Posts Tagged ‘healthcare reform’

The New Information Platform No One Sees Coming

December 6, 2012

I’d like to draw your attention to a fundamentally important area of disruptive innovations no one seems to see coming. The biggest thing rising in the world of science today that does not appear to be on anyone’s radar is measurement. Transformative potential beyond that of the Internet itself is available.

Realizing that potential will require an Intangible Assets Metric System. This system will connect together all the different ways any one thing is measured, bringing common languages for representing human, social, and economic value into play everywhere. We need these metrics on the front lines of education, health care, social services, and in human, reputation, and natural resource management, as well as in the economic models and financial spreadsheets informing policy, and in the scientific research conducted in dozens of fields.

All reading ability measures, for instance, should be transparently, inexpensively, and effortlessly expressed in a universally uniform metric, in the same way that standardized measures of weight and volume inform grocery store purchasing decisions. We have made starts at such systems for reading, writing, and math ability measures, and for health status, functionality, and chronic disease management measures. There oddly seems to be, however, little awareness of the full value that stands to be gained from uniform metrics in these areas, despite the overwhelming human, economic, and scientific value derived from standardized units in the existing economy. There has accordingly been virtually no leadership or investment in this area.

Measurement practice in business is woefully out of touch with the true paradigm shift that has been underway in psychometrics for years, even though the mantra “you manage what you measure” is repeated far and wide. In a fascinating twist, practically the only ones who notice the business world’s conceptual shortfall in measurement practice are the contrarians who observe that quantification can often be more of a distraction from management than the medium of its execution—but this is true only when measures are poorly conceived, designed, and implemented.

Demand for better measurement—measurement that reduces data volume not only with no loss of information but with the addition of otherwise unavailable interstitial information; that supports mass customized comparability for informed purchasing and quality improvement decisions; and that enables common product definitions for outcomes-based budgeting—is growing hand in hand with the spread of resilient, nimble, lean, and adaptive business models, and with the ongoing geometrical growth in data volume.

An even bigger source of demand for the features of advanced measurement is the increasing dependence of the economy on intangible assets, those forms of human, social, and natural capital that comprise 90% or more of the total capital under management. We will bring these now economically dead forms of capital to life by systematically standardizing representations of their quality and quantity. The Internet is the planetary nervous system through which basic information travels, and the Intangible Assets Metric System will be the global cerebrum, where higher order thinking takes place.

It will not be possible to realize the full potential of lean thinking in the information- and service-based economy without an Intangible Assets Metric System. Given the long-proven business value of standards and the role of measurement in management, it seems self-evident that our ongoing economic difficulties stem largely from our failure to develop and deploy an Intangible Assets Metric System providing common currencies for the exchange of authentic wealth. The future of sustainable and socially responsible business practices must surely depend extensively on universal access to flexible and practical uniform metrics for intangible assets.

Of course, for global intangible assets standards to be viable, they must be adaptable to local business demands and conditions without compromising their comparability. And that is just what is most powerfully disruptive about contemporary measurement methods: they make mass customization a reality. They’ve been doing so in computerized testing since the 1970s. Isn’t it time we started putting this technology to systematic use in a wide range of applications, from human and environmental resource management to education, health care, and social services?

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.
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Reimagining Capitalism Again, Part III: Reflections on Greider’s “Bold Ideas” in The Nation

September 10, 2011

And so, The Nation’s “Bold Ideas for a New Economy” is disappointing for not doing more to start from the beginning identified by its own writer, William Greider. The soul of capitalism needs to be celebrated and nourished, if we are to make our economy “less destructive and domineering,” and “more focused on what people really need for fulfilling lives.” The only real alternative to celebrating and nourishing the soul of capitalism is to kill it, in the manner of the Soviet Union’s failed experiments in socialism and communism.

The article speaks the truth, though, when it says there is no point in trying to persuade the powers that be to make the needed changes. Republicans see the market as it exists as a one-size-fits-all economic panacea, when all it can accomplish in its current incomplete state is the continuing externalization of anything and everything important about human, social, and environmental decency. For their part, Democrats do indeed “insist that regulation will somehow fix whatever is broken,” in an ever-expanding socialistic micromanagement of every possible exception to the rules that emerges.

