Resource, Quality and Data-Driven Management in the Oncology Hospital: Introduction 101 for Physicians

5. Quality indicators: the hospital’s vital signs

A quality indicator is a measure that turns one aspect of care into a number and compares it with a target; a single indicator does not make a diagnosis, but a few well-chosen indicators show the general condition of the hospital like a set of vital signs. The good news for the physician is that most of the strongest indicators in oncology consist of counting clinical work the physician already does.

5.1 Structure, process, outcome: the Donabedian triad

Donabedian (1988) proposed that quality of care can be assessed through structure, process and outcome. Their counterparts in oncology are shown below.

Indicator type	What does it measure?	Oncology example	Strength	Weakness
Structure	Resources and organisation	Linear accelerators per million population, existence of a tumour board	Easy to measure	Does not guarantee quality
Process	Was the right thing done at the right time?	At least 12 lymph nodes examined in colon resection, histological diagnosis before surgery	Gives fast feedback and translates into action	Its link with outcome must be proven
Outcome	What happened to the patient?	30-day mortality, unplanned readmission, survival	What the patient and society really want to know	Requires risk adjustment and large samples

The literature shows that process indicators dominate. Of 91 indicators in 89 studies comparing hospitals in surgical oncology, 64% were process, 29% outcome and 7% structure indicators; yet only 26% of these studies fed results back to the hospitals that supplied the data (McLeod et al., 2024). Measuring without feeding back is like ordering a test and never looking at the result.

5.2 The story of one indicator: 12 lymph nodes

In colon cancer, examination of at least 12 regional lymph nodes is associated with better staging and survival. When this criterion was tracked as a national indicator, the share of hospitals meeting the threshold in at least 75% of their patients rose from 15% in 1996 to 1997 to 38% in 2004 to 2005. In the same period, compliance was 78.1% in comprehensive cancer centres and 33.7% in community hospitals (Bilimoria et al., 2008). This example shows that a good process indicator both makes differences between institutions visible and can trigger improvement over time. Similar indicators give acceptable reliability at hospital level: hospital profiling with the 12 lymph node measure reached a median reliability of 0.72 with two years of data, whereas profiling individual surgeons with the same measure yielded only 0.31 even with four years of data (J. B. Liu et al., 2017). The lesson is that individual physician report cards built on small numbers are misleading.

5.3 Process or outcome? The physician’s dilemma

Patients and experts consider outcome indicators more important. In the Netherlands, patients with breast and colon cancer valued outcome indicators such as residual tumour and failure to rescue most when choosing a hospital (Salampessy et al., 2020). In China, among 47 indicators for cancer hospitals determined by the Delphi method (17 structure, 19 process, 11 outcome), experts gave outcome indicators a weight of 53.96% (M. C. Liu et al., 2024).

Statistics say something different. Among 113,942 breast cancer patients in 298 hospitals, the rankability of the indicator for cytological or histological diagnosis before surgery was 0.919, whereas that of the outcome indicator was only 0.011 (M. Y. Liu et al., 2024). Similarly, after risk adjustment very few hospitals are statistically distinguishable as good or poor in terms of survival (Shulman et al., 2018). Accredited cancer centres can look better on process and patient experience measures but worse on most crude outcome measures (Merkow et al., 2014), which suggests that centres admitting sicker patients may be unfairly penalised by comparisons without risk adjustment.

Practical conclusion: on the management dashboard, process indicators should serve as the daily compass and risk-adjusted outcome indicators as the annual report card, used together.

5.4 Composite indicators: the “textbook outcome”

The concept of textbook outcome was developed to gather many indicators into a single meaningful measure. Defined as the joint achievement of adequate lymph node yield, R0 resection, no prolonged stay, no readmission and guideline-concordant adjuvant treatment, this measure was examined in 852,988 resections; each 10% increase in a hospital’s adjusted textbook outcome rate was associated with 4% to 12% better survival depending on cancer type (Aquina et al., 2021). In the Netherlands, 16 of 21 breast cancer indicators in 91 hospitals were found valid, redundant ones were removed to form a summary measure of 13 indicators, and the median all-or-none score was 49% (Vos et al., 2020). The message: more indicators do not mean better management.

