Patient Flow: An Introduction (101)

In brief: Patient flow is the movement of patients through a health service, from arrival to departure, and through the units, staff and resources they need on the way. Good flow means that demand and capacity are matched so that each patient receives the right care at the right time without avoidable waiting, transfers or delays. Poor flow shows up as crowded emergency departments, patients boarding on trolleys, cancelled operations and delayed discharges.

Open the interactive module: Follow Emre, a new patient flow manager, through his first month: simulate a day of arrivals and see how variability creates queues, find the bottleneck, test what bed occupancy does to the emergency department, check the evidence on interventions, diagnose six flow problems and score your own hospital.
Flow Lab: Patient Flow Interactive Module →

Learning objectives

After reading this chapter you should be able to:

1. Define patient flow and distinguish it from throughput, crowding, length of stay, care pathways and capacity management.
2. Explain why flow is a whole-system property and why emergency department crowding is often caused elsewhere in the hospital.
3. Describe the main models used to study flow: queueing theory, simulation, lean thinking, network and process mining, and machine learning.
4. Summarise the evidence on interventions such as fast track, team triage, medical assessment units and capacity command centres.
5. Identify the main measurement problems and research gaps in patient flow research.
6. Apply flow thinking to a service you know, including the trade-off between flow efficiency and resource efficiency.

Key concepts

Patient flow, throughput, demand and capacity, variability, bottleneck, crowding, access block and boarding, length of stay, discharge, occupancy, queueing theory, discrete event simulation, lean healthcare, capacity command centre.

How this chapter was produced

Item	Detail
Database	Web of Science Core Collection, all indexes
Search string	TI=(“patient flow*” OR “patient throughput” OR “flow of patients” OR “hospital flow”)
Document types	Articles and reviews (reprints, notes, news items, corrections, book reviews, book chapters, letters, meeting abstracts, proceedings papers and editorial material excluded at search)
Export date and period	15 November 2026; no year limit (records span 1980 to 2026)
Inclusion	The record studies the movement of patients through, or between, health services
Exclusion	Duplicates; homonyms in which “flow of patients” means a physiological or laboratory flow (blood, saliva, inhaler, cytometry); uses in which “patient flows” means something else (budget distribution in pharmacoeconomic models, a patient population)
Numbers	Found 666; duplicates 1; excluded at screening 10; included 655
Coding	Setting, method and crisis context were coded from titles, keywords and abstracts with keyword rules and checked by reading; counts are indicative
Limitations	One database; title search only, so studies of flow that do not use these words in the title are missed; abstract-level reading; cited references were not in the export; 30 included records have no abstract

Introduction: why patient flow matters

It is Monday, 10 a.m. in a large public hospital. The emergency department is full. Twelve patients have been assessed, treated and admitted, but they are still lying on trolleys because no ward bed is free. Upstairs, the medical wards are full too: several patients are medically ready to go home but are waiting for a consultant review, a pharmacy prescription, transport or a place in a nursing home. In the operating theatres, a planned operation has been cancelled because the intensive care bed it needed is occupied by a patient who could have moved to a ward yesterday.

Every unit in this story is busy, and every member of staff is working hard. The problem is not effort. It is the way patients move, or fail to move, between the units. This is what patient flow describes. Flow problems are rarely located where they become visible: the crowded emergency department is usually a symptom of blocked beds elsewhere (Miró et al., 2003; Riahi et al., 2026).

Patient flow matters for three reasons. First, delay harms patients. Delayed emergency department and hospital throughput is described as a critical threat to patient safety (Franklin et al., 2022), and boarding time and daily census are independently associated with longer stays, later physician contact and more patients leaving before treatment (Timm et al., 2008). Second, flow determines how much care a fixed amount of capacity can deliver; a hospital with good flow can treat more patients with the same beds. Third, flow is where clinical work, management and data meet, which makes it one of the most practical topics in health management.

History and development

The earliest records in the corpus already contain the two ideas that still shape the field: measuring how patients move, and modelling it. Cox (1980) simulated involuntary admissions to a psychiatric ward by computer. Berger et al. (1982) followed all 265 adult patients who visited a Soweto polyclinic on one day: four in five arrived before 10 a.m., two in five waited in five or more queues, and patients spent on average 171 minutes in the clinic for 24 minutes of attention. The authors used computer models to test an appointment system, which most patients said they would accept.