To date, the president’s efforts at a nonpartisan third way amount only to vacillations between these opposing poles. The leadership that is needed, however, is something else altogether. Yes, as The Nation article says, capitalism needs to be made to serve the interests of society, and this will require deep structural change, not just new policies. But none of the contributors of the “bold ideas” presented propose deep structural changes of a kind that actually gets at the soul of capitalism. All of the suggestions are ultimately just new policies tweaking superficial aspects of the economy in mechanical, static, and very limited ways.

The article calls for “Democratizing reforms that will compel business and finance to share decision-making and distribute rewards more fairly.” It says the vision has different names but “the essence is a fundamental redistribution of power and money.” But corporate distortions of liability law, the introduction of boardroom watchdogs, and a tax on financial speculation do not by any stretch of the imagination address the root causes of social and environmental irresponsibility in business. They “sound like obscure technical fixes” because that’s what they are. The same thing goes for low-cost lending from public banks, the double or triple bottom lines of Benefit Corporations, new anti-trust laws, calls for “open information” policies, added personal stakes for big-time CEOs, employee ownership plans, the elimination of tax subsidies for, new standards for sound investing, new measures of GDP, and government guarantees of full employment.

All of these proposals sound like what ought to be the effects and outcomes of efforts addressing the root causes of capitalisms’ shortcomings. Instead, they are band aids applied to scratched fingers and arms when multiple by-pass surgery is called for. That is, what we need is to understand how to bring the spirit of capitalism to life in the new domains of human, social, and environmental interests, but what we’re getting are nothing but more of the same piecemeal ways of moving around the deck chairs on the Titanic.

There is some truth in the assertion that what really needs reinventing is our moral and spiritual imagination. As someone (Einstein or Edison?) is supposed to have put it, originality is simply a matter of having a source for an analogy no one else has considered. Ironically, the best model is often the one most taken for granted and nearest to hand. Such is the case with the two-sided scientific and economic effects of standardized units of measurement. The fundamental moral aspect here is nothing other than the Golden Rule, independently derived and offered in cultures throughout history, globally. Individualized social measurement is nothing if not a matter of determining whether others are being treated in the way you yourself would want to be treated.

And so, yes, to stress the major point of agreement with The Nation, “the new politics does not start in Washington.” Historically, at their best, governments work to keep pace with the social and technical innovations introduced by their peoples. Margaret Mead said it well a long time ago when she asserted that small groups of committed citizens are the only sources of real social change.

Not to be just one of many “advocates with bold imaginations” who wind up marginalized by the constraints of status quo politics, I claim my personal role in imagining a new economic future by tapping as deeply as I can into the positive, pre-existing structures needed for a transition into a new democratic capitalism. We learn through what we already know. Standards are well established as essential to commerce and innovation, but 90% of the capital under management in our economy—the human, social, and natural capital—lacks the standards needed for optimal market efficiency and effectiveness. An intangible assets metric system will be a vitally important way in which we extend what is right and good in the world today into new domains.

To conclude, what sets this proposal apart from those offered by The Nation and its readers hinges on our common agreement that “the most threatening challenge to capitalism is arguably the finite carrying capacity of the natural world.” The bold ideas proposed by The Nation’s readers respond to this challenge in ways that share an important feature in common: people have to understand the message and act on it. That fact dooms all of these ideas from the start. If we have to articulate and communicate a message that people then have to act on, we remain a part of the problem and not part of the solution.

As I argue in my “The Problem is the Problem” blog post of some months ago, this way of defining problems is itself the problem. That is, we can no longer think of ourselves as separate from the challenges we face. If we think we are not all implicated through and through as participants in the construction and maintenance of the problem, then we have not understood it. The bold ideas offered to date are all responses to the state of a broken system that seek to reform one or another element in the system when what we need is a whole new system.

