Archive for the ‘health care’ Category

Question Authority: Queries In the Back of the Wall Street Demonstrators’ Minds

October 2, 2011

I think the Wall Street demonstrators’ lack of goals and the admission of not having a solution is very important. All solutions offered so far are band-aids at best, and most are likely to do more harm than good.

I think I have an innovative way of articulating the questions people have on their minds. I thought of scattering small pieces of paper anywhere there are these demonstrations going on, with questions like these on them:

Feeling robbed of the trust, loyalty, and commitment you invested?

Unable to get a good return on your investment in your education?

Feeling robbed of your share of the world’s natural resources?

How many shares of social capital do you own?

How many shares of literacy capital do you have on the market?

How many shares of health capital do you own?

How many shares of natural capital do you own?

Wishing there was an easy way to know what return rate you get on your health investments?

Wishing there was an easy way to know what return rate you get on your education investments?

Why don’t you have legal title to your literacy capital shares?

Why don’t you have legal title to your social capital shares?

Why don’t you have legal title to your health capital shares?

Why don’t you have legal title to your natural capital shares?

Why don’t you know how many literacy capital shares are rightfully yours?

Why don’t you know how many social capital shares are rightfully yours?

Why don’t you know how many health capital shares are rightfully yours?

Why don’t you know how many natural capital shares are rightfully yours?

Why is there no common currency for trading on your literacy capital?

Why is there no common currency for trading on your health capital?

Why is there no common currency for trading on your social capital?

Why is there no common currency for trading on your natural capital?

Why aren’t corporations accountable for their impacts on your literacy capital investments?

Why aren’t corporations accountable for their impacts on your natural capital investments?

Why aren’t corporations accountable for their impacts on your social capital investments?

Why aren’t corporations accountable for their impacts on your health capital investments?

Why aren’t governments accountable for their impacts on your literacy capital investments?

Why aren’t governments accountable for their impacts on your natural capital investments?

Why aren’t governments accountable for their impacts on your social capital investments?

Why aren’t governments accountable for their impacts on your health capital investments?

Why are educational outcomes not comparable in a common metric?

Why are health care outcomes not comparable in a common metric?

Why are social program outcomes not comparable in a common metric?

Why are natural resource management program outcomes not comparable in a common metric?

Why do accounting and economics focus on land, labor, and manufactured capital instead of putting the value of ecosystem services, and health, literacy, and social capital, on the books and in the models, along with property and manufactured capital?

If we truly do manage what we measure, why don’t we have a metric system for literacy capital?

Can we effectively manage literacy capital if we don’t have a universally recognized and accepted metric for it?

If we truly do manage what we measure, why don’t we have a metric system for health capital?

Can we effectively manage health capital if we don’t have a universally recognized and accepted metric for it?

If we truly do manage what we measure, why don’t we have a metric system for social capital?

Can we effectively manage social capital if we don’t have a universally recognized and accepted metric for it?

If we truly do manage what we measure, why don’t we have a metric system for natural capital?

Can we effectively manage natural capital if we don’t have a universally recognized and accepted metric for it?

How is our collective imagination being stifled by the lack of a common language for literacy capital?

How is our collective imagination being stifled by the lack of a common language for health capital?

How is our collective imagination being stifled by the lack of a common language for social capital?

How is our collective imagination being stifled by the lack of a common language for natural capital?

How can the voice of the people be heard without common languages for things that are important to us?

How do we know where we stand as individuals and as a society if we can’t track the value and volume of our literacy, health, social, and natural capital shares?

Why don’t NIST and NSF fund new research into literacy, health, social, and natural capital metrics?

Why aren’t banks required to offer literacy, health, social, and natural capital accounts?

If we want to harmonize relationships between people, within and between societies, and between culture and nature, why don’t we tune the instruments on which we play the music of our lives?

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
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New Opportunities for Job Creation and Prosperity

August 17, 2011

What can be done to create jobs and revive the economy? There is no simple, easy answer to this question. Creating busywork is nonsense. We need fulfilling occupations that meet the world’s demand for products and services. It is not easy to see how meaningful work can be systematically created on a broad scale. New energy efficiencies may lead to the cultivation of significant job growth, but it may be unwise to put all of our eggs in this one basket.

So how are we to solve this puzzle? What other areas in the economy might be ripe for the introduction of a new technology capable of supporting a wave of new productivity, like computers did in the 1980s, or the Internet in the 1990s? In trying to answer this question, simplicity and elegance are key factors in keeping things at a practical level.

For instance, we know we accomplish more working together as a team than as disconnected individuals. New jobs, especially new kinds of jobs, will have to be created via innovation. Innovation in science and industry is a team sport. So the first order of business in teaming up for job creation is to know the rules of the game. The economic game is played according to the rules of law embodied in property rights, scientific rationality, capital markets, and transportation/communications networks (see William Bernstein’s 2004 book, The Birth of Plenty). When these conditions are met, as they were in Europe and North America at the beginning of the nineteenth century, the stage is set for long term innovation and growth on a broad scale.

The second order of business is to identify areas in the economy that lack one or more of these four conditions, and that could reasonably be expected to benefit from their introduction. Education, health care, social services, and environmental management come immediately to mind. These industries are plagued with seemingly interminable inflationary spirals, which, no doubt, are at least in part caused by the inability of investors to distinguish between high and low performers. Money cannot flow to and reward programs producing superior results in these industries because they lack common product definitions and comparable measures of their results.

The problems these industries are experiencing are not specific to each of them in particular. Rather, the problem is a general one applicable across all industries, not just these. Traditionally, economic thinking focuses on three main forms of capital: land, labor, and manufactured products (including everything from machines, roads, and buildings to food, clothing, and appliances). Cash and credit are often thought of as liquid capital, but their economic value stems entirely from the access they provide to land, labor, and manufactured products.

Economic activity is not really, however, restricted to these three forms of capital. Land is far more than a piece of ground. What are actually at stake are the earth’s regenerative ecosystems, with the resources and services they provide. And labor is far more than a pair of skilled hands; people bring a complex mix of abilities, motivations, and health to bear in their work. Finally, this scheme lacks an essential element: the trust, loyalty, and commitment required for even the smallest economic exchange to take place. Without social capital, all the other forms of capital (human, natural, and manufactured, including property) are worthless. Consistent, sustainable, and socially responsible economic growth requires that all four forms of capital be made accountable in financial spreadsheets and economic models.

The third order of business, then, is to ask if the four conditions laying out the rules for the economic game are met in each of the four capital domains. The table below suggests that all four conditions are fully met only for manufactured products. They are partially met for natural resources, such as minerals, timber, fisheries, etc., but not at all for nature’s air and water purification systems or broader genetic ecosystem services.

 Table

Existing Conditions Relevant to Conceiving a New Birth of Plenty, by Capital Domains

Human

Social

Natural

Manufactured

Property rights

No

No

Partial

Yes

Scientific rationality

Partial

Partial

Partial

Yes

Capital markets

Partial

Partial

Partial

Yes

Transportation & communication networks

Partial

Partial

Partial

Yes

That is, no provisions exist for individual ownership of shares in the total available stock of air and water, or of forest, watershed, estuary, and other ecosystem service outcomes. Nor do any individuals have free and clear title to their most personal properties, the intangible abilities, motivations, health, and trust most essential to their economic productivity. Aggregate statistics are indeed commonly used to provide a basis for policy and research in human, social, and natural capital markets, but falsifiable models of individually applicable unit quantities are not widely applied. Scientifically rational measures of our individual stocks of intangible asset value will require extensive use of these falsifiable models in calibrating the relevant instrumentation.

Without such measures, we cannot know how many shares of stock in these forms of capital we own, or what they are worth in dollar terms. We lack these measures, even though decades have passed since researchers first established firm theoretical and practical foundations for them. And more importantly, even when scientifically rational individual measures can be obtained, they are never expressed in terms of a unit standardized for use within a given market’s communications network.

So what are the consequences for teams playing the economic game? High performance teams’ individual decisions and behaviors are harmonized in ways that cannot otherwise be achieved only when unit amounts, prices, and costs are universally comparable and publicly available. This is why standard currencies and exchange rates are so important.

And right here we have an insight into what we can do to create jobs. New jobs are likely going to have to be new kinds of jobs resulting from innovations. As has been detailed at length in recent works such as Surowiecki’s 2004 book, The Wisdom of Crowds, innovation in science and industry depends on standards. Standards are common languages that enable us to multiply our individual cognitive powers into new levels of collective productivity. Weights and measures standards are like monetary currencies; they coordinate the exchange of value in laboratories and businesses in the same way that dollars do in the US economy.

Applying Bernstein’s four conditions for economic growth to intangible assets, we see that a long term program for job creation then requires

  1. legislation establishing human, social, and natural capital property rights, and an Intangible Assets Metrology System;
  2. scientific research into consensus standards for measuring human, social, and natural capital;
  3. venture capital educational and marketing programs; and
  4. distributed information networks and computer applications through which investments in human, social, and natural capital can be tracked and traded in accord with the rule of law governing property rights and in accord with established consensus standards.

Of these four conditions, Bernstein (p. 383) points to property rights as being the most difficult to establish, and the most important for prosperity. Scientific results are widely available in online libraries. Capital can be obtained from investors anywhere. Transportation and communications services are available commercially.

But valid and verifiable means of representing legal title to privately owned property is a problem often not yet solved even for real estate in many Third World and former communist countries (see De Soto’s 2000 book, The Mystery of Capital). Creating systems for knowing the quality and quantity of educational, health care, social, and environmental service outcomes is going to be a very difficult process. It will not be impossible, however, and having the problem identified advances us significantly towards new economic possibilities.

We need leaders able and willing to formulate audacious goals for new economic growth from ideas such as these. We need enlightened visionaries able to see our potentials from a new perspective, and who can reflect our new self-image back at us. When these leaders emerge—and they will, somewhere, somehow—the imaginations of millions of entrepreneurial thinkers and actors will be fired, and new possibilities will unfold.

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
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Science, Public Goods, and the Monetization of Commodities

August 13, 2011

Though I haven’t read Philip Mirowski’s new book yet (Science-Mart: Privatizing American Science. Cambridge, MA: Harvard University Press, 2011), a statement in the cover blurb given at Amazon.com got me thinking. I can’t help but wonder if there is another way of interpreting neoliberal ideology’s “radically different view of knowledge and discovery: [that] the fruits of scientific investigation are not a public good that should be freely available to all, but are commodities that could be monetized”?

