A taskforce for national improvement of innovation implementation in radiotherapy.

BACKGROUND AND PURPOSE: Previous research among Dutch radiotherapy centres (RTCs) showed that 69% of innovations was simultaneously implemented in 7/19 centres, with a success rate of 51%. However, no structure to share lessons learned about the implementation process existed. Therefore, a national Taskforce Implementation (TTI) was raised to stimulate efficient implementation of innovations. The aim of the current study was to develop and pilot-evaluate a website for facilitating mutual learning on implementation issues. MATERIAL AND METHODS: First, we made an inventory in all Dutch RTCs on their 10 most valuable innovations between 2019 and 2022. In-depth interviews, structured according to the Consolidated Framework for Implementation Research, were performed on the four most mentioned topics. A website was built, and pilot evaluated 1 year after the launch, using a qualitative survey amongst the TTI members. RESULTS: In 13/18 centres, 19 interviews were conducted on 1) automation, 2) patient participation, 3) adaptive radiotherapy 4) surface guided radiotherapy and tracking. Most innovations (13/16) were implemented with a delay, with many comparable challenges: e.g. shortage of personnel (7/16) and prioritization of projects (9/16). The website allows users to upload and search for projects, including implementation experiences. After 1 year, 14 projects were uploaded. The qualitative evaluation was largely positive with room for improvement, i.e.75 % would recommend the website to others. CONCLUSION: This study showed that RTCs experience comparable challenges when implementing innovations, thereby underlining the need for a platform to share implementation-lessons learned. The first concept of this platform was evaluated positively.

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

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.

Clues to address barriers for access to proton therapy in the Netherlands.

BACKGROUND AND PURPOSE: The Netherlands has National Indication Protocols on proton therapy (PT) to select patients who benefit most from PT. However, referrals to proton therapy centres (PTCs) are lagging. The objective of this research is to identify the barriers for access to PT and to design interventions to address these barriers. MATERIAL AND METHODS: We conducted a nationwide survey among radiation oncologists (ROs), and semi- structured in-depth interviews with ROs and patients. Subsequently, four workshops were held, in which ROs from one PTC and ROs from referring hospitals participated. The workshops were based on design-thinking research, where ideas were co-created on a multidisciplinary basis to encourage joint problem ownership. Kruskal Wallis and X2 tests were used to analyze data. RESULTS: The most prominent barriers mentioned by ROs were patient selection, poor logistics, and logistical worries about the combination of radiation treatment with chemotherapy. Patients pointed out the inefficient coordination between organisations, poor communication, travel issues and discomfort during treatment. Clues to increase referrals revealed the need for additional tools for patient selection and innovative ways to improve logistics. A case manager was identified as beneficial to the patients' journey as part of a multidisciplinary approach. Such an approach should include the active involvement of medical oncologists, surgeons and pulmonologists. CONCLUSION: Barriers for access to PT were identified and prioritized in the inter-organisational care- pathway of proton therapy patients in The Netherlands. Innovative solutions were co- designed to solve the barriers.

Analyzing the determinants to accept a virtual assistant and use cases among cancer patients: a mixed methods study.

BACKGROUND: Technological progress in artificial intelligence has led to the increasing popularity of virtual assistants, i.e., embodied or disembodied conversational agents that allow chatting with a technical system in a natural language. However, only little comprehensive research is conducted about patients' perceptions and possible applications of virtual assistant in healthcare with cancer patients. This research aims to investigate the key acceptance factors and value-adding use cases of a virtual assistant for patients diagnosed with cancer. METHODS: Qualitative interviews with eight former patients and four doctors of a Dutch radiotherapy institute were conducted to determine what acceptance factors they find most important for a virtual assistant and gain insights into value-adding applications. The unified theory of acceptance and use of technology (UTAUT) was used to structure perceptions and was inductively modified as a result of the interviews. The subsequent research model was triangulated via an online survey with 127 respondents diagnosed with cancer. A structural equation model was used to determine the relevance of acceptance factors. Through a multigroup analysis, differences between sample subgroups were compared. RESULTS: The interviews found support for all factors of the UTAUT: performance expectancy, effort expectancy, social influence and facilitating conditions. Additionally, self-efficacy, trust, and resistance to change, were added as an extension of the UTAUT. Former patients found a virtual assistant helpful in receiving information about logistic questions, treatment procedures, side effects, or scheduling appointments. The quantitative study found that the constructs performance expectancy (ß = 0.399), effort expectancy (ß = 0.258), social influence (ß = 0.114), and trust (ß = 0.210) significantly influenced behavioral intention to use a virtual assistant, explaining 80% of its variance. Self-efficacy (ß = 0.792) acts as antecedent of effort expectancy. Facilitating conditions and resistance to change were not found to have a significant relationship with user intention. CONCLUSIONS: Performance and effort expectancy are the leading determinants of virtual assistant acceptance. The latter is dependent on a patient's self-efficacy. Therefore, including patients during the development and introduction of a VA in cancer treatment is important. The high relevance of trust indicates the need for a reliable, secure service that should be promoted as such. Social influence suggests using doctors in endorsing the VA.