5.5 Certification, accreditation and indicators that cannot be measured

Indicator data on 195,342 patients in 218 breast centres certified by the German Cancer Society showed that target compliance was high and that differences between institutions narrowed over time; centres that miss a target must explain the deviation at the on-site audit (Kowalski et al., 2015). In 10 centres with OECI accreditation in the Nordic countries, compliance with standards ranged from 79 to 95%; the weak areas were governance, quality systems and multidisciplinarity, and one recurring improvement theme was access to quality dashboards (Nilbert et al., 2026).

Not every important indicator can be measured. In Belgium, 20 indicators for lung cancer could be computed from routine data, but 23 indicators considered relevant could not be measured for lack of data; median time from diagnosis to first active treatment was 20 days, and stage reporting to the cancer registry was 80% (Vrijens et al., 2018). In Asia, more than 60% of 21 countries have no published national data on stage distribution, time to diagnosis or time to treatment (Ong et al., 2024). An ESMO analysis covering 47 countries in the Asia-Pacific region also revealed marked differences between countries in oncology quality indicators (Kanesvaran et al., 2025).

5.6 Six questions for choosing a good indicator

Question	Why does it matter?	Evidence
Is its link with patient outcome proven?	An indicator without evidence wastes effort	Aquina et al., 2021
Can it discriminate between hospitals?	A measure on which everyone scores the same produces no decision	M. Y. Liu et al., 2024
Are the numbers sufficient?	With a small denominator, chance dominates	J. B. Liu et al., 2017
Can it be risk-adjusted?	A centre taking sicker patients looks unfairly bad	Merkow et al., 2014; Shulman et al., 2018
Are the data available and reliable?	If the record is poor, the indicator is computed wrongly	Dentler et al., 2014; Vrijens et al., 2018
To whom and when is the result fed back?	Without feedback there is no improvement	McLeod et al., 2024

6. Patient safety: first, do no harm

The oncology patient is particularly exposed to care-related harm, being older, staying longer, moving between many physicians and units, and receiving treatments with a narrow therapeutic range. Safety indicators are therefore the “alarm” part of quality indicators and, as we will see below, a resource indicator as well.

6.1 How often?

In Norway, a review of 6,720 patient charts with the Global Trigger Tool found adverse events in 24.2% of the admissions of cancer patients and 17.4% of those of other patients. Rates per 1,000 patient days were similar, however (37.1 and 36.0); the difference is explained by age, length of stay and surgery. Each day in bed raises the risk 1.05-fold and each year of age by 1.3% (Haukland et al., 2017). In Switzerland, at least one adverse event was found in 42% of the charts reviewed in oncology wards; there were 75.4 events per 100 admissions and 106.6 per 1,000 patient days, and the most frequent events were care-related pain, constipation and hospital-acquired infections (Gerber et al., 2020).

The picture is similar in outpatient treatment. Of unplanned admissions within 30 days after chemotherapy, 19.3% were caused directly by treatment-related adverse events; median stay in these admissions was 6 days, and 48.1% of the events were gastrointestinal symptoms and 11.1% neutropenic fever (Wong et al., 2019). The era of immunotherapy has opened a new safety field: immune-related adverse events of any grade occurred in 85% of melanoma patients receiving ipilimumab, and 35% required systemic corticosteroids (Horvat et al., 2015). Median survival was 12.9 months in metastatic patients hospitalised because of these events and 26.9 months in those managed as outpatients (Nurmela et al., 2025).

6.2 The price of safety: harm also consumes resources

The most concrete bridge between safety and resource prioritisation is cost. In gynaecological oncology surgery, the additional cost per patient with an adverse event averaged 12,780 Australian dollars; a 40% reduction in adverse events could save up to 20.6 million Australian dollars per year nationwide (Kondalsamy-Chennakesavan et al., 2011). In metastatic urothelial cancer, monthly costs were 6,130 US dollars higher in patients with severe adverse events (Grivas et al., 2019). The annual cost of recurrent venous thromboembolism was 29,142 US dollars higher than in patients with a single episode, and the difference was driven largely by admissions (Khorana et al., 2020). Chemotherapy-induced nausea and vomiting occurred in 64.4% of observed cycles, the delayed type was more frequent than the acute type (60.7% against 32.8%), and 32.6% of patients used additional health services for this reason (Ihbe-Heffinger et al., 2004). The unplanned 30-day readmission rate in gynaecological oncology was 11.0%, and the mean cost per readmission was 25,416 US dollars (Wilbur et al., 2016).