Corporations and governments are not the only ones investing in research and new product development, and they are not the only ones who could benefit from the monetization of the fruits of scientific investigation. Individuals make these investments as well, and despite ostensible rights to private ownership, no individuals anywhere have access to universally comparable, uniformly expressed, and scientifically valid information on the quantity or quality of the literacy, health, community, or natural capital that is rightfully theirs. They accordingly also then do not have any form of demonstrable legal title to these properties. In the same way that corporations have successfully advanced their economic interests by seeing that patent and intellectual property laws were greatly strengthened, so, too, ought individuals and communities advance their economic interests by, first, expanding the scope of weights and measures standards to include intangible assets, and second, by strengthening laws related to the ownership of privately held stocks of living capital.

The nationalist and corporatist socialization of research will continue only as long as social capital, human capital, and natural capital are not represented in the universally uniform common currencies and transparent media that could be provided by an intangible assets metric system. When these forms of capital are brought to economic life in fungible measures akin to barrels, bushels, or kilowatts, then they will be monetized commodities in the full capitalist sense of the term, ownable and purchasable products with recognizable standard definitions, uniform quantitative volumes, and discernable variations in quality. Then, and only then, will individuals gain economic control over their most important assets. Then, and only then, will we obtain the information we need to transform education, health care, social services, and human and natural resource management into industries in which quality is appropriately rewarded. Then, and only then, will we have the means for measuring genuine progress and authentic wealth in ways that correct the insufficiencies of the GNP/GDP indexes.

The creation of efficiently functioning markets for all forms of capital is an economic, political, and moral necessity (see Ekins, 1992 and others). We say we manage what we measure, but very little effort has been put into measuring (with scientific validity and precision in universally uniform and accessible aggregate terms) 90% of the capital resources under management: human abilities, motivations, and health; social commitment, loyalty, and trust; and nature’s air and water purification and ecosystem services (see Hawken, Lovins, & Lovins, 1999, among others). All human suffering, sociopolitical discontent, and environmental degradation are rooted in the same common cause: waste (see Hawken, et al., 1999). To apply lean thinking to removing the wasteful destruction of our most valuable resources, we must measure these resources in ways that allow us to coordinate and align our decisions and behaviors virtually, at a distance, with no need for communicating and negotiating the local particulars of the hows and whys of our individual situations. For more information on these ideas, search “living capital metrics” and see works like the following:

Ekins, P. (1992). A four-capital model of wealth creation. In P. Ekins & M. Max-Neef (Eds.), Real-life economics: Understanding wealth creation (pp. 147-15). London: Routledge.

Fisher, W. P., Jr. (2009). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 42(9), 1278-1287.

Hawken, P., Lovins, A., & Lovins, H. L. (1999). Natural capitalism: Creating the next industrial revolution. New York: Little, Brown, and Co.

Latour, B. (1987). Science in action: How to follow scientists and engineers through society. New York: Cambridge University Press.

Latour, B. (2005). Reassembling the social: An introduction to Actor-Network-Theory. (Clarendon Lectures in Management Studies). Oxford, England: Oxford University Press.

Miller, P., & O’Leary, T. (2007). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-34.

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Debt, Revenue, and Changing the Way Washington Works: The Greatest Entrepreneurial Opportunity of Our Time

July 30, 2011

“Holding the line” on spending and taxes does not make for a fundamental transformation of the way Washington works. Simply doing less of one thing is just a small quantitative change that does nothing to build positive results or set a new direction. What we need is a qualitative metamorphosis akin to a caterpillar becoming a butterfly. In contrast with this beautiful image of natural processes, the arguments and so-called principles being invoked in the sham debate that’s going on are nothing more than fights over where to put deck chairs on the Titanic.

What sort of transformation is possible? What kind of a metamorphosis will start from who and where we are, but redefine us sustainably and responsibly? As I have repeatedly explained in this blog, my conference presentations, and my publications, with numerous citations of authoritative references, we already possess all of the elements of the transformation. We have only to organize and deploy them. Of course, discerning what the resources are and how to put them together is not obvious. And though I believe we will do what needs to be done when we are ready, it never hurts to prepare for that moment. So here’s another take on the situation.

Infrastructure that supports lean thinking is the name of the game. Lean thinking focuses on identifying and removing waste. Anything that consumes resources but does not contribute to the quality of the end product is waste. We have enormous amounts of wasteful inefficiency in many areas of our economy. These inefficiencies are concentrated in areas in which management is hobbled by low quality information, where we lack the infrastructure we need.

Providing and capitalizing on this infrastructure is The Greatest Entrepreneurial Opportunity of Our Time. Changing the way Washington (ha! I just typed “Wastington”!) works is the same thing as mitigating the sources of risk that caused the current economic situation. Making government behave more like a business requires making the human, social, and natural capital markets more efficient. Making those markets more efficient requires reducing the costs of transactions. Those costs are determined in large part by information quality, which is a function of measurement.

It is often said that the best way to reduce the size of government is to move the functions of government into the marketplace. But this proposal has never been associated with any sense of the infrastructural components needed to really make the idea work. Simply reducing government without an alternative way of performing its functions is irresponsible and destructive. And many of those who rail on and on about how bad or inefficient government is fail to recognize that the government is us. We get the government we deserve. The government we get follows directly from the kind of people we are. Government embodies our image of ourselves as a people. In the US, this is what having a representative form of government means. “We the people” participate in our society’s self-governance not just by voting, writing letters to congress, or demonstrating, but in the way we spend our money, where we choose to live, work, and go to school, and in every decision we make. No one can take a breath of air, a drink of water, or a bite of food without trusting everyone else to not carelessly or maliciously poison them. No one can buy anything or drive down the street without expecting others to behave in predictable ways that ensure order and safety.

But we don’t just trust blindly. We have systems in place to guard against those who would ruthlessly seek to gain at everyone else’s expense. And systems are the point. No individual person or firm, no matter how rich, could afford to set up and maintain the systems needed for checking and enforcing air, water, food, and workplace safety measures. Society as a whole invests in the infrastructure of measures created, maintained, and regulated by the government’s Department of Commerce and the National Institute for Standards and Technology (NIST). The moral importance and the economic value of measurement standards has been stressed historically over many millennia, from the Bible and the Quran to the Magna Carta and the French Revolution to the US Constitution. Uniform weights and measures are universally recognized and accepted as essential to fair trade.

So how is it that we nonetheless apparently expect individuals and local organizations like schools, businesses, and hospitals to measure and monitor students’ abilities; employees’ skills and engagement; patients’ health status, functioning, and quality of care; etc.? Why do we not demand common currencies for the exchange of value in human, social, and natural capital markets? Why don’t we as a society compel our representatives in government to institute the will of the people and create new standards for fair trade in education, health care, social services, and environmental management?

Measuring better is not just a local issue! It is a systemic issue! When measurement is objective and when we all think together in the common language of a shared metric (like hours, volts, inches or centimeters, ounces or grams, degrees Fahrenheit or Celsius, etc.), then and only then do we have the means we need to implement lean strategies and create new efficiencies systematically. We need an Intangible Assets Metric System.

The current recession in large part was caused by failures in measuring and managing trust, responsibility, loyalty, and commitment. Similar problems in measuring and managing human, social, and natural capital have led to endlessly spiraling costs in education, health care, social services, and environmental management. The problems we’re experiencing in these areas are intimately tied up with the way we formulate and implement group level decision making processes and policies based in statistics when what we need is to empower individuals with the tools and information they need to make their own decisions and policies. We will not and cannot metamorphose from caterpillar to butterfly until we create the infrastructure through which we each can take full ownership and control of our individual shares of the human, social, and natural capital stock that is rightfully ours.

We well know that we manage what we measure. What counts gets counted. Attention tends to be focused on what we’re accountable for. But–and this is vitally important–many of the numbers called measures do not provide the information we need for management. And not only are lots of numbers giving us low quality information, there are far too many of them! We could have better and more information from far fewer numbers.

Previous postings in this blog document the fact that we have the intellectual, political, scientific, and economic resources we need to measure and manage human, social, and natural capital for authentic wealth. And the issue is not a matter of marshaling the will. It is hard to imagine how there could be more demand for better management of intangible assets than there is right now. The problem in meeting that demand is a matter of imagining how to start the ball rolling. What configuration of investments and resources will start the process of bursting open the chrysalis? How will the demand for meaningful mediating instruments be met in a way that leads to the spreading of the butterfly’s wings? It is an exciting time to be alive.

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
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Translating Gingrich’s Astute Observations on Health Care

June 30, 2011

“At the very heart of transforming health and healthcare is one simple fact: it will require a commitment by the federal government to invest in science and discovery. The period between investment and profit for basic research is too long for most companies to ever consider making the investment. Furthermore, truly basic research often produces new knowledge that everyone can use, so there is no advantage to a particular company to make the investment. The result is that truly fundamental research is almost always a function of government and foundations because the marketplace discourages focusing research in that direction” (p. 169 in Gingrich, 2003).

Gingrich says this while recognizing (p. 185) that:

“Money needs to be available for highly innovative ‘out of the box’ science. Peer review is ultimately a culturally conservative and risk-averse model. Each institution’s director should have a small amount of discretionary money, possibly 3% to 5% of their budget, to spend on outliers.”

He continues (p. 170), with some important elaborations on the theme:

“America’s economic future is a direct function of our ability to take new scientific research and translate it into entrepreneurial development.”

“The [Hart/Rudman] Commission’s second conclusion was that the failure to invest in scientific research and the failure to reform math and science education was the second largest threat to American security [behind terrorism].”

“Our goal [in the Hart/Rudman Commission] was to communicate the centrality of the scientific endeavor to American life and the depth of crisis we believe threatens the math and science education system. The United States’ ability to lead today is a function of past investments in scientific research and math and science education. There is no reason today to believe we will automatically maintain that lead especially given our current investments in scientific research and the staggering levels of our failures in math and science education.”

