Moderns Dehumanize Care and Cure
Late modern systems impose engineering, legal, and economic paradigms in the form of over-planned, over-engineered, over-managed, and ill-fitting algorithms. This has reduced selective random errors at the cost of imposing systematic ones. Many of the latter arise from fitting the management of medical arts that interface with life to production line models that fit simple-minded assumptions of complete control. In medicine, this perforce creates a virtual reality that substitutes for and may diverge radically from what a caring human being might detect. For example, pre-modern hospitals were called Houses of God and were run by nuns who brought in the dying to ease suffering and ensure that none died shunned or unattended. Now that we moderns judge hospitals by their death rates, hospital staff will do whatever they can—however wasteful, unwise or unkind—to defer death until the patient can be discharged, which pushes hospitals to return to the pre-modern ethos of de facto shunning or avoiding the dying. Likewise, modern systems that optimize selected processes exclude those who do not fit into pre-set algorithms of care, as those of us with emerging illnesses can attest.
In general, systematic errors occur for one of two reasons: the first being that neglected or rejected forms of care and cure are engineered out of the system, and the second being that modern systems handle complexity poorly. Errors of omission may be addressed when a patient lobby acts up, as with AIDS or in the case of the American “wars” on heart disease, cancer, and stroke. Errors inherent in modern models include the neglect of particularism and the reductionism that leads planners to take stock of data and to impose on every individual treatments “proven” to work on any potentially relevant group. Another error inherent in modern models is the tendency to assess “hard” outcomes like death and to disregard “soft” ones that are personal, subjective, and matter to the patient.
Neglect of the Human Scale
At the dawn of the modern era, the microscope and telescope revealed unseen scales of existence. Worldly powers took that as a threat, and thus channeled new vision into concrete mechanistic confines that divided humanity from the human scale, from subjective experience, and thus from common sense. The subsequent scientific obsession with indirect observations of the atomic and cosmic scales of existence also divided clinical medicine from the human scale. As a result, patients today are viewed on the one hand as organ systems, organs, or molecules and on the other as undifferentiated bundles of matter and energy. Neither can facilitate comprehensive, human, or integrative care or cure.
Losing Meaning
Late moderns have an insatiable appetite for data, especially for the raw, precisely constituted images of things hidden from our naked view. You and I probably value high-resolution images far beyond their utility. No diagnostic image of the body can provide as much data or as many constructs as a prepared and alert mind practiced and honed in its sense awareness. With a little experience, almost any nervous system can discern more than any device, however richly detailed that device’s output.
When I was a medical student, a chief resident shared his most valued modern pearl of wisdom, saying, “What you want is to look good.” This attention to appearance is hardly new but now, in a devolving culture that is visually illiterate and yet craves images, we humans cannot afford to mistake a richly detailed surface for a rich body or life. No image, however enticing, can access or assess your body, your state of being, or your state of becoming fruitful in life–that is, creating and contributing to life on Earth and to its future thriving as one.
Accepting Co-dependency with Chronic Illness
Familiar chronic diseases are on the rise: diabetes, cancer, autism, and neurodegenerative diseases such as Parkinson’s and multiple sclerosis. Treatments for these diseases have improved, and yet they remain chronic–that is, incurable and often progressive. Doctors and researchers have not looked to the living context to identify the causes of these diseases, or worked with patients or policymakers to prevent them. Instead, molecular-based hypotheses–including the analyses based on the Human Genome Project–aim to find a “silver bullet” or two to account for these diseases–and every such hypothesis disappoints.
To prevent an illness, we must comprehend it. We can no longer rely on modern mainstream methods to do this. Biology is complicated, and biological problems do not often have simple solutions. Most statistical analyses rely on the flimsy frame of Popperian proof–i.e. rejection of the null hypothesis–and presume to reduce the infinite intricacy of interconnected living systems to a few “main effects” and perhaps a compound effect or two. One reason for this is that the human mind can only handle three to seven variables at one time, and is content with false and often ill-fitting simplicity. Thus, we trim problems down to a comfortable size, and remain mystified by reality.
To solve our problems will require finding a new and better-fitting model of health and disease, and emerging one that pertains to the human scale and to biological time. Let’s look at how modern research methods failed, and consider the new epidemics of chronic disease before going on to consider living views and constructs.
Eliminating Care-synchronized, Experiential Learning
The idea that more data is better is based on a fundamental misconception of human learning. There is a strong belief that if only we had enough data, our statistical methods would start to work. (For more, see Thinking Fast and Slow by Daniel Kahneman.) At the same time, we ignore the fact that when data sets are large enough, any correlation–however small–will seem to “reach” significance. Once again, humans prefer any simplicity to bafflement by complexity. Human learning begins with a state of wonder, keen curiosity, and observation honed through a lifetime of experience and experiment. Learning advances as common sense. It is kinesthetic, the very thing big data can never be.
Educators have created a sequence that reveals much about the fallacy of big data. Higher order thinking evolved as a process that integrates data into information, information into knowledge, and knowledge into wisdom. This thinking is more like learning to walk than like learning cognitive dogmas or generalizations such as algorithms, and then projecting those generalizations onto life.
Moderns Dissociate Care and Cure From Life
Late modern systems remove our species and its ailments from the context of the web of life and its care and cure, imposing instead socially constructed views, ideas, and methods realized via engineered systems. These presume to codify the wild and wooly art of medical diagnosis and detection, an art best internalized as discipline and improvised as discovery. Internalizing this art frees the individual and groups alike to immerse themselves in and learn from life and time, that is, to participate in real-time experiential empiricism. This empiricism enables us to open up those human capacities that are actively suppressed, that are viewed by authors of modern science fiction stories as the gifts of drugs and machines that attempt to impose abilities best gained by the slow and steady forms of inner work devised during and after the Axial Age.
