see also:

• administrative aspects of Emergency Medicine in Australia
• queue theory
• ED overcrowding and bed access block
• Emergency Department performance, waiting times, KPIs, NEAT and current status

• ED patient complexity can be defined as the total diagnostic and procedural effort expended in assessing and managing a patient during an emergency department attendance ¹⁾

• language
• dependency
• social situation - home alone vs NH etc
• patient compliance
• presenting problem
• co-morbidities
• resuscitative measures
• investigations required
• procedures required
• multi-disciplinary assessments
• prescribing
• documentation and patient information - discharge letters, certificates, etc.
• diagnosis
• disposition
• bureaucratic / administrative requirements
• special circumstances

• Urgency Related Groups (URGs)
• Urgency and Disposition Groups (UDGs)
• Urgency Disposition and Age Groups (UDAGs)
• Summated procedures, investigations or consultations (PICsum)
  • only 2 groups
• Healthcare Resource Groups (HRGs)
  • only 11 groups for ED pts
• Ambulatory Payment Classifications (APCs)
  • groupings based on procedures done
• Ambulatory Patient Groups (APGs)
  • groups patients into classes based on major procedures, and where procedures are not available, diagnosis
• Emergency Department Groups (EDGs)
  • groups patients in to 216 classes using MDC, disposition, ICD-9-CM, diagnosis, age and physician procedures
• Comprehensive Ambulatory Care Classification System (CACS)
  • based on: emergency visit indicator, disposition, mode of visit, ambulatory care type, program area, presenting complaint, diagnosis and intervention
• Danish Ambulatory Grouping System (DAGS)
  • only a single category for emergency department visits within the almost 200 DAGS

• system complexity = static complexity + dynamic complexity
• currently no real time measurement systems have been developed for ED as they have been for the manufacturing industry
• ²⁾

• static complexity is determined by the number of resources(M) it has (i.e., people, machines, and so on), the number of possible states (S) for each resource, and the probability (pij) that a resource i is in state j at a given point in time
• is calculated by transforming state time distributions into probability distributions.
• pij = time in state j for each ith resource / total time interval being measured

• dynamic complexity reflects the extra amount of information required for defining the state of the system when it deviates from the expected behavior. It is primarily afunction of queues (queue variability or queue changes).
  • In the ED, queues are made up of several different entities.
  • Entities are patients or objects (e.g., laboratory specimens, x-rays) that must be processed for the ED to properly deliver health care.
  • Every resource that is utilized by the ED manages a queue.
  • A physician’s queue consists of the number of patients he or she simultaneously manages (i.e., patient volume).
  • A triage nurse’s queue consists of the number of patients waiting to be triaged at any given point in time.
  • A laboratory technician’s queue consists of the number of laboratory tests ordered and awaiting completion.
  • The measure of dynamic complexity quantifies uncertainty of the demands on the ED resources
  • considers both planned and un-planned events
  • modelling should also add interruptions given ED is a high interrupt-driven environment