What we need is a system that so fully embodies nature’s own ecological wisdom that the medium becomes the message. When the ground rules for economic success are put in place such that it is impossible to earn a profit without increasing stocks of human, social, and natural capital, there will be no need to spell out the details of a microregulatory structure of controlling new anti-trust laws, “open information” policies, personal stakes for big-time CEOs, employee ownership plans, the elimination of tax subsidies, etc. What we need is precisely what Greider reported from Innovest in his book: reliable, high quality information that makes human, social, and environmental issues matter financially. Situated in a context like that described by Bernstein in his 2004 The Birth of Plenty, with the relevant property rights, rule of law, scientific rationality, capital markets, and communications networks in place, it will be impossible to stop a new economic expansion of historic proportions.

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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.
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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.
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Permissions beyond the scope of this license may be available at http://www.livingcapitalmetrics.com.

A Technology Road Map for Efficient Intangible Assets Markets

February 24, 2011

Scientific technologies, instruments and conceptual images have been found to play vitally important roles in economic success because of the way they enable accurate predictions of future industry and market states (Miller & O’Leary, 2007). The technology road map for the microprocessor industry, based in Moore’s Law, has successfully guided market expectations and coordinated research investment decisions for over 40 years. When the earlier electromechanical, relay, vacuum tube, and transistor computing technology paradigms are included, the same trajectory has dominated the computer industry for over 100 years (Kurzweil, 2005, pp. 66-67).

We need a similar technology road map to guide the creation and development of intangible asset markets for human, social, and natural (HSN) capital. This will involve intensive research on what the primary constructs are, determining what is measurable and what is not, creating consensus standards for uniform metrics and the metrology networks through which those standards will function. Alignments with these developments will require comprehensively integrated economic models, accounting frameworks, and investment platforms, in addition to specific applications deploying the capital formations.

What I’m proposing is, in a sense, just an extension in a new direction of the metrology challenges and issues summarized in Table ITWG15 on page 48 in the 2010 update to the International Technology Roadmap for Semiconductors (http://www.itrs.net/about.html). Distributed electronic communication facilitated by computers and the Internet is well on the way to creating a globally uniform instantaneous information network. But much of what needs to be communicated through this network remains expressed in locally defined languages that lack common points of reference. Meaningful connectivity demands a shared language.

To those who say we already have the technology necessary and sufficient to the measurement and management of human, social, and natural capital, I say think again. The difference between what we have and what we need is the same as the difference between (a) an economy whose capital resources are not represented in transferable representations like titles and deeds, and that are denominated in a flood of money circulating in different currencies, and, (b) an economy whose capital resources are represented in transferable documents and are traded using a single currency with a restricted money supply. The measurement of intangible assets is today akin to the former economy, with little actual living capital and hundreds of incommensurable instruments and scoring systems, when what we need is the latter. (See previous entries in this blog for more on the difference between dead and living capital.)

Given the model of a road map detailing the significant features of the living capital terrain, industry-specific variations will inform the development of explicit market expectations, the alignment of HSN capital budgeting decisions, and the coordination of research investments. The concept of a technology road map for HSN capital is based in and expands on an integration of hierarchical complexity (Commons & Richards, 2002; Dawson, 2004), complex adaptive functionality (Taylor, 2003), Peirce’s semiotic developmental map of creative thought (Wright, 1999), and historical stages in the development of measuring systems (Stenner & Horabin, 1992; Stenner, Burdick, Sanford, & Burdick, 2006).

Technology road maps replace organizational amnesia with organizational learning by providing the structure of a memory that not only stores information, knowledge, understanding, and wisdom, but makes it available for use in new situations. Othman and Hashim (2004) describe organizational amnesia (OA) relative to organizational learning (OL) in a way that opens the door to a rich application of Miller and O’Leary’s (2007) detailed account of how technology road maps contribute to the creation of new markets and industries. Technology road maps function as the higher organizational principles needed for transforming individual and social expertise into economically useful products and services. Organizational learning and adaptability further need to be framed at the inter-organizational level where their various dimensions or facets are aligned not only within individual organizations but between them within the industry as a whole.