Emerging architects reach an accordance with life through biomimicry; doctors and patients can accord with life directly and thus leave behind policies that impose ephemeral trends and maladaptive strategies for care and cure. We can form and try more adaptive approaches based on the opening and honing of our senses, the disciplining of our understanding, and the search for clues that signal discord with life. When we can find such clues, we can trace them back to the source and restore accord and balance in concord with the web of life.
Evidence-Based Medicine Runs in Place
To discern barriers to surviving and thriving, including those that belong to your intangible personal body, you can take experience as your guide, illuminate the unknown, and construct support for dynamic, expanding processes of expanding psychosocial apprehension and comprehension. In recent paradigms, these have taken legalistic forms that have shaped late modern ideas of evidence and proof; in the emerging model they will metamorphose into living processes that sync with time and respond to unfolding events dynamically and interactively.
Emerging evidence comes of innocence and experience, of urgent need that sharpens curiosity and inspirits learning. Learning does not come from institutions entrapped in late modern patterns that target villain scapegoats and succumb to the kind of wishful thinking that seeks profit in popular molecules and technologies. New learning is the wild child of wonder and exploration that, when it succeeds in reaching escape velocity from institutional gravity, can weightlessly and timelessly derive new ways of living that free life of deathly acts and consequences.
Late modern research institutions are, in a way, insane. They are blithely dissociated from the web of life, obsessed with internal stimuli, and devoted to delusive expectations that run counter to reality. They are dead and thus unresponsive to chance or circumstance.
Emergence has already begun, but evidence-based medical ideas that emulate institutional forms have mired rational inquiry and discovery in forms that are confining and choking it. The slow process of allopathic consensus, once timely, is now slowed to a seeming standstill. It is focused too much on the center and on data that fill the sponge learner with nonsense and anti-sense. Centralized systems thus do at least as much harm as good for patients and their carers, and insist on distributing data from the center to the periphery rather than allowing feedback or facilitating experiential learning.
Evidence-based medicine has yet to evolve to the point that it extracts meaningful information in real time, filters and integrates it through networks of collaborating patients and doctors, and shares it transparently and yet wisely and sparingly. As this process develops, research will be able to avoid predictable errors such as responding to the database rather than to reality. For more, see John Brockman’s This Idea Must Die.
Devaluing Particularism
In the human body, afferent nerves carry peripheral stimuli to the center in order to prompt a response. As every doctor knows, sensory impulses flow inward and motor impulses flow outward; the repetition of this process establishes feedback loops that enable learning. In late modern philosophy, generalism that comes from the center is preferred over particularism that comes from the periphery. This renders new learning inefficient and increasingly ineffective. In neurobiological terms, this cuts the afferent impulses that enable reality testing, and converts medical science into a theoretical rather than empirical science. Medical systems may become effete and useless as well as disassociated and dangerous.
This bias is apparent in information systems that gather centralized data produced for academic or legal reasons and disseminate it without regard to the consequences. Clinical medicine responds increasingly to irrelevant and overwhelming data rather than to real secular trends such as the emergence of modern environmental epidemics. At the same time, managers suppress random errors that appear in the database while ignoring systematic errors that do not. In other words, they attack the remaining one percent of detectable errors while ignoring undetectable systematic errors that reach rates of one hundred percent.
Unfortunately, allopaths—unlike lawyers and businessmen—no longer place the case study at the core of professional education. Fortunately, emerging thinkers like David Sackett, who championed the innovative problem-based curriculum at McMaster University, and Oliver Sacks, who kept alive and popularized the neurological case study and who, in his Awakenings chapter of On the Move: A Life, reminds us that case studies and particularism remain essential. Their work also reminds us of two clinical pearls: common sense is uncommon, and half of what physicians learn in medical school is wrong—they just don’t know which half.
Education in information science and statistics once included pithy, practical reminders of the danger of simpleminded applications of tenuous methods. Two that have become more and more important even as they have fallen by the wayside are: garbage in, garbage out; and lies, damn lies, and statistics. Evidence-based medicine and statistically-based standards of care now obscure the limitations of research and cheat the system by pressing hospitals to cherry-pick patients and to push the poor and forgotten outside to die alone. And so obsession with the general fosters late modern depersonalization and dehumanization in medicine. In emergence, we are reminded to consider the specific, particular situation of an individual and the circumstances surrounding them. This is historically and once again the province of medical detection.
Misdiagnosing New Epidemics
In the 1990s, doctors began to see patients with new conditions that defied diagnosis. The Centers for Disease Control and other groups around the world created provisional syndrome definitions based on commonly reported symptoms, including myalgic encephalomyelitis, called “chronic fatigue syndrome” and fibromyalgia in the States. Other labels in use include Gulf War Syndrome; chemical sensitivity; food allergies; “mito;” “EHS,” and leaky bowel. Most of these conditions are being treated by “alternative providers,” and appear to be related in some way.
In the absence of evidence, blame quickly shifted to the patient. In the research world, investigators began to look at gene-environment interactions and found conventional evidence for some, when the numbers were just right to support their statistical methods. For example, Parkinson’s disease susceptibility runs in families, and manifests in members exposed to chemically-treated golf courses. This indicates that human-created chemicals–chemicals that were once absent from the ambient environment–could act with genetic variation to cause disease.
To go farther in the investigation of chronic ambient poisoning—symptomatic and subliminal—and its relation to emerging epidemics, we need new tools. Science involves asking questions and then devising methods that enable humans to answer them. At this time, modern methods are failing for reasons that will be explored in more detail in the Osprey and Bear Levels. In the meantime, the emerging viewpoint described here is a good place to begin rethinking prevention for a living future.