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The Value of Systems and Complexity Sciences for Healthcare pp 51–67Cite as

Coping with Complexity and Uncertainty: Insights from Studying Epidemiology in Family Medicine

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Abstract

Family medicine’s epidemiology is complex, it shows a power-law or Pareto distribution of consultation outcomes. Eighty percent of consultations end with an “uncertain diagnoses” [more accurately these consultations end with descriptive terms of symptom, group of symptoms or disease picture (or syndromes)] and only 20 % have a well-defined diagnosis. Robert N. Braun developed a pragmatic approach—a hierarchy of diagnostic certainty, and diagnostic protocols—to help general practitioners/family physicians to cope with the discipline’s complexities in everyday practice, and elaborated critically on the limitations of heuristics and Bayesian approaches as tools for clinical decision-making.

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Notes

  1. 1.

    Die Diagnose ist also ein Ausdruck für die Summe der Erkenntnis, die den Arzt zu seinem Handeln und Verhalten veranlasst. …Diagnostiziert wird nicht ein Krankheitsbegriff, sondern der Zustand eines einzelnen. [The diagnosis is an expression of the sum of insights that cause the physician to act and respond. …We do not diagnose the label for the person’s illness experience, i.e. the diagnosis, but rather the state of experience of that person.]

  2. 2.

    \(P(A\vert B) = \frac{P(B\vert A)P(A)} {P(B)}\); where A and B are events. P(A) and P(B) are the probabilities of A and B independent of each other. P(A | B), a conditional probability, is the probability of A given that B is true. P(B | A) is the probability of B given that A is true.

  3. 3.

    SEN Sensibility; PRE Prevalence; SPE Specificity.

  4. 4.

    Also see NN Taleb, Fooled by Randomness (2004, pp. 269–270). Taleb reiterated Meehl’s critique of the “clinical expert” in the 1950s.

  5. 5.

    Note: The Bayesian approach is not a tool designed for problems involving high degrees of uncertainty, like undifferentiated presentations in primary care. Bayes requires well-defined data, usually gained from specific sources (e.g. cardiac catheter examination) whose sensitivities and specificities are well known.

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Acknowledgements

Thanks to Frank H. Mader for discussing a faint idea of this manuscript in 2012.

Thanks to Klaus D. Kossow for providing the Praetorius reference [42].

Thanks to Joachim P. Sturmberg for thoroughly editing the manuscript.

Author information

Authors and Affiliations

  1. Hannover Medical School (MHH), Department of Family Medicine, Academic Teaching Practice, Bahnhofstr. 5, D-29690, Schwarmstedt, Germany

    Martin Konitzer

  2. Vienna University, Department of Public Health, Academic Teaching Practice, Haus Nr. 142, A-3722, Straning, Austria

    Waltraud Fink

  3. Compass Lexecon, 23 Square de Meeus (7th Floor), Brussels, 1000, Belgium

    Vilen Lipatov

  4. Vienna University, Department of Public Health, Academic Teaching Practice, Ollersbachgasse 144, A-2261, Angern, Austria

    Gustav Kamenski

  5. Family Physician, Hasenheide 5, D-29313, Hambühren, Germany

    Thorsten Knigge

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  1. Martin Konitzer
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Correspondence to Martin Konitzer .

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Editors and Affiliations

  1. Department of General Practice, Newcastle University, Newcastle, New South Wales, Australia

    Joachim P. Sturmberg

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Konitzer, M., Fink, W., Lipatov, V., Kamenski, G., Knigge, T. (2016). Coping with Complexity and Uncertainty: Insights from Studying Epidemiology in Family Medicine. In: Sturmberg, J. (eds) The Value of Systems and Complexity Sciences for Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-319-26221-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-26221-5_5

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