Don Roosan1, Charlene Weir2, Matthew Samore3, Makoto Jones4, Mumtahena Rahman5, Gregory J Stoddard6, Guilherme Del Fiol7. 1. 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. 2. 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. 3. 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. 4. 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. 5. Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Ste 140, Salt Lake City, UT 84018, USA. Electronic address: moom.rahman@utah.edu. 6. 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. 7. 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.
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.
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.
Authors: F J Huyse; P de Jonge; J P Slaets; T Herzog; A Lobo; J S Lyons; B C Opmeer; B Stein; V Arolt; N Balogh; G Cardoso; P Fink; M Rigatelli Journal: Psychosomatics Date: 2001 May-Jun Impact factor: 2.386
Authors: Jeffrey L Schnipper; Jeffrey A Linder; Matvey B Palchuk; Jonathan S Einbinder; Qi Li; Anatoly Postilnik; Blackford Middleton Journal: J Am Med Inform Assoc Date: 2008-04-24 Impact factor: 4.497