“Our ability to lead in 2025 will be a function of current decisions. Increasing our investment in science and discovery is a sound and responsible national security policy. No other federal expenditure will do more to create jobs, grow wealth, strengthen our world leadership, protect our environment, promote better education, or ensure better health for the country. We must make this increase now.”

On p. 171, this essential point is made:

“In health and healthcare, it is particularly important to increase our investment in research.”

This is all good. I agree completely. What NG says is probably more true than he realizes, in four ways.

First, the scientific capital created via metrology, controlled via theory, and embodied in technological instruments is the fundamental driver of any economy. The returns on investments in metrological improvements range from 40% to over 400% (NIST, 1996). We usually think of technology and technical standards in terms of computers, telecommunications, and electronics, but there actually is not anything at all in our lives untouched by metrology, since the air, water, food, clothing, roads, buildings, cars, appliances, etc. are all monitored, maintained, and/or manufactured relative to various kinds of universally uniform standards. NG is, as most people are, completely unaware that such standards are feasible and already under development for health, functionality, quality of life, quality of care, math and science education, etc. Given the huge ROIs associated with metrological improvements, there ought to be proportionately huge investments being made in metrology for human, social, and natural capital.

Second, NG’s point concerning national security is right on the mark, though for reasons that go beyond the ones he gives. There are very good reasons for thinking investments in, and meaningful returns from, the basic science for human, social, and natural capital metrology could be expected to undercut the motivations for terrorism and the retreats into fundamentalisms of various kinds that emerge in the face of the failures of liberal democracy (Marty, 2001). Making all forms of capital measured, managed, and accountable within a common framework accessible to everyone everywhere could be an important contributing factor, emulating the property titling rationale of DeSoto (1989, 2000) and the support for distributed cognition at the social level provided by metrological networks (Latour, 1987, 2005; Magnus, 2007), The costs of measurement can be so high as to stifle whole economies (Barzel, 1982), which is, broadly speaking, the primary problem with the economies of education, health care, social services, philanthropy, and environmental management (see, for instance, regarding philanthropy, Goldberg, 2009). Building the legal and financial infrastructure for low-friction titling and property exchange has become a basic feature of World Bank and IMF projects. My point, ever since I read De Soto, has been that we ought to be doing the same thing for human, social, and natural capital, facilitating explicit ownership of the skills, motivations, health, trust, and environmental resources that are rightfully the property of each of us, and that similar effects on national security ought to follow.

Third, NG makes an excellent point when he stresses the need for health and healthcare to be individual-centered, saying that, in contrast with the 20th-century healthcare system, “In the 21st Century System of Health and Healthcare, you will own your medical record, control your healthcare dollars, and be able to make informed choices about healthcare providers.” This is basically equivalent to saying that health capital needs to be fungible, and it can’t be fungible, of course, without a metrological infrastructure that makes every measure of outcomes, quality of life, etc. traceable to a reference standard. Individual-centeredness is also, of course, what distinguishes proper measurement from statistics. Measurement supports inductive inference, from the individual to the population, where statistics are deductive, going from the population to the individual (Fisher & Burton, 2010; Fisher, 2010). Individual-centered healthcare will never go anywhere without properly calibrated instrumentation and the traceability to reference standards that makes measures meaningful.

Fourth, NG repeatedly indicates how appalled he is at the slow pace of change in healthcare, citing research showing that it can take up to 17 years for doctors to adopt new procedures. I contend that this is an effect of our micromanagement of dead, concrete forms of capital. In a fluid living capital market, not only will consumers be able to reward quality in their purchasing decisions by having the information they need when they need it and in a form they can understand, but the quality improvements will be driven from the provider side in much the same way. As Brent James has shown, readily available, meaningful, and comparable information on natural variation in outcomes makes it much easier for providers to improve results and reduce the variation in them. Despite its central importance and the many years that have passed, however, the state of measurement in health care remains in dire need of dramatic improvement. Fryback (1993, p. 271; also see Kindig, 1999) succinctly put the point, observing that the U.S.

“health care industry is a $900 + billion [over $2.5 trillion in 2009 (CMS, 2011] endeavor that does not know how to measure its main product: health. Without a good measure of output we cannot truly optimize efficiency across the many different demands on resources.”

Quantification in health care is almost universally approached using methods inadequate to the task, resulting in ordinal and scale-dependent scores that cannot take advantage of the objective comparisons provided by invariant, individual-level measures (Andrich, 2004). Though data-based statistical studies informing policy have their place, virtually no effort or resources have been invested in developing individual-level instruments traceable to universally uniform metrics that define the outcome products of health care. These metrics are key to efficiently harmonizing quality improvement, diagnostic, and purchasing decisions and behaviors in the manner described by Berwick, James, and Coye (2003) without having to cumbersomely communicate the concrete particulars of locally-dependent scores (Heinemann, Fisher, & Gershon, 2006). Metrologically-based common product definitions will finally make it possible for quality improvement experts to implement analogues of the Toyota Production System in healthcare, long presented as a model but never approached in practice (Coye, 2001).

So, what does all of this add up to? A new division for human, social, and natural capital in NIST is in order, with extensive involvement from NIH, CMS, AHRQ, and other relevant agencies. Innovative measurement methods and standards are the “out of the box” science NG refers to. Providing these tools is the definitive embodiment of an appropriate role for government. These are the kinds of things that we could have a productive conversation with NG about, it seems to me….

References

 Andrich, D. (2004, January). Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care, 42(1), I-7–I-16.

Barzel, Y. (1982). Measurement costs and the organization of markets. Journal of Law and Economics, 25, 27-48.

Berwick, D. M., James, B., & Coye, M. J. (2003, January). Connections between quality measurement and improvement. Medical Care, 41(1 (Suppl)), I30-38.

Centers for Medicare and Medicaid Services. (2011). National health expenditure data: NHE fact sheet. Retrieved 30 June 2011, from https://www.cms.gov/NationalHealthExpendData/25_NHE_Fact_Sheet.asp.

Coye, M. J. (2001, November/December). No Toyotas in health care: Why medical care has not evolved to meet patients’ needs. Health Affairs, 20(6), 44-56.

De Soto, H. (1989). The other path: The economic answer to terrorism. New York: Basic Books.

De Soto, H. (2000). The mystery of capital: Why capitalism triumphs in the West and fails everywhere else. New York: Basic Books.

Fisher, W. P., Jr. (2010). Statistics and measurement: Clarifying the differences. Rasch Measurement Transactions, 23(4), 1229-1230 [http://www.rasch.org/rmt/rmt234.pdf].

Fisher, W. P., Jr., & Burton, E. (2010). Embedding measurement within existing computerized data systems: Scaling clinical laboratory and medical records heart failure data to predict ICU admission. Journal of Applied Measurement, 11(2), 271-287.

Fryback, D. (1993). QALYs, HYEs, and the loss of innocence. Medical Decision Making, 13(4), 271-2.

Gingrich, N. (2008). Real change: From the world that fails to the world that works. Washington, DC: Regnery Publishing.

Goldberg, S. H. (2009). Billions of drops in millions of buckets: Why philanthropy doesn’t advance social progress. New York: Wiley.

Heinemann, A. W., Fisher, W. P., Jr., & Gershon, R. (2006). Improving health care quality with outcomes management. Journal of Prosthetics and Orthotics, 18(1), 46-50 [http://www.oandp.org/jpo/library/2006_01S_046.asp].

Kindig, D. A. (1997). Purchasing population health. Ann Arbor, Michigan: University of Michigan Press.

Kindig, D. A. (1999). Purchasing population health: Aligning financial incentives to improve health outcomes. Nursing Outlook, 47, 15-22.

Latour, B. (1987). Science in action: How to follow scientists and engineers through society. New York: Cambridge University Press.

Latour, B. (2005). Reassembling the social: An introduction to Actor-Network-Theory. (Clarendon Lectures in Management Studies). Oxford, England: Oxford University Press.

Magnus, P. D. (2007). Distributed cognition and the task of science. Social Studies of Science, 37(2), 297-310.

Marty, M. (2001). Why the talk of spirituality today? Some partial answers. Second Opinion, 6, 53-64.

Marty, M., & Appleby, R. S. (Eds.). (1993). Fundamentalisms and society: Reclaiming the sciences, the family, and education. The fundamentalisms project, vol. 2. Chicago: University of Chicago Press.

National Institute for Standards and Technology. (1996). Appendix C: Assessment examples. Economic impacts of research in metrology. In Committee on Fundamental Science, Subcommittee on Research (Ed.), Assessing fundamental science: A report from the Subcommittee on Research, Committee on Fundamental Science. Washington, DC: National Standards and Technology Council

[http://www.nsf.gov/statistics/ostp/assess/nstcafsk.htm#Topic%207; last accessed 30 June 2011].

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Measurement, Metrology, and the Birth of Self-Organizing, Complex Adaptive Systems

February 28, 2011

On page 145 of his book, The Mathematics of Measurement: A Critical History, John Roche quotes Charles de La Condamine (1701-1774), who, in 1747, wrote:

‘It is quite evident that the diversity of weights and measures of different countries, and frequently in the same province, are a source of embarrassment in commerce, in the study of physics, in history, and even in politics itself; the unknown names of foreign measures, the laziness or difficulty in relating them to our own give rise to confusion in our ideas and leave us in ignorance of facts which could be useful to us.’

Roche (1998, p. 145) then explains what de La Condamine is driving at, saying:

“For reasons of international communication and of civic justice, for reasons of stability over time and for accuracy and reliability, the creation of exact, reproducible and well maintained international standards, especially of length and mass, became an increasing concern of the natural philosophers of the seventeenth and eighteenth centuries. This movement, cooperating with a corresponding impulse in governing circles for the reform of weights and measures for the benefit of society and trade, culminated in late eighteenth century France in the metric system. It established not only an exact, rational and international system of measuring length, area, volume and mass, but introduced a similar standard for temperature within the scientific community. It stimulated a wider concern within science to establish all scientific units with equal rigour, basing them wherever possible on the newly established metric units (and on the older exact units of time and angular measurement), because of their accuracy, stability and international availability. This process gradually brought about a profound change in the notation and interpretation of the mathematical formalism of physics: it brought about, for the first time in the history of the mathematical sciences, a true union of mathematics and measurement.”

