The Effect of Integrated Scheduling and Capacity Policies on Clinical Efficiency
Abstract
In outpatient healthcare clinics, capacity, patient flow, and scheduling are rarely managed in an integrated fashion, so a question of interest is whether clinic performance can be improved if the policies that guide these decisions are set jointly. Despite the potential importance of this issue, we find surprisingly few studies that look at how the allocation of capacity, paired with various appointment scheduling policies and different patient flow configurations, affects patient flow and clinical efficiency. In this paper, we develop an empirically based discrete‐event simulation to examine the interactions between patient appointment policies and capacity allocation policies (i.e., the number of available examination rooms) and how they jointly affect various performance measures, such as resource utilization and patient waiting time. Findings suggest that scheduling lower‐variance, shorter appointments earlier in the clinic (and, conversely, higher‐variance, longer appointments later) results in less overall patient waiting without reducing physician utilization or increasing clinic duration. Additionally, exam rooms exhibited classic bottleneck behavior: there was no effect on physician utilization by adding exam rooms beyond a certain threshold, but too few exam rooms were devastating to clinic throughput. Some significant interactions between these variables were observed, but were not influential to the level of managerial concern. Clinicians' intuition about managing capacity in healthcare settings may differ substantially from best policies.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Poor equipment and facility allocation can also lead to a bottleneck, hindering patient flow and decreasing clinician utilization.
“Poor equipment and facility allocation can also lead to a bottleneck, hindering patient flow and decreasing clinician utilization.”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [291]
Inefficient exam room allocation makes clinicians spend time waiting for availability, which reduces the number of patients they can see.
“For example, inefficient exam room allocation makes clinicians spend time waiting for availability, which reduces the number of patients they can see [291].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [291]
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 39 · Cited by 112
- Authors
- White, D. L., Froehle, C. M., & Klassen, K. J.
- Affiliations
- Cincinnati Children's Hospital Medical Center (US)
- Brock University (CA)
- Venue
- Production and Operations Management
- Publisher
- SAGE Publications
- Published
- 2011-05
- Volume
- 20
- Issue
- 3
- Pages
- 442-455
- ISSN
- 1059-1478 (ISSN-L)
- 1937-5956
- OpenAlex
- W2154232436
- Cited by
- 112
- References
- 39
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 9 · 2023: 9 · 2022: 14 · 2021: 10 · 2020: 7 · 2019: 11 · 2018: 10 · 2017: 6 · 2016: 7 · 2015: 4 · 2014: 4 · 2013: 10 · 2012: 2
- FWCI
- 8.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Production and Operations Management (publishedVersion)
- Topics
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Social Sciences › Business, Management and Accounting › Management Information Systems › Advanced Queuing Theory Analysis
- Keywords
- outpatient clinic scheduling, capacity allocation, patient flow, clinical efficiency, patient waiting time, resource utilization (health care), examination rooms, physician utilization, discrete event simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review