Prof. Dr. Mehmet Nurullah Kurutkan, Düzce University, Faculty of Business, Department of Health Management
Chapter summary. In an oncology hospital, physician and nurse workload and the capacity of chemotherapy chairs, radiotherapy machines, operating theatres and beds are links of a single chain: the narrowest link (the bottleneck) sets the pace of the whole hospital, and resource balancing is the art of widening that link and distributing the load fairly. This chapter filters the managerially relevant studies from four corpora compiled from the Web of Science (WoS) (physician and nurse workload: 50 records; capacity planning: 74 records; bottlenecks in oncology: 84 records; resource balancing in oncology: 164 records) and explains the subject step by step, from the simplest concepts to decision support models. The intended reader is the physician who has chosen to become a manager or who meets management every day.
Key concepts: workload, capacity, utilization, bottleneck, resource balancing, resource allocation, prioritisation, simulation
1. Introduction: why a “capacity gift” for physicians?
In oncology, time is a component of treatment: the number of days from diagnosis to treatment, the overall duration of radiotherapy or the number of weeks by which an operation is postponed directly affects survival and the patient’s quality of life (Ju et al., 2018; Vijayakumar et al., 2023). What determines this time is often not medical knowledge but capacity: a free chemotherapy chair, an available linear accelerator (LINAC) session, an operating theatre slot, a bed and, above all, a physician or nurse who is not exhausted.
Physicians live this reality every day in sentences such as “the outpatient clinic overflowed again”, “the radiotherapy queue has grown” and “we moved the operation because there was no room”. Yet the logic behind these sentences, that is, how demand meets supply, where the bottleneck sits and how resources can be shifted, is rarely taught in medical education. In the past thirty years, operations research, health economics and nursing science have produced fairly clear answers to this question (Ma et al., 2016; Tappenden et al., 2012; Wazqar et al., 2017).
The aim of this chapter is to translate those answers into the physician’s language. We therefore build it as a “gift package”:
- A glossary: six core concepts you will meet in management meetings (Section 2).
- A map: a single diagram showing the relationship between workload, the four capacity domains, bottlenecks and resource balancing (Section 8).
- A toolbox: simple calculations and questions you can try on your ward on Monday morning (Section 9).
- A reading list: the sources behind every claim, in APA 7 format (References).
Pocket note for physicians: Capacity management does not mean “seeing more patients”. It means being able to see the right patient at the right time without wearing out the team.
2. Core concepts: a six-word glossary
This whole chapter is built on six concepts, and confusing them is the most frequent source of disagreement in hospital meetings. The table below defines each concept in one sentence, gives an example from oncology and shows how it differs from its neighbour.
| Concept | Simple definition | Example from oncology | Often confused with |
|---|---|---|---|
| Demand | The amount of patients and procedures needing service in a given period | Weekly new patient consultations, number of sessions | Queue: the part of demand that cannot be met and waits |
| Capacity | The maximum service a resource can produce in a given period | Daily sessions of one LINAC, chair hours | Effective capacity: the real capacity left after staff, drug preparation and scheduling rules are deducted (Cataldo et al., 2023) |
| Workload | The amount of work carried by a person or unit, in time and intensity | Nursing hours per patient per day | Number of patients: the same number of patients can generate very different workloads (Balaminut et al., 2024) |
| Utilization | The ratio of capacity used to total capacity | The percentage of working hours during which chairs are occupied | Efficiency: high utilization is not always good; waiting grows rapidly as it approaches 100% |
| Bottleneck | The link in the patient flow chain whose capacity falls furthest short of demand | Biopsy appointment, plastic surgery slot, planning physicist | Constraint: a bottleneck is a physical or organisational link; a constraint can also be a budget, a regulation or a policy (Goldratt & Cox, 1984) |
| Resource balancing | Redistributing people, equipment, space and time according to demand to balance both patient flow and staff load | Spreading consultations across mornings and afternoons | Resource allocation: balancing is an in-hospital, mostly operational act; allocation is a budget and policy-level and often ethical decision (Winkler, 2011) |
One simple formula connects these six concepts. According to Little’s law, the average number of patients in a system equals the arrival rate multiplied by the average time spent in the system (Little, 1961):
L = λ × W
Here L is the average number of patients in the system, λ (lambda) is the number of patients arriving per unit of time, and W is the average time a patient spends in the system. For the physician this means: in a day treatment unit that receives 40 patients a day who each stay 5 hours on average, 5 patients arrive per hour over an 8-hour working day, and the unit needs space, chairs and nurses for an average of 5 × 5 = 25 patients at the same time.
