This is the first set of multidisciplinary QIs for HNC care, to assess quality of integrated care agreed by patients and professionals. This set can be used to build other oncological quality dashboards for integrated care.

BackgroundAudit and feedback on professional practice and health care outcomes are the most often used interventions to change behaviour of professionals and improve quality of health care. However, limited information is available regarding preferred feedback for patients, professionals and health insurers.ObjectiveInvestigate the (differences in) preferences of receiving feedback between stakeholders, using the Dutch Head and Neck Audit as an example.MethodsA total of 37 patients, medical specialists, allied health professionals and health insurers were interviewed using semi‐structured interviews. Questions focussed on: “Why,” “On what aspects” and “How” do you prefer to receive feedback on professional practice and health care outcomes?ResultsAll stakeholders mentioned that feedback can improve health care by creating awareness, enabling self‐reflection and reflection on peers or colleagues, and by benchmarking to others. Patients prefer feedback on the actual professional practice that matches the health care received, whereas medical specialists and health insurers are interested mainly in health care outcomes. All stakeholders largely prefer a bar graph. Patients prefer a pie chart for patient‐reported outcomes and experiences, while Kaplan‐Meier survival curves are preferred by medical specialists. Feedback should be simple with firstly an overview, and 1‐4 times a year sent by e‐mail. Finally, patients and health professionals are cautious with regard to transparency of audit data.ConclusionsThis exploratory study shows how feedback preferences differ between stakeholders. Therefore, tailored reports are recommended. Using this information, effects of audit and feedback can be improved by adapting the feedback format and contents to the preferences of stakeholders.

IntroductionInnovations in head and neck cancer (HNC) treatment are often subject to economic evaluation prior to their reimbursement and subsequent access for patients. Mapping functions facilitate economic evaluation of new treatments when the required utility data is absent, but quality of life data is available. The objective of this study is to develop a mapping function translating the EORTC QLQ-C30 to EQ-5D-derived utilities for HNC through regression modeling, and to explore the added value of disease-specific EORTC QLQ-H&N35 scales to the model.MethodsData was obtained on patients with primary HNC treated with curative intent derived from two hospitals. Model development was conducted in two phases: 1. Predictor selection based on theory- and data-driven methods, resulting in three sets of potential predictors from the quality of life questionnaires; 2. Selection of the best out of four methods: ordinary-least squares, mixed-effects linear, Cox and beta regression, using the first set of predictors from EORTC QLQ-C30 scales with most correspondence to EQ-5D dimensions. Using a stepwise approach, we assessed added values of predictors in the other two sets. Model fit was assessed using Akaike and Bayesian Information Criterion (AIC and BIC) and model performance was evaluated by MAE, RMSE and limits of agreement (LOA).ResultsThe beta regression model showed best model fit, with global health status, physical-, role- and emotional functioning and pain scales as predictors. Adding HNC-specific scales did not improve the model. Model performance was reasonable; R2 = 0.39, MAE = 0.0949, RMSE = 0.1209, 95% LOA of -0.243 to 0.231 (bias -0.01), with an error correlation of 0.32. The estimated shrinkage factor was 0.90.ConclusionsSelected scales from the EORTC QLQ-C30 can be used to estimate utilities for HNC using beta regression. Including EORTC QLQ-H&N35 scales does not improve the mapping function. The mapping model may serve as a tool to enable cost-effectiveness analyses of innovative HNC treatments, for example for reimbursement issues. Further research should assess the robustness and generalizability of the function by validating the model in an external cohort of HNC patients.

Monitoring the identified themes in integrated HNC care, fitting in the Picker model, will enable us to respond better to the needs and preferences of patients, and patient-centred care in oncological care can be enhanced.

Background: Monitoring and effectively improving oncologic integrated care requires dashboard information based on quality registrations. The dashboard includes evidence-based quality indicators (QIs) that measure quality of care. This study aimed to assess the quality of current integrated head and neck cancer care with QIs, the variation between Dutch hospitals, and the influence of patient and hospital characteristics. Methods: Previously, 39 QIs were developed with input from medical specialists, allied health professionals, and patients' perspectives. QI scores were calculated with data from 1,667 curatively treated patients in 8 hospitals. QIs with a sample size of >400 patients were included to calculate reliable QI scores. We used multilevel analysis to explain the variation. Results: Current care varied from 29% for the QI about a case manager being present to discuss the treatment plan to 100% for the QI about the availability of a treatment plan. Variation between hospitals was small for the QI about patients discussed in multidisciplinary team meetings (adherence: 95%, range 88%-98%), but large for the QI about malnutrition screening (adherence: 50%, range 2%-100%). Higher QI scores were associated with lower performance status, advanced tumor stage, and tumor in the oral cavity or oropharynx at the patient level, and with more curatively treated patients (volume) at hospital level. Conclusions: Although the quality registration was only recently launched, it already visualizes hospital variation in current care. Four determinants were found to be influential: tumor stage, performance status, tumor site, and volume. More data are needed to assure stable results for use in quality improvement.