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I have personally been involved in providing health care and patient service for eighteen-plus years and have witnessed, along with the rest of the U.S. population, staggering growth and fundamental change with regard to the dollars and resources required to deliver care. Health care accounts for over one-sixth of the U.S. economy and has become its largest sector. The U.S. dedicates an estimated $1 out of every $6 spent on final goods and services to the health sector, and its per-capita health care spending (over $8,000) greatly exceeds that of any other country (Folland & Goodman, 2013). The last six years or so of my career has been devoted to Health IT and, considering the size and scope of health care in the U.S., it comes as no surprise that information management has become such an important facet of this nation’s health care delivery system. The U.S. Government, through its HealthIT.gov website, describes health information technology (health IT) as a means for health care providers to better manage patient care through secure use and sharing of health information (P., A.,2014). Health information is data that has meaning and has been processed or organized in a manner that is useful for decision making. Management of this data involves overseeing patient health information and medical records, administering computer information systems, and coding diagnoses and procedures for health care services provided to patients (Green & Bowie, 2011). The use of health IT has been spurred on by the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009, a part of the American Recovery and Reinvestment Act (ARRA). According to Buntin, Burke, Hoaglin, and Blumenthal, “HITECH makes an estimated $14–27 billion in incentive payments available to hospitals and health professionals to adopt certified electronic health records and use them effectively in the course of care” (Buntin, Burke, Hoaglin, & Blumenthal, 2011). The ARRA is an example of fiscal policy, the use of government spending and taxes to manage aggregate demand (Krugman & Wells, 2012). Considering this, it is reasonable to conclude that fiscal policy has, over the last five or so years, had a substantial influence on the economic viability of the health information management industry, specifically.

According to Thomas and Maurice, the six principal variables that influence the quantity demanded of a good or service are the price of the good or service, the incomes of consumers, the prices of related goods and services, the tastes or preference patterns of consumers, the expected price of the product in future periods, and the number of consumers in the market (Thomas & Maurice, 2013). One can question whether the typical economic methods and study apply to health care: are consumers of healthcare rational and do they calculate optimally at the margin? Do consumers question the price of emergency services during a time of need? In terms of consumer choice, there is a perception that patients leave medical decisions up to their providers. However, a considerable amount of decision-making is left up to patients in terms of elective procedures and routine examinations and preventative care, creating opportunities for rational choices to be made. Research by such studies as the RAND Health Insurance Experiment in the 1970’s indicates that people consume more health care as the care becomes less costly in terms of dollars paid out-of-pocket (Folland & Goodman, 2013). The trend toward consumer-driven healthcare has forced consumers to become more cost conscious of what they are spending on both healthcare and health insurance.

Simple economics has contributed to other areas of healthcare, as well. From a health information technology perspective, recent advancements in technology (i.e. semiconductor manufacturing) which have contributed to a decline in the prices of information technology equipment has steadily enhanced the role of IT investment as a source of American economic growth. “Productivity growth in IT-producing industries,” Jorgenson reports, “has gradually risen in importance and a productivity revival is now underway in the rest of the economy” (Jorgenson, 2002). Computers, as well as related digital communication technology, have the power to reduce the costs of coordination, communications, and information processing in the health care industry. “Thus, it is not surprising that the massive reduction in computing and communications costs has engendered a substantial restructuring of the economy. The majority of modern industries are being significantly affected by computerization” (Brynjolfsson & Hitt, 2000, p.24). Adoption and integration of information technology provides potential improvements in health care, but also possibilities of increasing costs.

In my opinion, macroeconomic indicators for both health care and health information technology are similar, however health IT is still a growing sector and there is a level of uncertainty that surrounds the industry. The most relevant indicator for healthcare overall would seem to be the National Health Expenditures Estimates, which represents the amount of health spending in the U.S. monthly and what percentage of GDP the spending represents. As stated earlier, the U.S. devotes by far the largest share of GDP to health care spending. For example, the Bureau of Economic Analysis reported in April, 2014 that expenses for health care rose at a 5.6% annual rate in the fourth quarter of 2013, signaling an upward revision in the government’s estimate of consumer spending overall and accounted for nearly a quarter of the economy’s 2.6% annualized growth in the last three months of 2013 (Davidson, 2014). The Consumer Price Index is another measure of health-related economics which depicts price changes for out-of-pocket expenditures. The CPI for medical care services also includes an indirect measure of price change for health insurance coverage purchased directly by consumers (Seifert, Heffler, & Donham, Winter, 1999). Yet another influential indicator is job openings and labor turnover: a July, 2013 report by Pellegrini, Rodriguez-Monguio, and Qian indicates that the healthcare sector was one of the few sectors of the U.S. economy that created new positions in spite of the recent economic downturn (Pellegrini, Rodriguez-Monguio, & Qian, 2014). More specifically, a 2009, fourth-quarter survey of more than 130 U.S.-based IT organizations conducted by Computer Economics indicated high demand in the healthcare space, particularly for software developers with skills in mobile app development and those with experience in supporting infrastructure virtualization (Monegain, 2012). There are several other lesser-known, industry specific, microeconomic indicators which economists utilize that point toward the condition of the industry. These would include the measure of the contribution of health care to population health, as represented by the elasticity of health with respect to expenditure on health care inputs, or the % change in health / % change in health care expenditures. Others include morbidity rates, capacity utilization, and average cost per patient (Folland & Goodman, 2013).

