Clinical Complexity in Medicine: A Measurement Model of Task and Patient Complexity.

Area of Science:

• Medical Informatics
• Clinical Research Methodology
• Healthcare Systems Analysis

Background:

• Medicine requires simplification for comprehension by researchers and clinicians.
• Existing literature lacks models addressing both task and patient complexity.
• Clinical complexity measurement is crucial for effective healthcare delivery.

Purpose of the Study:

• To develop an integrated approach for measuring clinical complexity.
• To incorporate both task and patient complexity components within the infectious disease domain.
• To adapt and validate a measurement model for the healthcare setting.

Main Methods:

• Observation, audio-recording, and transcription of three clinical infectious disease teams.
• Independent coding of complexity attributes from transcripts by authors.
• Validation through expert consensus, iterative discussions, and calculation of inter-rater reliability using Cohen's kappa.

Main Results:

• A clinical complexity model comprising two components: clinical task complexity and patient complexity.
• The clinical task complexity model includes 13 contributing factors across 7 dimensions.
• The patient complexity model includes 11 contributing factors across 5 dimensions.

Conclusions:

• The developed measurement model offers a valuable tool for assessing clinical complexity.
• It provides a framework for future research and industry applications in healthcare.
• This model aids in understanding and simplifying complexity in medical settings.