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

Empirical analysis of Zipf’s law, power law, and lognormal distributions in medical discharge reports

“…One other study explored whether patient physiologic derangements encoded as symbols followed a power law distribution, the data generated from virtual patients in a digital training simulator [19]. Epidemiology of GP clinical encounters has also been found to follow a power law distribution [20]. None of these analysed a corpus of medical text to determine whether Zipf's law arises.…”

“…To date, no work has explored Zipf's law in medical and clinical language, such as analysis discharge report text. Zipf's law has been explored in clinical codes [17,18], clinical diagnoses [18], virtual patient physiologic derangements [19], and epidemiology [20], but not in unstructured medical text. We aim to test whether medical discharge reports in the MIMIC-III dataset [21] follow Zipf's law, a power law distribution, and assess the fit of alternative probability distributions.…”

Applying a complex adaptive system's understanding of health to primary care

“…Highlighting the definitional problems and distinctions is especially important for primary care physicians as many people present with unspecific symptoms rather than a “specific diagnosis” 3 . Diagnoses are socially constructed 4 and have become a “tyranny for clinicians” 5 . From a complexity perspective thinking about patients’ complaints as “conditions” or “states of being” clearly fosters more creative approaches to patient care and the emergence of more satisfactory outcomes.…”