The health economy merits attention for its sheer size in terms of its large share of GDP and the substantial capital investment it represents. It also maintains a large and growing share of the labor force. It has become clear to me that observing and scrutinizing the well-being of the health care sector allows for perspective on the well-being of the economy as a whole. As such, it’s evident that the new technologies which will grow out of a need to improve health care delivery over the coming years will have a lasting effect on the wealth of this nation. Krugman writes, “as much as possible, you should spend on things of lasting value, things that, like roads and bridges, will make us a richer nation. Upgrade the infrastructure behind the Internet; upgrade the electrical grid; improve information technology in the healthcare sector, a crucial part of any healthcare reform” (Monegain, 2009, January 19).    

Resources:

Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011). The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Affairs, 30(3), 464-471.

Brynjolfsson, E., & Hitt, L. M. (2000). Beyond computation: Information technology, organizational transformation and business performance. The Journal of Economic Perspectives, 23-48.

Brynjolfsson, E. (2011). Wired for innovation: how information technology is reshaping the economy. MIT Press Books, 1.

Davidson, P. (2014, April 1). Health care spending growth hits 10-year high. USA Today. Retrieved April 25, 2014, from http://www.usatoday.com/story/money/business/2014/03/30/health-care-spending/7007987/

Folland, S., & Goodman, A. C. (2013). The economics of health and health care (7th ed.). Upper Saddle River, N.J.: Pearson.

Green, M. A., & Bowie, M. J. (2011). Essentials of health information management: principles and practices. Clifton Park, NY: Delmar Cengage Learning.

Jorgenson, D. W. (2002). Information technology and the US economy. 2002), Economic Policy Issues of the New Economy, 37-80.

Krugman, P., & Wells, R. (2012). Economics. (3rd ed.). Worth Publishers.

Mandl, K. D., & Kohane, I. S. (2009). No small change for the health information economy. New England Journal of Medicine, 360(13), 1278-1281.

Monegain, B. (2009, January 19). Economist calls for healthcare IT as part of reform. Healthcare IT News. Retrieved April 26, 2014, from http://www.healthcareitnews.com/news/economist-calls-healthcare-it-part-reform

Monegain, B. (2012, February 17). IT pros likely to fare better in healthcare. Healthcare IT News. Retrieved April 26, 2014, from http://www.healthcareitnews.com/news/it-pros-likely-fare-better-healthcare

P., A. (2014). ABOUT HealthIT.gov. HealthIT.gov. Retrieved , from http://www.healthit.gov/

Pellegrini, L. C., Rodriguez-Monguio, R., & Qian, J. (2014). The US healthcare workforce and the labor market effect on healthcare spending and health outcomes. International journal of health care finance and economics, 1-15.

Seifert, M., Heffler, S., & Donham, C. (Winter, 1999). Hospital, Employment, and Price Indicators for the Health Care Industry: Second Quarter 1999. HEALTH CARE FINANCING REVIEW, 21, 239-279.

Thomas, C. R., & Maurice, S. C. (2013). Managerial economics: foundations of business analysis and strategy (11th ed.). New York: McGraw-Hill/Irwin.

Why is national competitiveness a necessary prerequisite to our U.S. superior standard of living?