Through the 1980s and 1990s, patient flow studies were small and local: family planning clinics, recovery rooms, outpatient radiology and geriatric units. Output rose sharply after 2005. Emergency department crowding became the dominant problem, and studies began to show that it was driven by factors outside the department. Miró et al. (2003) recorded why each patient remained in a Barcelona emergency department and found that both department-related and hospital-related factors kept patients there; after a reorganisation, waiting time to be seen fell from 87 to 24 minutes.

From around 2010, systematic reviews consolidated the evidence on triage and fast-track interventions (Oredsson et al., 2011; Harding et al., 2011) and on modelling approaches (Wiler et al., 2011; Sobolev et al., 2011; Bhattacharjee & Ray, 2014). Operations management researchers brought theory from manufacturing, such as the theory of swift, even flow (Devaraj et al., 2013). Since 2020, three developments stand out: the COVID-19 pandemic, which turned flow into a crisis-management problem; machine learning for forecasting and discharge prediction (El-Bouri et al., 2021); and hospital capacity command centres that manage flow in real time (Franklin et al., 2022; Paterson et al., 2024). Figure 2 summarises this trajectory.

Definitions and conceptual scheme

There is no single agreed definition. The corpus offers several, each stressing a different dimension.

Source	Year	Discipline	Definition (paraphrased)	Dimension stressed
Knarr & MacArthur	2012	Nursing leadership	The observable process a patient experiences during the delivery of care	Patient experience of the process
Devaraj et al.	2013	Operations management	Movement of patients that should be both swift (short throughput time) and even (low variability)	Speed and variability
Bhattacharjee & Ray	2014	Industrial engineering	Patient flows as the object of modelling for capacity planning, resource allocation and scheduling	Decision support
El-Bouri et al.	2021	Biomedical engineering	The problem of moving patients through healthcare services, from demand to discharge	Prediction across stages
Nguyen et al.	2022	Health informatics	The progression of patients along a pathway of care, such as from admission to discharge	Pathway
Benjamin & Jacelon	2022	Nursing (concept analysis)	Patient flow management as holistic perspectives, dynamic data and multiple priorities applied to enable timely, efficient, high-quality care	Management and judgement
Wang	2023	Systems engineering	The movement of patients through a healthcare facility	Physical movement
Manning & Islam	2023	Health planning	Effective flow exists when demand and capacity are matched	Demand and capacity balance

The common core is movement over time through a sequence of steps that use limited resources. The definitions diverge on three questions. What is the unit of analysis: a facility, a pathway or a whole system? Is flow an objective process to be measured, or a managed activity that requires judgement? And is the goal speed, evenness or the match between demand and capacity? This chapter uses the following working definition: patient flow is the movement of patients through and between care settings over time, shaped by the match between demand and capacity at each step. Figure 3 shows the scheme.

Three ideas from operations management help to read the scheme. Demand and capacity: when arrivals exceed the rate at which a step can serve patients, a queue forms. Variability: even when average capacity is sufficient, uneven arrivals and service times create queues; variability in arrivals and length of stay has a major impact on capacity requirements, so models based on averages are misleading (de Bruin et al., 2005). Bottleneck: the step with the least spare capacity sets the pace of the whole pathway, and improvements elsewhere do not increase throughput.

Distinguishing neighbouring concepts

Patient flow is often used interchangeably with several related terms. They describe parts of flow, its symptoms or its management.

Concept	What it describes	Relation to patient flow
Throughput	Number of patients served per unit of time	An output measure of flow; high throughput can coexist with long waits (Heaton et al., 2016)
Crowding	Demand exceeding the capacity of a department	A symptom of poor flow, usually driven by output blocks (Timm et al., 2008)
Access block and boarding	Admitted patients held in the emergency department because no bed is free	The point where hospital-wide flow failure becomes visible (Riahi et al., 2026)
Length of stay	Time a patient spends in a unit or hospital	The most common flow metric, but only one dimension (Fieldston et al., 2014)
Care pathway	A planned sequence of care for a patient group	Defines the intended route; can improve or constrain flow (Vallandingham et al., 2024)
Capacity management	Planning beds, staff and equipment against demand	The supply side of flow (Bhattacharjee & Ray, 2014)
Patient journey	The patient’s experience across contacts	The experiential side; flow research rarely captures it (Gualandi et al., 2020)

Figure 4 compares these concepts on five attributes.

Types and variants

The corpus describes flow at different levels and in different conditions.

