As the nation still struggles with high healthcare costs that consume larger and larger portions of patient budgets as well as government coffers, the search for ways to get costs under control continues. Total healthcare spending in the U.S. now represents almost 18 percent of our entire economy. One promising cost-savings approach is called “reference pricing,” where the insurer establishes a price ceiling on selected services (joint replacement, colonoscopy, lab tests, etc.). Often, this price cap is based on the average of the negotiated prices for providers in its network, and anything above the reference price has to be covered by the insured consumer.

A study published in JAMA Internal Medicine by James Robinson and colleagues analyzed grocery store Safeway’s experience with reference pricing for laboratory services such as such as a lipid panel, comprehensive metabolic panel or prostate-specific antigen test. Safeway’s non-union employees were given information on prices at all laboratories through a mobile digital platform and told what Safeway would cover. Patients who chose a lab charging above the payment limit were required to pay the full difference themselves.

Employers see this type of program as a way to incentivize employees to think through the price of services when making healthcare decisions. Employees enjoy savings when they switch to a provider whose negotiated price is below the reference price, whereas if they choose services above it, they are responsible for the additional cost.

Robinson’s results show substantial savings to both Safeway and to its covered employees from reference pricing. Compared to trends in prices paid by insurance enrollees not subject to the caps of reference pricing, costs paid per test went down almost 32 percent, with a total savings over three years of $2.57 million – patients saved $1.05 million in out-of-pocket costs and Safeway saved $1.7 million.

I wrote an accompanying editorial in JAMA Internal Medicine focusing on different types of consumer-driven approaches to obtain lower prices; I argue that approaches that make the job simpler for consumers are likely to be even more successful. There is some work involved for patients to make reference pricing work, and many may have little awareness of price differences across laboratories, especially differences between those in some physicians’ offices, which tend to be more expensive but also more convenient, and in large commercial laboratories. Safeway helped steer their employees with accessible information: they provided employees with a smartphone app to compare lab prices.

But high-deductible plans like Safeway’s that provide extensive price information to consumers often have only limited impact because of the complexity of shopping for each service involved in a course of treatment -- something close to impossible for inpatient care. In addition, high deductibles are typically met for most hospitalizations (which tend to be the very expensive), so those consumers are less incentivized to comparison shop.

Plans that have limited provider networks relieve the consumer of much complexity and steer them towards providers with lower costs. Rather than review extensive price information, the consumer can focus on whether the provider is in the network. Reference pricing is another approach that simplifies—is the price less than the reference price? What was striking about Robinson’s results is that reference pricing for laboratories was employed in a high-deductible plan, showing that the savings achieved—in excess of 30 percent compared to a control—were beyond what the high deductible had accomplished.

While promising, reference pricing cannot be applied to all medical services: it works best for standardized services and where variation in quality is less of a concern. It also can be applied only to services that are “shoppable,” which is only about one-third of privately-insured spending. Even if reference pricing expanded to a number of other medical services, other cost containment approaches, including other network strategies, are needed to successfully contain health spending and lower costs for non-shoppable medical services.

Editor's note: This piece originally appeared in JAMA.

Editor's Note: At a Zócalo Public Square* event, several experts were asked to weigh in on the following question: How should we most effectively measure happiness? Here is Carol Graham's response-

We must make it a measure that’s meaningful to the average person

Happiness is increasingly in the media. Yet it is an age-old topic of inquiry for psychologists, philosophers, and even the early economists (before the science got dismal). The pursuit of happiness is even written into the Declaration of Independence (and into the title of my latest Brookings book, I might add). Public discussions of happiness rarely define the concept. Yet an increasing number of economists and psychologists are involved in a new science of measuring well-being, a concept that includes happiness but extends well beyond it.