Breast cancer patients' most important quality of life themes for a radiotherapy decision aid.

BACKGROUND AND AIM: The BRASA patient decision aid (BRASA-PtDA) facilitates shared decision making for breast cancer patients (BCPs) facing a radiotherapy treatment decision. During evaluations, patients indicated the wish for quantitative information on side effects. Therefore, this study assessed BCPs opinion on which and how information on side effects should be incorporated in the BRASA-PtDA. METHODS: A workshop was organized with BCPs (n = 9), researchers (n = 5) and clinicians (n = 3). Subsequently, a survey was sent to BCPs (n = 744) investigating the generalisability of the workshop findings, and posing additional questions. The survey entailed multiple choice questions on quality of life themes, the use of a decision aid and risk communication. RESULTS: The workshop revealed BCPs wish for a layered, all encompassing information system. Information on the impact of side effects on daily life was preferred above the risk of these side effects. The survey revealed that important quality of life (QoL) themes were having energy (81%; n = 605), arm function (61%; n = 452), pain (55%; n = 410). Despite the focus on qualitative effects in the workshop, 89% of the survey respondents also wanted to be informed on individualized risks of side effects. 54% Of the survey respondents had never heard of a PtDA. CONCLUSIONS: BCPs preferred information on the impact of side effects, but also their individualized risks on side effects. Most important QoL themes were having enough energy, arm function and pain. Consequently, the BRASA-PtDA should be reshaped, starting with quality of life themes, rather than side effects.

The barriers and facilitators of radical innovation implementation in secondary healthcare: a systematic review.

PURPOSE: This study aimed to identify the barriers and facilitators related to the implementation of radical innovations in secondary healthcare. DESIGN/METHODOLOGY/APPROACH: A systematic review was conducted and presented in accordance with a PRISMA flowchart. The databases PubMed and Web of Science were searched for original publications in English between the 1st of January 2010 and 6th of November 2020. The level of radicalness was determined based on five characteristics of radical innovations. The level of evidence was classified according to the level of evidence scale of the University of Oxford. The Consolidated Framework for Implementation Research was used as a framework to classify the barriers and facilitators. FINDINGS: Based on the inclusion and exclusion criteria, nine publications were included, concerning six technological, two organizational and one treatment innovation. The main barriers for radical innovation implementation in secondary healthcare were lack of human, material and financial resources, and lack of integration and organizational readiness. The main facilitators included a supportive culture, sufficient training, education and knowledge, and recognition of the expected added value. ORIGINALITY/VALUE: To our knowledge, this is the first systematic review examining the barriers and facilitators of radical innovation implementation in secondary healthcare. To ease radical innovation implementation, alternative performance systems may be helpful, including the following prerequisites: (1) Money, (2) Added value, (3) Timely knowledge and integration, (4) Culture, and (5) Human resources (MATCH). This study highlights the need for more high-level evidence studies in this area.

Factors predicting timely implementation of radiotherapy innovations: the first model.

OBJECTIVE: The improvement of radiotherapy depends largely on the implementation of innovations, of which effectivity varies widely. The aim of this study is to develop a prediction model for successful innovation implementation in radiotherapy to improve effective management of innovation projects. METHODS: A literature review was performed to identify success factors for innovation implementation. Subsequently, in two large academic radiotherapy centres in the Netherlands, an inventory was made of all innovation projects executed between 2011 and 2017. Semi-structured interviews were performed to record the presence/absence of the success factors found in the review for each project. Successful implementation was defined as timely implementation, yes/no. Cross-tables, Χ2 tests, t-tests and Benjamin-Hochberg correction were used for analysing the data. A multivariate logistic regression technique was used to build a prediction model. RESULTS: From the 163 identified innovation projects, only 54% were successfully implemented. We found 31 success factors in literature of which 14 were significantly related to successful implementation in the innovation projects in our study. The prediction model contained the following determinants: (1) sufficient and competent employees, (2) complexity, (3) understanding/awareness of the project goals and process by employees, (4) feasibility and desirability. The area Under the curve (AUC) of the prediction model was 0.86 (0.8-0.92, 95% CI). CONCLUSION: A prediction model was developed for successful implementation of innovation in radiotherapy. ADVANCES IN KNOWLEDGE: This prediction model is the first of its kind and, after external validation, could be widely applicable to predict the timely implementation of radiotherapy innovations.

