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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 specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians. METHOD: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. 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 transcripts 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 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.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Roosan D,Weir C,Samore M,Jones M,Rahman M,Stoddard GJ,Del Fiol G

doi

10.1016/j.jbi.2016.10.018

subject

Has Abstract

pub_date

2017-07-01 00:00:00

pages

S13-S21

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(16)30155-1

journal_volume

71S

pub_type

杂志文章

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