Pocket note for physicians: Capacity and workload are two sides of the same coin. Capacity is the language of machines and space; workload is the language of people. The bottleneck appears where the two collide, and resource balancing is the method for resolving the collision.
3. Historical development: from the nurse’s ledger to the algorithm
Thinking on capacity and workload in oncology has developed in four waves; each wave did not erase the previous one but added a new layer on top.
First wave (1980s and 1990s): counting and noticing scarcity. Early studies focused on making workload visible. The study that followed the workload of oncology nurses at the Netherlands Cancer Institute for 14 years is an early example (van Dam, 1990). In the same period, the financing of cancer centres began to be discussed as a problem of “scarce resource allocation” (Korn, 1989), and decision analysis approaches were proposed that assess effectiveness and cost-effectiveness of treatments together (Smith et al., 1993). The idea that outpatient appointments should be scheduled not as independent events but as sequential, linked activities was also voiced in this period at the Johns Hopkins Oncology Center (Majidi et al., 1994).
Second wave (2000s): justice and prioritisation. Concrete squeezes such as radiotherapy waiting lists were debated with the principles of egalitarian justice and evidence-based medicine (Schäfer et al., 2005). A warning was issued that complex technologies such as intensity-modulated radiotherapy (IMRT) can lengthen waiting times in departments short of staff and equipment (Probst & Griffiths, 2006). The stratified approach for low-resource countries, which divides treatments into four resource levels (basic, limited, enhanced and maximal), is also a product of this wave (Eniu et al., 2006).
Third wave (2009 to 2019): modelling and simulating. Operations research entered oncology: simulation for radiotherapy patient scheduling (Ogulata et al., 2009), simulation-based optimisation for a skin cancer “one-stop-shop” clinic (Romero et al., 2013) and capacity planning models for new patient consultations (Ma et al., 2016) were developed. In the same period, health economics proposed “whole disease modelling” to think about resource allocation across the entire pathway from diagnosis to death (Tappenden et al., 2012). The nursing literature began to measure burnout and work stress systematically (Gómez-Urquiza et al., 2016).
Fourth wave (2020 onwards): shock, resilience and artificial intelligence. The COVID-19 pandemic showed how fragile capacity is: in the United Kingdom, head and neck cancer surgery capacity fell in the second wave to 58% of the pre-pandemic level (Shaw, 2021). International consensus recommendations for acute and severe resource constraint (Mehanna et al., 2020) and prioritisation guidance (Lugnier et al., 2024) were published. Today the agenda includes bottleneck detection with process mining (Monteiro et al., 2026), patient segmentation with explainable machine learning (Lopéz-Flores et al., 2026) and automated radiotherapy planning supported by artificial intelligence (Netherton et al., 2026).
Pocket note for physicians: The literature has travelled from the question “how many patients are there?” to the question “which patient, with which resource, when, and at the cost of whose fatigue?”
4. Physician and nurse workload
The real capacity of an oncology hospital is determined not by the number of machines but by the number of rested people who can operate those machines safely. Workload is therefore both an input and an outcome of capacity management.
4.1 How is workload measured?
There are three basic ways to measure workload, ordered here from simple to complex:
- Volume measures: the daily number of patients, sessions or prescriptions. Easy to collect, but blind to the patient’s severity.
- Patient dependency and activity scales: tools such as the Nursing Activities Score (NAS) estimate the care time of each patient. In hospitalised women with breast and gynaecological cancer, mean NAS was 29.3%, corresponding to about 7 hours of nursing care per patient per day; workload rose with gynaecological cancer, medical treatment and low functional capacity (Balaminut et al., 2024). Dedicated scales have also been developed for risk-based human resource allocation in the day hospital (Kohen et al., 2013).
