Katie Spencer

Background: Reliable cancer-system data are needed for capacity planning, resource allocation and accountability. We assessed which patient- and department-level data elements professionals consider important for cancer-service planning and how feasible they are to collect across diverse settings.Methods: We conducted a cross-sectional online survey of cancer-system professionals and service leaders. Respondents rated the importance and local feasibility of 27 data elements linked to 14 recurring planning questions on parallel 5-point scales. Value-feasibility gaps were calculated as importance minus feasibility and summarised by domain and World Bank income group.Results: Among 82 valid respondents from 75 institutions in 48 countries, importance ratings were consistently high, while feasibility was lower and more variable. All 14 planning questions showed positive mean gaps. The largest concerned outcomes, toxicity, patient-reported outcomes, patient experience and treatment-related economic burden. In the small low-income subgroup, substantial gaps also affected baseline patient and tumour characteristics, diagnosis and treatment counts, and timelines. Equipment and workforce data were more closely aligned.Conclusions: Cancer-system data are widely valued for planning but are not consistently feasible to collect. The findings support a harmonised minimum indicator set with staged implementation adapted to local data maturity and resources.

Introduction: The use of electronic patient reported outcome measures (ePROMs) is increasing in routine cancer care, with benefit demonstrated in improving patient survival, satisfaction and response time. ePROMs represent a complex intervention, with successful implementation reliant upon a range of questionnaires, platform, patient and clinician characteristics alongside the wider organisational readiness and environment. Key performance indicators (KPIs) assess the performance of a system. A KPI framework would offer value in assessing ePROM implementation projects, however the outcomes and indicators of importance are not clear. Method: A modified Delphi methodology was used to define a framework of KPIs for assessing the deployment of ePROMs in routine cancer care. Potential KPIs were identified through literature searches, de-duplicated and allocated to a matrix of domains. Delphi participants were identified through a literature review and study team networks. KPIs were presented to participants for prioritisation using an online platform. A final set of KPIs was identified through two rounds of consensus with participants rating each KPI for relevance. Results: The literature search generated a list of 196 potential KPIs of which 48 were considered by 15 experts in the Delphi process. Consensus was reached to include 12 KPIs in the first round and a further 2 KPIs in the second round. Participant’s open text responses were analysed, suggesting a number of areas of debate regarding which KPIs are most pertinent. Discussion: This work provides a framework of 14 KPIs, covering those of relevance to patients, clinicians and health services and recognising the acceptability, feasibility and impact of ePROMs. This framework offers a means to appraise the implementation of ePROMs, supporting teams as they implement ePROMs in routine cancer care and other healthcare settings.

PURPOSE: This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice.METHODS: We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO use in prediction modeling, computing, implementation, and decision science. The discussions presented here focused on identifying challenges to the development, implementation and use of prediction models incorporating PROs, and suggesting possible solutions.RESULTS: Specific challenges and solutions were identified across three broad areas. (1) Understanding decision making and implementation: necessitating multidisciplinary collaboration in the early stages and throughout; early stakeholder engagement to define the decision problem and ensure acceptability of PROs in prediction; understanding patient/clinician interpretation of PRO predictions and uncertainty to optimize prediction impact; striving for model integration into existing electronic health records; and early regulatory alignment. (2) Recognizing the limitations to PRO collection and their impact on prediction: incorporating validated, clinically important PROs to maximize model generalizability and clinical engagement; and minimizing missing PRO data (resulting from both structural digital exclusion and time-varying factors) to avoid exacerbating existing inequalities. (3) Statistical and modeling challenges: incorporating statistical methods to address missing data; ensuring predictive modeling recognizes complex causal relationships; and considering temporal and geographic recalibration so that model predictions reflect the relevant population.CONCLUSION: Developing and implementing PRO-based prediction models in cancer care requires extensive multidisciplinary working from the earliest stages, recognition of implementation challenges because of PRO collection and model presentation, and robust statistical methods to manage missing data, causality, and calibration. Prediction models incorporating PROs should be viewed as complex interventions, with their development and impact assessment carried out to reflect this.

Objectives: Radiotherapy is a key cancer treatment modality but is poorly understood by doctors. We sought to evaluate radiation oncology (RO) teaching in medical schools within the United Kingdom (UK) and Republic of Ireland (RoI), as well as any impacts on RO teaching delivery from the coronavirus disease 2019 (COVID-19) pandemic. Methods: A bespoke online survey instrument was developed, piloted and distributed to oncology teaching leads at all UK and RoI medical schools. Questions were designed to capture information on the structure, format, content and faculty for RO teaching, as well as both the actual and the predicted short- and long-term impacts of COVID-19. Results: Responses were received from 29/41 (71%) UK and 5/6 (83%) RoI medical schools. Pre-clinical and clinical oncology teaching was delivered over a median of 2 weeks (IQR 1–6), although only 9 (27%) of 34 responding medical schools had a standalone RO module. RO teaching was most commonly delivered in clinics or wards (n = 26 and 25 respectively). Few medical schools provided teaching on the biological basis for radiotherapy (n = 11) or the RO career pathway (n = 8), and few provide teaching delivered by non-medical RO multidisciplinary team members. There was evidence of short- and long-term disruption to RO teaching from COVID-19. Conclusions: RO teaching in the UK and RoI is limited with minimal coverage of relevant theoretical principles and little exposure to radiotherapy departments and their non-medical team members. The COVID-19 pandemic risks exacerbating trainee doctors’ already constrained exposure to radiotherapy. Advances in knowledge: This study provides the first analysis of radiotherapy-related teaching in the UK and RoI, and the first to explore the impact of the COVID-19 pandemic on radiationoncology teaching.