The mediation of the individual and organizational levels, and of the organizational and inter-organizational levels, is facilitated by measurement. In the microprocessor industry, Moore’s Law enabled the creation of technology road maps charting the structure, processes, and outcomes that had to be aligned at the individual, organizational, and inter-organizational levels to coordinate the entire microprocessor industry’s economic success. Such road maps need to be created for each major form of human, social, and natural capital, with the associated alignments and coordinations put in play at all levels of every firm, industry, and government.

It is a basic fact of contemporary life that the technologies we employ every day are so complex that hardly anyone understands how they do what they do. Technological miracles are commonplace events, from transportation to entertainment, from health care to manufacturing. And we usually suffer little in the way of adverse consequences from not knowing how an automatic transmission, a thermometer, or digital video reproduction works. It is enough to know how to use the tool.

This passive acceptance of technical details beyond our ken extends into areas in which standards, methods, and products are much less well defined. Managers, executives, researchers, teachers, clinicians, and others who need measurement but who are unaware of its technicalities are then put in the position of being passive consumers accepting the lowest common denominator in the quality of the services and products obtained.

And that’s not all. Just as the mass market of measurement consumers is typically passive and uninformed, in complementary fashion the supply side is fragmented and contentious. There is little agreement among measurement experts as to which quantitative methods set the standard as the state of the art. Virtually any method can be justified in terms of some body of research and practice, so the confused consumer accepts whatever is easily available or is most likely to support a preconceived agenda.

It may be possible, however, to separate the measurement wheat from the chaff. For instance, measurement consumers may value a way of distinguishing among methods that is based in a simple criterion of meaningful utility. What if all measurement consumers’ own interests in, and reasons for, measuring something in particular, such as literacy or community, were emphasized and embodied in a common framework? What if a path of small steps from currently popular methods of less value to more scientific ones of more value could be mapped? Such a continuum of methods could range from those doing the least to advance the users’ business interests to those doing the most to advance those interests.

The aesthetics, simplicity, meaningfulness, rigor, and practical consequences of strong theoretical requirements for instrument calibration provide such criteria for choices as to models and methods (Andrich, 2002, 2004; Busemeyer and Wang, 2000; Myung, 2000; Pitt, Kim, Myung, 2003; Wright, 1997, 1999). These criteria could be used to develop and guide explicit considerations of data quality, construct theory, instrument calibration, quantitative comparisons, measurement standard metrics, etc. along a continuum from the most passive and least objective to the most actively involved and most objective.

The passive approach to measurement typically starts from and prioritizes content validity. The questions asked on tests, surveys, and assessments are considered relevant primarily on the basis of the words they use and the concepts they appear to address. Evidence that the questions actually cohere together and measure the same thing is not needed. If there is any awareness of the existence of axiomatically prescribed measurement requirements, these are not considered to be essential. That is, if failures of invariance are observed, they usually provoke a turn to less stringent data treatments instead of a push to remove or prevent them. Little or no measurement or construct theory is implemented, meaning that all results remain dependent on local samples of items and people. Passively approaching measurement in this way is then encumbered by the need for repeated data gathering and analysis, and by the local dependency of the results. Researchers working in this mode are akin to the woodcutters who say they are too busy cutting trees to sharpen their saws.

An alternative, active approach to measurement starts from and prioritizes construct validity and the satisfaction of the axiomatic measurement requirements. Failures of invariance provoke further questioning, and there is significant practical use of measurement and construct theory. Results are then independent of local samples, sometimes to the point that researchers and practical applications are not encumbered with usual test- or survey-based data gathering and analysis.

As is often the case, this black and white portrayal tells far from the whole story. There are multiple shades of grey in the contrast between passive and active approaches to measurement. The actual range of implementations is much more diverse that the simple binary contrast would suggest (see the previous post in this blog for a description of a hierarchy of increasingly complex stages in measurement). Spelling out the variation that exists could be helpful for making deliberate, conscious choices and decisions in measurement practice.