Those of us involved focus on two distinct dimensions: hedonic well-being, a daily experience component; and evaluative well-being, the way in which people think about their lives as a whole, including purpose or meaning. Jeremy Bentham focused on the former and proposed increasing the happiness and contentment of the greatest number of individuals possible in a society as the goal of public policy. Aristotle, meanwhile, thought of happiness as eudemonia, a concept that combined two Greek words: “eu” meaning abundance and “daimon” meaning the power controlling an individual’s destiny. Using distinct questions and methods, we are able to measure both. We can look within and across societies and see how people experience their daily lives and how that varies across activities such as commuting time, work, and leisure time on the one hand, and how they feel about their lives as a whole—including their opportunities and past experiences, on the other. Happiness crosses both dimensions of well-being. If you ask people how happy they felt yesterday, you are capturing their feelings during yesterday’s experiences. If you ask them how happy they are with their lives in general, they are more likely to think of their lives as a whole.

The metrics give us a tool for measuring and evaluating the importance of many non-income components of people’s lives to their overall welfare. The findings are intuitive. Income matters to well-being, and not having enough income is bad for both dimensions. But income matters more to evaluative well-being, as it gives people more ability to choose how to live their lives. More income cannot make them experience each point in the day better. Other things, such as good health and relationships, matter as much if not more to well-being than income. The approach provides useful complements to the income-based metrics that are already in our statistics and in the GDP. Other countries, such as Britain, have already begun to include well-being metrics in their national statistics. There is even a nascent discussion of doing so here.

Perhaps what is most promising about well-being metrics is that they seem to be more compelling for the average man (or woman) on the street than are complex income measures, and they often tell different stories. There are, for example, endless messages about the importance of exercising for health, the drawbacks of smoking, and the expenses related to long commutes. Yet it is likely that they are most often heard by people who already exercise, don’t smoke, and bicycle to work. And exercise does not really enter into the GNP, while cigarette purchases and the gasoline and other expenses related to commuting enter in positively. If you told people that exercising made them happier and that smoking and commuting time made them unhappy (and yes, these are real findings from nationwide surveys), then perhaps they might listen?

Read other responses to this question at zocalopublicsquare.org »

*Zócalo Public Square is a not-for-profit daily ideas exchange that blends digital humanities journalism and live events. 

Created within a year of each other, the World Bank and the United Nations were born out of a shared response to the Second World War. The war created a constituency willing to invest resources and ideals in a system of multilateral cooperation. In the words of one of their architects, these institutions were to create a “New Deal for a new world.”

Today we face another period of global disorder. The number of armed conflicts worldwide has tripled from four to 11 since 2007. 2014 was the most lethal year since the end of the Cold War, according to the Uppsala Conflict Data Program. In the same year, the total number of deaths from terrorism increased by 80 percent, to close to 37,000, the largest yearly increase in the last 15 years, according to the Institute for Economics and Peace.

The fallout is clear. The number of people affected by humanitarian crises has almost doubled in the past decade, with 125 million people requiring humanitarian assistance. Displacement is at a post-World War II high with 60 million people around the world forced from their homes, often within their own countries. Roughly two-thirds of U.N. peacekeepers today and almost 90 percent of personnel in U.N. Special Political Missions are working in and on countries where there is little peace to keep.

Responding to this challenge, the U.N. and its member states led major reviews in 2015 of the tools and approaches used to respond to conflict. These reviews looked at peacekeeping operations, the implementation of Security Council Resolution 1325 on Women, Peace, and Security, and the U.N.’s peacebuilding architecture.

These reviews underscored that while humanitarian assistance can mitigate suffering, and peacekeepers can stabilize situations, they alone cannot create lasting peace, development, and prosperity. 

Responding to this challenge requires a new global partnership to prevent violent conflict, reduce humanitarian need, and sustain peace. This partnership must reaffirm our commitment to humanity and chart a course for change, as the secretary-general has called for in his recent report for the World Humanitarian Summit.

Taking place just before the World Humanitarian Summit, the ministerial meeting of the International Dialogue on Peacebuilding and Statebuilding (IDPS) in Stockholm is a key moment at which the principles of the New Deal for Engagement in Fragile States, in particular the TRUST and FOCUS components, could be used to provide a foundation for this effort.