As it was in the seventeenth and eighteenth centuries for physics, so it has also been in the twentieth and twenty-first for the psychosocial sciences. The creation of exact, reproducible and well maintained international standards is a matter of increasing concern today for the roles they will play in education, health care, the work place, business intelligence, and the economy at large.

As the economic crises persist and perhaps worsen, demand for common product definitions and for interpretable, meaningful measures of impacts and outcomes in education, health care, social services, environmental management, etc. will reach a crescendo. We need an exact, rational and international system of measuring literacy, numeracy, health, motivations, quality of life, community cohesion, and environmental quality, and we needed it fifty years ago. We need to reinvigorate and revive a wider concern across the sciences to establish all scientific units with equal rigor, and to have all measures used in research and practice based wherever possible on consensus standard metrics valued for their accuracy, stability and availability. We need to replicate in the psychosocial sciences the profound change in the notation and interpretation of the mathematical formalism of physics that occurred in the eighteenth and nineteenth centuries. We need to extend the true union of mathematics and measurement from physics to the psychosocial sciences.

Previous posts in this blog speak to the persistent invariance and objectivity exhibited by many of the constructs measured using ability tests, attitude surveys, performance assessments, etc. A question previously raised in this blog concerning the reproductive logic of living meaning deserves more attention, and can be productively explored in terms of complex adaptive functionality.

In a hierarchy of reasons why mathematically rigorous measurement is valuable, few are closer to the top of the list than facilitating the spontaneous self-organization of networks of agents and actors (Latour, 1987). The conception, gestation, birthing, and nurturing of complex adaptive systems constitute a reproductive logic for sociocultural traditions. Scientific traditions, in particular, form mature self-identities via a mutually implied subject-object relation absorbed into the flow of a dialectical give and take, just as economic systems do.

Complex adaptive systems establish the reproductive viability of their offspring and the coherence of an ecological web of meaningful relationships by means of this dialectic. Taylor (2003, pp. 166-8) describes the five moments in the formation and operation of complex adaptive systems, which must be able

  • to identify regularities and patterns in the flow of matter, energy, and information (MEI) in the environment (business, social, economic, natural, etc.);
  • to produce condensed schematic representations of these regularities so they can be identified as the same if they are repeated;
  • to form reproductively interchangeable variants of these representations;
  • to succeed reproductively by means of the accuracy and reliability of the representations’ predictions of regularities in the MEI data flow; and
  • adaptively modify and reorganize representations by means of informational feedback from the environment.

All living systems, from bacteria and viruses to plants and animals to languages and cultures, are complex adaptive systems characterized by these five features.

In the history of science, technologically-embodied measurement facilitates complex adaptive systems of various kinds. That history can be used as a basis for a meta-theoretical perspective on what measurement must look like in the social and human sciences. Each of Taylor’s five moments in the formation and operation of complex adaptive systems describes a capacity of measurement systems, in that:

  • data flow regularities are captured in initial, provisional instrument calibrations;
  • condensed local schematic representations are formed when an instrument’s calibrations are anchored at repeatedly observed, invariant values;
  • interchangeable nonlocal versions of these invariances are created by means of instrument equating, item banking, metrological networks, and selective, tailored, adaptive instrument administration;
  • measures read off inaccurate and unreliable instruments will not support successful reproduction of the data flow regularity, but accurate and reliable instruments calibrated in a shared common unit provide a reference standard metric that enhances communication and reproduces the common voice and shared identity of the research community; and
  • consistently inconsistent anomalous observations provide feedback suggesting new possibilities for as yet unrecognized data flow regularities that might be captured in new calibrations.

Measurement in the social sciences is in the process of extending this functionality into practical applications in business, education, health care, government, and elsewhere. Over the course of the last 50 years, measurement research and practice has already iterated many times through these five moments. In the coming years, a new critical mass will be reached in this process, systematically bringing about scale-of-magnitude improvements in the efficiency of intangible assets markets.

How? What does a “data flow regularity” look like? How is it condensed into a a schematic and used to calibrate an instrument? How are local schematics combined together in a pattern used to recognize new instances of themselves? More specifically, how might enterprise resource planning (ERP) software (such as SAP, Oracle, or PeopleSoft) simultaneously provide both the structure needed to support meaningful comparisons and the flexibility needed for good fit with the dynamic complexity of adaptive and generative self-organizing systems?

Prior work in this area proposes a dual-core, loosely coupled organization using ERP software to build social and intellectual capital, instead of using it as an IT solution addressing organizational inefficiencies (Lengnick-Hall, Lengnick-Hall, & Abdinnour-Helm, 2004). The adaptive and generative functionality (Stenner & Stone, 2003) provided by probabilistic measurement models (Rasch, 1960; Andrich, 2002, 2004; Bond & Fox, 2007; Wilson, 2005; Wright, 1977, 1999) makes it possible to model intra- and inter-organizational interoperability (Weichhart, Feiner, & Stary, 2010) at the same time that social and intellectual capital resources are augmented.

Actor/agent network theory has emerged from social and historical studies of the shared and competing moral, economic, political, and mathematical values disseminated by scientists and technicians in a variety of different successful and failed areas of research (Latour, 2005). The resulting sociohistorical descriptions ought be translated into a practical program for reproducing successful research programs. A metasystem for complex adaptive systems of research is implied in what Roche (1998) calls a “true union of mathematics and measurement.”

Complex adaptive systems are effectively constituted of such a union, even if, in nature, the mathematical character of the data flows and calibrations remains virtual. Probabilistic conjoint models for fundamental measurement are poised to extend this functionality into the human sciences. Though few, if any, have framed the situation in these terms, these and other questions are being explored, explicitly and implicitly, by hundreds of researchers in dozens of fields as they employ unidimensional models for measurement in their investigations.

If so, might then we be on the verge of a yet another new reading and writing of Galileo’s “book of nature,” this time restoring the “loss of meaning for life” suffered in Galileo’s “fateful omission” of the means by which nature came to be understood mathematically (Husserl, 1970)? The elements of a comprehensive, mathematical, and experimental design science of living systems appear on the verge of providing a saturated solution—or better, a nonequilbrium thermodynamic solution—to some of the infamous shortcomings of modern, Enlightenment science. The unity of science may yet be a reality, though not via the reductionist program envisioned by the positivists.

Some 50 years ago, Marshall McLuhan popularized the expression, “The medium is the message.” The special value quantitative measurement in the history of science does not stem from the mere use of number. Instruments are media on which nature, human or other, inscribes legible messages. A renewal of the true union of mathematics and measurement in the context of intangible assets will lead to a new cultural, scientific, and economic renaissance. As Thomas Kuhn (1977, p. 221) wrote,

“The full and intimate quantification of any science is a consummation devoutly to be wished. Nevertheless, it is not a consummation that can effectively be sought by measuring. As in individual development, so in the scientific group, maturity comes most surely to those who know how to wait.”

Given that we have strong indications of how full and intimate quantification consummates a true union of mathematics and measurement, the time for waiting is now past, and the time to act has come. See prior blog posts here for suggestions on an Intangible Assets Metric System, for resources on methods and research, for other philosophical ruminations, and more. This post is based on work presented at Rasch meetings several years ago (Fisher, 2006a, 2006b).

References

Andrich, D. (2002). Understanding resistance to the data-model relationship in Rasch’s paradigm: A reflection for the next generation. Journal of Applied Measurement, 3(3), 325-59.

Andrich, D. (2004, January). Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care, 42(1), I-7–I-16.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Fisher, W. P., Jr. (2006a, Friday, April 28). Complex adaptive functionality via measurement. Presented at the Midwest Objective Measurement Seminar, M. Lunz (Organizer), University of Illinois at Chicago.

Fisher, W. P., Jr. (2006b, June 27-9). Measurement and complex adaptive functionality. Presented at the Pacific Rim Objective Measurement Symposium, T. Bond & M. Wu (Organizers), The Hong Kong Institute of Education, Hong Kong.

Husserl, E. (1970). The crisis of European sciences and transcendental phenomenology: An introduction to phenomenological philosophy (D. Carr, Trans.). Evanston, Illinois: Northwestern University Press (Original work published 1954).

Kuhn, T. S. (1977). The function of measurement in modern physical science. In T. S. Kuhn, The essential tension: Selected studies in scientific tradition and change (pp. 178-224). Chicago: University of Chicago Press. [(Reprinted from Kuhn, T. S. (1961). Isis, 52(168), 161-193.]

Latour, B. (1987). Science in action: How to follow scientists and engineers through society. New York: Cambridge University Press.

Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. (Clarendon Lectures in Management Studies). Oxford, England: Oxford University Press.

Lengnick-Hall, C. A., Lengnick-Hall, M. L., & Abdinnour-Helm, S. (2004). The role of social and intellectual capital in achieving competitive advantage through enterprise resource planning (ERP) systems. Journal of Engineering Technology Management, 21, 307-330.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests (Reprint, with Foreword and Afterword by B. D. Wright, Chicago: University of Chicago Press, 1980). Copenhagen, Denmark: Danmarks Paedogogiske Institut.

Roche, J. (1998). The mathematics of measurement: A critical history. London: The Athlone Press.

Stenner, A. J., & Stone, M. (2003). Item specification vs. item banking. Rasch Measurement Transactions, 17(3), 929-30 [http://www.rasch.org/rmt/rmt173a.htm].

Taylor, M. C. (2003). The moment of complexity: Emerging network culture. Chicago: University of Chicago Press.

Weichhart, G., Feiner, T., & Stary, C. (2010). Implementing organisational interoperability–The SUddEN approach. Computers in Industry, 61, 152-160.

Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Wright, B. D. (1977). Solving measurement problems with the Rasch model. Journal of Educational Measurement, 14(2), 97-116 [http://www.rasch.org/memo42.htm].

Wright, B. D. (1997, Winter). A history of social science measurement. Educational Measurement: Issues and Practice, 16(4), 33-45, 52 [http://www.rasch.org/memo62.htm].