1. Unit flow. Movement within one department, such as an emergency department, an imaging suite or an outpatient clinic. This is the most studied level.
2. Hospital-wide flow. Movement between units: from emergency department to ward, from theatre to intensive care, from ward to discharge. Network analysis of two acute sites of a London hospital trust showed that a core subnetwork containing only 13 to 17% of transfer routes carried 83 to 90% of the patient flow (Bean et al., 2017).
3. Flow between care settings. Movement between primary care, hospitals, rehabilitation and long-term care. A 2026 systematic review found 24 studies using seven methods for this level and called for more work on chronic conditions (Yang et al., 2026).
4. Regional and international flows. Movement of patients between hospitals, regions or countries, including medical tourism (Lunt et al., 2013).
5. Scheduled and unscheduled flow. Elective surgery and appointments can be planned and smoothed; emergency arrivals cannot, although they follow daily, weekly and seasonal rhythms (Dauncey et al., 2021).
6. Surge and disaster flow. Pandemics, mass casualty incidents and natural disasters create sudden surges. Of the 655 records, 69 deal with such crises, 52 of them published since 2020. Disaster flow adds transport and coordination problems between scene and hospital (Magana et al., 2026).

A further distinction is between unidirectional flows, in which patients move to lower levels of care, and bidirectional flows, in which some patients move back up. Models that assume one direction underestimate capacity needs (Perera & Uslu, 2022).

Theoretical frameworks and models

Patient flow research borrows most of its theory from operations research and management. Seven frameworks appear in the corpus.

• Queueing theory. Treats each step as a server with arrival and service rates. Madsen and Kofoed-Enevoldsen (2011) found that a simple single-server queue model fitted a Danish emergency department with 27,000 patient cases almost perfectly, and that its capacity was surprisingly constant despite variations in staffing.
• Discrete event simulation and system dynamics. Simulate individual patients or aggregate stocks and flows to test changes before implementing them (Hung et al., 2007; Rohleder et al., 2011; Al-Karkhi & Byatt, 2025).
• Theory of swift, even flow. From manufacturing: productivity rises with the speed and evenness of flow. In 567 US hospitals, information technology was associated with swift and even patient flow, which in turn was associated with better financial and quality performance (Devaraj et al., 2013).
• Lean healthcare. Identifies and removes non-value-adding steps. A review of 40 ambulatory care studies found reductions in length of stay in 19 of 22 and in waiting time in 24 of 26 (Tlapa et al., 2020).
• Network analysis and process mining. Reconstruct real patient routes from administrative data and identify the routes that matter most for performance (Bean et al., 2017).
• Machine learning and forecasting. Predict arrivals, admissions, length of stay and discharge readiness (Peck et al., 2012; El-Bouri et al., 2021; Bishop et al., 2021; Susnjak & Maddigan, 2023).
• Human factors, complex systems and paradox theory. Treat flow as the product of people, teams and competing goals: control centres as socio-technical systems (Paterson et al., 2024), hospitals as complex systems that need whole-system approaches (Manning & Islam, 2023), and the tension between flow efficiency and resource efficiency (Vallandingham et al., 2024).

Themes and bibliometric profile

The 655 included records span 1980 to 2026. Output was below 20 records per five-year period until 2004, then rose to 53 in 2005 to 2009, 97 in 2010 to 2014, 157 in 2015 to 2019 and 219 in 2020 to 2024 (Figure 5). Two thirds of the records (435, 66%) were published from 2016 onward. The records have been cited 10,878 times in the Core Collection (median 8); the most cited is the systematic review of triage-related interventions by Oredsson et al. (2011). The leading journals are BMC Health Services Research (17 records), Health Care Management Science (13), Academic Emergency Medicine (13) and BMJ Open Quality (12). Author addresses are concentrated in the USA (212 records), the UK (89), Australia (65) and Canada (47).

The emergency department dominates: 248 records concern it, compared with 132 on inpatient wards and beds, 132 on outpatient, diagnostic and therapy services, 71 on surgery and intensive care, 42 on flows between settings or regions and 15 on mental health. A further 177 address the hospital as a whole or do not name a setting. Simulation is the most common method (147 records), followed by lean and quality improvement (92), queueing and mathematical models (83), information technology and real-time location systems (55) and machine learning (44). Figure 6 shows how settings and methods combine: simulation and lean dominate in the emergency department, while machine learning and network methods remain rare everywhere.

Six themes recur: emergency department crowding and front-end redesign; bed management, discharge and hospital-wide flow; modelling and forecasting; lean and quality improvement; information technology and command centres; and surge and crisis management.

