Summary
Background: Complexity in medicine needs to be reduced to simple components in a way that is
comprehensible to researchers and clinicians. Few studies in the current literature
propose a measurement model that addresses both task and patient complexity in medicine.
Objective: The objective of this paper is to develop an integrated approach to understand and
measure clinical complexity by incorporating both task and patient complexity components
focusing on the infectious disease domain. The measurement model was adapted and modified
for the healthcare domain. Methods: Three clinical infectious disease teams were observed, audio-recorded and transcribed.
Each team included an infectious diseases expert, one infectious diseases fellow,
one physician assistant and one pharmacy resident fellow. The transcripts were parsed
and the authors independently coded complexity attributes. This baseline measurement
model of clinical complexity was modified in an initial set of coding processes and
further validated in a consensus-based iterative process that included several meetings
and email discussions by three clinical experts from diverse backgrounds from the
Department of Biomedical Informatics at the University of Utah. Inter-rater reliability
was calculated using Cohen’s kappa. Results: The proposed clinical complexity model consists of two separate components. The first
is a clinical task complexity model with 13 clinical complexity-contributing factors
and 7 dimensions. The second is the patient complexity model with 11 complexity-contributing
factors and 5 dimensions. Conclusion: The measurement model for complexity encompassing both task and patient complexity
will be a valuable resource for future researchers and industry to measure and understand
complexity in healthcare.
Keywords
Decision Complexity - Decision Support Systems - Clinical/Utilization - Social Medicine/Methods
- Humans - Quality Assurance - Health Care/methods - Software Design - Information
Technology