Peacebuilding and statebuilding, however, are political. Technical instruments must be aligned with and informed by a political strategy owned by national governments and developed in consultation with its people. This is as true at the global level as it is in each country.

The first step is normative. In 2015, through the Addis Ababa Action Agenda and the 2030 Agenda for Sustainable Development, member states committed to a future that aims to leave no one behind. The International Dialogue, the New Deal, and the g7+ were important foundations, asserting the links between development and peace captured in the Sustainable Development Goals (SDG). However, the SDGs are universal. Goal 16 on just, peaceful, and inclusive societies is an ambition of all countries, not only those identified internationally as conflict-affected, and other goals—for example SDG 1 on ending poverty and SDG 10 on reducing inequality—are critical to peace in conflict-affected states. A statement at Stockholm should be made clarifying the linkages between the specific focus of the New Deal and the universal goals of the SDGs (and their affiliated processes).

The second is ownership. Peace and development are first and foremost a national responsibility. The New Deal provides a framework that brings together multilateral and bilateral partners of conflict-affected countries. However, it has functioned primarily as a tool for the targeting of aid, not its management. To achieve the SDGs in 2030 we need to equip national partners with the tools to address the drivers of conflict. That is where a revitalized New Deal can play an important role. While the SDGs are now the overarching framework, making more significant progress on the TRUST and FOCUS components of the New Deal will be essential contributions to the implementation of the 2030 Agenda. Commitments to ownership, the use of country systems, and mobilization of national resources should be restated and given life in Stockholm.

The last is resources. Resolving conflict requires multi-year financing addressing the drivers of conflict rather than short-term responses.  While official development assistance (ODA) to conflict-affected countries has increased over the last dozen years or so, in 2013, peacebuilding support to legitimate politics, security, and justice systems represented only 16 percent (or $6.8 billion) of the $42 billion in gross development assistance for 31 conflict-affected countries (see Figure 1). At a very moment of global crisis, as of January 1, 2016 and for the first time in its history, the United Nations Peacebuilding Fund will not reach its $100 million annual allocation target endorsed by the secretary-general and donors. Stockholm needs to demonstrate a commitment to peacebuilding and statebuilding that goes beyond words, and commit to more resources devoted to conflict-affected countries and more resources targeting the drivers of conflict.

The U.N. has been a supporter of the New Deal from the beginning, recognizing it as a model for partnership between conflict-affected states and their development partners. A political, prioritized strategy for peacebuilding and statebuilding is necessary to support full implementation of the Sustainable Development Goals in conflict-affected states. The New Deal provides inspiration for such a strategy. The question for Stockholm is whether inspiration alone will be sufficient.

Note: Special thanks goes to Jago Salmon for his contributions. This blog reflects the views of the author only and does not reflect the views of the Africa Growth Initiative. Similarly, the views expressed herein are those of the author(s) and do not necessarily reflect the views of the United Nations.

Almost two years ago, I alerted readers to a contest, sponsored by the Bill and Melinda Gates Foundation through the Global Development Network, to develop new ideas to improve the impact of development cooperation. The Next Horizons Essay contest 2014 received 1,470 submissions from 142 countries, from which 13 winners were selected.

Four of the winners took part in a roundtable at the Brookings Institution yesterday. Here’s a quick synopsis of the main takeaways.

There is a lot of experimentation happening in the delivery of aid, and most aid agencies are thinking hard about how to position themselves to contribute more to the sustainable development goals. In part, this is because these agencies are mission-driven to improve impact. The current system of aid replenishments of multilateral institutions forces them to compete with each other by persuading donors that they are best deserving of the scarce aid budgets being allocated. Even bilateral aid agencies find themselves under budgetary stress, asked to justify the impact of their lending compared to a counterfactual of channeling the money through a multilateral agency or of contributing to an appeal from the United Nations for humanitarian assistance or climate financing.