The situation in Türkiye

Three records have an author address in Türkiye. Gunes and Yaman (2005) modelled the reallocation of resources and the matching of patients to hospitals after the consolidation of health insurance funds. Gül et al. (2017) analysed patient flow in a university hospital emergency department with fuzzy critical path and fuzzy programme evaluation techniques, using time data from 1,500 patients. Perera and Uslu (2022) simulated a neonatal unit with bidirectional patient flows for capacity planning. Given the scale of emergency department use and hospital occupancy pressures in Türkiye, this is a marked gap. Turkish studies published in national journals or indexed only in TR Dizin are outside this corpus.

What works: evidence on interventions

The corpus contains a substantial evidence base on interventions, most of it from emergency departments.

Intervention	Evidence in the corpus	Strength
Fast track for less severe patients	Shorter waiting time and length of stay, fewer patients leaving unseen (Oredsson et al., 2011)	Moderate
Team triage with a physician	Probably shorter waits and stays, fewer leaving unseen (Oredsson et al., 2011); no significant effect in four cluster RCTs (Ming et al., 2016)	Mixed
Primary care professionals at triage	Shorter length of stay in 29 of 30 studies, but mostly low-quality designs (Jeyaraman et al., 2022)	Moderate, low quality
Triage with initial treatment	Positive effect on flow when combined with treatment of low-resource cases; triage that only prioritises has conflicting evidence (Harding et al., 2011)	Moderate
Advanced practice nursing, physician-assisted triage, medical assessment units	Shorter stays and fewer patients leaving unseen, but study quality limited (Elder et al., 2015; Canetta et al., 2024)	Limited
Scribes	No effect on length of stay; small increase in patients seen per hour (Heaton et al., 2016)	Moderate
Lean in ambulatory care	Reduced waiting time and length of stay in most studies; little evidence on savings (Tlapa et al., 2020)	Moderate, mostly before-after
Capacity command centres	All reports positive, but only four peer-reviewed studies (Franklin et al., 2022)	Very limited
Health information systems	Help identify blockages and coordinate care; mechanisms unclear (Nguyen et al., 2022)	Limited

The pattern is consistent. Interventions that separate simple from complex patients early, and that bring decision-making forward, improve front-end flow. Interventions at the front door cannot fix blocked beds at the back door.

Debates and contradictions in the literature

1. Is crowding an emergency department problem or a hospital problem? Most interventions target the emergency department front end: triage, fast track, scribes. Yet the strongest evidence points to output block. In 25 Queensland hospitals, a 10% rise in bed occupancy was associated with a 19-minute longer emergency department stay, and 33 minutes for admitted patients (Riahi et al., 2026). Miró et al. (2003) and Timm et al. (2008) reached the same conclusion earlier. Manning and Islam (2023) conclude that only a whole-system approach works. The field has invested most in the part of the system that least determines its performance.

2. Flow efficiency or resource efficiency? Managers are often asked to raise occupancy and cut beds. Queueing theory warns that high occupancy with variable demand produces queues (de Bruin et al., 2005), and Riahi et al. (2026) found that the critical occupancy threshold varies by hospital, challenging the traditional 85% target. Vallandingham et al. (2024) frame this as a paradox: maximising resource use and maximising flow pull in opposite directions, and care pathways must manage both.

3. Does faster flow harm patients? Singh and Terwiesch (2012) found that a cardiac intensive care unit discharged patients earlier when occupancy was high, which led to more readmissions among severely ill patients and reduced effective capacity. In contrast, Bahalkeh et al. (2026) found that wards discharged regular patients faster when long-stay patients occupied beds, without a rise in 30-day readmissions, and Devaraj et al. (2013) found that swift, even flow improved quality as well as finances. The answer seems to depend on patient severity and on whether speed comes from removing waste or from cutting care.

4. Do models change practice? Simulation is the most common method, but a review of 21 UK emergency department simulation studies found that only three reported implementing the changes they supported (Mohiuddin et al., 2017), and only 26% of surgical simulation studies involved managers or policy-makers (Sobolev et al., 2011). Successful cases exist (Rohleder et al., 2011), but the gap between model and decision is the field’s most persistent weakness.

