pith. sign in

arxiv: 2605.19139 · v1 · pith:CXPRA2WNnew · submitted 2026-05-18 · 💻 cs.PF

Reducing Waiting Time for Medical Tourists Through Hybrid Agent-Based and Discrete-Event Simulation: A Hospital Case Study

Melika Baghi , Hadi Mosadegh This is my paper

Pith reviewed 2026-05-20 08:18 UTC · model grok-4.3

classification 💻 cs.PF
keywords medical tourismhybrid simulationagent-based modelingdiscrete-event simulationwaiting timehospital operationsschedulingcase study
0
0 comments X

The pith

Hybrid simulation model reduces medical tourist waiting time from 13.7 days to 2.4 days in hospital case study.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper develops a hybrid simulation model for an international patient department that combines discrete-event simulation for procedural steps such as registration, consultation, admission, bed allocation, and discharge with agent-based logic for the behaviors of patients, physicians, and wards. It applies a two-level fractional factorial design across sixteen factors to evaluate impacts on six performance measures, with the primary focus on average waiting time in the hospital queue. The hybrid approach is shown to cut waiting time substantially compared with a discrete-event simulation model alone while uncovering behavioral patterns like dropouts and emergency escalations. A sympathetic reader would care because shorter waits for medical tourists directly lower their accommodation and travel costs and help hospitals manage shared resources more effectively.

Core claim

The hybrid agent-based and discrete-event simulation model represents registration, consultation, admission, bed allocation, and discharge through discrete-event simulation while representing patient, physician, and ward behaviours through agent-based logic. In the Tehran hospital case study, the hybrid model reduces the average waiting time of medical tourists in the hospital queue from 13.666 days in a DES-only counterpart to 2.416 days. It also reveals dropout and emergency-escalation patterns that a purely procedural representation suppresses. Bed capacity, patient-priority rules, channel design, and clinic slot interval emerge as the most influential levers.

What carries the argument

The hybrid agent-based and discrete-event simulation model that integrates procedural process flows with behavioral decision rules for patients, physicians, and wards.

If this is right

  • Bed capacity, patient-priority rules, channel design, and clinic slot interval are the most influential levers for managing tourist waiting time.
  • The hybrid model reveals dropout and emergency-escalation patterns that a purely procedural representation suppresses.
  • Hospital managers gain a decision-support tool for balancing shared capacity and service differentiation in medical tourism operations.
  • A 256-run fractional factorial design over 16 controllable factors evaluates the effects across six performance measures.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar hybrid models could be tested in other hospital departments that share capacity between local and international patients.
  • Updating the model with real-time operational data could support ongoing scheduling adjustments rather than static scenario analysis.
  • The approach might extend to multi-hospital networks to coordinate bed allocation across facilities serving medical tourists.

Load-bearing premise

The agent-based representations of patient, physician, and ward behaviours accurately capture the real dynamics and decision rules in the international patient department of the Tehran hospital case study.

What would settle it

A direct comparison of the hybrid model's predicted waiting times, dropout rates, and escalation patterns against observed historical data from the Tehran hospital's international patient department.

read the original abstract

Medical tourists face a scheduling problem that differs from that of local patients. Treatment delays extend not just care delivery time, but also accommodation and travel costs. This study develops a hybrid agent-based and discrete-event simulation model for an international patient department in a Tehran hospital case study. The model represents registration, consultation, admission, bed allocation, and discharge through discrete-event simulation, while patient, physician, and ward behaviours are represented through agent-based logic. A 256-run two-level fractional factorial design over 16 controllable factors is used to evaluate bed capacity, specialist counts, online consultation shares, bed-scheduling rules, patient-priority policy, and clinic slot interval across six performance measures. The primary outcome is the average waiting time of medical tourists in the hospital queue. In the case study, the hybrid model reduces this measure from 13.666 days in a DES-only counterpart to 2.416 days. It also reveals dropout and emergency-escalation patterns that a purely procedural representation suppresses. The results indicate that bed capacity, patient-priority rules, channel design, and clinic slot interval are the most influential levers for managing tourist waiting time. The study contributes a case-grounded decision-support model for hospital managers balancing shared capacity and service differentiation in medical tourism operations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript develops a hybrid agent-based and discrete-event simulation (ABM+DES) model for an international patient department in a Tehran hospital case study. Discrete-event components handle registration, consultation, admission, bed allocation, and discharge, while agent-based logic captures patient, physician, and ward behaviors including dropout and emergency escalation. A 256-run two-level fractional factorial design evaluates 16 controllable factors (including bed capacity, specialist counts, online consultation shares, bed-scheduling rules, patient-priority policy, and clinic slot interval) across six performance measures. The primary claim is that the hybrid model reduces average medical tourist waiting time from 13.666 days in a DES-only counterpart to 2.416 days, while also identifying bed capacity, patient-priority rules, channel design, and clinic slot interval as the most influential levers.