Stephen Mwangi Macharia talked about using development assistance to promote social impact investing. He noted the problems of sustainability, dependence, and ownership that can arise in traditional aid relationships and argued that social entrepreneurs can avoid such pitfalls. The question then becomes how donors can best help build the market infrastructure to support such efforts. Stephen’s idea: develop a social impact network initiative to build entrepreneurs’ capacity to develop “bankable” projects and to have a database to help match entrepreneurs and funders. 

There is certainly a lot of interest in social impact investing. According to the Global Impact Investing Network, around $60 billion are already under management (although mostly in developed countries) and the market is growing rapidly. Some questioned the role of aid donors however, noting that they could reduce incentives for others (universities, non-profits, etc.) who charge a fee for business development, awareness raising, and other market services. Others questioned the risk tolerance of donors for impact investing and a culture in many countries where business is viewed suspiciously when it tries to intentionally generate positive social and environmental impacts. As an aside, Judith Rodin, president of the Rockefeller Foundation, has noted that the development of impact investing was one of the accomplishments that she was most proud of.

Ray Kennedy suggested that vertical funds, because of better governance and a sharper focus, should be a preferred channel for development assistance. Interestingly, his argument was not based on advocacy for a particular sector, but on the improved adaptability of these institutions. His evidence provided several examples of how vertical funds changed in response to changing global conditions, and, he argued, such change is a highly desirable virtue in our rapidly changing times.

Of course, the recommendation to favor vertical funds did not go unchallenged. There was a lively discussion about the comparative advantage of different institutions and the dangers of mission creep by more effective institutions into space left open by less effective institutions. Yet, most agreed that new platforms were being fluidly created to solve new problems, and that a “mixed coalition,” to borrow a phrase from one of the participants, was part of the preferred solution.

Yuen Yuen Ang took on the problem of local ownership directly. It is easy to talk about local ownership, she said, but few agencies do anything about it in their actual operations. Instead, they promote best practice ideas, some of which may fail even the basic test of “do no harm.” Basing her arguments on the complexity of how organizations change, she advocates specific internal reforms: diversify staff experiences and backgrounds beyond economics and finance; carve out time for staff to pursue “non-standard” approaches; and build a bank of examples about “best-fit” approaches that have been shown to work in weak institutional settings.

A lively discussion followed on best-fit versus best-practice approaches and, indeed, on whether there is a trade-off between the two or whether the issue is how to balance both at the same time. There was agreement that best-practice applies to some issues, especially where global standards have developed (debt management or anti-money laundering, perhaps). Best-fit is more useful when judgement and a deep understanding of local conditions are required. Some questioned the role of external donor agencies in such contexts, however.

Dan Honig argued for greater autonomy of field-based staff. Based on an extensive and unique data set, he was able to test the impact of the degree of autonomy on project success. The econometrics show significant impact of autonomy on certain activities and in certain situations. When the context is fluid and unpredictable, as in fragile states for example, or when judgement is required, as in institutional development, then autonomy can help. But when desired outcomes are easily measurable, such as school or road construction, then autonomy makes little difference.

During the discussion, there was agreement that too much of a focus on metrics could be distortionary and, in fluid situations, could be damaging. The theme of donor risk aversion came up again, but this time coupled with the idea that metrics, however false and misleading they might be, provide comfort and cover for bureaucrats. A sympathetic hearing was given to former United States Agency for International Development Administrator Andrew Natsios’ concept of “obsessive measurement disorder.” But, participants also warned of the need to show that the costs of autonomy, in the form of larger field presence and a limited ability to scale up, outweighed the benefits.

It was refreshing to see new evidence and multidisciplinary approaches being brought to bear on development effectiveness. The four themes highlighted in these essays—making markets work for the poor, improving agency governance, local ownership and contextualization, and decentralization and autonomy—resonated with those participants who are, or had been, active in aid agencies. I thank the Global Development Network and the Bill and Melinda Gates Foundation for this initiative, as well as to the winning scholars for injecting new ideas into the discourse.