For this reason, when proposing “investment in safety” to a manager, the strongest argument in the physician’s hands is this: every adverse event prevented is a bed and time opened for another patient.

6.3 Institutional interventions that work

Intervention	Setting	Result	Source
Hospital-wide sepsis pathway	Sepsis after cancer surgery	Appropriate antibiotics 94.4% against 66.7%; median time to first dose 85 against 315 minutes; stay 15 against 30 days	Hiong et al., 2019
Systematic delirium prevention programme	Hospitalised cancer patients	Delirium from 7.1% to 4.3%; adverse events such as falls from 3.5% to 2.6%	Ogawa et al., 2019
Clinical pharmacist on the ward	Chemotherapy in thoracic cancer	Frequency of grade 2 or higher adverse events from 39.4% to 17.8% with interventions	Iihara et al., 2021
Pharmacist-managed oral chemotherapy clinic and dashboard	Monitoring of oral chemotherapy	A treatment-related problem identified in 45% of patients; missed monitoring caught by the dashboard	Battis et al., 2017
Paediatric early warning system (PEWS)	Paediatric oncology wards	Perceived to improve quality of care at different resource levels	Garza et al., 2021
Strategic programme for immunotherapy toxicity (IOTOX)	Comprehensive cancer centre	Standard clinical practice, education and a research platform under one roof	Fayle et al., 2024

6.4 Reporting culture

The raw material of a safety indicator is the report. In Switzerland, an oncology and haematology department made 392 of 5,000 hospital-wide critical incident reports, and 42 improvement measures were implemented in oncology; the authors stress that this is possible only in a climate of trust (Rose & Hess, 2008). In a blaming culture, reports decline and the “silence” of the dashboard is mistaken for safety. The physician’s role here is simple: report near misses and show the team that reporting is not punished.

7. Capacity, bottlenecks and resource prioritisation

Resource prioritisation is the art of directing limited beds, equipment, staff and budget to where they contribute most to patient outcome; the precondition for doing it well is knowing where the system is blocked, that is, the bottleneck. In clinical terms: however much fluid you give distal to a stenosis, the narrowing determines the flow. Likewise, if a pathology report takes three weeks, increasing operating theatre capacity will not shorten the time from diagnosis to treatment.

7.1 Why is time a quality indicator?

In oncology, waiting is not only a comfort issue but a clinical risk factor. In 51,655 patients with head and neck cancer, the risk of death rose when time from diagnosis to treatment exceeded 46 to 52 days; median survival was 71.9 months below this threshold and 46.6 months above 67 days (Murphy et al., 2016). Starting radiotherapy later than six weeks after breast-conserving surgery increased the risk of local recurrence by 19% (Punglia et al., 2010). During COVID-19 in England, it was estimated that even a 25% backlog created by a three-month lockdown in the urgent referral pathway would lead to 181 additional deaths and the loss of 3,316 life years (Sud et al., 2020). The effect of delay on prognosis in lung cancer has also long been debated (Jensen et al., 2002).

A study from Türkiye shows that how the time is measured also matters. In breast cancer, the “admission to surgery” and “biopsy result to surgery” intervals could not be used interchangeably, and discordant cases pointed to different bottlenecks; for quality dashboards the authors recommended monitoring both intervals together and briefly reviewing discordant cases coded by reason (Tandogan et al., 2025).