Wright, B. D. (1999). Fundamental measurement for psychology. In S. E. Embretson & S. L. Hershberger (Eds.), The new rules of measurement: What every educator and psychologist should know (pp. 65-104 [http://www.rasch.org/memo64.htm]). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

Parameterizing Perfection: Practical Applications of a Mathematical Model of the Lean Ideal

April 2, 2010

To properly pursue perfection, we need to parameterize it. That is, taking perfection as the ideal, unattainable standard against which we judge our performance is equivalent to thinking of it as a mathematical model. Organizations are intended to realize their missions independent of the particular employees, customers, suppliers, challenges, products, etc. they happen to engage with at any particular time. Organizational performance measurement (Spitzer, 2007) ought to then be designed in terms of a model that posits, tests for, and capitalizes on the always imperfectly realized independence of those parameters.

Lean thinking (Womack & Jones, 1996) focuses on minimizing waste and maximizing value. At every point at which resources are invested in processes, services, or products, the question is asked, “What value is added here?” Resources are wasted when no value is added, when they can be removed with no detrimental effect on the value of the end product. In their book, Natural Capitalism: Creating the Next Industrial Revolution, Hawken, Lovins, and Lovins (1999, p. 133) say

“Lean thinking … changes the standard for measuring corporate success. … As they [Womack and Jones] express it: ‘Our earnest advice to lean firms today is simple. To hell with your competitors; compete against perfection by identifying all activities that are muda [the Japanese term for waste used in Toyota’s landmark quality programs] and eliminating them. This is an absolute rather than a relative standard which can provide the essential North Star for any organization.”

Further, every input should “be presumed waste until shown otherwise.” A constant, ongoing, persistent pressure for removing waste is the basic characteristic of lean thinking. Perfection is never achieved, but it aptly serves as the ideal against which progress is measured.

Lean thinking sounds a lot like a mathematical model, though it does not seem to have been written out in a mathematical form, or used as the basis for calibrating instruments, estimating measures, evaluating data quality, or for practical assessments of lean organizational performance. The closest anyone seems to have come to parameterizing perfection is in the work of Genichi Taguchi (Ealey, 1988), which has several close parallels with Rasch measurement (Linacre, 1993).  But meaningful and objective quantification, as required and achieved in the theory and practice of fundamental measurement (Andrich, 2004; Bezruczko, 2005; Bond & Fox 2007; Smith & Smith, 2004; Wilson, 2005; Wright, 1999), in fact asserts abstract ideals of perfection as models of organizational, social, and psychological processes in education, health care, marketing, etc. These models test the extent to which outcomes remain invariant across examination or survey questions, across teachers, students, schools, and curricula, or across treatment methods, business processes, or policies.

Though as yet implemented only to a limited extent in business (Drehmer, Belohlav, James, & Coye, 2000; Drehmer & Deklava, 2001;  Lunz & Linacre, 1998; Salzberger, 2009), advanced measurement’s potential rewards are great. Fundamental measurement theory has been successfully applied in research and practice thousands of times over the last 40 years and more, including in very large scale assessments and licensure/certification applications (Adams, Wu, & Macaskill, 1997; Masters, 2007; Smith, Julian, Lunz, et al., 1994). These successes speak to an opportunity for making broad improvements in outcome measurement that could provide more coherent product definition, and significant associated opportunities for improving product quality and the efficiency with which it is produced, in the manner that has followed from the use of fundamental measures in other industries.

Of course, processes and outcomes are never implemented or obtained with perfect consistency. This would be perfectly true only in a perfect world. But to pursue perfection, we need to parameterize it. In other words, to raise the bar in any area of performance assessment, we have to know not only what direction is up, but we also need to know when we have raised the bar far enough. But we cannot tell up from down, we do not know how much to raise the bar, and we cannot properly evaluate the effects of lean experiments when we have no way of locating measures on a number line that embodies the lean ideal.

To think together collectively in ways that lead to significant new innovations, to rise above what Jaron Lanier calls the “global mush” of confused and self-confirming hive thinking, we need the common languages of widely accepted fundamental measures of the relevant processes and outcomes, measures that remain constant across samples of customers, patients, employees, students, etc., and across products, sales techniques, curricula, treatment processes, assessment methods, and brands of instrument.

We are all well aware that the consequences of not knowing where the bar is, of not having product definitions, can be disastrous. In many respects, as I’ve said previously in this blog, the success or failure of health care reform hinges on getting measurement right. The Institute of Medicine report, To Err is Human, of several years ago stresses the fact that system failures pose the greatest threat to safety in health care because they lead to human errors. When a system as complex as health care lacks a standard product definition, and product delivery is fragmented across multiple providers with different amounts and kinds of information in different settings, the system becomes dangerously cumbersome and over-complicated, with unacceptably wide variations and errors in its processes and outcomes, not to even speak of its economic inefficiency.

In contrast with the widespread use of fundamental measures in the product definitions of other industries, health care researchers typically implement neither the longstanding, repeatedly proven, and mathematically rigorous models of fundamental measurement theory nor the metrological networks through which reference standard metrics are engineered. Most industries carefully define, isolate, and estimate the parameters of their products, doing so in ways 1) that ensure industry-wide comparability and standardization, and 2) that facilitate continuous product improvement by revealing multiple opportunities for enhancement. Where organizations in other industries manage by metrics and thereby keep their eyes on the ball of product quality, health care organizations often manage only their own internal processes and cannot in fact bring the product quality ball into view.

In his message concerning the Institute for Healthcare Improvement’s Pursuing Perfection project a few years ago, Don Berwick, like others (Coye, 2001; Coye & Detmer, 1998), observed that health care does not yet have an organization setting new standards in the way that Toyota did for the auto industry in the 1970s. It still doesn’t, of course. Given the differences between the auto and health care industries uses of fundamental measures of product quality and associated abilities to keep their eyes on the quality ball, is it any wonder then, that no one in health care has yet hit a home run? It may well be that no one will hit a home run in health care until reference standard measures of product quality are devised.

The need for reference standard measures in uniform data systems is crucial, and the methods for obtaining them are widely available and well-known. So what is preventing the health care industry from adopting and deploying them? Part of the answer is the cost of the initial investment required. In 1980, metrology comprised about six percent of the U.S. gross national product (Hunter, 1980). In the period from 1981 to 1994, annual expenditures on research and development in the U.S. were less than three percent of the GNP, and non-defense R&D was about two percent (NIST Subcommittee on Research, National Science and Technology Council, 1996). These costs, however, must be viewed as investments from which high rates of return can be obtained (Barber, 1987; Gallaher, Rowe, Rogozhin, et al., 2007; Swann, 2005).

For instance, the U.S. National Institute of Standards and Technology estimated the economic impact of 12 areas of research in metrology, in four broad areas including semiconductors, electrical calibration and testing, optical industries, and computer systems (NIST, 1996, Appendix C; also see NIST, 2003). The median rate of return in these 12 areas was 147 percent, and returns ranged from 41 to 428 percent. The report notes that these results compare favorably with those obtained in similar studies of return rates from other public and private research and development efforts. Even if health care metrology produces only a small fraction of the return rate produced in physical metrology, its economic impact could still amount to billions of dollars annually. The proposed pilot projects therefore focus on determining what an effective health care outcomes metrology system should look like. What should its primary functions be? What should it cost? What rates of return could be expected from it?

Metrology, the science of measurement (Pennella, 1997), requires 1) that instruments be calibrated within individual laboratories so as to isolate and estimate the values of the required parameters (Wernimont, 1978); and 2) that individual instruments’ capacities to provide the same measure for the same amount, and so be traceable to a reference standard, be established and monitored via interlaboratory round-robin trials (Mandel, 1978).

Fundamental measurement has already succeeded in demonstrating the viability of reference standard measures of health outcomes, measures whose meaningfulness does not depend on the particular samples of items employed or patients measured. Though this work succeeds as far as it goes, it being done in a context that lacks any sense of the need for metrological infrastructure. Health care needs networks of scientists and technicians collaborating not only in the first, intralaboratory phase of metrological work, but also in the interlaboratory trials through which different brands or configurations of instruments intended to measure the same variable would be tuned to harmoniously produce the same measure for the same amount.

Implementation of the two phases of metrological innovation in health care would then begin with the intralaboratory calibration of existing and new instruments for measuring overall organizational performance, quality of care, and patients’ health status, quality of life, functionality, etc.  The second phase takes up the interlaboratory equating of these instruments, and the concomitant deployment of reference standard units of measurement throughout a health care system and the industry as a whole. To answer questions concerning health care metrology’s potential returns on investment, the costs for, and the savings accrued from, accomplishing each phase of each pilot will be tracked or estimated.

When instruments measuring in universally uniform, meaningful units are put in the hands of clinicians, a new scientific revolution will occur in medicine. It will be analogous to previous ones associated with the introduction of the thermometer and the instruments of optometry and the clinical laboratory. Such tools will multiply many times over the quality improvement methods used by Brent James, touted as holding the key to health care reform in a recent New York Times profile. Instead of implicitly hypothesizing models of perfection and assessing performance relative to them informally, what we need is a new science that systematically implements the lean ideal on industry-wide scales. The future belongs to those who master these techniques.

References

Adams, R. J., Wu, M. L., & Macaskill, G. (1997). Scaling methodology and procedures for the mathematics and science scales. In M. O. Martin & D. L. Kelly (Eds.), Third International Mathematics and Science Study Technical Report: Vol. 2: Implementation and Analysis – Primary and Middle School Years (pp. 111-145). Chestnut Hill, MA: Boston College.

Andrich, D. (2004, January). Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care, 42(1), I-7–I-16.

Barber, J. M. (1987). Economic rationale for government funding of work on measurement standards. In R. Dobbie, J. Darrell, K. Poulter & R. Hobbs (Eds.), Review of DTI work on measurement standards (p. Annex 5). London: Department of Trade and Industry.

Berwick, D. M., James, B., & Coye, M. J. (2003, January). Connections between quality measurement and improvement. Medical Care, 41(1 (Suppl)), I30-38.

Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM Press.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Coye, M. J. (2001, November/December). No Toyotas in health care: Why medical care has not evolved to meet patients’ needs. Health Affairs, 20(6), 44-56.

Coye, M. J., & Detmer, D. E. (1998). Quality at a crossroads. The Milbank Quarterly, 76(4), 759-68.

Drehmer, D. E., Belohlav, J. A., & Coye, R. W. (2000, Dec). A exploration of employee participation using a scaling approach. Group & Organization Management, 25(4), 397-418.

Drehmer, D. E., & Deklava, S. M. (2001, April). A note on the evolution of software engineering practices. Journal of Systems and Software, 57(1), 1-7.