5. Whose concept is patient flow? The literature defines flow management through data, coordination and efficiency (Benjamin & Jacelon, 2022). Emergency nurses define it differently: as a nurse-driven process whose ultimate goal is the collective safety of patients (Benjamin & Wolf, 2022). Nurses’ experiential knowledge is central to successful flow interventions, but the emotional labour involved is rarely counted (Sharma et al., 2020).

Measurement problems

• Single time measures dominate. Length of stay, waiting time and the proportion leaving without being seen are the most common outcomes (Oredsson et al., 2011; Nguyen et al., 2022). They are easy to collect but capture only one dimension. A five-domain composite scorecard gave a more complete picture in a children’s hospital (Fieldston et al., 2014).
• Process outcomes crowd out clinical and patient outcomes. Most improvement studies use process efficiency indicators; clinical outcomes are poorly analysed and patients’ experience is rarely considered (Gualandi et al., 2020).
• Variability is rarely measured. Flow theory stresses evenness, but few studies measure it. Exceptions show its importance: in Finnish delivery hospitals, the smallest units had quiet days on 43% of days and busy days on 16% (Vilkko et al., 2021), and acute medical unit occupancy followed daily, weekly and seasonal rhythms (Dauncey et al., 2021).
• Weak reporting of models. Simulation studies report assumptions poorly and rarely describe the emergency department modelled (Mohiuddin et al., 2017). Machine learning studies lack shared datasets for benchmarking (El-Bouri et al., 2021).
• Operational definitions vary. The same term is measured as length of stay, waiting time and number of handovers in one study (van Gils-van Rooij et al., 2018), and as visit duration by patient category in another (Vanderby et al., 2010). Results are hard to compare across studies.
• Study designs are weak. Before-after designs without control for confounding are the norm; randomised trials are few (Elder et al., 2015; Jeyaraman et al., 2022).

Research gaps and agenda

1. What is the safe occupancy threshold for a given hospital, and how should it be set? Evidence shows that thresholds vary by site, but there is no method for setting them (Riahi et al., 2026).
2. Which whole-system interventions improve both emergency department and inpatient flow? Most studies evaluate single units; whole-hospital designs are rare (Gualandi et al., 2020; Manning & Islam, 2023).
3. What is the effect of flow on clinical outcomes and patient experience? Process measures dominate and patient outcomes are underreported (Gualandi et al., 2020).
4. How can models be turned into decisions? The low implementation rate of simulation studies needs research on co-production with managers (Mohiuddin et al., 2017; Sobolev et al., 2011).
5. Do capacity command centres work, at what cost? Deployment is outpacing evidence (Franklin et al., 2022; Paterson et al., 2024).
6. How does flow work between care settings, especially for chronic conditions? This level is understudied (Yang et al., 2026).
7. Can machine learning models transfer between hospitals? Most are trained and tested in one site (El-Bouri et al., 2021).
8. What is the situation in Türkiye? Only three corpus records have a Turkish address; emergency department crowding, boarding and discharge delay in Turkish hospitals need systematic study.

Present-day relevance and implications for practice

• Look downstream first. When the emergency department is crowded, measure occupancy and discharge timing on the wards. Crowding is usually a hospital-wide signal (Riahi et al., 2026).
• Separate streams early. Fast track and triage with initial treatment are the front-end interventions with the strongest evidence (Oredsson et al., 2011; Harding et al., 2011).
• Do not run beds at full occupancy. Plan capacity for variability, not averages (de Bruin et al., 2005).
• Measure more than length of stay. Use a balanced set of flow, quality and experience measures (Fieldston et al., 2014; Gualandi et al., 2020).
• Involve nurses. Nurses hold much of the tacit knowledge on which flow depends (Sharma et al., 2020; Benjamin & Wolf, 2022).
• Pretest changes with models, then implement and evaluate. Simulation can test changes safely, but its value depends on implementation (Mohiuddin et al., 2017).
• Treat flow as a patient safety issue. Delays and crowding are safety risks, not only efficiency problems (Franklin et al., 2022).

Summary

• Patient flow is the movement of patients through and between care settings over time, shaped by the match between demand and capacity.
• Variability and bottlenecks, not only average workload, create queues and delays.
• Emergency department crowding is usually a symptom of hospital-wide flow problems, especially high bed occupancy.
• The field draws on queueing theory, simulation, lean, network analysis, machine learning and human factors.
• Fast track and triage with early treatment have the strongest evidence; command centres and machine learning tools are promising but untested.
• Measurement relies too heavily on single time measures; clinical outcomes and variability are rarely measured.
• 655 Web of Science records from 1980 to 2026 show rapid growth, but only three have an author address in Türkiye.