7.2 Where does the bottleneck hide?

Bottleneck type	How it appears in the clinic	Evidence from the literature
Equipment and infrastructure	Waiting list for radiotherapy	The number of linear accelerators needed per million population in Europe averages 5.9 (4.0 to 8.1 by country), with large shortfalls in many countries (Bentzen et al., 2005)
Access	A patient with an indication never reaches treatment	Radiotherapy access in England was 37% against an expected 50%; an estimated 33,881 patients did not receive expected radiotherapy (Williams & Drinkwater, 2009)
Decision process	A tumour board decision cannot be implemented	19.5% of board recommendations were not implemented; median time to treatment rose from 24 to 35 days (Goolam-Hossen et al., 2011)
Daily flow	Queues at the laboratory, pharmacy and infusion chair	In outpatient chemotherapy, waiting can be reduced by flow and appointment balancing (Liang et al., 2015; Suss et al., 2017)
Beds	Oncology patients waiting for admission in the emergency department	A simulated change in bed allocation shortened the wait for admission by 30 minutes or more in four wards (Mokashi et al., 2025)
Use of scale	Some hospitals are full while others are idle	In England, 40.4% of hospitals performing breast surgery and 50.3% of those performing colorectal surgery used less than 80% of their maximum capacity; spare capacity was about 25% of the volume performed (Aggarwal et al., 2023)

7.3 Four ways to increase capacity

A capacity problem is usually imagined as “a new machine, a new building”. The literature points to four different routes, two of which are directly in the physician’s hands.

• Planned investment. In the Netherlands, the radiotherapy waiting lists of the late 1990s had almost disappeared by 2010 thanks to equipment and staff investment planned for demand growing by 3.5 to 4% per year (Slotman & Vos, 2013).
• The capacity effect of clinical decisions. If hypofractionated radiotherapy had been given to all eligible breast patients, 14 more patients could have been treated per month and cost would have fallen by 24% (Dwyer et al., 2010). Compared with open surgery, minimally invasive surgery shortened length of stay by 1.87 days in lung lobectomy and 1.34 days in colon resection (Ricciardi et al., 2022). In other words, the choice of protocol is a capacity decision.
• Process redesign. In the outpatient pharmacy of a comprehensive cancer centre, lean management cut waiting time for prescriptions with few drugs from 22.3 to 8.1 minutes (Hammoudeh et al., 2021). In skin cancer, a one-stop-shop model for diagnosis and treatment in a single visit shortened the time by more than 90% with the same resources (Romero et al., 2013).
• Creative use of the workforce. Adding pharmacy trainees to an oral anticancer drug service provided about 0.43 full-time pharmacist equivalents of capacity, worth 69,300 Canadian dollars, at an annual cost of 44,700 Canadian dollars (Peragine et al., 2026).

At policy level, the strongest evidence is for activity-based payment, purchasing of local capacity and targets backed by strong incentives. Better use of existing capacity is also supported by evidence, whereas short-term injections of funds and unmonitored targets have the weakest evidence (Kreindler, 2010).

7.4 Where should the resources go? Prioritising with data

Studies of healthcare resource utilization show where the money goes. In Japan, the hospital-based cancer registry was linked with service use data to calculate first-year costs of five common cancers in 304,698 patients; the cancer type with the longest length of stay was found to have the highest cost (Watanabe et al., 2021). This shows how powerful a compass for management is created when registry and cost data are combined. In Mexico, a study that grouped 5,296 hospitalised oncology patients into three clinically meaningful clusters with explainable machine learning found length of stay and number of procedures to be decisive, and showed that this information can be transferred to surgical planning and bed management (López-Flores et al., 2026). Simulation is also a strong tool: a review of 51 simulation studies in cancer services reported that projects not conducted together with clinicians and managers were implemented less successfully (Gjerloev et al., 2024).

For physician-friendly prioritisation, each proposal can be scored from 1 to 5 on the six criteria below. This table is a suggested framework; weights should be adapted to the priorities of the institution.

Criterion	Question to ask	Which data feed it?
Clinical impact	How much does it change patient outcome?	Quality indicators, literature
Time sensitivity	Does delay worsen the outcome?	Diagnosis-to-treatment time data
Number of patients covered	How many patients does it affect?	Cancer registry
Safety effect	Does it reduce adverse events?	Safety reports, trigger tool
Cost and feasibility	How much resource and time does it require?	Resource utilization and cost data
Equity	Does it improve access for disadvantaged groups?	Sociodemographic breakdown in the registry

After scoring, proposals can be placed in a simple matrix by clinical impact and ease of implementation.

8. The management dashboard: the table where data become decisions

A management dashboard is a screen that shows selected indicators at a glance, up to date and with thresholds; but a dashboard alone improves nothing. What improves things is the team that looks at the dashboard and decides. The intensive care monitor analogy is instructive here: the monitor sounds the alarm and the physician treats the patient; if nobody hears the alarm, or if unnecessary alarms ring every minute, the monitor is useless.