- Process mapping and time measurement: measures how much time each step takes from which professional group. A study of 24 radiotherapy treatment categories in six institutions showed that the same treatment consumes very different resources from one institution to another and that hypofractionation, automation and standardisation are the main reasons for this difference (Halvorsen et al., 2023). In an analysis with time-driven activity-based costing (TDABC), staff cost accounted for 76% of the cost of IMRT and 71% of the cost of vaginal cuff brachytherapy (Ning et al., 2019b).
4.2 Nurse workload: the invisible capacity
The workload of the oncology nurse is not only physical; it has emotional, ethical and safety dimensions. Five themes stand out in the literature:
- Stress and burnout: nurse stress in outpatient oncology units (Ko & Kiser-Larson, 2016), the prevalence and risk factors of burnout in adult and paediatric oncology nurses (Gómez-Urquiza et al., 2016; De la Fuente-Solana et al., 2020) and the factors that upset work-life balance (Gribben & Semple, 2021) have been examined in detail.
- Compassion fatigue and moral distress: the antecedents of compassion fatigue (Banks et al., 2025) and the psychological burden and moral distress of oncologists and oncology nurses during the pandemic (Sommerlatte et al., 2023) have been documented.
- Job demands and intention to leave: perceived job demands are related to the professional quality of life and intention to leave of haematology nurses (S. L. Tan et al., 2024); work-related fatigue and burden are also high in different countries (El-Khawaga et al., 2025; Zemni et al., 2024).
- Safety: the structure and process characteristics of outpatient oncology units are associated with nurses’ exposure to chemotherapy drugs (Friese et al., 2012); use of personal protective equipment and hazardous drug spills are also the safety face of workload (He et al., 2017).
- Erosion of role boundaries: the role perceptions and workplace challenges of oncology nurses in Türkiye and Australia have been compared (Sahin et al., 2026), and the psychosocial consequences of eroding professional boundaries have been shown qualitatively (Kamisli & Saribudak, 2026).
At hospital level, general hospital data have long shown that as the number of patients per nurse rises, patient mortality, burnout and job dissatisfaction increase (Aiken et al., 2002). A new dimension of this relationship in oncology is the effect on nurse well-being of the nurse-patient assignment generated by the electronic health record (Le Roux et al., 2025).
4.3 Physician workload: consultation, specialisation and task sharing
There are three important findings in the literature on physician workload. First, the number of slots reserved for new patient consultations, the appointment rule and the way oncologists specialise together determine both the patient’s access time and the physician’s load (Ma et al., 2016). Second, rather than locking all oncologists to a single tumour type, having a small group of flexible oncologists markedly increases the share of demand that is met, and limiting the workload difference between physicians is an acceptable trade-off (Keshtzari & Norman, 2024). Third, task sharing works: in the United Kingdom, a breast cancer clinic run by a prescribing pharmacist wrote 13.4% of all systemic therapy prescriptions and completed 81.5% of consultations without support from a specialist physician (Sladkowski, 2026). In Canada, a pharmacy trainee programme for oral anticancer drugs similarly expanded institutional capacity at low cost (Peragine et al., 2026).
The seriousness of the problem at system level is also discussed more and more openly: the warning that clinicians do not have enough capacity for England’s national cancer plan is a current example (Wilkinson, 2026).
Pocket note for physicians: Do not discuss workload only in numbers of patients. Ask “how many minutes, how many nursing hours and how many decisions per patient?” Task sharing is the cheapest way to reserve the physician’s time for work that only a physician can do.
5. Four capacity domains: chemotherapy, radiotherapy, operating theatre, beds
Each of the four domains works with a different “unit of capacity” and requires a different planning logic: chemotherapy is measured in chair and nurse hours, radiotherapy in machines and the planning team, the operating theatre in room hours and the surgical team, and beds in days.
5.1 Chemotherapy: the triangle of chair, nurse and pharmacy
Outpatient chemotherapy depends at the same time on a fixed capacity (the chair) and a shared capacity (the nurse). While preparing a patient for infusion, a nurse is fully tied to that patient; once the infusion has started, the same nurse can monitor several patients at once. For this reason, models that solve appointment scheduling and nurse assignment together can serve the same number of patients with fewer nurse full-time equivalents (Hesaraki et al., 2020). A fixed slot template bridges the tactical capacity plan and daily appointment booking; because appointments range from 15 minutes to more than 8 hours, such a template is especially valuable (Hesaraki et al., 2023).
