2027 Queueing Theory for MHA Students: What Average Wait Times Hide
Co-Founder and Chief Data Scientist
A clinic can report a reasonable average wait while some patients wait far longer than others. For healthcare administrators, that gap matters: staffing, appointment design, and discharge decisions affect both access and patient experience.
This guide explains how queueing theory reveals what an average conceals, how MHA students can apply it, and which program features support that work. It will help prospective students decide whether an MHA offers the right preparation for a healthcare operations career.
Key Things You Should Know
- Queueing theory connects arrival patterns, service times, and capacity; an average wait alone cannot show peak-hour congestion or unusually long waits.
- In an illustrative single-server model, increasing arrivals from 8 to 9 patients per hour, with capacity unchanged at 10, raises the estimated average queue wait from 24 to 54 minutes.
- For operations-focused MHA study, prioritize applied patient-flow projects and instruction in analytics over a course title that merely mentions operations.
What Is Queueing Theory in Healthcare Administration?
Queueing theory studies how people or tasks move through a system with limited capacity. In healthcare, patients may wait for registration, a clinician, imaging, an inpatient bed, or discharge. Administrators use models of arrivals, service times, and available staff or equipment to test where delays arise and how proposed changes might affect them.
A queue is not always a physical line. An appointment backlog is a queue for future capacity, while a patient awaiting a bed may be occupying an emergency department space. An MHA student should therefore define the start and end of each wait before analyzing it. Students comparing foundational degree routes can examine institutionally accredited colleges with online healthcare administration degrees; graduate-level queueing work builds on, rather than replaces, that foundation.
How Does Queueing Theory Explain Average Patient Wait Times?
Consider a simplified clinic with one service station that can handle an average of 10 patients per hour. Under a standard M/M/1 model - random independent arrivals, exponentially distributed service times, first-come-first-served service, and stable demand - 8 arrivals per hour produce an estimated average queue wait of 24 minutes. At 9 arrivals per hour, the estimate rises to 54 minutes. These are model outputs, not observed clinic results.
The increase is disproportionate because there is less spare capacity to absorb random clusters of arrivals or longer-than-usual visits. The model does not include time spent receiving care. Real clinics also use appointments, multiple staff members, and clinical priorities, so administrators must check whether its assumptions fit before using its estimates to set staffing. Even a compressed graduate curriculum should give students time to test those assumptions; readers comparing one-year master's curricula in healthcare administration can ask how much applied modeling each includes.
What Do Average Wait Times Hide About Healthcare Operations?
An overall average can conceal a long upper tail: most patients may be seen promptly while a smaller group waits much longer. It can also conceal time-of-day peaks, differences between urgent and routine cases, and delays that move from one department to another. Shortening the registration queue is not a system improvement if patients then wait longer for an exam room.
Measurement choices matter too. A "wait to see a clinician" measure differs from time to an appointment, time in a waiting room, or total visit length. Combining these measures - or mixing patient groups without identifying them - can make comparisons misleading. This is one reason to compare degree routes in healthcare administration and HIM: operations leaders decide what a measure means and how to act on it, while health information management specialists often focus more directly on the quality and governance of the underlying records.
MHA programs commonly approach patient flow through operations management, quality improvement, healthcare analytics, and finance rather than a course devoted entirely to queueing theory. Students may map a care process, analyze timestamps, model capacity, and weigh a staffing change against cost, safety, and access. The strongest preparation connects calculations to decisions clinicians and administrators can implement.
Course titles alone are weak evidence of depth. Ask to see syllabi and examples of assignments: does a student work with variable arrivals, service-time distributions, and bottlenecks, or only calculate an overall average? Applicants who still need an undergraduate pathway can consider accelerated healthcare management undergraduate programs, but should verify their quantitative preparation and admission eligibility before treating that route as a shortcut to an MHA.
Use measures that describe both the distribution of waits and the system producing them. The table shows what each adds to an average - and where interpretation still requires care.
| Metric | What it reveals | Key limitation |
| Median and upper-percentile wait | Typical experience and the longer waits an average can mask | Requires a consistent definition of when waiting starts and ends |
| Arrivals and waits by time block | Whether congestion is concentrated in particular hours | Small groups can produce unstable comparisons |
| Service-time variation and capacity use | Whether unpredictable work and limited spare capacity drive delays | High capacity use does not, by itself, identify the right staffing level |
| Patients leaving before evaluation | A possible consequence of delayed access | Other factors may also influence departures |
Reliable timestamps and consistent patient categories are prerequisites for all four. Students interested in the data-governance side of that work can explore CAHIIM-accredited health information management study options; HIM training and an operations-focused MHA serve different professional goals.
Begin with a narrowly defined decision, such as whether adding appointment capacity in a peak period reduces delays without creating a downstream imaging backlog. A manageable project follows a sequence:
- Map each step and define precisely when a patient enters and leaves each queue.
- Collect appropriately authorized, de-identified arrival, service, and staffing data by relevant time period and patient group.
