collaborations and data sharing with academics for mutually beneficial research partnerships are taking new, largely unstructured forms to meet rising demand and interest. There is fresh demand for new kinds of collaboration models derived from data sharing needs, and exploration into ways of leveraging research practices and incorporating academic research curiosity across firms. This dissertation concludes by summarizing the importance of reconsidering the role of the firm within the broader R&D ecosystem and broader policy considerations. Programs to help structure and incentivize private/academic research collaborations should be considered, and private firms should consider their internal protocols and strategies in light of this changing landscape