Chemotherapy scheduling has several stages: first the day is chosen, then the time. In a study with real data from Chile, a method using the concept of “effective treatment capacity” performed better than manual scheduling almost always in terms of completion time, resource use, overtime and patient referral (Cataldo et al., 2023). The same group showed that using the capacity of several centres as a network could cut operating costs by 20% and halve the wait for the first session, but that the benefit materialises only if the plan is actually implemented (Wenzel et al., 2024). Clustering and stochastic simulation make it possible to handle uncertainties such as patient arrival patterns and nurse acuity levels together (Hadid et al., 2022).
Clinical choices also create capacity. In early breast cancer, giving trastuzumab subcutaneously rather than intravenously reduced preparation time by 71.7% and chair time by 89.3%; the authors recommended this switch for departments with constrained capacity (Farolfi et al., 2017). Moving chemotherapy from inpatient to outpatient care is one of the known ways of creating bed capacity (Cunningham et al., 2012; Abasta et al., 2025).
5.2 Radiotherapy: expensive machines, long queues
Radiotherapy consists of sessions that run for weeks and must not be interrupted; a balance therefore has to be struck constantly between taking on new patients and not disrupting the treatment of current ones (Ogulata et al., 2009). Reserving a fixed percentage of capacity for urgent patients is a common solution, but it can both delay urgent patients and waste capacity; dynamic scheduling based on forecasts reduces both problems together (Pham et al., 2023). Algorithms that include waiting time in the priority list have also been proposed (Riff et al., 2016).
At strategic level the question is “how many machines, and where?” In Ontario, a needs-based model for single brain metastasis calculated 5.9 full-time radiosurgery units, and in sensitivity analysis this number ranged from 2.5 to 12.2 (Greenspoon et al., 2012). A LINAC shortage index developed in Brazil was used to show in which regions new machines should be placed first (Viani et al., 2022). It has been stressed that more than half of patients worldwide cannot access radiotherapy and that this can be overcome with health technology assessment (Rodin et al., 2017).
The most powerful ways to increase capacity in radiotherapy are clinical and technological: hypofractionated and shortened schedules (Braunstein et al., 2020), resource-sparing brachytherapy schedules delivered in a single insertion (L. T. Tan et al., 2026) and automated planning systems designed for low-resource clinics (Kisling et al., 2019; Netherton et al., 2026).
5.3 Operating theatre: surgical capacity and prioritisation
In oncological surgery, a capacity gap turns directly into a treatment compromise. In the second wave of COVID-19 in the United Kingdom, treatment was substantially affected for 50% of head and neck cancer patients needing surgery: 28% were postponed, 10% received radiotherapy instead of surgery and 12% had more limited surgery (Shaw, 2021). Surgical capacity is also a prioritisation question in terms of societal preferences; a choice experiment with 1,046 people in the Netherlands showed that participants gave priority to a patient with breast cancer over a patient with knee osteoarthritis or hearing loss (Reckers-Droog et al., 2025).
In the operating theatre the bottleneck sometimes appears in an unexpected place: in breast cancer, the plastic surgeon’s schedule for immediate reconstruction can lengthen the time to definitive surgery from a few days to several weeks (Orosco et al., 2026). The launch of screening programmes also requires surgical capacity demand to be planned in advance (Eccles et al., 2023).
