Study
Commercial ProductionHigh ImpactModerate effect

Overbooking strategy reduces patient no-shows by 15% in pediatric hospital settings

Implementing an overbooking strategy, informed by discrete event simulation and Agile Six Sigma principles, can significantly mitigate patient no-shows and optimize resource utilization in healthcare.

International Journal of Environmental Research and Public Health · 2020

01

Key Findings

  • 01The overbooking strategy was effective in ensuring fairness of access to services.
  • 02All patients respected the waiting list times without favoritism, due to the replacement logic.
  • 03No statistically significant difference was found between a real sample of bookings and a simulated sample designed to improve no-shows.
02

Application

Design takeaway

Designers and managers in healthcare should consider implementing data-driven overbooking strategies, supported by simulation, to improve patient flow and resource efficiency.

How to apply

Use discrete event simulation software to model existing patient scheduling and waiting list processes. Introduce an overbooking strategy within the simulation and compare outcomes (e.g., no-show rates, waiting times, resource utilization) against the baseline.

Project actions

  • 01When modeling healthcare processes, clearly define the 'events' (e.g., patient arrival, appointment start, appointment end) and 'entities' (e.g., patients, doctors, rooms).
  • 02Consider the ethical implications of overbooking and how to communicate this strategy to patients.
03

Method & Evidence

AimTo investigate the effectiveness of an overbooking strategy, combined with Agile Six Sigma and discrete event simulation, in reducing patient no-shows and improving access fairness in a pediatric hospital setting.
MethodDiscrete Event Simulation (DES) combined with Agile Six Sigma principles.
ProcedureA real-world waiting list process at a pediatric hospital was modelled and simulated using DES software. The simulation incorporated an overbooking strategy, and various scenarios were analyzed to assess its impact on patient access and no-shows. Staff activities were also reproduced within the model.
ContextPediatric hospital waiting list management.

Variables

IV["Overbooking strategy (implemented vs. not implemented)","Simulation parameters (e.g., overbooking percentage)"]
DV["Patient no-show rate","Waiting list adherence","Resource utilization","Fairness of access"]
CV["Hospital type (pediatric)","Staff activities","Patient arrival patterns (in baseline)"]
04

Strengths & Limitations

Strengths

  • +Application of simulation to a real-world healthcare problem.
  • +Integration of Agile and Six Sigma principles.

Limitations

The simulation model is a simplification of reality and may not capture all nuances of patient behavior or hospital operations.

Reliability & validity

The study's validity is supported by the use of discrete event simulation, which allows for controlled experimentation. Reliability could be enhanced by repeating the simulation multiple times with different random seeds to ensure consistent results.

Think critically

How might the 'fairness of access' be quantified and measured in different healthcare contexts beyond just respecting waiting list times?

05

Design Principles

"Optimize service delivery through predictive modeling and adaptive scheduling strategies."

Healthcare systems face constant pressure to improve efficiency and patient access. This research demonstrates a practical, data-driven approach to address common operational challenges like patient absenteeism, which directly impacts resource allocation and service delivery.

06

What This Means for Your Design

This study shows that by strategically booking slightly more patients than there are slots (overbooking) and using computer simulations to figure out the best way to do it, hospitals can reduce the number of patients who don't show up for their appointments, making the system fairer and more efficient.

How to use in your project

  • 1.Reference this study when discussing the optimization of scheduling systems or the application of simulation techniques to solve real-world operational problems.
07

Add to My Project

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Quick Cite

(2020). Agile Six Sigma in Healthcare: Case Study at Santobono Pediatric Hospital. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph17031052 Retrieved from https://designdex.org/study/af85b858-f7a9-4a1a-8d24-18e91f4251de/overbooking-strategy-reduces-patient-no-shows-by-15-in-pediatric-hospital-settings

Paragraph starter

The study by Improta et al. (2020) highlights the efficacy of overbooking strategies, supported by discrete event simulation and Agile Six Sigma, in mitigating patient no-shows within healthcare settings. Their findings suggest that such approaches can lead to fairer access and improved resource management, offering valuable insights for optimizing service delivery systems.

09

Source

International Journal of Environmental Research and Public Health

Agile Six Sigma in Healthcare: Case Study at Santobono Pediatric Hospital

journal · 2020

View source

Questions about this research

What does the research say about overbooking strategy reduces patient no-shows by 15% in pediatric hospital settings?
Designers and managers in healthcare should consider implementing data-driven overbooking strategies, supported by simulation, to improve patient flow and resource efficiency. Evidence: International Journal of Environmental Research and Public Health (2020).
Why does "Overbooking strategy reduces patient no-shows by 15% in pediatric hospital settings" matter for design?
Healthcare systems face constant pressure to improve efficiency and patient access. This research demonstrates a practical, data-driven approach to address common operational challenges like patient absenteeism, which directly impacts resource allocation and service delivery.
How can designers apply this research?
Designers and managers in healthcare should consider implementing data-driven overbooking strategies, supported by simulation, to improve patient flow and resource efficiency.
What were the main findings?
The overbooking strategy was effective in ensuring fairness of access to services.. All patients respected the waiting list times without favoritism, due to the replacement logic.. No statistically significant difference was found between a real sample of bookings and a simulated sample designed to improve no-shows.
What research method was used?
Discrete Event Simulation (DES) combined with Agile Six Sigma principles..
How strong is the evidence?
Evidence strength is rated Moderate effect, based on a 2020 journal from International Journal of Environmental Research and Public Health.
What should I do differently in my next project?
Use discrete event simulation software to model existing patient scheduling and waiting list processes. Introduce an overbooking strategy within the simulation and compare outcomes (e.g., no-show rates, waiting times, resource utilization) against the baseline.
What are the limitations?
The study focused on a specific pediatric hospital; generalizability to other healthcare settings may vary.
Is there evidence that patient affects design outcomes?
The study found that an overbooking strategy, when simulated using discrete event modeling and Agile Six Sigma, effectively reduced patient no-shows and ensured equitable access to services without introducing bias. Healthcare systems face constant pressure to improve efficiency and patient access. This research demons Source: International Journal of Environmental Research and Public Health (2020).
Where does this overbooking strategy research apply?
Pediatric hospital waiting list management. It sits within commercial production research on designdex.org.

Related research topics

patient design research · evidence on patient · does patient improve design outcomes · overbooking strategy studies for designers · patient and overbooking strategy findings · commercial production research evidence