Central Appointment Allocation

• Central appointment allocation for outpatient specialist services in the German healthcare system evaluated
• Sophia Willis authored this in December 2024

Key Aspects

• Appointment allocation significantly impacts efficiency, fairness, and participant satisfaction in service-based systems
• Simulations compare decentralized against centralized appointment allocation
• The aim is to assess efficiencies and implications, providing insights into their relative advantages and disadvantages

Contents Overview

• This document includes an abstract, lists of figures and tables, and abbreviations
• Introduces methodology, referral systems, a proposed model, discussion, conclusion, and appendices

Methodology

• Compares centralized and decentralized patient allocation through simulation
• Systematically varies key parameters and tests different scenarios for evidence-based assessment
• Evaluates centralized vs. decentralized patient allocation effectiveness in a healthcare system by simulation with controlled experimentation
• Simulations model two alternative allocation mechanisms: Centralized and Decentralized allocation

Simulation Design Details

• Centralized Allocation involves appointment requests are pooled and patients are assigned to available doctors based on predefined criteria and allocation rules
• In Decentralized Allocation patients independently select doctors based on personal preference.

Parameters and Scenarios

• 13 simulations are performed systematically varying key parameters, with the first serving as the baseline
• Key parameters are the number of arriving patients per period, number of participating doctors, the capacity per doctor, and the patient preference distribution

Performance Metrics and Evaluation

• The two allocation mechanisms are compared by waiting times, queue lengths, and utilisation rates
• Waiting times are measured as average waiting time per patient
• Queue length is tracked at the end of each period and development is visualized in a graph
• Utilisation rates are calculated for each doctor individually and for the entire system and displayed in a graph

Key Limitations and Assumptions

• Simplifies patient behavior by assuming binary preferences
• Bases central allocation on specific predefined rules, which may need more dynamic decision-making
• Has limited scope of system factors, without explicitly modeling external influences
• Requires real-world validation, despite being validated against literature-based benchmarks

Focus on Single-Entry Model (SEM) based Referral Systems

• Utilizes a waiting time management strategy (WTMS) with supply- and demand-sided benefits
• Based on queuing theory principles
• The most common elements are pooled waiting lists and centralized intake through a single-entry point
• Triage is used based on urgency and appropriateness

SEM Implementation in Healthcare

• Traditional systems involve each specialist/hospital department managing their own queues which involves patients being referred to a specialist by primary care physicians
• With a SEM open appointment slots consolidate into one single queue, and allocation happens is on urgency
• Several countries are successfully implementing referral system SEM regionally (Winnipeg, Alberta, Saskatchewan)

Winnipeg Central Intake System (WCIS)

• Implemented in Winnipeg in 2012 to reduce wait times for total hip and knee replacement surgeries
• Patients can be referred to a specific surgeon or the next available physician
• Key elements include a WCIS Office, standard referral form, Patient Access Registry Tool (PART), Regional Joint Replacement Registry, screening criteria, pre-consultation questionnaire (PCQ), and acknowledgement letters

Alberta Facilitated Access to Specialized Treatment (FAST)

• Launched in 2022 as part of Alberta Surgical Initiative by Alberta Health Services to ensure treatment within clinically appropriate wait-time
• FAST manages surgical referrals and allocates them across Alberta for triage and booking, aiming to improve the process and reduce burden on physicians
• A referring physician must submit the referral to FAST, offering options via fax, eReferral, or Connect Care, and considering patient preferences

Saskatchewan Surgical Initiative (SSI)

• A four-year program started in 2010 that committed to reduce surgical wait times, ensuring no patient waited over 3 months
• New Saskatchewan Referral Management Service (RMS) was developed by Healthline Saskatchewan (GPs list patients and provides province-wide set of criteria for patient assessment)

SEM Summary

• All three referral systems (WCIS, FAST, SSI) are built upon the single-entry point principle and includes a core set of features
• The patients are given the option to choose a doctor or see the next available physician

Implementation Results

• WCIS optimized the referral process and improved patient access
• Nearly 50% of TKR patients received surgery within the recommended time frame, variations in wait-times was reduce across specialists

Policy Evaluation

• A SEM referral system has overall improved accessible and expected waiting times
• The success is dependant on GPs, patient, and specialist cooperation
• GPs did not adapt fast due to the work loud of training and implementation
• A safety net with regular referral is viewed negatively by specialist offices
• Standard triage with unified criteria ensures more fairness

German Healthcare System Factors

• The healthcare system consists of SHI (Statutory Health Insurance) and PSC (Private Substitutive Coverage)
• There the separation of IMPATIENT care and OUPATIENT care
• Family doctors are not among the relevant agents in the German market

Central Allocation Models

• Patient preference dictates the doctor assignments within the model
• Double booking was is a problem with the current allocation model

Allocation Prioritization

• Involves deciding who is allocated first based on urgency or necessity which includes wait times
• Is best done by introducing an algorithm to match patients and specialist

Proposed Model Overview

• Aims to streamline scheduling to ensure more equitable distribution of patients across multiple doctors
• Patient Preferences affect implementation by determining how patients express preferences over doctors
• Prioritization is achieved through an algorithm

Centralized Appointment Allocation Mechanics

• Patients submit their request, send the referral. One request is to enquire for doctor
• System takes in account preferences. Patients can pick from acceptable doctor choices
• Doctors have preferences. What if a patient prefers doctors, yet doctors also have preferences over patients

Algorithm Details

• Prioritization is determined by formula which respects assignments, urgency and waiting time
• P = preference
• U = the urgency level
• W = patient waiting time

Model Requirements Overview

• For the model to work it must have a good definition for urgency
• Doctors that are grant the data to be read and written
• A limit must be agreed to plan

Simulation Model

• Has patients' preferences, urgency, and doctor availability in mind
• Multiple periods and iterations account for doctor utilization and patient times

Parameters Governing the Study

• Involves number of doctors, patient range, and periods

Determining Acceptability

• Values determined if doctors want a certain patient to indicates the probabilities for a specific doctor
• It does not add up to 1!

Patient Assignment Specifics

• Doctors get rotational assignments by a shift at each period
• Lexicographic sorting mechanisms ensures by the tie breaking
• They assign patients based on their highest score until the doctors reach capacity

Simulation Execution and Waitlist Integration

• Run for a bunch of times at certain levels
• Each period generates preferences on said doctor's acceptance
• Add the patients of waiting lists

Simulation Tracking and Metrics

• Collect information of two levels of indicators of performance data of time, data and utilities
• Total patients give us the results and the doctor

Decentralized Allocation Simulation Details

• It checks the preferred model or ability which assigns a patient
• The new one has each patient ID stored for the next period

In the Results Section

• The effects of overall system are the variations of each parameter
• And those included in the appendix has comprehensive documentation for the results

The Waiting Analysis

• Waiting times will show if there is high numbers
• The overall average and doctors are there as well so the doctor also has a chance too

Policy Consideration

• Involve those affected to be in more efficient change

Further Points

• In both systems the patient seems to behave as they always do
• You cannot apply it for someone who is going back to the surgeon often
• 1,3,5 - has not been solved

Conclusion

• Use an SEM to help them while they wait while not harming.