Ealey, L. A. (1988). Quality by design: Taguchi methods and U.S. industry. Dearborn MI: ASI Press.

Gallaher, M. P., Rowe, B. R., Rogozhin, A. V., Houghton, S. A., Davis, J. L., Lamvik, M. K., et al. (2007). Economic impact of measurement in the semiconductor industry (Tech. Rep. No. 07-2). Gaithersburg, MD: National Institute for Standards and Technology.

Hawken, P., Lovins, A., & Lovins, H. L. (1999). Natural capitalism: Creating the next industrial revolution. New York: Little, Brown, and Co.

Hunter, J. S. (1980, November). The national system of scientific measurement. Science, 210(21), 869-874.

Linacre, J. M. (1993). Quality by design: Taguchi and Rasch. Rasch Measurement Transactions, 7(2), 292.

Lunz, M. E., & Linacre, J. M. (1998). Measurement designs using multifacet Rasch modeling. In G. A. Marcoulides (Ed.), Modern methods for business research. Methodology for business and management (pp. 47-77). Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc.

Mandel, J. (1978, December). Interlaboratory testing. ASTM Standardization News, 6, 11-12.

Masters, G. N. (2007). Special issue: Programme for International Student Assessment (PISA). Journal of Applied Measurement, 8(3), 235-335.

National Institute for Standards and Technology (NIST). (1996). Appendix C: Assessment examples. Economic impacts of research in metrology. In C. o. F. S. Subcommittee on Research (Ed.), Assessing fundamental science: A report from the Subcommittee on Research, Committee on Fundamental Science. Washington, DC: National Standards and Technology Council [http://www.nsf.gov/statistics/ostp/assess/nstcafsk.htm#Topic%207; last accessed 18 February 2008].

National Institute for Standards and Technology (NIST). (2003, 15 January). Outputs and outcomes of NIST laboratory research. Retrieved 12 July 2009, from http://www.nist.gov/director/planning/studies.htm#measures.

Pennella, C. R. (1997). Managing the metrology system. Milwaukee, WI: ASQ Quality Press.\

Salzberger, T. (2009). Measurement in marketing research: An alternative framework. Northampton, MA: Edward Elgar.

Smith, R. M., Julian, E., Lunz, M., Stahl, J., Schulz, M., & Wright, B. D. (1994). Applications of conjoint measurement in admission and professional certification programs. International Journal of Educational Research, 21(6), 653-664.

Smith, E. V., Jr., & Smith, R. M. (2004). Introduction to Rasch measurement. Maple Grove, MN: JAM Press.

Spitzer, D. (2007). Transforming performance measurement: Rethinking the way we measure and drive organizational success. New York: AMACOM.

Swann, G. M. P. (2005, 2 December). John Barber’s pioneering work on the economics of measurement standards [Electronic version]. Retrieved http://www.cric.ac.uk/cric/events/jbarber/swann.pdf from Notes for Workshop in Honor of John Barber held at University of Manchester.

Wernimont, G. (1978, December). Careful intralaboratory study must come first. ASTM Standardization News, 6, 11-12.

Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Womack, J. P., & Jones, D. T. (1996, Sept./Oct.). Beyond Toyota: How to root out waste and pursue perfection. Harvard Business Review, 74, 140-58.

Wright, B. D. (1999). Fundamental measurement for psychology. In S. E. Embretson & S. L. Hershberger (Eds.), The new rules of measurement: What every educator and psychologist should know (pp. 65-104 [http://www.rasch.org/memo64.htm]). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

The Role of Measurement in Reducing Transaction Costs and Creating Efficient Markets for Externalities

March 31, 2010

Coase (1960, 1990) suggests that well-defined property rights drive transaction costs down, and so could, in principle, overcome the market frictions introduced by externalities. Because transaction costs are usually not negligible, they make the initial allocation of property rights a crucial factor in market efficiency. It is generally accepted that property rights should then be assigned to those able to realize the greatest value from them, that the nature of the firm is contingent on its capacities to reduce transaction costs relative to their costs outside of the firm, and that a legitimate role for government is in facilitating efficient corrections of misallocated resources via laws and agencies that reduce transaction costs.

Most of the complex discussions involving these issues, broadly referred to as consequences of the Coase Theorem, take place in the context of extensive assumptions about the nature of capital and capitalism (McChesney, 2006; Cooter, 2000; also many others). Most problematic among these assumptions are those involving a short-term focus on financial bottom lines. No mention is made of sustainable long-term profitability’s dependence on trusting relationships with healthy, motivated workforces and communities living in clean and pleasant environments. The fact that everyone owns, has shares in, and wants to maximize the value of the local stock of social and natural capital goes unrecognized, as is the fact that everyone has the right to increase the value of, and/or contract out, shares of their human capital.

But examples of externality problems rarely put due emphasis on the maximization of social welfare. Even in McChesney’s (2006) third example, in which the cost of a government solution is added, rights are not defined to maximize the social welfare, even though the social value of the pollution abatement is included as a hypothetical factor in all the examples. Where the steel mill and the laundry both have owners and/or stockholders who are financially rewarded or penalized in the short term by the freedom to pollute or by the restriction on pollution, the value associated with the maximization of social welfare is vaguely defined. Its costs and benefits are realized in an aggregate way that shows up only indirectly in anyone’s accounting spreadsheets, as health care costs increase or decrease, as employee recruitment and retention becomes more difficult or easier, as community trust and loyalty spills over into appreciation or lawsuits.

To define rights in a way that would lead to the greatest potential for the maximization of social welfare from the start, we need only think of what it means for markets to function efficiently and apply those considerations to the ownership of human, social, and natural capital. Transaction costs are most fundamentally affected by information on the identification, volume, and quality of what is traded (Barzel, 1982, Benham & Benham, 2000). Common product definitions, universal uniform metrics, and reliable quality indicators make or break markets by the way they clarify or obscure property rights.

For example, it is one thing to own a vineyard in the Margaux terrain producing 20,000 cases (12 bottles of 0.75 liters each) of premier cru 1990 vintage wine, and quite another to own a hospital in New Orleans producing gains of 37.2 units expressed in ordinal health status survey scores of unknown reliability and cross-instrument convertability. Superior value is unmistakable in the widely recognized identity of the Margaux estate, the immediately understood measure of volume, and the well-known quality attached to the premier cru designation and the 1990 year. There is a literal transparency in the measure of quantity, in that each bottle of wine could be shown to contain the same 0.75 liters, no matter where in the world it is measured, no matter who measures it, and no matter which particular instrument of volume measurement is employed. The French and international oenology communities go to great lengths, via their standards associations and in collaborations facilitated by the international Treaty of the Meter, to ensure the global recognition of the value of Margaux wines.

The identity, amount, and quality of the hospital’s health status gain score is quite obscure, however. What exactly is being referred to as a change in health status? Is it a reduction in symptoms associated with a specific disease or condition? Or is it a change in physical or emotional functionality? What is the unit being counted? How much change in health status does a unit stand for? How does this amount of change compare with the changes produced by other health care providers? Is it more or less, and what immediate and long term value is obtained per dollar spent?

Fortunately, over 20 years of research shows that health status surveys, and many other kinds of tests, surveys, and assessments, often can be calibrated to measure in constant linear units (Bezruczko, 2005; Bond & Fox, 2007; Fisher & Wright, 1994), and appear to have the potential to support universally uniform reference standard metrics (Fisher, 1997a, 1997b, 1998, 2000; Fisher, Eubanks, & Marier, 1997; Fisher, Harvey, & Kilgore, 1995; Fisher, Harvey, Taylor, Kilgore, & Kelly, 1995). The opportunities before us in health care, in education, in human and environmental resource management, and in other areas are directly analogous to the historical births of many markets, such as the transformation of the London coal trade effected in the 1830s (Velkar, 2008) the origins of the electrical industry in the 1870s (Hunt, 1994; Schaffer, 1992), or the establishment of standard time in 1883 and the subsequent distribution and sale of uniform time units by astronomic observatories (Bartky, 2000), and the use of the clock in the regulation of the work day.

Capital is made fungible when its monetary expressions can be summed across properties owned, when it can be divided into shares for sale to investors, and when its value can be transparently and efficiently represented via some form of currency or instrument recognized and accepted at any point across a network of linked agencies, such as trading centers, banks, courts, laboratories, etc. (Ashworth, 2004; Barzel, 1982; De Soto, 2000; Latour, 1987, p. 223; Fisher, 2002). The unification of measurements and standardization of quantitative expressions facilitate common product definitions, lower transaction costs, and market transparency by enabling more precise determinations of property rights—who owns what, how much of it, and of what quality.

Probabilistic models for calibrating and equating ability tests, attitude surveys, and performance assessments (Rasch, 1960; Wright, 1977, 1999; Andrich, 1988) offer a scientific basis for rigorous, practical, and convenient methods of unifying the measurement of human, social, and natural capital (Fisher, 2002, 2005, 2007, 2009, 2010). One way in which governments could, then, perform their optimal role would be to introduce laws and institutions focused on reducing living capital transaction costs, as would occur via the expansion of the Treaty of the Meter to include uniform standards for the measurement of the various forms of human, social, and natural capital.

The origins of the metric system (SI) were explicitly framed in terms of a mutual conformity between universal rights for all people and universal standards of measurement (Alder, 2002, p. 3). The question we face today is, what might a metric system for human, social, and natural capital imply for the co-production of new scientific, economic and social orders? In particular, how might business practices be made more just, sustainable, and profitable through the better management of intangible assets? How and when will we as individuals be able to represent our hireability, promotability, retainability, and productivity in terms recognized and accepted as far and wide as the terms for expressing time, distance, weight, and temperature are? Can universal human rights to equal opportunities be realized in the absence of fair and comparable metrics for representing abilities, health, performance, and credibility? Will we get ahead of this issue and proactively develop the systems we need, or will we need to be driven to action by yet another new crisis?  We hold the keys to our liberation.

References

Alder, K. (2002). The measure of all things: The seven-year odyssey and hidden error that transformed the world. New York: The Free Press.

Andrich, D. (1988). Rasch models for measurement. (Vols. series no. 07-068). Sage University Paper Series on Quantitative Applications in the Social Sciences). Beverly Hills, California: Sage Publications.