Significance. If the behavioral rules and parameters can be shown to reflect actual hospital operations, the work would provide a practical decision-support tool for managing shared capacity in medical tourism settings. The factorial experiment systematically explores interactions among operational levers, and the hybrid approach's ability to surface dropout and escalation patterns (suppressed in pure DES) offers a concrete advantage for understanding patient flow dynamics.

major comments (2)
  1. [Model description] Model description section: The agent-based representations of patient dropout probabilities, priority thresholds, consultation durations, and ward behaviors are described in detail but without any reported calibration or validation against logs, observed queue lengths, or expert elicitation from the Tehran hospital's international patient records. This is load-bearing for the central claim, as the 13.666-to-2.416-day reduction and the revelation of suppressed patterns could arise simply from embedding more responsive rules that the DES-only version lacks by construction.
  2. [Results] Results section (256-run factorial): Concrete numerical reductions are reported, yet the text provides no details on statistical significance testing, confidence intervals, or sensitivity analysis for the identified influential factors (bed capacity, patient-priority rules, channel design, clinic slot interval). Without these, the ranking of levers and the cross-factor claims rest on point estimates alone.
minor comments (2)
  1. [Abstract] Abstract: Add one sentence summarizing input data sources or validation steps to give readers immediate context for the reported reductions.
  2. [Experimental design] Experimental design: A table listing all 16 factors with their two levels and the exact performance measures would improve reproducibility and clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. Below we provide point-by-point responses to the major comments and indicate the revisions we intend to make.

read point-by-point responses

  1. Referee: [Model description] Model description section: The agent-based representations of patient dropout probabilities, priority thresholds, consultation durations, and ward behaviors are described in detail but without any reported calibration or validation against logs, observed queue lengths, or expert elicitation from the Tehran hospital's international patient records. This is load-bearing for the central claim, as the 13.666-to-2.416-day reduction and the revelation of suppressed patterns could arise simply from embedding more responsive rules that the DES-only version lacks by construction.

    Authors: We acknowledge this valid concern regarding model validation. The parameters for agent behaviors, such as dropout probabilities and priority thresholds, were based on a synthesis of published studies on medical tourism patient flows, general hospital operational data, and expert-informed assumptions to capture dynamic behaviors not present in standard DES models. Direct access to the Tehran hospital's patient logs for calibration was not possible in this academic case study due to data privacy regulations. In the revised manuscript, we will add explicit documentation of parameter sources and a limitations subsection that discusses the implications of the lack of empirical validation for the reported waiting time reductions. We will also emphasize that the hybrid model is intended to demonstrate the value of incorporating behavioral elements rather than to provide validated predictions for immediate implementation. revision: partial

  2. Referee: [Results] Results section (256-run factorial): Concrete numerical reductions are reported, yet the text provides no details on statistical significance testing, confidence intervals, or sensitivity analysis for the identified influential factors (bed capacity, patient-priority rules, channel design, clinic slot interval). Without these, the ranking of levers and the cross-factor claims rest on point estimates alone.

    Authors: We agree that the results section would benefit from more rigorous statistical support. We will revise the manuscript to include a statistical analysis of the 256-run factorial design, specifically reporting the results of ANOVA tests for main effects and two-factor interactions on the six performance measures. This will include p-values to establish statistical significance, as well as confidence intervals around the mean waiting times and other metrics. Sensitivity analysis will be added to confirm the robustness of the identified influential factors (bed capacity, patient-priority rules, channel design, and clinic slot interval). These additions will ensure that the claims are supported by more than point estimates. revision: yes