8.1 Dashboard types: who is asking which question?

Dashboard type	Main user	Question answered	Example indicators	Example from the literature
Strategic (governance)	Chief physician, board	Are we progressing towards institutional goals?	Treatment completion, equipment utilization, patient experience, staff training	A balanced scorecard-based dashboard of 21 indicators in 5 dimensions in a public oncology centre in Morocco (Bejja & Najdi, 2026)
Operational (flow and capacity)	Ward and unit leads	Where are we blocked today?	Bed occupancy, wait for admission	Bed plan supported by simulation (Mokashi et al., 2025)
Clinical quality	Discipline and tumour board teams	Are we meeting guidelines and time targets?	Radiotherapy within 6 weeks after surgery	Baseline 61% and a 2026 target of 80% in head and neck cancer (R. Liu et al., 2026)
Patient-reported outcome	Physician, nurse and patient	How does the patient feel?	Symptom scores, quality of life	Symptom management integrated with the electronic record in six health systems (Hassett et al., 2022)
Tumour board	Board members	What do we need when deciding for this patient?	Patient, disease and treatment metrics	Information architecture for a head and neck tumour board (Oeser et al., 2018)
Data quality assurance	Quality and registry team	Are our data reliable?	Missing or implausible values	2,302 quality events in an international radiotherapy study, at least one in 52% of patients (Ecker et al., 2023)

Strategic dashboards are rooted in the balanced scorecard, which proposes monitoring performance not only through finance but together with customer, internal process and learning dimensions (Kaplan & Norton, 1992). In the Moroccan example, operational indicators such as treatment completion and equipment utilization were high, at around 90%, while diagnostic delays, late-stage presentation and psychosocial support coverage remained weak (Bejja & Najdi, 2026). The dashboard thus made possible a balanced diagnosis such as “our technical capacity is good, but the beginning and end of the patient journey are weak”.

8.2 Bringing the patient voice to the dashboard

The entry of patient-reported outcomes into dashboards is the most important development of recent years. In the Canadian province of Alberta, patient-reported measures from more than 60,000 patients and more than 300,000 screening records were turned into dashboards at micro (patient), meso (clinic) and macro (programme) level and began to be used in staff and capacity planning (Watson et al., 2021). In breast reconstruction, a digital dashboard and “scale champions” raised the completion rate of the patient scale from 42.8% to 87.6% (Nelson et al., 2023). With a dashboard co-designed with patients with advanced cancer and chronic kidney disease, the share of patients reporting the highest level of shared decision-making rose by 17 percentage points in six months and anxiety fell (Cella et al., 2024); among cancer patients using the same dashboard, chemotherapy-related emergency visits or admissions were significantly fewer (Amagai et al., 2025).

8.3 Why does a dashboard sometimes fail?

The existence of a dashboard does not guarantee its use. In one primary care system, 25.8% of physicians never opened their dashboard, and no relationship was found between the number of views and change in quality score (Twohig et al., 2019). Oncology nurse leaders stated that the reports they could access were not accurate, usable or relevant to patient care, and that real-time, unit-specific data were needed (Mazzella-Ebstein et al., 2022). In automated data extraction, agreement of up to 99% was achieved for most variables, but agreement was only 20% for one variable and the deviation approached 25% in one indicator; the authors stress that data validation is a precondition for a reliable dashboard (Ebbers et al., 2023).

8.4 Ten rules for a physician-friendly dashboard

1. Few and meaningful indicators: a limited number of indicators, each tied to a decision, is better than a long list; in the Dutch example, 21 indicators were reduced to a summary measure of 13 (Vos et al., 2020).
2. Target and threshold: green, amber and red thresholds should be defined in advance for every indicator.
3. Time series: show the trend, not the value of a single month.
4. Risk adjustment: comparisons between institutions or teams should be adjusted for case mix (Shulman et al., 2018).
5. Data date and source: state which record each indicator comes from and when.
6. Ownership: every indicator should have an owner.
7. Link to action: for an indicator that turns red, who will do what and when should be written down.
8. Co-design: design together with the physicians and nurses who will use it (Janssen et al., 2020).
9. Common data standard: open standards such as FHIR allow clinicians without technical knowledge to filter the data (Borys et al., 2025).
10. Regular meeting rhythm: unless it is the agenda of a weekly or monthly management meeting, a dashboard is only decoration (Twohig et al., 2019).