5.4 Beds: the last stop of the flow
Bed capacity is where the load accumulated by all the preceding links is carried. A discrete event simulation model built at Memorial Sloan Kettering Cancer Center modelled admission patterns, the different care needs of patient groups and floor-level bed use together, and tested a scenario of cohorting patients on specific floors by clinical service (Mokashi et al., 2025). How an ambulatory care model would change bed needs in bone marrow transplantation and intensive chemotherapy has also been estimated by simulation (Hummel et al., 2012). The effect of admissions through the emergency department on length of stay and resource use (Udovica et al., 2019) and equity in the regional distribution of oncology beds (Pu & Zhong, 2026) are two further faces of this domain.
| Domain | Unit of capacity | Typical bottleneck | Evidence-supported lever |
|---|---|---|---|
| Chemotherapy | Chair hours and nurse full-time equivalents | Morning peak, drug preparation in the pharmacy | Scheduling appointments and nurse assignment together, subcutaneous formulations (Hesaraki et al., 2020; Farolfi et al., 2017) |
| Radiotherapy | LINAC sessions, planning team hours | Planning, squeeze from urgent patients | Hypofractionation, automated planning, dynamic scheduling (Braunstein et al., 2020; Pham et al., 2023) |
| Operating theatre | Room hours, surgical team, intensive care | Transition from diagnosis to surgery, reconstruction slot | Running preoperative processes in parallel, prioritisation rules (Ju et al., 2018; Lugnier et al., 2024) |
| Beds | Bed-days | Emergency admissions, delayed discharge | Shifting to ambulatory care, floor and ward-level simulation (Cunningham et al., 2012; Mokashi et al., 2025) |
Pocket note for physicians: Every choice of protocol is also a capacity decision. As long as it is evidence-based, a hypofractionated schedule or a subcutaneous formulation is a gift both to the patient and to the next patient waiting in line.
6. Bottlenecks: the narrowest link of the chain
A patient journey moves only as fast as its slowest step. This is the core idea of the theory of constraints: speeding up a step outside the bottleneck does not increase the output of the system; it only enlarges the queue in front of the bottleneck (Goldratt & Cox, 1984).
6.1 Separating the concept from its neighbours
When “oncology” and “bottleneck” are searched together in WoS, three different meanings appear, and it is important for physicians to tell them apart. The first is the biological bottleneck: limiting steps in metabolic pathways, chromatin or the metastatic process (Sayin et al., 2019; Szczurek et al., 2020). The second is the algorithmic bottleneck: “information bottleneck” layers in deep learning architectures (Wang et al., 2023). The third is the managerial bottleneck, the subject of this chapter: the link in the care process where the patient waits longest and capacity falls furthest short of demand (Ju et al., 2018). There is also the concept of “resource constraint”, which is broader and describes a scarcity that envelops the whole system, especially in low and middle-income countries (Sengar et al., 2022; Thongprasert & Premsuwan, 2013).
6.2 Types of managerial bottleneck
| Type | What becomes the bottleneck? | Example from the corpus |
|---|---|---|
| Diagnostic | Biopsy, imaging, colonoscopy, pathology | In lung cancer, the times from detection of the lesion to biopsy and from staging to surgery emerged as the two most critical bottlenecks (Ju et al., 2018); colonoscopy capacity determines the design of the screening programme (McFerran et al., 2022) |
| Specialist workforce | The schedule of a particular specialist | The plastic surgeon’s schedule can delay definitive surgery in breast cancer (Orosco et al., 2026) |
| Timing and flow | Appointments piling up within the day | In a multidisciplinary clinic, 57% of patients were concentrated between 09:00 and 12:00; the problem lay not in service time but in the distribution of appointments (Ning et al., 2019a) |
| Equipment and space | LINAC, operating theatre, beds | The LINAC shortage differs markedly between regions (Viani et al., 2022) |
| Drugs and supply | Supply of oncology drugs | Although drug bottlenecks in oncology are better managed, the threat persists (“Drug bottlenecks in oncology”, 2020) |
| Regulatory and administrative | Ethics committee, diagnostic device approval, eligibility screening | In Europe, approval of biomarker-based studies took 129.4 days on average (Hersey et al., 2026); in lung cancer, 15.7% of patients were found eligible for a clinical trial and 5.1% enrolled (Gonzalez et al., 2013) |
| Information and data | Variant interpretation, access to data | Attempts have been made to ease the cancer variant interpretation bottleneck with a community-based knowledge base (Krysiak et al., 2022; Manley & Lee, 2026) |
6.3 How is a bottleneck found?
The methods for finding a bottleneck can also be ordered from simple to complex:
- Card tracking and Pareto: give the patient a tracking card on arrival and have the time written at each step, then use a Pareto chart to see where delays cluster. Ning and colleagues (2019a) examined 2,951 appointments with this method and corrected the distribution of appointments through Plan, Do, Study, Act (PDSA) cycles.