Ashworth, W. J. (2004, 19 November). Metrology and the state: Science, revenue, and commerce. Science, 306(5700), 1314-7.

Bartky, I. R. (2000). Selling the true time: Nineteenth-century timekeeping in America. Stanford: Stanford University Press.

Barzel, Y. (1982). Measurement costs and the organization of markets. Journal of Law and Economics, 25, 27-48.

Benham, A., & Benham, L. (2000). Measuring the costs of exchange. In C. Ménard (Ed.), Institutions, contracts and organizations: Perspectives from new institutional economics (pp. 367-375). Cheltenham, UK: Edward Elgar.

Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM Press.

Bond, T., & Fox, C. (2007). Applying the Rasch model: Fundamental measurement in the human sciences, 2d edition. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Coase, R. (1960, October). The problem of social cost. Journal of Law and Economics, 3, 1-44.

Coase, R. (1990). The firm, the market, and the law. Chicago, Illinois: University of Chicago Press.

Cooter, R. D. (2000). Law from order: Economic development and the jurisprudence of social norms. In M. Olson & S. Kahkonen (Eds.), A not-so-dismal science: A broader view of economies and societies (pp. 228-244). New York: Oxford University Press.

De Soto, H. (2000). The mystery of capital: Why capitalism triumphs in the West and fails everywhere else. New York: Basic Books.

Dohrn-van Rossum, G. (1996). History of the hour: Clocks and modern temporal orders. Chicago, Illinois: University of Chicago Press.

Fisher, W. P., Jr. (1997a). Physical disability construct convergence across instruments: Towards a universal metric. Journal of Outcome Measurement, 1(2), 87-113.

Fisher, W. P., Jr. (1997b, June). What scale-free measurement means to health outcomes research. Physical Medicine & Rehabilitation State of the Art Reviews, 11(2), 357-373.

Fisher, W. P., Jr. (1998). A research program for accountable and patient-centered health status measures. Journal of Outcome Measurement, 2(3), 222-239.

Fisher, W. P., Jr. (1999). Foundations for health status metrology: The stability of MOS SF-36 PF-10 calibrations across samples. Journal of the Louisiana State Medical Society, 151(11), 566-578.

Fisher, W. P., Jr. (2000). Objectivity in psychosocial measurement: What, why, how. Journal of Outcome Measurement, 4(2), 527-563 [http://www.livingcapitalmetrics.com/images/WP_Fisher_Jr_2000.pdf].

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2005). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-9 [http://www.livingcapitalmetrics.com/images/FisherJAM05.pdf].

Fisher, W. P., Jr. (2007, Summer). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-3 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

Fisher, W. P., Jr. (2010). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 11, in press [http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf].

Fisher, W. P., Jr., Eubanks, R. L., & Marier, R. L. (1997). Equating the MOS SF36 and the LSU HSI physical functioning scales. Journal of Outcome Measurement, 1(4), 329-362.

Fisher, W. P., Jr., Harvey, R. F., & Kilgore, K. M. (1995). New developments in functional assessment: Probabilistic models for gold standards. NeuroRehabilitation, 5(1), 3-25.

Fisher, W. P., Jr., Harvey, R. F., Taylor, P., Kilgore, K. M., & Kelly, C. K. (1995, February). Rehabits: A common language of functional assessment. Archives of Physical Medicine and Rehabilitation, 76(2), 113-122.

Fisher, W. P., Jr., & Wright, B. D. (1994). Introduction to probabilistic conjoint measurement theory and applications (W. P. Fisher, Jr., & B. D. Wright eds.) [Special issue]. International Journal of Educational Research, 21(6), 559-568.

Hunt, B. J. (1994). The ohm is where the art is: British telegraph engineers and the development of electrical standards. Osiris: A Research Journal Devoted to the History of Science and Its Cultural Influences, 9, 48-63.

McChesney, F. S. (2006, Winter). Coase, Demsetz, and the unending externality debate. Cato Journal, 26(1), 179-200.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests (Reprint, with Foreword and Afterword by B. D. Wright, Chicago: University of Chicago Press, 1980). Copenhagen, Denmark: Danmarks Paedogogiske Institut.

Schaffer, S. (1992). Late Victorian metrology and its instrumentation: A manufactory of Ohms. In R. Bud & S. E. Cozzens (Eds.), Invisible connections: Instruments, institutions, and science (pp. 23-56). Bellingham, WA: SPIE Optical Engineering Press.

Velkar, A. (2008, 4 April). Caveat Emptor: Abolishing public measurements, standardizing quantities, and enhancing market transparency in the London coal trade c1830. Enterprise and Society, http://es.oxfordjournals.org/cgi/content/abstract/khn003v1.

Wright, B. D. (1977). Solving measurement problems with the Rasch model. Journal of Educational Measurement, 14(2), 97-116 [http://www.rasch.org/memo42.htm].

Wright, B. D. (1999). Fundamental measurement for psychology. In S. E. Embretson & S. L. Hershberger (Eds.), The new rules of measurement: What every educator and psychologist should know (pp. 65-104 [http://www.rasch.org/memo64.htm]). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

Assignment from Wired’s Predict What’s Next page: “Imagine the Future of Medical Bills”

March 20, 2010

William P. Fisher, Jr.

william@livingcapitalmetrics.com
New Orleans, Louisiana
20 March 2010

Consider the following, formulated in response to Wired magazine’s 18.04 request for ideas on the future of medical bills, for possible use on the Predict What’s Next page. For background on the concepts presented here, see previous posts in this blog, such as https://livingcapitalmetrics.wordpress.com/2010/01/13/reinventing-capitalism/.

Visualize an online image of a Maiuetic Renaissance Bank’s Monthly Living Capital Stock, Investment, and Income Report. The report is shown projected as a vertical plane in the space above an old antique desk. Credits and debits to and from Mary Smith’s health capital account are listed, along with similar information on all of her capital accounts. Lying on the desk is a personalized MRB Living Capital Credit/Debit card, evidently somehow projecting the report from the eyes of Mary’s holographic image on it.

The report shows headings and entries for Mary Smith’s various capital accounts:

  • liquid (cash, checking and savings),
  • property (home, car, boat, rental, investments, etc.),
  • social capital (trust, honesty, commitment, loyalty, community building, etc.) credits/debits:
    • personal,
    • community’s,
    • employer’s,
    • regional,
    • national;
  • human capital:
    • literacy credits (shown in Lexiles; http://www.lexile.com),
    • numeracy credits (shown in Quantiles; http://www.quantiles.com),
    • occupational credits (hireability, promotability, retainability, productivity),
    • health credits/debits (genetic, cognitive reasoning, physical function, emotional function, chronic disease management status, etc.); and
  • natural capital:
    • carbon credits/debits,
    • local and global air, water, ecological diversity, and environmental quality share values.

Example social capital credits/debits shown in the report might include volunteering to build houses in N’Awlins Ninth Ward, tutoring fifth-graders in math, jury duty, voting, writing letters to congress, or charitable donations (credits), on the one hand, or library fines, a parking ticket, unmaintained property, etc. (debits), on the other.

Natural capital credits might be increased or decreased depending on new efficiencies obtained in electrical grid or in power generation, a newly installed solar panel, or by a recent major industrial accident, environmental disaster, hurricane, etc.

Mary’s share of the current value of the overall Genuine National Product, or Happiness Index, is broken out by each major form of capital (liquid, property, social, human, natural).

The monetary values of credits are shown at the going market rates, alongside the changes from last month, last year, and three years ago.

One entry could be a deferred income and property tax amount, given a social capital investment level above a recommended minimum. Another entry would show new profit potentials expressed in proportions of investments wasted due to inefficiencies, with suggestions for how these can be reduced, and with time to investment recovery and amount of new social capital generated also indicated.

The health capital portion of the report is broken out in a magnified overlay. Mary’s physical and emotional function measures are shown by an arrow pointing at a level on a vertical ruler. Other arrows point at the average levels for people her age (globally, nationally, regionally, and locally), for women and women of different ages, living in different countries/cities, etc.

Mary’s diabetes-specific chronic disease management metric is shown at a high level, indicating her success in using diet and exercise to control her condition. Her life expectancy and lifetime earning potentials are shown, alongside comparable values for others.

Recent clinical visits for preventative diabetes and dental care would be shown as debits against one account and as an investment in her health capital account. The debits might be paid out of a sale of shares of stock from her quite high social or natural capital accounts, or from credits transferred from those to her checking account.

Cost of declining function in the next ten years, given typical aging patterns, shown as lower rates of new capital investment in her stock and lower ROIs.

Cost of maintaining or improving function, in terms of required investments of time and resources in exercise, equipment, etc. balanced against constant rate of new investments and ROI.

Also shown:

A footnote could read: Given your recent completion of post-baccalaureate courses in political economy and advanced living capital finance, your increased stocks of literacy, numeracy, and occupational capital qualify you for a promotion or new positions currently compensated at annual rates 17.7% higher than your current one. Watch for tweets and beams from new investors interested in your rising stock!

A warning box: We all pay when dead capital lies unleveragable in currencies expressed in ordinal or otherwise nonstandard metrics! Visit http://www.CapitalResuscitationServices.com today to convert your unaccredited capital currencies into recognized value. (Not responsible for fraudulent misrepresentations of value should your credits prove incommensurable or counterfeit. Always check your vendor’s social capital valuations before investing in any stock offering. Go to http://www.Rasch.org for accredited capital metrics equating information, courses, texts, and consultants.)

Ad: Click here to put your occupational capital stock on the market now! Employers are bidding $$$, ¥¥¥ and €€€ on others at your valuation level!

Ad: You are only 110 Lexiles away from a literacy capital stock level on which others receive 23% higher investment returns! Enroll at BobsOnlineLiteracyCapitalBoosters.com now for your increased income tomorrow! (Past performance is not a guarantee of future results. Your returns may vary. Click here to see Bob’s current social capital valuations.)

Bottom line: Think global, act local! It is up to you to represent your shares in the global marketplace. Only you can demand the improvements you seek by shifting and/or intensifying your investments. Do so whenever you are dissatisfied with your own, your global and local business partners’, your community’s, your employer’s, your region’s, or your nation’s stock valuations.