Electronic Health Record implementation in a large academic radiotherapy department: Temporarily disruptions but long-term benefits.

PURPOSE: To study the number of disruptions in patient processes in a radiotherapy centre after the replacement of an Electronic Health Record (EHR), integrating information tools for patient care and billing. METHODS: Our self-made Electronic Medical Record was replaced by a new EHR, including clinical path and workflow-management. A social-technological approach was used to reduce complexity. We measured disruptions in patient processes by the number and type of EHR related root causes and EHR-related incidents that reached patients, in our patient safety system 12 months before implementing the new EHR, 6 months after implementation (transition period) and 24 months after the transition period. We used Mann-Whitney U and X² tests to compare data before and after implementation. RESULTS: An increase of disruptions occurred only temporarily during 6 months. After this period, the number stabilized to the level before implementation while having more functionalities and benefits. Neither the number nor the severity of incidents reaching patients increased. CONCLUSIONS: Disruptions in patient processes are considered as a main barrier for implementing an EHR. Using a social/technical approach, the increase in disruptions did only temporarily occur and did not reach patients. We think it is important to share this insight with physicians because literature shows that their long-term opinion regarding the usefulness of the EHR is often based on the experience in the first months after implementation. Management of expectations is recommended. ADVANCES IN KNOWLEDGE: This study is the first of its kind measuring long-term effects of EHR on patient processes in radiotherapy.

What is the impact of innovation on output in healthcare with a special focus on treatment innovations in radiotherapy? A literature review.

OBJECTIVE: To analyse how often innovations in healthcare are evaluated regarding output, especially in radiotherapy. Output was defined as either survival, toxicity, safety, service, efficiency or cost-effectiveness. METHODS: A systematic literature review was conducted, using three search strategies: (1) innovations in general healthcare; (2) radiotherapy-specific innovations, i.e. organizational innovations and general implementation of innovations; (3) innovations per tumour group/radiotherapy technique. Scientific levels were classified according to the system used in European Society for Medical Oncology guidelines. Finally, we calculated the percentage of implemented innovations in Dutch radiotherapy centres for which we found evidence regarding output in the literature review. RESULTS: Only 94/1072 unique articles matched the inclusion criteria. Significant results on patient outcome, service or safety were reported in 65% of papers, which rose to 76% if confined to radiotherapy reviews. A significant technological improvement was identified in 26%, cost-effectiveness in 10% and costs/efficiency in 36% of the papers. The scientific level of organizational innovations was lower than that of clinical papers. Dutch radiotherapy treatment innovations were adequately evaluated on outcome data before implementation in clinical routine in a minimum of 64-92% of cases. CONCLUSION: Only few studies report on output when considering innovations in general, but radiotherapy reviews give a reasonably good insight into innovation output effects, with a higher level of evidence. In Dutch radiotherapy centres only small improvements are possible regarding evaluation of treatment innovations before implementation. Advances in knowledge: This study is the first of its kind measuring how innovations are evaluated in scientific literature, before implementation in clinical practice.

What is the degree of innovation routinely implemented in Dutch radiotherapy centres? A multicentre cross-sectional study.

OBJECTIVE: To study the implementation of innovation activities in Dutch radiotherapy (RT) centres in a broad sense (product, technological, market and organizational innovations). METHODS: A descriptive cross-sectional study was conducted in 15 Dutch RT centres. A list of innovations implemented from 2011 to 2013 was drawn up for each centre using semi-structured interviews. These innovations were classified into innovation categories according to previously defined innovation indicators. Where applicable, each innovation was rated by each centre on the effort required to implement it and on its expected effects, to get an impression of how far reaching and radical the innovations were and to be able to compare the number of innovations between centres. RESULTS: The participating RT centres in the Netherlands implemented 12 innovations per year on average (range 5-25); this number was not significantly different for academic (n = 13) or non-academic centres (n = 10). Several centres were dealing with the same innovations at the same time. The average required effort and expected output did not differ significantly between product, technological and organizational innovation or between academic and non-academic centres. CONCLUSION: The number of innovations observed per centre varied across a large range, with a large overlap in terms of the type of innovations that were implemented. Registering innovations using the innovation indicators applied in our study would make it possible to improve collaboration between centres, e.g. with common training modules, to avoid duplication of work. Advances in knowledge: This study is the first of its kind investigating innovation implementation in RT in a broad sense.