Circularity Check

0 steps flagged

Simulation outputs generated from explicit process rules and factorial experiments, not reduced to inputs by construction

full rationale

The paper constructs a hybrid ABM+DES model from explicit descriptions of hospital processes (registration, consultation, admission, bed allocation, discharge) and agent-based logic for patient/physician/ward behaviors. It then runs a 256-run two-level fractional factorial design over 16 factors and reports simulation outputs for waiting time (2.416 days hybrid vs. 13.666 days DES-only). These numerical results are produced by executing the defined model rules and experimental design; they are not obtained by fitting parameters to the target waiting-time values, nor by any self-definitional equation, self-citation chain, or ansatz that makes the outcome equivalent to the inputs. No equations, uniqueness theorems, or prior-author citations appear in the provided text to create load-bearing circularity. The derivation is therefore self-contained as a case-grounded simulation study.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 0 invented entities

The model rests on several domain assumptions about agent behaviors and process flows drawn from the case study; no new physical entities are postulated.

free parameters (3)
  • bed capacity
    One of the 16 controllable factors varied across the fractional factorial design.
  • specialist counts
    Varied to assess impact on queue times and other measures.
  • clinic slot interval
    Scheduling parameter tested as an influential lever.
axioms (2)
  • domain assumption Patient, physician, and ward behaviours can be represented through agent-based logic that includes dropout and emergency-escalation patterns.
    Central to the hybrid model's ability to reveal patterns suppressed by pure DES.
  • domain assumption The Tehran hospital case-study data and process descriptions are representative of medical tourist flows.
    Basis for calibrating the simulation and interpreting the 2.416-day result.

pith-pipeline@v0.9.0 · 5760 in / 1405 out tokens · 59142 ms · 2026-05-20T08:18:36.380690+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

39 extracted references · 39 canonical work pages

  1. [1]

    and Kucukusta, Deniz and Song, Honggen , title =

    Heung, Vincent C.S. and Kucukusta, Deniz and Song, Honggen , title =. Tourism Management , volume =. 2011 , publisher =

    work page 2011

  2. [2]

    , title =

    Singh, T. , title =. International Journal of Healthcare Management , volume =

    work page

  3. [3]

    Abdalkareem, Z. A. and Amir, A. and Al-Betar, M. A. and Ekhan, P. and Hammouri, A. I. , title =. Health and Technology , volume =

    work page

  4. [4]

    and Vishnoi, S

    Bagga, T. and Vishnoi, S. K. and Jain, S. and Sharma, R. , title =. International Journal of Scientific and Technology Research , volume =

    work page

  5. [5]

    , title =

    Connell, J. , title =. Tourism Management , volume =

    work page

  6. [6]

    and Ismail, I

    Rosenbusch, J. and Ismail, I. R. and Ringle, C. M. , title =. Journal of Hospitality and Tourism Technology , volume =

    work page

  7. [7]

    and Stephano, R.-M

    Fetscherin, M. and Stephano, R.-M. , title =. Tourism Management , volume =

    work page

  8. [8]

    and Baloglu, S

    Suess, C. and Baloglu, S. and Busser, J. A. , title =. Tourism Management , volume =

    work page

  9. [9]

    Liang, L. J. and Choi, H. C. and Joppe, M. and Lee, W. , title =. International Journal of Tourism Research , volume =

    work page

  10. [10]

    Crooks, V. A. and Kingsbury, P. and Snyder, J. and Johnston, R. , title =. BMC Health Services Research , volume =

    work page

  11. [11]

    , title =

    Turner, L. , title =. Globalization and Health , volume =

    work page

  12. [12]

    and Falconer, E

    Kalton, A. and Falconer, E. and Docherty, J. and Alevras, D. and Brann, D. and Johnson, K. , title =. Journal of Medical Systems , volume =

    work page

  13. [13]

    and Wu, S

    Alibrahim, A. and Wu, S. , title =. Health Care Management Science , volume =

    work page

  14. [14]

    and Ying, K.-C

    Kittipittayakorn, C. and Ying, K.-C. , title =. Journal of Healthcare Engineering , volume =

    work page

  15. [15]

    and Simonsen, T

    Viana, J. and Simonsen, T. B. and Faraas, H. E. and Schaanning, N. and Doreen, S. and Dahl, F. A. , title =. Proceedings of the 2018 Winter Simulation Conference (WSC) , publisher =

    work page 2018

  16. [16]

    and German, R

    Djanatliev, A. and German, R. , title =. Proceedings of the 2013 Winter Simulation Conference (WSC) , publisher =

    work page 2013

  17. [17]