Last month, we experienced yet another breakthrough in the epic battle of man against machine. Google’s AlphaGo won against the reigning Go champion Lee Sedol. This success, however, was different than that of IBM’s Deep Blue against Gary Kasparov in 1987. While Deep Blue still applied “brute force” to calculate all possible options ahead, AlphaGo was learning as the game progressed. And through this computing breakthrough that we can learn how to better leverage the data revolution.

In the game of Go, brute-force strategies don’t help because the total number of possible combinations exceeds the number of atoms in the universe. Some games, including some we played since childhood, were immune to computing “firepower” for a long time. For example, Connect Four wasn’t solved until 1995 with the conclusion being the first player can force a win. And checkers wasn’t until 2007, when Jonathan Schaeffer determined that in a perfect game, both sides could force a draw. For chess, a safe strategy has yet to be developed, meaning that we don’t know yet if white could force a win or, like in checkers, black could manage to hold on to a draw.

But most real-life situations are more complicated than chess, precisely because the universe of options is unlimited and solving them requires learning. If computers are to help, beyond their use as glorified calculators, they need to be able to learn. This is the starting point of the artificial intelligence movement.  In a world where perfection is impossible, you need well-informed intuition in order to advance. The first breakthrough in this space occurred when IBM’s Watson beat America’s Jeopardy! champions in 2011. These new intelligent machines operate in probabilities, not in certainty.

That being said, perfection remains important, especially when it comes to matters of life and death such as flying airplanes, constructing houses, or conducting heart surgery, as these areas require as much attention to detail as possible. At the same time, in many realms of life and policymaking we fall into a perfection trap. We often generate obsolete knowledge by attempting to explain things perfectly, when effective problem solving would have been better served by real-time estimates. We strive for exactitude when rough results, more often than not, are good enough.

By contrast, some of today’s breakthroughs are based on approximation. Think of Google Translate and Google’s search engine itself. The results are typically quite bad, but compared to the alternative of not having them at all, or spending hours leafing through an encyclopedia, they are wonderful. Moreover, once these imperfect breakthroughs are available, one can improve them iteratively. Only once the first IBM and Apple PCs were put on the market in the 1980s did the cycle of upgrading start, which still continues today.

In the realm of social and economic data, we have yet to reach this stage of “managed imperfection” and continuous upgrading. We are producing social and economic forecasts with solid 20th century methods. With extreme care we conduct poverty assessments and maps, usually taking at least a year to produce as they involve hundreds of enumerators, lengthy interviews and laborious data entry. Through these methods we are able to perfectly explain past events, but we fail to estimate current trends—even imperfectly.

The paradox of today’s big data era is that most of that data is poor and messy, even though the possibilities for improving it are unlimited. Almost every report from development institutions starts with a disclaimer highlighting “severe data limitations.” This is because only 0.5 percent of all the available data is actually being curated to be made usable. If data is the oil of the 21st century, we need data refineries to convert the raw product into something that can be consumed by the average person.

Thanks to the prevalence of mobile device and rapid advances in satellite technology, it is possible to produce more data faster, better, and cheaper. High-frequency data also makes it possible to make big data personal, which also increases the likelihood that people act on it. Ultimately, the breakthroughs in big data for development will be driven by managerial cultures, as has been the case with other successful ventures. Risk averse cultures pay great attention to perfection. They nurture the fear of mistakes and losing. Modern management accepts failure, encourages trial and error, and reaches progress through interaction and continuous upgrading.

Event Information

Since the Rose Revolution in November 2003, Georgia has grappled with the many challenges of building a modern, Western-oriented state, including implementing political and economic reforms, fighting corruption, and throwing off the vestiges of the Soviet legacy. On the path toward a functioning and reliable democracy, Georgia has pursued these domestic changes in an often difficult international environment, as evidenced by the Russia-Georgia conflict in 2008.

On March 17, the Center on the United States and Europe at Brookings (CUSE) hosted President Mikheil Saakashvili to discuss Georgia’s approach to these challenges. A leader of Georgia’s 2003 Rose Revolution, Saakashvili was elected president of Georgia in January 2004 and reelected for a second term in January 2008.

