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Complexity and Management and Policy: Why Our Interventions Go Astray

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Complex Systems in Medicine

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Abstract

The increasing number of administrators in healthcare reflects a mental model of managerialism – a belief in the value of professional managers and of the concepts and methods they use. Among these methods is the use of “performance indicators.” These performance indicators are part of the control systems that administrators use to keep the health care “machine” running. This image of a machine emphasizes the aspects of the organization that are (supposedly) predictable, controllable and reproducible. While it is often hard to see the similarities between health care systems and other systems, such control systems are manifest in the hormonal axes that endocrinologists deal with every day. This chapter shows how a cybernetic model of hormonal axes involved in insulin secretion can serve as an analogy for the development and operation of performance measures for diabetes including some of the unintended consequences. Unintended consequences of actions in complicated or complex systems are a fact of life. Simple systems are predictable. Complicated systems may be computationally tractable and therefore predictable, although expertise is required. Lacking that expertise or the correct computer model, unintended consequences may come as a surprise. Unintended (and often unexpected) consequences are characteristic of action on complex systems; they emerge.

This chapter is to a large extent based on reference 5.

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Notes

  1. 1.

    State legislation establishing Medi-Cal enacted November 15, 1965; implemented March 1, 1966. It is California’s version of the Nation’s major publicly financed health care program providing benefits to low-income individuals, including families, seniors, persons with disabilities, children in foster care, pregnant women, and childless adults with incomes below a certain percentage of federal poverty level.

  2. 2.

    Bureau of Labor Statistics, the National Center for Health Statistics, and the United States Census Bureau’s Current Population Survey. Himmelstein and Woolhandler analysis of current population survey. http://www.pnhp.org/. 4 Aug 15.

  3. 3.

    Cybernetics is the science of communications and automatic control systems in both machines and living things. The term itself goes back to the ancient Greeks and was borrowed by Norbert Weiner in his book Wiener, Norbert (1948). Hermann & Cie, ed. Cybernetics; or, Control and communication in the animal and the machine. Paris: Technology Press. Weiner was a leader in establishing the discipline of cybernetics. https://en.wikipedia.org/wiki/Cybernetics. 10 July 18.

  4. 4.

    Of course, in this circumstance the insulin is provided to and not made by the pump, but the insulin could be synthesized in a cell-free system. When I was an endocrine fellow 40 years ago, we had an artificial pancreas. It was about the size of a dormitory room refrigerator and was transported on a wheeled cart. It was a royal pain to use and required an intravenous line as well as the equivalent of an arterial line. The latter was achieved by an IV in the subject’s hand that was warmed up to get “arteriolized” blood. You had to keep a close eye on the subject’s hand because the heat could result in burns if you weren’t careful. (You can figure out for yourself why I know this.) What was more of a pain was that the sensor lasted only a day so the whole device was useful only for experiments. Practical, implantable closed-loop systems are now coming into use. See: [40].

  5. 5.

    A 2006 conference on assessing quality of care for diabetes sponsored by Agency for Research on Healthcare Quality, the National Institute for Diabetes and Digestive and Kidney Diseases, and the VA Office of Quality and Performance (OQP) identified the characteristics of good measures and recommended that performance measures should: (1) give credit for achieving the measure that is commensurate with the likelihood of benefit to the patient, consistent with the Institute of Medicine definitions of quality, i.e., the most credit should be given for achieving goals or clinical actions with large potential benefits in downstream outcomes for the patient (e.g., based on life expectancy, comorbidity, etc.); (2) be applied such that the eligible population reflects the population(s) that will receive the benefit; (3) motivate improvements in quality while minimizing problems with patient safety and unintended consequences; (4) incorporate, when possible, considerations of patient preferences and patient choice; and (5) incorporate patient assessments of quality.

  6. 6.

    The classic joke about cost goes something like this: An administrator wants to know how much something in the hospital costs, e.g., a procedure, so she asks an accountant in the finance department. The accountant replies: How much do you want it to cost? (Costs can be allocated any way you like.)

  7. 7.

    It is useful to make a distinction between their use for public accountability and their use to support internal quality improvement efforts. First, the measures might be different. In general, accountability measures which are used in public reporting and pay-for-performance require higher levels of validity, i.e., greater strength of evidence that the intervention or treatment will have population benefit, that the measure has scientific soundness, and that the data are feasible to collect. Of critical importance is their independence from effects of patient characteristics on measures of quality. This means having a robust method of adjustment for case mix. Measures for quality improvement efforts need only “satisfice,” i.e., be good enough, although it is critical that those using the measures understand their limitations. Second, even if the measure itself is the same, the different uses mandate different interpretations of and action in response to the numbers. When one’s salary or market share depends upon how close to 100% one’s measured performance is, it is more difficult to take into account the difference between population measures, which are good “on the average,” but may not be good for a particular patient. Achieving the measure for all might actually harm specific individuals, as illustrated by the history of the A1C <7% measure for glycemic control. See: [23, 41].

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  1. School of Medicine and Weatherhead School of Management, Case Western Reserve University and Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA

    David C. Aron

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Aron, D.C. (2020). Complexity and Management and Policy: Why Our Interventions Go Astray. In: Complex Systems in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-24593-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-24593-1_12

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