9. An integrative model: the Record-to-Decision loop

When registry, indicator, safety, capacity and dashboard are not connected, each is experienced as a separate bureaucratic burden; when they are connected, a single loop emerges that closely resembles clinical reasoning. The “Record-to-Decision” loop proposed in this chapter consists of six links, each dependent on the quality of the one before it.

Link	Clinical counterpart	Question to ask	Main evidence
1. Record	History and chart	Are the data complete, correct and timely?	Bray & Parkin, 2009; Fagundes et al., 2026
2. Measure	Vital signs and tests	Are we computing the right indicator, with risk adjustment?	Aquina et al., 2021; M. Y. Liu et al., 2024
3. Display	Monitor and alarm	Does the dashboard show the deviation in time and to the right person?	Ebbers et al., 2023; Twohig et al., 2019
4. Diagnose	Differential diagnosis	Where is the bottleneck or the safety gap?	Goolam-Hossen et al., 2011; Tandogan et al., 2025
5. Prioritise and allocate	Treatment plan	Which intervention has the highest impact and is feasible?	Dwyer et al., 2010; Kreindler, 2010
6. Monitor and learn	Follow-up visit	Did the intervention change the indicator, and did the new data enter the record correctly?	McLeod et al., 2024; R. Liu et al., 2026

9.1 Worked example: time from diagnosis to surgery and radiotherapy in breast cancer

The example below is a teaching scenario showing how the loop might work in an oncology hospital; the steps rest on findings in the literature, and no institution name or institutional figures are used.

1. Record. In the hospital cancer registry, the dates of biopsy, pathology result, admission, surgery and start of radiotherapy are kept as structured fields. If dates remain as free text in the physician’s note, the indicator cannot be computed (Dentler et al., 2014).
2. Measure. Two intervals are computed together: biopsy result to surgery, and admission to surgery. These two do not substitute for each other and point to different bottlenecks (Tandogan et al., 2025). The interval from surgery to radiotherapy is added; an interval beyond six weeks is associated with the risk of local recurrence (Punglia et al., 2010).
3. Display. On the clinical quality dashboard, these three intervals are shown as monthly trends together with target thresholds. The reason for each case exceeding the threshold is marked with a short code (for example additional imaging, patient preference, theatre list, change in the board decision).
4. Diagnose. When the reason codes are brought together, the bottleneck becomes visible. For example, failure to implement tumour board recommendations can markedly delay the start of treatment; the reasons are mostly incomplete comorbidity information reaching the board and new clinical information (Goolam-Hossen et al., 2011).
5. Prioritise and allocate. The options are scored on the six criteria in Section 7.4: a standard information form before the board (low cost, high impact), wider use of hypofractionated radiotherapy in eligible patients (which frees machine capacity; Dwyer et al., 2010), use of spare theatre capacity in the region (Aggarwal et al., 2023), or investment in new equipment (high cost, long lead time; Slotman & Vos, 2013).
6. Monitor and learn. The chosen intervention is tested with a Plan-Do-Study-Act (PDSA) cycle; in head and neck cancer, a similar approach was designed with a patient dashboard inside the electronic patient record to raise the share of patients starting radiotherapy within six weeks after surgery from a baseline of 61% to a target of 80% (R. Liu et al., 2026). Results are fed back to the team and the loop starts again.

9.2 A second short example: immunotherapy toxicity

As immune checkpoint inhibitors have spread, immune-related adverse events have become both a safety and a capacity problem. At the record level, the grade and management of these events should be kept in structured form (Horvat et al., 2015); at the indicator level, the rate of event-related admission should be monitored (Nurmela et al., 2025); at the dashboard level, unplanned presentations after outpatient treatment should be tracked (Wong et al., 2019); and at the resource level, the establishment of a multidisciplinary toxicity programme should be considered (Fayle et al., 2024).

10. Pitfalls, the physician’s pocket list and a look ahead

The greatest risk of data-driven management is not missing data but misread data. Each of the pitfalls below has a concrete example in the literature.