Vice President Martin Indyk, director of Foreign Policy at Brookings, provided introductory remarks and Senior Fellow and CUSE Director Fiona Hill moderated the discussion. After the program, President Saakashvili took audience questions.

Video

• Georgia Is a Transformed Country
• Georgia Is a Valuable Asset to Europe
• The Key to Effective Change Is Youth

Audio

• From Popular Revolutions to Effective Reforms: The Georgian Experience

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• Uncorrected Transcript (.pdf)

Event Materials

• 20110317_saakashvili_transcript

Many proposals to accelerate and improve medical product innovation and regulation focus on reforming the product development and regulatory review processes that occur before drugs and devices get to market. While important, such proposals alone do not fully recognize the broader opportunities that exist to learn more about the safety and effectiveness of drugs and devices after approval. As drugs and devices begin to be used in larger and more diverse populations and in more personalized clinical combinations, evidence from real-world use during routine patient care is increasingly important for accelerating innovation and improving regulation.

First, further evidence development from medical product use in large populations can allow providers to better target and treat individuals, precisely matching the right drug or device to the right patients. As genomic sequencing and other diagnostic technologies continue to improve, postmarket evidence development is critical to assessing the full range of genomic subtypes, comorbidities, patient characteristics and preferences, and other factors that may significantly affect the safety and effectiveness of drugs and devices. This information is often not available or population sizes are inadequate to characterize such subgroup differences in premarket randomized controlled trials.

Second, improved processes for generating postmarket data on medical products are necessary for fully realizing the intended effect of premarket reforms that expedite regulatory approval. The absence of a reliable postmarket system to follow up on potential safety or effectiveness issues means that potential signals or concerns must instead be addressed through additional premarket studies or through one-off postmarket evaluations that are more costly, slower, and likely to be less definitive than would be possible through a better-established infrastructure. As a result, the absence of better systems for generating postmarket evidence creates a barrier to more extensive use of premarket reforms to promote innovation.

These issues can be addressed through initiatives that combine targeted premarket reforms with postmarket steps to enhance innovation and improve evidence on safety and effectiveness throughout the life cycle of a drug or device. The ability to routinely capture clinically relevant electronic health data within our health care ecosystem is improving, increasingly allowing electronic health records, payer claims data, patient-reported data, and other relevant data to be leveraged for further research and innovation in care. Recent legislative proposals released by the House of Representatives’ 21st Century Cures effort acknowledge and seek to build on this progress in order to improve medical product research, development, and use. The initial Cures discussion draft included provisions for better, more systematic reporting of and access to clinical trials data; for increased access to Medicare claims data for research; and for FDA to promulgate guidance on the sources, analysis, and potential use of so-called Real World Evidence. These are potentially useful proposals that could contribute valuable data and methods to advancing the development of better treatments.

What remains a gap in the Cures proposals, however, is a more systematic approach to improving the availability of postmarket evidence. Such a systematic approach is possible now. Biomedical researchers and health care plans and providers are doing more to collect and analyze clinical and outcomes data. Multiple independent efforts – including the U.S. Food and Drug Administration’s Sentinel Initiative for active postmarket drug safety surveillance, the Patient-Centered Outcomes Research Institute’s PCORnet for clinical effectiveness studies, the Medical Device Epidemiology Network (MDEpiNet) for developing better methods and medical device registries for medical device surveillance and a number of dedicated, product-specific outcomes registries – have demonstrated the potential for large-scale, systematic postmarket data collection. Building on these efforts could provide unprecedented evidence on how medical products perform in the real-world and on the course of underlying diseases that they are designed to treat, while still protecting patient privacy and confidentiality.

These and other postmarket data systems now hold the potential to contribute to public-private collaboration for improved population-based evidence on medical products on a wider scale. Action in the Cures initiative to unlock this potential will enable the legislation to achieve its intended effect of promoting quicker, more efficient development of effective, personalized treatments and cures.

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