NHS cancer patients are waiting too long for surgery or radiotherapy—the two treatments most likely to cure solid tumours—and no one has yet built a systematic way to fix the delays. This matters because bowel, breast, lung, and oesophageal cancers together account for a large share of UK cancer cases, and national treatment targets are routinely missed. The problem is not simply a lack of capacity; it is that hospitals, clinicians, and policymakers lack a structured method to identify where delays happen, why they happen, and which solutions actually work. The TACTIC project will build a "cancer learning system"—a repeatable model that combines national waiting-time data, an online community of staff and patients, and co-design workshops to understand bottlenecks and test fixes. If successful, the model could become an enduring tool for NHS England and Cancer Alliances, allowing them to spot which trusts are performing well and which are falling behind, then rapidly adapt proven solutions. The project will also generate evidence on how treatment delays affect survival and quality of life for different cancer stages, helping clinicians decide which patients should be prioritised. The ultimate change would be a quieter, more reliable cancer pathway—fewer agonising waits, less variation between hospitals, and a system that learns from its own data rather than reacting to crises.
View original technical description
Background For people with cancer, NHS targets for timely treatment are not being met for surgery or radiotherapy which are the two main curative treatments for solid tumours. It is unclear how we can make better use of all available capacity for surgery and radiotherapy to provide more timely treatment. We have consulted with Public and Patient Involvement (PPI) contributors who have lived experience of cancer, and with clinicians, NHS England (NHSE), Cancer Alliances, and those working in radiotherapy and surgical directorates. All are highly supportive of our proposal for developing a sound evidence base to improve the timeliness of cancer treatment. Aim and objectives Our aim, in this 3.5 year project, is investigate how to improve timeliness of curative surgery or radiotherapy for people with cancer by using an innovative cancer learning system model. Our model will combine high quality data, innovative co-design techniques, and an inclusive online community to provide large-scale capability for understanding problems, developing solutions, and evaluating them. Our objectives are: 1. To develop and test a cancer learning system model comprising three components: high quality cancer data and analytics, an online collaborative community, and rigorous co-design methods 2. To understand influences on timeliness to initiation of cancer treatment (radiotherapy and surgery) following diagnosis for four high-burden cancers (bowel, breast, lung and oesophageal cancer) 3. To co-design possible solutions (process improvements) for improving timeliness of curative cancer treatments 4. To generate evidence about the impacts of treatment delays on outcomes for different cancer types and stages to inform decisions about which treatment pathways should be accelerated and for whom 5. To evaluate the potential impact of the proposed solutions on processes, outcomes, costs and inequalities Methods We will develop and test a cancer learning system approach to improve the timeliness of curative surgery or radiotherapy for people with bowel, breast, lung, or oesophageal cancer. National clinical guidelines recommend surgery or radiotherapy as primary curative treatments for early stages of these cancers, yet there are important variations across NHS trusts in whether these treatments are provided on time. We will develop an inclusive collaboration of diverse staff, and patient groups to understand why these treatments are delayed. We will use national cancer data and waiting time audits to identify trusts with short and long times to curative treatment. Our stakeholders will use this information to co-design solutions that can improve treatment timeliness. We will also generate evidence to inform development of accelerated care pathways by assessing the impact of providing more timely treatment on outcomes. We will evaluate the impact of potential solutions on waiting times and inequalities, and assess cost-effectiveness. Timelines: Months -3 to 12: recruit stakeholders, internal pilot. 0-18: understand variations, propose solutions. 18-36: impact of delays on outcomes,18-39: evaluate solutions. 0-42: translation. Impact: NHSE, lead clinicians, service managers and PPI contributors will help ensure findings have a major impact on cancer pathways, and patients. Our infographics, videos, animations, and easy-read summaries will make findings widely accessible. This cancer learning system will be an enduring asset for improving cancer services.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know