In their definition of the purpose of business and economic activity, Michael Porter and fellow Harvard Magazine editor Jan Rivkin state that “U.S. competitiveness [is] the ability of firms in the U.S. to succeed in the global marketplace while raising the living standards of the average American” (Porter and Rivkin, 2012). Throughout a series of interviews published in Harvard Magazine in 2012, Porter, Rivkin, and their fellow editors at the publication examined national competitiveness, raising questions about the U.S. economy, businesses, and the political system. Their study found evidence that job creation and the wage level in the U.S. began to stagnate around 2000, ahead of the recent recession (Porter and Rivkin, 2012). Corporations began to move production out of the U.S., seeking lower wage rates, better access to skilled labor, and fewer or less expensive regulations. This is of particular concern because we know from Mankiw’s Ten Principles of Economics that our standard of living depends on our ability to produce goods and services, “and that highly productive workers are highly paid, and less productive workers are less highly paid” (Mankiw, 2011, p.388). This is an indication that our ability to compete in a global economy has a direct correlation to the well-being of the U.S. citizen. Krugman states that, “sustained economic growth occurs only when the amount of output produced by the average worker increases steadily” (Krugman, 2009, p.646). However, Porter and Rivkin’s analysis indicates that improvement in the U.S.’s long-run productivity has been hindered by factors such as a complex tax code, a highly regulatory environment, and failures in K-12 education (Porter and Rivkin, 2012). Their concern is whether the U.S. can effectively compete in the areas of physical and human capital and technology.

 
The questions raised by the Harvard study relate to other statistical measures of the Nation’s competitiveness and the performance of the overall economy. Porter and Rivkin indicate that, while the U.S. has experienced GDP growth over the past few decades, it’s been highly concentrated among top earners (Porter and Rivkin, 2012). The implications of this observation are related to the fact that the GDP is one of the most closely watched economic statistics “because it is thought to be the best single measure of a society’s economic well-being” (Mankiw, 2011, p. 492). However, Mankiw goes on to raise an important point in terms of the absoluteness of the GDP: considering the supposed comprehensive nature of Porter and Rivkin’s Harvard study, should we factor in the verity that the GDP doesn’t account for leisure, the value of activity outside the marketplace, and the quality of the environment into the nation’s well-being?

 
Krugman, P. (2009). Economics. (2nd ed.). Worth Publishers.

Mankiw, N. G. (2011). Principles of economics. Cengage Learning.

Porter, M., Rivkin, J.W., (2012, September – October). Can america compete? strategies for economic revival Harvard Magazine, Retrieved from http://harvardmagazine.com/2012/09/can-america-compete

In their book, Social Business by Design: Transformative Social Media Strategies for the Connected Company, authors Dion Hinchcliffe and Peter Kim consider the strategy surrounding social media marketing, stating that, “the objectives of customer acquisition, satisfaction, and retention remain, but relationship management requires fundamental rethinking” (Hinchcliffe, D., & Kim, P. 2012, pg 62). Marketing in the past consisted of analytics such as reach, response rate, and conversation ratio and were defined by a limited number of methods in which products move from the manufacturer to the distributer and then onto the end user.  Marketing in the internet age has made available a larger number of channels to facilitate the movement of product, including hundreds of social networks, communities, and blogs. At the heart of this social marketing strategy is what Thackeray, Neiger & Keller describe as, “the opportunity to create ongoing conversations and dialogue with an audience in the ‘exchange of ideas and opinions’. These conversations are the starting point for creating deeper connections and longer term relationships with audience members (Thackeray, Neiger & Keller, 2012, p.165).

This approach is evident in the research case, “Digital Marketing at Nike: From Communication to Dialogue”, as authors Purkayastha and Rao point out that Nike’s plan is to, “allocate a major share of its advertising spending on some form of service to its customers like workout advice, online communities, and local sports competitions” (Purkayastha & Rao, 2012 p. 9).  Indeed, I am part of Nike’s “Nike+” social community, and I am motivated by the shared experience to work toward improving my physical fitness.  Author Dave Evans, in his book, Social Media Marketing: An Hour a Day, describes a situation that illustrates how the Nike+ technology plays an important part in the company’s understanding the social media marketing feedback loop. “One of the more interesting pieces of user-generated content that I’ve seen,” Evans relates, “is the ‘How To’ on converting any brand of running shoe into a “Nike Plus” compatible shoe. The content — which includes instructions, photos (Flickr), and video (YouTube) — provides the step-by-step process to cut away a section of the inner sole of the shoe and install the Apple transponder that tracks and stores details of your last run for upload when you’re back home” (Evans, D. 2012).  Examples of this are available here http://www.instructables.com/id/Nike%2B-iPod-Nano-Shoe-Mod/ and here http://www.youtube.com/watch?v=4s5x6GiTjg8. Evans contends that most companies would have utilized legal means to stop this type of activity, demanding that the content be removed and these contributors be stopped. However, Nike has remained neutral with regard to the hacked Nike+ running shoe, neither endorsing nor condemning. Evans uses this narrative to demonstrate the value that Nike places on the social media marketing, stating that part of the challenge of today’s internet marketing environment is knowing when to get involved and when to simply stay out of the way.

Evans, D. (2012). Social media marketing: An hour a day. Sybex.

Hinchcliffe, D., & Kim, P. (2012). Social Business by Design: Transformative Social Media Strategies for the Connected Company. Jossey-Bass.