- Cycle time and takt time: compare the actual duration of a step with the rhythm required by demand. In an academic cancer centre, the process steps of the research scheduling team were cut from 15 to 10 and cycle time by 11% (Dang et al., 2018).
- Value stream mapping and statistical process control: the redesign of an entire outpatient cancer centre within a learning health system framework (Fung-Kee-Fung et al., 2022).
- Simulation: trying out the question “what if we did it this way?” without touching the patient. In the outpatient department of a cancer institute in India, simulation showed that rooms and physicians were over-utilised while support staff were under-utilised (Singh et al., 2022).
- Process mining: extracting the real process from time-stamped records in the hospital information system. In northern Portugal, deviations and bottlenecks in colorectal cancer screening were made visible with 6.96 million event records for 1.18 million people (Monteiro et al., 2026).
For what to do once the bottleneck is found, the five steps of Goldratt and Cox (1984) are a useful sequence for physicians too: identify the bottleneck, do not let it run idle, subordinate the other steps to it, elevate its capacity if necessary, and go back to the start when the bottleneck moves.
Pocket note for physicians: The sentence “everyone is very busy” does not reveal the bottleneck. Look at where the queue builds up; the bottleneck is usually not in the waiting room but in front of the next step.
7. Resource balancing and allocation
Resource balancing means relocating people, equipment, space and time to widen the bottleneck and distribute the load fairly; it is done at three levels, each with a different decision-maker.
7.1 Three decision levels
| Level | Time horizon | Typical question | Decision-maker | Example from the corpus |
|---|---|---|---|---|
| Operational | Day, week | Which patient goes to which chair with which nurse today? | Ward lead, unit physician | Solving appointments and nurse assignment together (Hesaraki et al., 2020); simulation and optimisation in multi-period treatment plans (Martins et al., 2021) |
| Tactical | Month, quarter | How many clinic days and slots for which tumour group? | Chief physician, head of department | Allocating capacity to cancer types under uncertain demand (Keshtzari & Norman, 2024); case management by disease site with quarterly readjustment (Fung-Kee-Fung et al., 2022) |
| Strategic | Year and beyond | How many machines and specialists, and in which region? | Ministry, provincial and hospital management | An 11-year forecast of workforce and facility needs in Indonesia (Melyda et al., 2022); capacity planning for diagnostic tests based on aggregate demand (Arruda et al., 2021) |
7.2 Four tools of balancing
- Balancing in time: spread demand across the day and the week. Dispersing the morning peak can remove the perception of a bottleneck without additional resources (Ning et al., 2019a).
- Balancing between people: flexible specialisation and task sharing. Even a small group of flexible oncologists strongly increases the share of demand met (Keshtzari & Norman, 2024); pharmacist-led and nurse-led clinics free physician time (Sladkowski, 2026; Ni, 2026).
- Balancing between spaces and centres: distribute patients across floors or centres with a network logic (Mokashi et al., 2025; Wenzel et al., 2024). Centralisation can improve outcomes but must be balanced against access (Usman et al., 2026).
- Reducing demand wisely: removing unnecessary procedures also creates resources. In an imaging audit of lymphoma patients, 21% of examinations were found to have been requested for routine surveillance (Schwartz et al., 2006). The multidisciplinary tumour board also validates resource use: in a low-resource centre, the board added to the surgeon’s initial plan in 38.4% of cases and changed the plan entirely in 25% (Qureshi et al., 2025).
7.3 The ethical and economic face of allocation
Three concepts are often confused in debates on resource allocation. Rationalisation means using efficiency reserves; rationing means withholding an effective service for reasons of cost, and it can also occur implicitly through waiting time or budgets; prioritisation means determining with explicit criteria which service will be delivered first (Winkler, 2011). The critical point for physicians is this: a lengthening waiting list is implicit rationing even when nobody has taken a decision.
