Identifying complexity in infectious diseases inpatient settings: An observation study.
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.
Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Ste 140, Salt Lake City, UT 84018, USA; IDEAS Center of Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84148, USA; Health Services Research Section, Baylor College of Medicine, 2450 Holcombe Blvd, Houston, TX 77030, USA. Electronic address: roosan.islam@utah.edu.
Don
D
Roosan
Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Ste 140, Salt Lake City, UT 84018, USA; IDEAS Center of Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84148, USA. Electronic address: charlene.weir@utah.edu.
Charlene
C
Weir
IDEAS Center of Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84148, USA. Electronic address: matthew.samore@hsc.utah.edu.
Matthew
M
Samore
IDEAS Center of Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84148, USA. Electronic address: makoto.jones@hsc.utah.edu.
Makoto
M
Jones
Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Ste 140, Salt Lake City, UT 84018, USA. Electronic address: moom.rahman@utah.edu.
Mumtahena
M
Rahman
Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Ste 140, Salt Lake City, UT 84018, USA; IDEAS Center of Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84148, USA. Electronic address: greg.stoddard@hsc.utah.edu.
Gregory J
GJ
Stoddard
Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Ste 140, Salt Lake City, UT 84018, USA; IDEAS Center of Innovation, VA Salt Lake City Health System, 500 Foothill Drive, Salt Lake City, UT 84148, USA. Electronic address: guilherme.delfiol@utah.edu.
Guilherme
G
Del Fiol
D003141:Communicable Diseases; D020000:Decision Support Systems, Clinical; D006801:Humans; D007297:Inpatients; D008490:Medical Informatics; D010820:Physicians; D012044:Regression Analysis
Clinical complexity; Clinical decision support design; Health information technology; Infectious disease; Medical informatics; Uncertainty