Identifying complexity in infectious diseases inpatient settings: An observation study

Area of Science:

• Healthcare Informatics
• Clinical Medicine
• Health Systems Research

Background:

• Understanding healthcare complexity can reduce uncertainty in clinical decision-making and treatment.
• Identifying patient and task complexity can inform health information technology (HIT) system design and task allocation.

Purpose of the Study:

• To identify factors contributing to clinical complexity within the infectious disease domain.
• To examine the relationship between objectively measured complexity and clinicians' perceived complexity.

Main Methods:

• Observation and audio recording of clinical rounds in three infectious disease teams.
• Coding of 30 observed cases based on a clinical complexity model and statistical analysis.
• Factor analysis to identify complexity-contributing factors and regression analysis to assess perceived vs. objective complexity.

Main Results:

• Factor analysis revealed three key complexity factors: task interaction/goals, urgency/acuity, and psychosocial behavior.
• No statistically significant association was found between physician-perceived complexity and objectively measured complexity (R-squared=0.13, p=0.61).
• Physician effects did not significantly influence the rating of perceived complexity.

Conclusions:

• Task complexity is a significant component of overall complexity in infectious disease care.
• Identified complexity factors can guide the design of intuitive HIT systems and decision support tools.
• Further research into domain-specific complexity is needed to improve clinical reasoning and task allocation.