Germany’s pandemic-era guidance offers the most concrete framework for prioritisation criteria. The basic principle is to minimise harm, and decisions rest on three criteria: urgency to prevent harm, the likelihood of success of diagnosis or treatment, and the availability of alternative treatment options. Where there is a risk of harm, the decision should be made by a team (Lugnier et al., 2024). The values that society considers important have also been explored with citizens’ juries; the number of patients who would benefit, current health status, prognosis without the technology, health outcome with the technology, age and persons for whose care the patient is responsible came to the fore (Stafinski et al., 2014).
In economic terms, oncology is an expensive field. With German data from 2002 to 2004, the cost per life year gained in malignant neoplasms was calculated at about 140,750 euros (Hartmann et al., 2008). Value measures therefore gain importance: countries’ reimbursement criteria have been compared using the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS) (Lindgren et al., 2017), the economic rationale for assessing cancer drugs in a separate process has been questioned (McDonald et al., 2015), and “onco-exceptionalism” has been debated anew from the standpoint of equity (Frühwein et al., 2026). Guidelines stratified by resource level show what should be built first in constrained settings (Anderson et al., 2011; Eniu et al., 2006; Sengar et al., 2022).
Pocket note for physicians: Balancing usually starts without asking for new resources: first spread the time, then share the task, then remove unnecessary work. Requests for new machines and posts should come last and be backed by data.
8. An integrated model: how do the four elements connect?
Workload, capacity, bottleneck and resource balancing form a single loop: demand reaches capacity, the real limit of capacity is drawn by the workforce, the link where demand most exceeds this limit becomes the bottleneck, outputs are measured, and resource balancing readjusts the loop.
The diagram rests on four propositions:
- When demand exceeds capacity, a queue is inevitable. By Little’s law, the number of patients waiting is the product of arrival rate and time in the system (Little, 1961); as time in the system lengthens, the same demand requires more capacity.
- Effective capacity is set by the workforce, not the machine. Even if the chair is free, the infusion does not start unless the nurse is ready (Hesaraki et al., 2020); even if the LINAC is working, treatment does not start if the planning team cannot keep up (Halvorsen et al., 2023; Probst & Griffiths, 2006).
- The bottleneck moves. Widening one link shifts the constraint to the next (Goldratt & Cox, 1984); improvement is therefore not a one-off project but a continuous loop of measuring and adjusting. The fact that even network-based planning delivers benefit only when it is actually implemented illustrates this (Wenzel et al., 2024).
- Excessive workload erodes capacity. High job demands lower professional quality of life and raise intention to leave (S. L. Tan et al., 2024); every experienced nurse or physician who leaves reduces effective capacity and increases the load on those who remain. This is a vicious circle, and the way to break it is to make workload a direct variable of capacity planning (Gribben & Semple, 2021; Keshtzari & Norman, 2024).
Pocket note for physicians: When applying this diagram to your own unit, first measure the outputs, then find where the queue builds up, and only then decide which level of balancing (daily, quarterly, yearly) can solve the problem.
9. A toolbox for physicians and a projection to the present
The tools in this section can be used with a calculator and one week of data; the aim is not to build a perfect model but to ask the right question with the right number.
9.1 Tool 1: how many chairs do I need?
The rough chair requirement of a day treatment unit is calculated as follows:
Chairs needed = (patients per day × average chair hours per patient) / (opening hours per day × target utilization)
Example: with 60 patients per day, an average chair time of 3 hours, 10 opening hours and a target utilization of 85%, 60 × 3 / (10 × 0.85) = about 21 chairs are needed. If you take target utilization as 100%, the result is 18 chairs; the 3 chairs in between are the safety margin left for delays, drug preparation and urgent add-on patients. The 85% in this example is not a rule but a teaching assumption; it should be set according to the variability of your own unit. In real planning, the number of nurses and pharmacy capacity must enter the same calculation (Hadid et al., 2022; Hesaraki et al., 2020).
9.2 Tool 2: a five-question bottleneck checklist
- Where does the patient wait longest, and has this wait increased over the past three months? (Ju et al., 2018)
- Are appointments distributed evenly within the day and across the week? (Ning et al., 2019a)
- Does the bottleneck resource (for example the planning physicist or the biopsy room) ever stand idle, or is it kept busy with other work?