It is inevitable that we would start from the materials we have at hand, and that we would then move through a hierarchy of increasing efficiency and predictive control as understanding of any given variable grows. Previous considerations of the problem have offered different categorizations for the transformations characterizing development on this continuum. Stenner and Horabin (1992) distinguish between 1) impressionistic and qualitative, nominal gradations found in the earliest conceptualizations of temperature, 2) local, data-based quantitative measures of temperature, and 3) generalized, universally uniform, theory-based quantitative measures of temperature.

The latter is prized for the way that thermodynamic theory enables the calibration of individual thermometers with no need for testing each one in empirical studies of its performance. Theory makes it possible to know in advance what the results of such tests would be with enough precision to greatly reduce the burden and expenses of instrument calibration.

Reflecting on the history of psychosocial measurement in this context, it then becomes apparent that these three stages can then be further broken down. The previous post in this blog lists the distinguishing features for each of six stages in the evolution of measurement systems, building on the five stages described by Stenner, Burdick, Sanford, and Burdick (2006).

And so what analogue of Moore’s Law might be projected? What kind of timetable can be projected for the unfolding of what might be called Stenner’s Law? Guidance for reasonable expectations is found in Kurzweil’s (2005) charting of historical and projected future exponential increases in the volume of information and computer processing speed. The accelerating growth in knowledge taking place in the world today speaks directly to a systematic integration of criteria for what shall count as meaningful new learning. Maps of the roads we’re traveling will provide some needed guidance and make the trip more enjoyable, efficient, and productive. Perhaps somewhere not far down the road we’ll be able to project doubling rates for growth in the volume of fungible literacy capital globally, or the halving rates in the cost of health capital stocks. We manage what we measure, so when we begin measuring well what we want to manage well, we’ll all be better off.

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.

Busemeyer, J. R., & Wang, Y.-M. (2000, March). Model comparisons and model selections based on generalization criterion methodology. Journal of Mathematical Psychology, 44(1), 171-189 [http://quantrm2.psy.ohio-state.edu/injae/jmpsp.htm].

Commons, M. L., & Richards, F. A. (2002, Jul). Organizing components into combinations: How stage transition works. Journal of Adult Development, 9(3), 159-177.

Dawson, T. L. (2004, April). Assessing intellectual development: Three approaches, one sequence. Journal of Adult Development, 11(2), 71-85.

Kurzweil, R. (2005). The singularity is near: When humans transcend biology. New York: Viking Penguin.

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

Myung, I. J. (2000). Importance of complexity in model selection. Journal of Mathematical Psychology, 44(1), 190-204.

Othman, R., & Hashim, N. A. (2004). Typologizing organizational amnesia. The Learning Organization, 11(3), 273-84.

Pitt, M. A., Kim, W., & Myung, I. J. (2003). Flexibility versus generalizability in model selection. Psychonomic Bulletin & Review, 10, 29-44.

Stenner, A. J., Burdick, H., Sanford, E. E., & Burdick, D. S. (2006). How accurate are Lexile text measures? Journal of Applied Measurement, 7(3), 307-22.

Stenner, A. J., & Horabin, I. (1992). Three stages of construct definition. Rasch Measurement Transactions, 6(3), 229 [http://www.rasch.org/rmt/rmt63b.htm].

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

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.

Build it and they will come

February 8, 2011

“It” in the popular Kevin Costner movie, “Field of Dreams,” was a baseball diamond. He put it in a corn field. Not only did a ghost team conjure itself from the corn, so did a line of headlights on the road. There would seem to have been a stunning lack of preparation for crowds of fans, as parking, food, and toilet facilities were nowhere in sight.

Those things would be taken care of in due course, but that’s another story. The point has nothing to do with being realistic and everything to do with making dreams come true. Believing in yourself and your dreams is hard. Dreams are inherently unrealistic. As George Bernard Shaw said, reasonable people adapt to life and the world. It’s unreasonable people who think the world should adapt to them. And, accordingly, change comes about only because unreasonable and unrealistic people act to make things different.

I dream of a playing field, too. I can’t just go clear a few acres in a field to build it, though. The kind of clearing I’m dreaming of is more abstract. But the same idea applies. I, too, am certain that, if we build it, they will come.

