Unknown

Dataset Information

0

Medical practices display power law behaviors similar to spoken languages.


ABSTRACT: Medical care commonly involves the apprehension of complex patterns of patient derangements to which the practitioner responds with patterns of interventions, as opposed to single therapeutic maneuvers. This complexity renders the objective assessment of practice patterns using conventional statistical approaches difficult.Combinatorial approaches drawn from symbolic dynamics are used to encode the observed patterns of patient derangement and associated practitioner response patterns as sequences of symbols. Concatenating each patient derangement symbol with the contemporaneous practitioner response symbol creates "words" encoding the simultaneous patient derangement and provider response patterns and yields an observed vocabulary with quantifiable statistical characteristics.A fundamental observation in many natural languages is the existence of a power law relationship between the rank order of word usage and the absolute frequency with which particular words are uttered. We show that population level patterns of patient derangement: practitioner intervention word usage in two entirely unrelated domains of medical care display power law relationships similar to those of natural languages, and that-in one of these domains-power law behavior at the population level reflects power law behavior at the level of individual practitioners.Our results suggest that patterns of medical care can be approached using quantitative linguistic techniques, a finding that has implications for the assessment of expertise, machine learning identification of optimal practices, and construction of bedside decision support tools.

SUBMITTER: Paladino JD 

PROVIDER: S-EPMC3766655 | BioStudies | 2013-01-01T00:00:00Z

REPOSITORIES: biostudies

Similar Datasets

2019-01-01 | S-EPMC7514953 | BioStudies
2012-01-01 | S-EPMC3517984 | BioStudies
2013-01-01 | S-EPMC3558701 | BioStudies
2009-01-01 | S-EPMC2770836 | BioStudies
2020-01-01 | S-EPMC7323847 | BioStudies
2017-01-01 | S-EPMC5345836 | BioStudies
2019-01-01 | S-EPMC6716390 | BioStudies
2015-01-01 | S-EPMC4388647 | BioStudies
2020-01-01 | S-EPMC7000533 | BioStudies
1000-01-01 | S-EPMC6001944 | BioStudies