- Can only a physician perform this step, or can it be shared with a trained nurse or pharmacist? (Sladkowski, 2026)
- Could a protocol change (hypofractionation, subcutaneous formulation, ambulatory treatment) deliver the same clinical outcome with less capacity? (Braunstein et al., 2020; Farolfi et al., 2017)
9.3 Tool 3: a weekly mini indicator set
| Indicator | Simple definition | Why does it matter? |
|---|---|---|
| Days from diagnosis to treatment | Median days from first pathological diagnosis to first treatment | The flow indicator that most directly affects clinical outcome (Ju et al., 2018) |
| Overall radiotherapy treatment time | Days from the start to the end of radiotherapy | Prolongation reflects patient and system factors together (Vijayakumar et al., 2023) |
| Chair and machine utilization | Hours used / hours scheduled | Very low shows waste, very high shows fragility (Cataldo et al., 2023) |
| Overtime hours | Weekly overtime per staff member | The bill that the capacity gap presents to people (Hadid et al., 2022) |
| Care load per nurse | Care hours calculated with NAS or a similar scale | Shows the severity hidden by the number of patients (Balaminut et al., 2024) |
| Burnout pulse | A short survey once or twice a year | An early warning of intention to leave (S. L. Tan et al., 2024) |
9.4 A projection to today and the near future
The 2025 and 2026 records of the corpus clearly show five trends:
- Creating capacity with artificial intelligence: automated contouring and planning ease the planning bottleneck in radiotherapy (Netherton et al., 2026); explainable machine learning clusters hospitalised oncology patients by resource need (Lopéz-Flores et al., 2026); decision support is being developed for intensive care admission of febrile oncology patients (Iyer et al., 2026).
- From data to process: process mining makes it possible to find the bottleneck from real records rather than by guesswork (Monteiro et al., 2026).
- Thinking as a network: capacity sharing between centres (Wenzel et al., 2024) and the balance of centralisation and access (Usman et al., 2026) make the regional network, rather than the single hospital, the unit of planning.
- Task sharing and virtual care: pharmacist clinics (Peragine et al., 2026; Sladkowski, 2026) and virtual care (Lee et al., 2026) redistribute physician and nurse time.
- Protecting the workforce: the growing number of studies on compassion fatigue, erosion of role boundaries and intention to stay (Banks et al., 2025; Kamisli & Saribudak, 2026; Yi et al., 2026) shows that the workforce is now seen as the main component of capacity.
Pocket note for physicians: The contribution of technology to capacity is real only when it targets the bottleneck. Automating a step outside the bottleneck only moves the queue to another door.
10. Conclusion: ten sentences to remember and questions for discussion
In an oncology hospital, workforce, workload, capacity, bottleneck and resource balancing are not separate topics but different views of a single system; a physician who understands this system becomes able to manage both the patient’s waiting time and the team’s fatigue.
Ten sentences to remember
- In oncology, time is a component of treatment; capacity determines that time.
- Capacity is the language of the machine and workload the language of people; the two must be read together.
- The number of patients is not workload; the patient’s severity must also be measured.
- A system is as fast as its narrowest link.
- Improvement outside the bottleneck only moves the queue.
- Pushing utilization to 100% increases waiting and burnout.
- In chemotherapy the chair and the nurse, and in radiotherapy the machine and the planning team, must be planned together.
- The choice of clinical protocol is also a capacity decision.
- Balancing starts with time, task sharing and removing unnecessary work; new resources come last.
- A lengthening waiting list is implicit rationing; prioritisation should be done with explicit criteria and as a team.
Questions for discussion
- In your own unit, at which step does the patient wait longest, and with which data can you show it?
- Which tasks in your unit can be safely shared with a non-physician professional group?
- Is there an evidence-based alternative requiring less capacity for a protocol used last year?
- On a day when resources truly are insufficient, who makes the prioritisation decision, and with which criteria?
- How can you add nurse and physician burnout as an indicator to your capacity planning?
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Kurutkan, M. N. (2026, November 14). Workforce, Workload and Capacity Management in the Oncology Hospital: An Introduction (101). The Health Topics. https://healthtopic.org/topics/oncology-hospital-workforce-workload-capacity-management-an-introduction-101/