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Understanding complexity in healthcare has the potential to reduce decision and treatment uncertainty. Therefore, identifying both patient and task complexity may offer better task allocation and design recommendation for next-generation health information technology system design.

To identify specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians.

We observed and audio recorded clinical rounds of three infectious disease teams. Thirty cases were observed for a period of four consecutive days. Transcripts were coded based on clinical complexity-contributing factors from the clinical complexity model. Ratings of complexity on day 1 for each case were collected. We then used statistical methods to identify complexity-contributing factors in relationship to perceived complexity of clinicians.

A factor analysis (principal component extraction with varimax rotation) of specific items revealed three factors (eigenvalues > 2.0) explaining 47% of total variance, namely task interaction and goals (10 items, 26%, Cronbach’s Alpha = 0.87), urgency and acuity (6 items, 11%, Cronbach’s Alpha = 0.67), and psychosocial behavior (4 items, 10%, Cronbach’s alpha = 0.55). A linear regression analysis showed no statistically significant association between complexity perceived by the physicians and objective complexity, which was measured from coded transcripts by three clinicians (Multiple R-squared = 0.13, p = 0.61). There were no physician effects on the rating of perceived complexity.

Task complexity contributes significantly to overall complexity in the infectious diseases domain. The different complexity-contributing factors found in this study can guide health information technology system designers and researchers for intuitive design. Thus, decision support tools can help reduce the specific complexity-contributing factors. Future studies aimed at understanding clinical domain-specific complexity-contributing factors can ultimately improve task allocation and design for intuitive clinical reasoning.


NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Biomedical Informatics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Biomedical Informatics, volume 71, in 2016.

The Creative Commons license below applies only to this version of the article.

Dr. Moom Roosan was known as Mumtahena Rahman at the time of publication.



Creative Commons License

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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.



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