Patients Are More Complex Than We Realize

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Complexity in Health Care

Source: Springer

Complexity in Health Care: A Paradigm shift for Clinical Practice (Springer 2023) by Steven A. Frankel, Steven D. Thurber, and James A. Bourgeois offers a healthy departure from standard approaches to diagnosis and treatment in psychiatry.

Standard approaches follow set protocols and algorithms, such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and various diagnostic tests, such as the Hamilton Depression Scale and the PHQ-9.

While not eschewing standard methods, Complexity in Health Care complements them by integrating myriad other features of care to improve patient outcomes. The authors caution that “Exclusion of seemingly inconsequential factors from clinical consideration is likely to introduce distortions into case formulation and treatment.…”

But it’s daunting to consider the hundreds of new features that might be involved. Indeed, they can come from any part of the patient’s unique past, present, and imagination. It is here, though, that Complexity in Health Care shines brightest.

It provides numerous case studies from psychiatry patients cared for by one of the authors (Frankel) that demonstrate how to identify the variables not usually considered—and shows how recognizing them leads to successes in previously refractory care.

Complexity in Health Care defines complexity pragmatically:

Clinical situations…not fully understandable or treatable by established clinical means, whether medical, psychotherapeutic, pharmacological, environmental change or a combination of these modalities.

The book outlines three types of complexity variables:

  1. standard, definable, and understood by others, such as algorithms and explicit criteria in DSM-V
  2. more subjective, more complex to define, and less subject to measurement, such as emotion, habitual behaviors, judgment, motivation, anxiety, or depression
  3. microscopic, such as genetic and metabolic changes that influence behavior and mental constructs or beliefs of the patient and clinician.

Diagnostically, the authors describe how we can sort out these variables deductively and inductively. But they go beyond this to tout the value of “abduction,” a subtype of induction that amounts to an informed guess.

Abductive reasoning employs a tentative, working hypothesis that may or may not be accurate. Irrespective of its initial accuracy, its repeated use provides an effective means for arriving at the current best understanding. It often requires what the authors label a “leap of inference.”

Done collaboratively with the patient, it can lead to radical revisions of the diagnostic and therapeutic approach. Or it can be wrong, falling flat with the clinician and patient. In the latter instance, one uses the best hypothesis about the patient.

Complexity in Health Care cogently addresses how clinicians should think about patients, striving to balance intuitive and subjective responses with the slower, more analytic responses we usually depend upon. Not surprisingly, the authors emphasize the importance of the subjective variables, especially the emotions of the patient and the clinician.

Extensive case reports demonstrate how these emotional features can be integrated into care. Eliciting and understanding emotions are especially important to forming a collaborative relationship. When effectively formed, the clinician may develop a sense of “awe” in their profound understanding of the patient and themselves.

Reassuringly, the authors embrace standard research thinking. They discuss how the diversity of patient variables can be accommodated within statistical concepts, emphasizing the role of path analysis and other multivariable models.

By identifying the salient variables, the clinician and patient can construct a diagram or “snapshot” of the patient’s complexity as a guide for treatment. Indeed, the book suggests estimating the one- and two-way correlations of the relevant interacting factors in a path diagram for each patient.

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More broadly, they note that complexity research on groups of patients is still rudimentary. However, they do review several measures recently developed for complexity research and indicate that they are now using them for their studies.

I have a few concerns about the book, but they are minor. For example, I can’t entirely agree that their approach represents a paradigm shift. While it’s a more expansive approach, it is still encompassed by the patient-centered, biopsychosocial model, which most would consider the new medical paradigm.

Next, while the authors reveal their awareness in dealing with the complexities of patients, I would like it more if the book had made explicit that personal awareness is the essential need for establishing a collaborative relationship with the patient.

The latter, itself, is the key to successful therapeutic outcomes.

Finally, the book refers to some variables as randomly occurring, but I think they are better understood as deriving from the unconscious, which operates by rules that may appear random. Indeed, the authors seem in other places to acknowledge that the variables may not be random.

All told, Complexity in Health Care is extraordinarily unique and can be a valuable addition to all clinicians’ bookshelves. It is one of the rare expositions of now-overlooked factors we must consider in diagnosis and treatment.

Medicine is complex, but Complexity in Health Care provides the best guide for entering this dark area of medicine. Readers will appreciate medicine’s true complexity and come away with some confidence in how to approach it.

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