Purkayastha, D., & Rao, A. (2012). Digital marketing at nike: From communication to dialogue. IBS Center for Management Research.

Thackeray, R., Neiger, B. L., & Keller, H. (2012). Integrating social media and social marketing : A four-step process. Health Promotion Practice, 13(2), 165– 168.

In their article, The Hidden Traps in Decision Making, authors John S. Hammond, Ralph L Keeney, and Howard Raiffa introduce six common, yet often disguised, psychological pitfalls that every decision maker faces when contemplating an important choice. These hidden traps include:

•The Anchoring Trap
•The Status-Quo Trap
•The Sunk-Cost Trap
•The Confirming-Evidence Trap
•The Framing Trap
•The Estimating and Forecasting Traps (Hammond, Keeney & Raiffa, 2006)

According to Hammond, Keeney, and Raiffa, decision makers are particularly susceptible to estimating and forecasting traps- those involving uncertainty – because most of us aren’t proficient at judging chances. “We are adept at judging time, distance, weight, and volume,” the Authors explain, “because we’re constantly making judgments about these variables and getting quick feedback about their accuracy. Through daily practice, our minds become finely calibrated. Judging uncertainty, however, is a different matter. Though we often make forecasts about uncertain events, we rarely get clear feedback about our accuracy” (Hammond, Keeney & Raïffa, 2002 p.213). Gilboa offers the example of a roulette wheel which has come up “black” that last five spins. Given a choice, he states that many respondents would now rather bet on “red” as the result of the next spin. There is no logical answer as to why individuals subscribe to this “Gambler’s Fallacy”, though Gilboa tends towards an over-interpretation of the law of large numbers as the rationale. As he explains it, the law states that, “if you observe a long sequence of Independent and identically distributed random variables, their average will, with very high probability, be very close to their (joint) expectation. Thus, if the roulette wheel is indeed fair, then, as the number of spins goes to infinity, the relative frequency of “red” and of “black” will converge to the same number” (Gilboa, 2010 p.74). Simply put, if one observes five “blacks” and no “reds”, the mind rationalizes that red must “catch up”. This expectation is, of course, false, as nature cares little about correcting any perceived bias or deviations from the expected frequency. As Hammond, Keeney, and Raiffa put it, “despite our innate desire to see patterns, random phenomena remain just that random” (Hammond, Keeney & Raïffa, 2002 p.211).

If one considers Professor Zlatev’s discussion of the foundations of strategic decision making and his statement that, “uncertainty limits the ability of a given organization to control the outcomes of its strategic decisions” (Zlatev, 2013), then one must be concerned with Hammond, Keeney, and Raiffa’s contention that individual’s struggle to make decisions when confronted with uncertainty. For instance, consider an organization principally involved in a technology-based service, has been providing the service for many years, and is considered an expert in the field. The organization has evaluated new technology entering the market and, ultimately, fails in implementing a strategy involving this technology because decision-makers underestimate the impact it would have on the industry. The decision not to adopt the new technology is based on several factors, including the reliability and performance of current technology and the fact that the company possesses a high level of comfort with existing methods. These factors influenced leadership to ignore emerging trends, thus assuming consumer’s tastes would reflect their own. In attempting to make strategic decisions about the company’s future, the decision-makers misjudged the distance between where they were as an organization at the time and where they wanted to be. It is clear from the outcome of this failed strategy that, in order to make effective decisions, one must be predictive, not reactive. As Taylor states in his examination of adaptable, analytic systems designed to handle changing circumstances and allow for continuous process improvement, “passing only historical data into a Decision Management System would be like driving with only the rear view mirror—every decision being made would be based on out-of date and backward-looking data” (Taylor, 2011 p.61).

Gilboa, I. (2010). Making better decisions: Decision theory in practice. (1 ed.). Wiley-Blackwell.

Hammond, J. S., Keeney, R. L., & Raïffa, H. (2002). Smart choices, a practical guide to making better life decisions. Broadway.

Hammond, J. S., Keeney, R. L. & Raiffa, H. (2006, January). The hidden traps in decision making. harvard business review, Retrieved from http://hbr.org/2006/01/the-hidden-traps-in-decision-making/ar/1

Taylor, J. (2011). Decision management systems: A practical guide to using business rules and predictive analytics. IBM Press.

Zlatev, V. (2013, March). Lecture 04 – strategic decision making in organizations. Lecture Retrieved from https://onlinecampus.bu.edu/webapps/portal/frameset.jsp?tab_group=courses&url=/webapps/blackboard/execute/displayLearningUnit?course_id=_5747_1&content_id=_843891_1&framesetWrapped=true