What is it? Who are they? “It” is a better way for each of us to represent who we are to the world, and to see where we stand in it. It is a new language for speaking the truth of what we are each capable of. It is a way of tuning the instruments of a new science that will enable us to harmonize relationships of all kinds: personal, occupational, social, and economic.

Which brings us to who “they” are. They are us. Humanity. We are the players on this field that we will clear. We are the ones who care and who desire meaning. We are the ones who have been robbed of the trust, loyalty, and commitment we’ve invested in governments, corporations, and decades of failed institutions. We are the ones who know what has been lost, and what yet could still be gained. We are the ones who possess our individual skills, motivations, and health, but yet have no easy, transparent way to represent how much of any one of them we have, what quality it is, or how much it can be traded for. We are the ones who all share in the bounty of the earth’s fecund capacity for self-renewal, but who among us can show exactly how much the work we do every day adds or subtracts from the quality of the environment?

So why do I say, build it and they will come? Because this sort of thing is not something that can be created piecemeal. What if Costner’s character in the movie had not just built the field but had instead tried to find venture capital, recruit his dream team, set up a ticket sales vendor, hire management and staff, order uniforms and equipment, etc.? It never would have happened. It doesn’t work that way.

And so, finally, just what do we need to build? Just this: a new metric system. The task is to construct a system of measures for managing what’s most important in life: our relationships, our health, our capacity for productive and creative employment. We need a system that enables us to track our investments in intangible assets like education, health care, community, and quality of life. We need instruments tuned to the same scales, ones that take advantage of recently developed technical capacities for qualitatively meaningful quantification; for information synthesis across indicators/items/questions; for networked, collective thinking; for adaptive innovation support; and for creating fungible currencies in which human, social, and natural capital can be traded in efficient markets.

But this is not a system that can be built piecemeal. Infrastructure on this scale is too complex and too costly for any single individual, firm, or industry to create by itself. And building one part of it at a time will not work. We need to create the environment in which these new forms of life, these new species, these new markets for living capital, can take root and grow, organically. If we create that environment, with incentives and rewards capable of functioning like fertile soil, warm sun, and replenishing rain, it will be impossible to stop the growth.

You see, there are thousands of people around the world using new measurement methods to calibrate tests, surveys and assessments as valid and reliable instruments. But they are operating in an environment in which the fully viable seeds they have to plant are wasted. There’s no place for them to take root. There’s no sun, no water.

Why is the environment for the meaningful, uniform measurement of intangible assets so inhospitable? The primary answer to this question is cultural. We have ingrained and highly counterproductive attitudes toward what are often supposed to be the inherent properties of numbers. One very important attitude of this kind is that it is common to think that all numbers are quantitative. But lots of scoring systems and percentage reporting schemes involve numbers that do not stand for something that adds up. There is nothing automatic or simple about the way any given unit of calibrated measurement remains the same all up and down a scale. Arriving at a way to construct and maintain such a unit requires as much intensive research and imaginative investigation in the social sciences as it does in the natural sciences. But where the natural sciences and engineering have grown up around a focus on meaningful measurement, the social sciences have not.

One result of mistaken preconceptions about number is that even when tests, surveys, and assessments measure the same thing, they are disconnected from one another, tuned to different scales. There is no natural environment, no shared ecology, in which the growth of learning can take place in field-wide terms. There’s no common language in which to share what’s been learned. Even when research results are exactly the same, they look different.

But if there was a system of consensus-based reference standard metrics, one for each major construct–reading, writing, and math abilities; health status; physical and psychosocial functioning; quality of life; social and natural capital–there would be the expectation that instruments measuring the same thing should measure in the same unit. Researchers could be contributing to building larger systems when they calibrate new instruments and recalibrate old ones. They would more obviously be adding to the stock of human knowledge, understanding, and wisdom. Divergent results would demand explanations, and convergent ones would give us more confidence as we move forward.

Most importantly, quality improvement and consumer purchasing decisions and behaviors would be fluidly coordinated with no need for communicating and negotiating the details of each individual comparison. Education and health care lack common product definitions because their outcomes are measured in fragmented, incommensurable metrics. But if we had consensus-based reference standard metrics for every major form of capital employed in the economy, we could develop reasonable expectations expressed in a common language for how much change should typically be obtained in fifth-grade mathematics or from a hip replacement.