Discussion questions

1. Why can an emergency department be crowded even when its own staff and processes are efficient?
2. Choose a pathway you know. Where is the bottleneck, and how would you prove it?
3. Why is planning bed capacity on average demand misleading?
4. Is a faster discharge always a better discharge? Use the readmission evidence in your answer.
5. Your hospital plans to open a capacity command centre. What evidence would you ask for, and what would you measure in the first year?
6. Design a balanced scorecard of five patient flow measures for a Turkish public hospital.
7. Why do so few simulation studies lead to implemented changes, and how could this be improved?

Further reading

• Oredsson et al. (2011): the most cited review of triage-related interventions, with evidence graded by GRADE.
• Bhattacharjee and Ray (2014): a classification of patient flow modelling approaches with a generic framework.
• Devaraj et al. (2013): the theory of swift, even flow applied to hospitals.
• Wiler et al. (2011): a clear comparison of modelling approaches for emergency department flow and crowding.
• Gualandi et al. (2020): a systematic review of hospital-wide flow improvement and its enablers.
• Riahi et al. (2026): recent large-scale evidence on occupancy and emergency department access.
• Benjamin and Jacelon (2022): a concept analysis of patient flow management from nursing.
• Vallandingham et al. (2024): the paradox between flow efficiency and resource efficiency in care pathways.

Frequently asked questions

What is patient flow in a hospital? The movement of patients through the hospital, from arrival in the emergency department or clinic, through diagnosis, treatment and the wards, to discharge, and the way capacity is matched to demand at each step.

What is the difference between patient flow and throughput? Throughput is the number of patients served per unit of time. Patient flow is the whole movement process, including waits, transfers and delays. A unit can have high throughput and still poor flow.

Why does poor patient flow matter? It causes crowding, long waits, boarding, cancelled operations and delayed discharges, and it is associated with worse patient safety and outcomes.

How is patient flow measured? Most often with length of stay, waiting time, the proportion of patients leaving without being seen, occupancy and discharge timing. Composite scorecards combine several measures.

How can hospitals improve patient flow? By separating simple and complex patients early, bringing decisions forward, planning capacity for variability, improving discharge processes and managing flow across the whole hospital rather than unit by unit.

What is access block or boarding? The situation in which admitted patients remain in the emergency department because no inpatient bed is available. It is the most visible sign of hospital-wide flow failure.

Glossary

Term	Short definition
Patient flow	Movement of patients through and between care settings over time
Throughput	Number of patients served per unit of time
Bottleneck	The step with the least spare capacity, which sets the pace of the pathway
Variability	Unevenness in arrivals and service times
Access block (boarding)	Admitted patients held in the emergency department for lack of a bed
Occupancy	Proportion of available beds in use
Fast track	A separate stream for patients with less severe conditions
Discrete event simulation	Computer model that follows individual patients through a system
Queueing theory	Mathematical study of waiting lines
Capacity command centre	A unit that manages hospital-wide flow with real-time data

Related on The Health Topics

• Flow Lab: Patient Flow Interactive Module
• Bottleneck Management 101: An Interdisciplinary Introduction with a Focus on Healthcare
• Managing Waiting in Health Services: Strategies, Techniques and Measurement
• Streamlining Patient Discharge with Six Sigma and Discrete Event Simulation
• Process Mining in Healthcare: Characteristics and Challenges
• Forecasting Methods in Health Management
• Appointment Scheduling: Theory, Methods, Health Service Applications and the Turkish Context
• Managing an Oncology Hospital as a System: Patient Journey, Process, Bottleneck and Capacity

References

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• Fieldston, E. S., Zaoutis, L. B., Agosto, P. M., Guo, A., Jonas, J. A., & Tsarouhas, N. (2014). Measuring patient flow in a children’s hospital using a scorecard with composite measurement. Journal of Hospital Medicine, 9(7), 463-468. https://doi.org/10.1002/jhm.2202
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Data and supplementary material

The full list of the 655 included records with setting, method and crisis codes, the screening log for all 666 records with decisions and reasons, the 11 excluded records and the verification report are provided in a supplementary Excel file (Patient_flow_supplementary_material.xlsx), available from the author on request. The record list is not reproduced in this chapter.

Cite this chapter: Kurutkan, M. N. (2026). Patient flow: An introduction 101. The Health Topics. https://healthtopic.org/topics/patient-flow-an-introduction-101/