10.1 Ten common pitfalls and their antidotes

Pitfall	How is it noticed?	Antidote	Source
Skipping data quality	The indicator differs between the electronic record and the quality registry	Make data validation a precondition of the dashboard	Dentler et al., 2014; Ebbers et al., 2023
Indicator inflation	Nobody remembers all the indicators	Reduce to a few valid, non-redundant indicators	Vos et al., 2020
Comparison without risk adjustment	A centre taking sicker patients always looks bad	Adjust for case mix	Merkow et al., 2014; Shulman et al., 2018
Individual report cards on small numbers	One physician’s score jumps from month to month	Institution-level and long-term data	J. B. Liu et al., 2017
Looking only at outcomes	The outcome indicator does not discriminate between hospitals	Monitor process and outcome together	M. Y. Liu et al., 2024
A single measure of time	“Waiting” looks good but the patient still waits	Measure different intervals of the journey separately	Tandogan et al., 2025
Ignoring what cannot be measured	Important areas without data drop off the agenda	List unmeasurable indicators and plan data collection	Vrijens et al., 2018
Treating the dashboard as decoration	The dashboard is not opened or discussed in meetings	Tie the dashboard to the management meeting agenda	Twohig et al., 2019
Giving no feedback	The team knows it is measured but never sees the result	Return results regularly to those who produce the data	McLeod et al., 2024
Blaming culture	Reports decline and “silence” is mistaken for safety	A climate of trust and an easy reporting portal	Rose & Hess, 2008

10.2 The physician’s pocket list: ten questions for Monday morning

• Is any stage or performance status missing from this week’s records on my ward?
• Where does our diagnosis-to-treatment time stand relative to the target threshold?
• Is there any patient whose interval from surgery to adjuvant treatment exceeds six weeks?
• Of my patients with an unplanned admission or emergency visit last week, how many were treatment-related adverse events?
• Which of the reported near misses was discussed in the team?
• Was there a patient whose tumour board decision could not be implemented, and why?
• Where is this week’s longest queue: pathology, imaging, pharmacy, beds or the operating theatre?
• To open this bottleneck, should the process, the protocol or the resources change first?
• Do the red indicators on the dashboard have an owner and an action plan?
• Did last month’s improvement step change the indicator?

10.3 From today to tomorrow: five trends

• Patient voice and value. Combining patient-reported outcomes with clinical and cost data in the same record makes value-based healthcare dashboards possible; in the Australian lung cancer registry, 49% of patients completed the baseline scale, and quality of life was associated with stage, performance status and guideline-concordant treatment (Harden et al., 2026; Abdalla et al., 2022).
• A common data language. Standards such as mCODE and FHIR reduce the manual work between registry and dashboard (Borys et al., 2025; Osterman et al., 2020).
• Explainable artificial intelligence. In a multimodal model for predicting radiotherapy interruption, discrimination remained moderate (area under the curve 0.689); this is a reminder that artificial intelligence should support the decision without replacing the physician (Zink et al., 2026; López-Flores et al., 2026).
• Tying spending to capacity. A form of governance is proposed that ties oncology spending to diagnostic and service capacity, publishes transparent capacity dashboards and actively withdraws low-value practices (Benitez-Fuentes et al., 2026). Linking payment models to quality is part of the same trend (Wen et al., 2018).
• Crisis-resilient oncology. Real-time drug dashboards and regional reserve systems have been put on the agenda in response to pandemics, conflict and supply chain crises (AlJohani, 2026).

10.4 Conclusion

Good management in an oncology hospital is not a specialty separate from good clinical practice; it is the same practice carried to the scale of the institution. When the cancer registry is kept correctly, quality and safety indicators gain meaning; when indicators are displayed correctly on the dashboard, bottlenecks become visible; and when bottlenecks are visible, resources are allocated by evidence rather than by guesswork. The physician’s share in this chain is decisive from the first link to the last: the physician is the one who first writes the data, knows the clinical meaning of the indicator, sees the cause of the bottleneck on the ground and first notices the effect of the intervention on the patient. That is what this chapter offers the physician: a translation of the clinical reasoning you already know into a language you can apply to the whole hospital.

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