As is well-known in the business world, innovation is highly dependent on standards. We cannot empower the front line with the authority to make changes when decisions have to be based on information that is unavailable or impossible to interpret. Most of the previous entries in this blog take up various aspects of this situation.

All of this demands a very different way of thinking about what’s possible in the realm of measurement. The issues are complex. They are usually presented in difficult mathematical terms within specialized research reports. But the biggest problem has to do with thinking laterally, with moving ideas out of the vertical hierarchies of the silos where they are trapped and into a new field we can dream in. And the first seeds to be planted in such a field are the ones that say the dream is worth dreaming. When we hear that message, we are already on the way not just to building this dream, but to creating a world in which everyone can dream and envision more specific possibilities for their lives, their families, their creativity.

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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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You see, there are thousands of people around the world using these
new measurement methods to calibrate tests, surveys and assessments as
valid and reliable instruments. But they are operating in an
environment in which the fully viable seeds they have to plant are
wasted. There’s no place for them to take root. There’s no sun, no
water. 

This is because the instruments being calibrated are all disconnected.
Even instruments of the same kind measuring the same thing are
isolated from one another, tuned to different scales. There is no
natural environment, no shared ecology, in which the growth of
learning can take place. There’s no common language in which to share
what’s been learned. Even when results are exactly the same, they look
different.

 

You see, there are thousands of people around the world using these new measurement methods to calibrate tests, surveys and assessments as valid and reliable instruments. But they are operating in an environment in which the fully viable seeds they have to plant are wasted. There’s no place for them to take root. There’s no sun, no water. This is because the instruments being calibrated are all disconnected. Even instruments of the same kind measuring the same thing are isolated from one another, tuned to different scales. There is no natural environment, no shared ecology, in which the growth of learning can take place. There’s no common language in which to share what’s been learned. Even when results are exactly the same, they look different.

How bad will the financial crises have to get before…?

April 30, 2010

More and more states and nations around the world face the possibility of defaulting on their financial obligations. The financial crises are of epic historical proportions. This is a disaster of the first order. And yet, it is so odd–we have the solutions and preventative measures we need at our finger tips, but no one knows about them or is looking for them.

So,  I am persuaded to once again wonder if there might now be some real interest in the possibilities of capitalizing on

  • measurement’s well-known capacity for reducing transaction costs by improving information quality and reducing information volume;
  • instruments calibrated to measure in constant units (not ordinal ones) within known error ranges (not as though the measures are perfectly precise) with known data quality;
  • measures made meaningful by their association with invariant scales defined in terms of the questions asked;
  • adaptive instrument administration methods that make all measures equally precise by targeting the questions asked;
  • judge calibration methods that remove the person rating performances as a factor influencing the measures;
  • the metaphor of transparency by calibrating instruments that we really look right through at the thing measured (risk, governance, abilities, health, performance, etc.);
  • efficient markets for human, social, and natural capital by means of the common currencies of uniform metrics, calibrated instrumentation, and metrological networks;
  • the means available for tuning the instruments of the human, social, and environmental sciences to well-tempered scales that enable us to more easily harmonize, orchestrate, arrange, and choreograph relationships;
  • our understandings that universal human rights require universal uniform measures, that fair dealing requires fair measures, and that our measures define who we are and what we value; and, last but very far from least,
  • the power of love–the back and forth of probing questions and honest answers in caring social intercourse plants seminal ideas in fertile minds that can be nurtured to maturity and Socratically midwifed as living meaning born into supportive ecologies of caring relations.

How bad do things have to get before we systematically and collectively implement the long-established and proven methods we have at our disposal? It is the most surreal kind of schizophrenia or passive-aggressive avoidance pathology to keep on tormenting ourselves with problems for which we have solutions.

For more information on these issues, see prior blogs posted here, the extensive documentation provided, and http://www.livingcapitalmetrics.com.

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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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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.

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