- Choose a model whose assumptions fit the workflow, then compare its predictions with observed waits.
- Test alternatives, including appointment spacing or cross-trained staffing, and examine cost and patient-safety trade-offs.
- Monitor the wait distribution and downstream effects after a change rather than declaring success from one improved average.
Students coming from another industry can bring process-analysis skills, but need clinical partners to interpret triage, care priorities, and privacy constraints. For entry-route considerations, see MHA study pathways for students from other industries. A model should support clinical judgment, not override it.
The delivery format does not determine mathematical rigor. Either format can teach modeling through recorded lessons, live problem-solving, simulation software, and projects. The consequential difference is often access to useful feedback and a feasible applied setting: ask who reviews model assumptions, whether group work requires scheduled meetings, and how students obtain a suitable project if they do not work for a healthcare organization.
Online study may reduce travel, but it does not necessarily eliminate live sessions, group coordination, or fieldwork. Campus study may offer easier face-to-face collaboration without automatically providing a stronger operations curriculum. Readers evaluating time constraints can compare online graduate pathways with shorter healthcare management schedules, then verify actual course sequencing and project requirements rather than assuming a shorter calendar means less work.
Verify institutional accreditation first, then check whether the specific MHA holds programmatic accreditation from the Commission on Accreditation of Healthcare Management Education (CAHME). Institutional and programmatic accreditation answer different questions; neither establishes that a particular queueing course exists. Review the current course catalog and ask about faculty expertise, analytics tools, access to real-world projects, and examples of assessed operations work.
An MHA generally prepares students to manage services, not conduct advanced original research in operations research. Someone seeking a research-intensive route should compare doctoral curricula and the planning the duration of a healthcare administration doctorate before committing. The degree level and the program's actual methods training matter more than a broad promise to teach healthcare innovation.
Patient-flow coordinators, clinic operations managers, quality improvement specialists, and hospital capacity managers may use queueing concepts to plan schedules, identify bottlenecks, or evaluate service changes. An experienced clinician or healthcare analyst may build on existing workflow knowledge; someone new to healthcare may first need an analyst or coordinator role. Senior responsibility typically depends on operational experience and leadership, not an MHA alone.
For salary context, the U.S. Bureau of Labor Statistics reports a May 2025 median annual wage of $126,520 for all management occupations. That broad figure is not a salary estimate for patient-flow roles or MHA graduates. Compare specific job descriptions and local pay when evaluating healthcare administration career opportunities by role.
How Should Students Choose an MHA Program for Healthcare Operations Careers?
Choose according to the work you want to perform. If your goal is to lead patient-flow improvements, look for substantive analytics and operations coursework, a feasible applied project, and access to healthcare practitioners. Ask admissions staff for recent syllabi, project expectations, delivery schedules, total tuition and mandatory fees, and career support available to students in your format.
Then compare the degree with the shortest credible path to your goal. An MHA may suit someone seeking healthcare-specific management breadth; targeted analytics training or an employer-sponsored improvement project may be enough for an established manager with a narrower skills gap. An MBA may fit a broader business-management goal, but a healthcare elective is not equivalent to sustained patient-flow training. If considering that route, compare low-cost healthcare management MBA options online against MHA curricula and full costs - not price alone.
Other Things You Should Know About Healthcare Administration
Not necessarily. Many management applications emphasize interpreting models and testing assumptions. Check a program's prerequisites if you want to know how much mathematics its courses require.
Little's Law states that, in a stable system, the average number of items in a process equals their average arrival rate multiplied by their average time in that process. It is useful for checking whether flow measurements are consistent.
Only with appropriate authorization and safeguards under the organization's policies and applicable privacy rules. Students should confirm access requirements before proposing a project.
No. A simple formula can clarify a decision when its assumptions fit. Simulation is more useful when schedules, priorities, or multiple interacting services make a simple model inadequate.
- Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies - PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC8716576/
- Improving Timeliness of Emergency Department Care | ahrq.gov https://psnet.ahrq.gov/web-mm/some-patients-cant-wait-improving-timeliness-emergency-department-care
- Little's law flow analysis of observation unit impact and sizing | nih.gov https://pubmed.ncbi.nlm.nih.gov/21314778/
- Emergency Department Triage - StatPearls - NCBI Bookshelf https://www.ncbi.nlm.nih.gov/books/NBK557583/
- a survey of Queuing Theory applications in healthcare | umd.edu https://drum.lib.umd.edu/bitstreams/1493f710-6b3b-46e1-8546-6303e987e2ba/download
- Innovation and Best Practices in Health Care Scheduling | NAM https://nam.edu/perspectives/innovation-and-best-practices-in-health-care-scheduling/
- Waiting times: Health at a Glance 2025 | oecd.org https://www.oecd.org/en/publications/2025/11/health-at-a-glance-2025_a894f72e/full-report/waiting-times_3a1021fa.html
- Using Queuing Theory to Reduce Wait, Stay in Emergency Department | AAPL https://www.physicianleaders.org/pages/articles/queuing-theory-reducing-wait-stay