    Jacobson, S. H. and Hall, S. N. and Swisher, J. R. , title =. Patient Flow: Reducing Delay in Healthcare Delivery , pages =. 2013 , publisher =

    work page 2013

  18. [18]

    Vázquez-Serrano, J. I. and Peimbert-García, R. E. and Cárdenas-Barrón, L. E. , title =. International Journal of Environmental Research and Public Health , volume =

    work page

  19. [19]

    Chan, W. K. V. and Son, Y.-J. and Macal, C. M. , title =. Proceedings of the 2010 Winter Simulation Conference (WSC) , publisher =

    work page 2010

  20. [20]

    and Khasawneh, M

    Rezaeiahari, M. and Khasawneh, M. T. , title =. Operations Research for Health Care , volume =

    work page

  21. [21]

    and Khasawneh, M

    Rezaeiahari, M. and Khasawneh, M. T. , title =. Expert Systems with Applications , volume =

    work page

  22. [22]

    and Dai, T

    Zhou, M. and Dai, T. and Liu, J. and Saghafian, S. , title =. Manufacturing & Service Operations Management , volume =

    work page

  23. [23]

    Abera, A. K. and O'Reilly, M. M. and Holland, B. R. and Fackrell, M. , title =. Stochastic Models , volume =

    work page

  24. [24]

    , title =

    Guido, R. , title =. International Transactions in Operational Research , year =

    work page

  25. [25]

    and Kannan, D

    Eshghali, M. and Kannan, D. and Salmanzadeh-Meydani, N. and Mehdizadeh, A. , title =. Annals of Operations Research , pages =

    work page

  26. [26]

    Turhan, A. M. and Bilgen, B. , title =. Computers & Operations Research , volume =

    work page

  27. [27]

    , title =

    Grömping, U. , title =. Journal of Statistical Software , volume =

    work page

  28. [28]

    Journal of Simulation , volume =

    Kar, Eyup and Fakhimi, Masoud and Turner, Christopher and Eldabi, Tillal , title =. Journal of Simulation , volume =. 2025 , doi =

    work page 2025

  29. [29]

    IISE Transactions , volume =

    Wang, Lien and Demeulemeester, Erik , title =. IISE Transactions , volume =. 2023 , doi =

    work page 2023

  30. [30]

    Flexible bed allocations for hospital wards , journal =

    Bekker, Ren. Flexible bed allocations for hospital wards , journal =. 2017 , doi =

    work page 2017

  31. [31]

    Computers & Operations Research , volume =

    Gong, Xuran and Wang, Xiuxian and Zhou, Liping and Geng, Na , title =. Computers & Operations Research , volume =. 2022 , doi =

    work page 2022

  32. [32]

    Monks, Thomas and Currie, Christine S. M. and Onggo, Bhakti Stephan and Robinson, Stewart and Kunc, Martin and Taylor, Simon J. E. , title =. Journal of Simulation , pages =. 2018 , doi =

    work page 2018

  33. [33]

    and Rogowski, Wolf H

    Zhang, Xiange and Lhachimi, Stefan K. and Rogowski, Wolf H. , title =. Value in Health , volume =. 2020 , doi =

    work page 2020

  34. [34]

    Efficient patient care in the digital age: impact of online appointment scheduling in a medical practice and a university hospital on the ``no-show''-rate , journal =

    Kammrath Betancor, Paola and Boehringer, Daniel and Jordan, Jens and L. Efficient patient care in the digital age: impact of online appointment scheduling in a medical practice and a university hospital on the ``no-show''-rate , journal =. 2025 , doi =

    work page 2025

  35. [35]

    , title =

    Sargent, Robert G. , title =. Proceedings of the 2010 Winter Simulation Conference , pages =. 2010 , publisher =

    work page 2010

  36. [36]

    2014 , publisher =

    Robinson, Stewart , title =. 2014 , publisher =

    work page 2014

  37. [37]

    2013 , publisher =

    Borshchev, Andrei , title =. 2013 , publisher =

    work page 2013

  38. [38]

    Box, George E. P. and Hunter, J. Stuart and Hunter, William G. , title =. 2005 , publisher =

    work page 2005

  39. [39]

    , title =

    Montgomery, Douglas C. , title =. 2017 , publisher =

    work page 2017