For background on the concepts involved in this scenario, see:

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2005). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-9 [http://www.livingcapitalmetrics.com/images/FisherJAM05.pdf].

Fisher, W. P., Jr. (2007, Summer). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-3 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

Fisher, W. P.. Jr. (2009). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital (Tech. Rep. No. http://www.livingcapitalmetrics.com/images/FisherNISTWhitePaper2.pdf). New Orleans: http://www.LivingCapitalMetrics.com.

Fisher, W. P., Jr. (2010). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 11, in press [http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf].

Creative Commons License
LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

How Measurement, Contractual Trust, and Care Combine to Grow Social Capital: Creating Social Bonds We Can Really Trade On

October 14, 2009

Last Saturday, I went to Miami, Florida, at the invitation of Paula Lalinde (see her profile at http://www.linkedin.com/pub/paula-lalinde/11/677/a12) to attend MILITARY 101: Military Life and Combat Trauma As Seen By Troops, Their Families, and Clinicians. This day-long free presentation was sponsored by The Veterans Project of South Florida-SOFAR, in association with The Southeast Florida Association for Psychoanalytic Psychology, The Florida Psychoanalytic Society, the Soldiers & Veterans Initiative, and the Florida BRAIVE Fund. The goals of the session “included increased understanding of the unique experiences and culture related to the military experience during wartime, enhanced understanding of the assessment and treatment of trauma specific difficulties, including posttraumatic stress disorder, common co-occurring conditions, and demands of treatment on trauma clinicians.”

Listening to the speakers on Saturday morning at the Military 101 orientation, I was struck by what seemed to me to be a developmental trajectory implied in the construct of therapy-aided healing. I don’t recall if anyone explicitly mentioned Maslow’s hierarchy but it was certainly implied by the dysfunctionality that attends being pushed down to a basic mode of physical survival.

Also, the various references to the stigma of therapy reminded me of Paula’s arguments as to why a community-based preventative approach would be more accessible and likely more successful than individual programs focused on treating problems. (Echoes here of positive psychology and appreciative inquiry.)

In one part of the program, the ritualized formality of the soldier, family, and support groups’ stated promises to each other suggested a way of operationalizing the community-based approach. The expectations structuring relationships among the parties in this community are usually left largely unstated, unexamined, and unmanaged in all but the broadest, and most haphazard, ways (as most relationships’ expectations usually are). The hierarchy of needs and progressive movement towards greater self-actualization implies a developmental sequence of steps or stages that comprise the actual focus of the implied contracts between the members of the community. This sequence is a measurable continuum along which change can be monitored and managed, with all parties accountable for their contracted role in producing specific outcomes.

The process would begin from the predeployment baseline, taking that level of reliability and basis of trust existing in the community as what we want to maintain, what we might want to get back to, and what we definitely want to build on and surpass, in time. The contract would provide a black-and-white record of expectations. It would embody an image of the desired state of the relationships and it could be returned to repeatedly in communications and in visualizations over time. I’ll come back to this after describing the structure of the relational patterns we can expect to observe over the course of events.

The Saturday morning discussion made repeated reference to the role of chains in the combat experience: the chain of command, and the unit being a chain only as strong as its weakest link. The implication was that normal community life tolerates looser expectations, more informal associations, and involves more in the way of team interactions. The contrast between chains and teams brought to mind work by Wright (1995, 1996a, 1996b; Bainer, 1997) on the way the difficulties of the challenges we face influence how we organize ourselves into groups.

Chains tend to form when the challenge is very difficult and dangerous; here we have mountain climbers roped together, bucket brigades putting out fires, and people stretching out end-to-end over thin ice to rescue someone who’s fallen through. In combat, as was stressed repeatedly last Saturday, the chain is one requiring strict follow-through on orders and promises; lives are at risk and protecting them requires the most rigorous adherence to the most specific details in an operation.

Teams form when the challenge is not difficult and it is possible to coordinate a fluid response of partners whose roles shift in importance as the situation changes. Balls are passed and the lead is taken by each in turn, with others getting out of the way or providing supports that might be vitally important or merely convenient.

A third kind of group, packs, forms when the very nature of the problem is at issue; here, individuals take completely different approaches in an exploratory determination of what is at issue, and how it might be addressed. Examples include the Manhattan Project, for instance, where scientists following personal hunches went in their own directions looking for solutions to complex problems. Wolves and other hunting parties form packs when it is impossible to know where the game might be. And though the old joke says that the best place to look for lost keys is where there’s the most light, if you have others helping you, it’s best to split up and not be looking for them in the same place.

After identifying these three major forms of organization, Wright (1996b) saw that individual groups might transition to and from different modes of organization as the nature of the problem changed. For instance, a 19th-century wagon train of settlers heading through the American West might function well as a team when everyone feels safe traveling along with a cavalry detachment, the road is good, the weather is pleasant, and food and water are plentiful. Given vulnerability to attacks by Native Americans, storms, accidents, lack of game, and/or muddy, rutted roads, however, the team might shift toward a chain formation and circle the wagons, with a later return to the team formation after the danger has passed. In the worst case scenario, disaster breaks the chain into individuals scattered like a pack to fend for themselves, with the limited hope of possibly re-uniting at some later time as a chain or team.

In the current context of the military, it would seem that deployment fragments the team, with the soldier training for a position in the chain of command in which she or he will function as a strong link for the unit. The family and support network can continue to function together and separately as teams to some extent, but the stress may require intermittent chain forms of organization. Further, the separation of the soldier from the family and support would seem to approach a pack level of organization for the three groups taken as a whole.

An initial contract between the parties would describe the functioning of the team at the predeployment stage, recognize the imminent breaking up of the team into chains and packs, and visualize the day when the team would be reformed under conditions in which significant degrees of healing will be required to move out of the pack and chain formations. Perhaps there will be some need and means of countering the forcible boot camp enculturation with medicinal antidote therapies of equal but opposite force. Perhaps some elements of the boot camp experience could be safely modified without compromising the operational chain to set the stage for reintegrating the family and community team.

We would want to be able to draw qualitative information from all three groups as to the nature of their experiences at every stage. I think we would want to focus the information on descriptions of the extent to which each level in Maslow’s hierarchy is realized. This information would be used in the design of an assessment that would map out the changes over time, set up the evaluation framework, and guide interventions toward reforming the team. Given their experience with the healing process, the presenters from last Saturday have obvious capacities for an informed perspective on what’s needed here. And what we build with their input would then also plainly feed back into the kind of presentation they did.

There will likely be signature events in the process that will be used to trigger new additions to the contract, as when the consequences of deployment, trauma, loss, or return relative to Maslow’s hierarchy can be predicted. That is, the contract will be a living document that changes as goals are reached or as new challenges emerge.

This of course is all situated then within the context of measures calibrated and shared across the community to inform contracts, treatment, expectations, etc. following the general metrological principles I outline in my published work (see references).

The idea will be for the consistent production of predictable amounts of impact in the legally binding contractual relationships, such that the benefits produced in terms of individual functionality will attract investments from those in positions to employ those individuals, and from the wider society that wants to improve its overall level of mental health. One could imagine that counselors, social workers, and psychotherapists will sell social capital bonds at prices set by market forces on the basis of information analogous to the information currently available in financial markets, grocery stores, or auto sales lots. Instead of paying taxes, corporations would be required to have minimum levels of social capitalization. These levels might be set relative to the value the organization realizes from the services provided by public schools, hospitals, and governments relative to the production of an educated, motivated, healthy workforce able to get to work on public roads, able to drink public water, and living in a publicly maintained quality environment.

There will be a lot more to say on this latter piece, following up on previous blogs here that take up the topic. The contractual groundwork that sets up the binding obligations for formal agreements is the thought of the day that emerged last weekend at the session in Miami. Good stuff, long way to go, as always….

References
Bainer, D. (1997, Winter). A comparison of four models of group efforts and their implications for establishing educational partnerships. Journal of Research in Rural Education, 13(3), 143-152.

Fisher, W. P., Jr. (1995). Opportunism, a first step to inevitability? Rasch Measurement Transactions, 9(2), 426 [http://www.rasch.org/rmt/rmt92.htm].

Fisher, W. P., Jr. (1996, Winter). The Rasch alternative. Rasch Measurement Transactions, 9(4), 466-467 [http://www.rasch.org/rmt/rmt94.htm].

Fisher, W. P., Jr. (1997a). Physical disability construct convergence across instruments: Towards a universal metric. Journal of Outcome Measurement, 1(2), 87-113.

Fisher, W. P., Jr. (1997b, June). What scale-free measurement means to health outcomes research. Physical Medicine & Rehabilitation State of the Art Reviews, 11(2), 357-373.

Fisher, W. P., Jr. (1998). A research program for accountable and patient-centered health status measures. Journal of Outcome Measurement, 2(3), 222-239.

Fisher, W. P., Jr. (2000). Objectivity in psychosocial measurement: What, why, how. Journal of Outcome Measurement, 4(2), 527-563 [http://www.livingcapitalmetrics.com/images/WP_Fisher_Jr_2000.pdf].

Fisher, W. P., Jr. (2004, October). Meaning and method in the social sciences. Human Studies: A Journal for Philosophy and the Social Sciences, 27(4), 429-54.

Fisher, W. P., Jr. (2005). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-9 [http://www.livingcapitalmetrics.com/images/FisherJAM05.pdf].

Fisher, W. P., Jr. (2008). Vanishing tricks and intellectualist condescension: Measurement, metrology, and the advancement of science. Rasch Measurement Transactions, 21(3), 1118-1121 [http://www.rasch.org/rmt/rmt213c.htm].

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

Wright, B. D. (1995). Teams, packs, and chains. Rasch Measurement Transactions, 9(2), 432 [http://www.rasch.org/rmt/rmt92j.htm].

Wright, B. D. (1996a). Composition analysis: Teams, packs, chains. In G. Engelhard & M. Wilson (Eds.), Objective measurement: Theory into practice, Vol. 3 (pp. 241-264). Norwood, New Jersey: Ablex [http://www.rasch.org/memo67.htm].

Wright, B. D. (1996b). Pack to chain to team. Rasch Measurement Transactions, 10(2), 501 [http://www.rasch.org/rmt/rmt102s.htm].

Creative Commons License
LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.