Identifying complexity in infectious diseases inpatient settings: An observation study

Abstract

Background 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. Objective To identify the specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians. Method We observed and audio recorded the clinical rounds of three infectious disease teams. Thirty cases were observed for a period of four consecutive days. Transcripts were coded based on the 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. Results 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 transcript by three clinicians (Multiple R-squared=0.13, p=0.61). There were no physician effects on the rating of perceived complexity. Conclusion Task complexity contributes significantly to overall complexity in the infectious disease domain. The different complexity-contributing factors found in this study can guide health information technology system designers and researchers for intuitive design. Different types of decision support tools can help to reduce the specific complexity- contributing factors found in this study. Future studies aimed at understanding clinical domain-specific complexity-contributing factors can ultimately improve task allocation and design for intuitive clinical reasoning.

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