External validation of a prediction model for timely implementation of innovations in radiotherapy

Radiotherapy and Oncology, vol. 179, pp. 109459

Abstract

BACKGROUND AND PURPOSE: The aim of this study was to externally validate a model that predicts timely innovation implementation, which can support radiotherapy professionals to be more successful in innovation implementation. MATERIALS AND METHODS: A multivariate prediction model was built based on the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) criteria for a type 4 study (1). The previously built internally validated model had an AUC of 0.82, and was now validated using a completely new multicentre dataset. Innovation projects that took place between 2017-2019 were included in this study. Semi-structured interviews were performed to retrieve the prognostic variables of the previously built model. Projects were categorized according to the size of the project; the success of the project and thepresence of pre-defined success factors were analysed. RESULTS: Of the 80 included innovation projects (32.5% technological, 35% organisational and 32.5% treatment innovations), 55% were successfully implemented within the planned timeframe. Comparing the outcome predictions with the observed outcomes of all innovations resulted in an AUC of the external validation of the prediction model of 0.72 (0.60-0.84, 95% CI). Factors related to successful implementation included in the model are sufficient and competent employees, desirability and feasibility, clear goals and processes and the complexity of a project. CONCLUSION: For the first time, a prediction model focusing on the timely implementation of innovations has been successfully built and externally validated. This model can now be widely used to enable more successful innovation in radiotherapy.

Authors 8

1. Rachelle Swart

   Maastricht University Medical Centre · Maastricht University

   Affiliation as printed

   Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands. Electronic address: r.swart@maastrichtuniversity.nl

2. Rianne Fijten

   Maastricht University Medical Centre · Maastro Clinic

   Affiliation as printed

   Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands

3. Liesbeth Jorinne Boersma

   Maastricht University Medical Centre · Maastro Clinic

   Affiliation as printed

   Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands

4. Petros Kalendralis

   Maastricht University Medical Centre · Maastro Clinic

   Affiliation as printed

   Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands

5. Myra D Behrendt

   Tilburg University

   Affiliation as printed

   Tilburg School of Economics and Management, Tilburg University, Tilburg, The Netherlands

6. Martijn Ketelaars Aachen Department of Radiation Oncology

   Leiden University Medical Center

   Affiliation as printed

   Department of Radiation Oncology, Leiden University Medical Center, Leiden, The Netherlands

7. Cheryl Roumen

   Maastricht University Medical Centre · Maastro Clinic

   Affiliation as printed

   Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands

8. Maria J.G. Jacobs

   Tilburg University

   Affiliation as printed

   Tilburg School of Economics and Management, Tilburg University, Tilburg, The Netherlands