Problems with Health Systems and Care

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Increasing Complexity

Application of biomedical knowledge to clinical practice has increased for nearly two centuries with the dramatic rise of the medical industrial complex, including the pharmaceutical industry, since World War II. This is reflected in the growth in health care expenditure per person per year, with an increase in both the scale and scope of available interventions driving up both the price and volume of services. The growth in complexity associated with technologies and industry sector size is also reflected in the growth and specialization of the health workforce, with sustained increases in the number and type of health professionals and health occupations. In the medical profession in the US there has been a shift from generalist to specialist and subspecialist. The first specialty of ophthalmology began in 1916 with the American Board of Medical Specialties growing from around a dozen in 1940, including paediatrics, radiology and internal medicine, to over 150 in 2008. Paediatrics started in 1933 and now has 20 separate subspecialties. Family medicine (a generalist practice!) even became a specialty in 1969.

Recent advances in systems biology, linking genes proteins and disease networks, are driving even more subspecialties, for example Medical Biochemical Genetics within the Board of Medical Genetics.

The same proliferation of health professions and occupations has occurred outside the medical professions with other clinical professions, health management, health planning and policy, public health and associated biomedical and social sciences and technologies. This complex mix of separate disciplines who have less and less shared knowledge, language and experience and less time to spend together learning to understand each other and make sense of the world. It is easy to imagine how a “Tower of Babel” of specialized but related disciplines can lead to fragmentation, miscommunication and frustration, with a lack of shared understanding. Yet they face the daily challenge of connecting at the individual patient level to deliver care that is both technically sophisticated and humane. Some patients have described their experience of hospital ward rounds as a procession of “strangers by the bedside”, a complex, bewildering interplay among many professionals.

Continual change

Apart from the growth in clinical practice specialties, health care is changing in many ways. Milstein (Hygeia’s Constellation p36) describes transitions in our understanding of causes of population health problems that have taken decades and lifetimes. There has been a shift from considering the major diseases of pestilence and famine as “acts of god”, through tackling epidemics of acute communicable diseases, to delaying the onset and progression of degenerative and man-made disease. Complex causation has moved from single-disease single-cause to multiple interacting causes of multiple diseases. This transition can be seen in the progression of heart disease causes from infectious rheumatic fever, through the “heart attack” to the current focus on the interaction among obesity, diabetes and cardiovascular disease.

Also, we have seen transitions in the organization of health care and health policy challenges. These changes occur over years and decades and are often related to political election cycles. Key concerns have shifted from financing health care, through containing costs to improving quality and equity of access. At a more detailed work team level, major shifts in organizing and managing clinical tasks have moved from individual medical professionalism, through multidisciplinary interdependent team interactions to corporate managerialism and back to individual self-managed chronic care by consumers rather than patients. Some of these shifts have reflected the changes in the broader social context of health-care and the relative popularity of government, market and professional network control mechanisms.


More rapid transitions in the clinical practice of diagnosis and treatment have been driven by continual introduction of new disruptive products, methods and services. These disruptions have been driven by both incremental technology innovation and changing expectations of health and wellbeing that accumulate over generations. We expect to play tennis at the age of 90 with the help of a prosthetic joint or two, whereas our parents were happy to play cards until they turned 80. Their parents were happy to have survived until 65.

On a personal note, my most noteworthy experiences of changes in healthcare over nearly half a century are the wider use of lifesaving technologies like dialysis and transplantation, immunization to prevent acute communicable diseases, the emergence of AIDS as an infectious disease, treating of peptic ulcer with antibiotics rather than rest and surgery, the advances in knowledge of the immune system and the genome, the growth in the internet, the shift from saving young people from premature death to assisting older people living with disability.

Dynamic complexity produces unintended consequences

Within this change some things have remained reasonably constant. The preoccupation with cost, quality and access persist despite many practice and policy changes. Sterman, in a NIH Videocast has described the qualities of these persistent complex health problems which resist policy solutions as

  • Dynamic,
  • Tightly coupled (connected)
  • Governed by Feedback (with delays)
  • Non-linear
  • Multi-scale
  • Self-organizing
  • Adaptive
  • Evolving

This dynamic complexity refers to surprising behaviour over time. The hallmark of dynamic complexity is unintended consequences. The problems either resist all solutions (policy resistance or gridlock, where large changes have small effects), or show “tipping points” where small changes have large effects. This non-linearity can also be manifested as “sensitive dependence on initial conditions”. Other writers distinguish between complex and complicated. Ravel instructed his music students, “Your playing should be complex, but never complicated.” Complicated mechanisms, sometimes referred to as static or structural or operational complexity, hold few surprises, whereas dynamic or behavioural complexity is considered puzzling or surprising. Of course surprise depends on the understanding of the person surprised. Another type of complexity is called analytic or evaluation complexity, where problems and causes are fuzzy and indistinct and values and views are so contested there is no way even to agree on a framework to analyse the issue. This is the territory of “unknown unknowns” and distinguishes uncertainty from risk. Risk is considered quantifiable, whereas uncertainty is not.

Mental models limit learning from shared experience

Mental models are our way of making sense of the world, the beliefs inside our heads that we use to explain what we see and give us the confidence to act well. We are capable of many levels of abstraction and we act quickly using fast but fallible decision making rules. Sterman again lists some of the problems with mental models as

  • Focus on “here and now”
  • Stop at a single simple explanation
  • Ignore feedback loops
  • “Get it wrong” for
    • Chance and uncertainty
    • Time delays
    • Accumulations
    • Non-linearities

These mental models of cause and effect are learnt from our individual past experiences and from hearing and reading the stories and thoughts of others. They are therefore personal, disconnected and fail to take account of complex dynamic nonlinear feedback interactions. Fragmented disciplines, the jargon language of management and confusion of concepts and diversity of values make these mental models difficult to describe, share and improve. To avoid embarrassment we mix with like-minded people. We value focussed analytical tunnel vision that ignores complexity, rather than more imaginative synthetic ways to reason and plan what to do in a complex world. This can lead to limiting our ways of knowing and learning by isolating parts of the world rather than exploring connections. We tend to focus on fixing processes that fall within our narrow range of expertise and span of control rather than seeking explanations in independent interactions. As interdependencies increase, so does the likelihood that a given action will generate unintended consequences that may unfold over distant space and time. The more unintended consequences that are generated, the less likely it is that the intended consequences of the action will be achieved and/or sustained. The conflicting mental models of health and healthcare in the heads of participants drive the health system as much as the external institutions and rules that were shaped by mental models of past leaders. From this viewpoint the health system is seen as a strife of interests, or an endless conflict among countervailing powers. Indeed it is a lot like the challenge of climate change.

We need better tools to help us to share our deep knowledge of cause and effect

We need tools and methods to shape the future and build consensus about taking effective action, tools to help us think clearly, to explain, design and manage complex social and technical systems This collection explores the potential for using concept maps and computer models to help us agree on how to shape a challenging future. It is about computer assisted thinking, synthesis and experimenting and learning from virtual experience. It introduces basic complex systems science and engineering methods and applies them to a range of health and health care problems using maps and models we have found useful in the past.

References

Questions & Comments to Geoff McDonnell
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