Keywords

Introduction

Head and neck cancer (HNC) and its treatment can have a major impact on the physical, psychological and social aspects of health-related quality of life [1,2,3,4,5,6]. The overall aim of supportive cancer care is to reduce symptoms and improve health related quality of life in people living with and beyond cancer. In many countries, government policy statements and national guidelines reflect broad scientific and societal support for an integrated approach to supportive care, including rehabilitation, psychosocial care and lifestyle interventions [7,8,9,10]. Currently, much effort is undertaken to use the concept of value based health care to optimize care, which can be translated into three components: tailoring of care to the needs of the individual patient (patient-centered care), offering effective care (quality care) and offering cost-effective care (affordable care) [11, 12]. Self-management can be an effective part of value based health care and includes, among others, navigating across the cancer care trajectory, managing biopsychosocial sequelae of cancer and its treatment, applying healthy lifestyle behavior to reduce cancer recurrence and late effect risks and adjusting to the end of life phase in case of incurable cancer [13, 14]. Patient reported outcome measures are increasingly used to monitor symptoms and health related quality of life over time and to identify those patients who might benefit from supportive care [15, 16]. However, the diversity and unconnectedness of available supportive care options remain a problem and many patients have unmet needs [17]. Digital technologies may facilitate accessible and sustainable supportive care in cancer. This paper describes the use of digital technologies in supportive care, which is of relevance considering the ongoing shortage in healthcare services and the increasing incidence and survival rates in head and neck cancer.

Supportive Care and Self-management

The Multinational Association of Supportive Care in Cancer (MASCC) defined supportive care as the prevention and management of the adverse effects of cancer and its treatment. It involves the provision of services to meet physical, psychosocial, informational, practical, spiritual and lifestyle needs from diagnosis and treatment to long-term survivorship or end-of-life care [18,19,20,21,22]. Supportive care needs are diverse and vary between people and also over time. It is estimated that over 60% of HNC patients have unmet supportive care needs [23,24,25,26]. To improve the benefits of supportive care, people with cancer are expected to adopt an active role in managing their own care. Self-management is defined as “the individual’s ability to manage the symptoms, treatment, physical and psychosocial consequences and lifestyle changes inherent in living with a chronic condition” [27] and “those tasks that individuals undertake to deal with the medical, role and emotional management of their health condition(s)” [28]. Self-management support is a dynamic, interactive and daily process, to help patients to engage in three self-management tasks—medical management, role management and emotional management—and six self-management skills—problem solving, decision making, resource utilization, the formation of a patient-provider partnership, action planning and self-tailoring [29]. In cancer, self-management interventions such as psycho-educational interventions, exercise programs and healthy lifestyle courses, aim to achieve optimal health and well-being, while living with and beyond cancer [30]. Benefits of self-management include reduction of symptoms, improvement of health related quality of life and its potential to be cost-effective [31, 32]. To better integrate self-management as part of high quality supportive cancer care, the Global Partners on Self-Management in Cancer described six priority areas for action [32] (Table 22.1). It is clear that a lot of work has yet to be done to achieve the goals of these actions. A promising development that may facilitate supportive care including self-management in cancer is the use of digital technologies.

Table 22.1 Six priority areas for action to better integrate self-management as part of high quality supportive cancer care, as described by the Global Partners on Self-Management in Cancer [32]
Full size table

Digital Supportive Care

Digital technologies are part of our daily life and the use of these technologies in eHealth to ease the living with and beyond head and neck cancer is growing. eHealth is defined as “an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the internet and related technologies” [33, 34]. Behavioral intervention technologies can be used in supportive cancer care to support behavior change related to physical, psychological and social problems. Examples are websites, mobile apps and wearable devices to help users address or change behaviors, cognitions and emotional states. Interventions use varying formats, including text, audio, video or games. Some behavioral intervention technologies are designed to be used by users themselves (fully automated behavioral intervention technologies). Others are intended to be used as a component of care that is delivered by a health care provider (adjunctive behavioral intervention technologies) or as a key aspect of care with support from a health care provider (guided behavioral intervention technologies) [35].

Evidence about clinical and cost-effectiveness of digital technologies in supportive cancer care is growing but still limited and implementation remains a challenge [36]. To enhance adoption of digital care in clinical practice, it is essential to integrate research methods during both the development and evaluation of eHealth applications. By using participatory design methods in the development of digital care applications, the effectiveness and usefulness of these applications can be optimized. Participatory design is a method that actively involves users and other stakeholders in the design process of technological solutions, to make sure that the application fits the users’ needs [37,38,39]. Participatory design generally consists of several iterative phases: (1) needs assessment or contextual inquiry: the identification of end users needs through active participation of users, (2) idea generation or value specification: generating ideas following the identification of needs, gaining insight into the perceived benefits and barriers of the application and define requirements, resulting in prototypes that address the end users’ needs, (3) testing and retesting, the design phase: testing the prototypes in pilot studies and further developing them before implementation, (4) operationalization: the phase in which the application is introduced into practice and (5) evaluation: assessment of effectiveness and contribution to the quality of care after implementation. The RE-AIM framework is often used to research the reach, effectiveness, adoption, implementation and maintenance of (digital) care options [40].

As an example of developing, researching and implementing digital supportive care, we describe our approach on an application called “Oncokompas”.

The Case of Oncokompas

Oncokompas is a fully automated self-management application to monitor physical, psychological, social and spiritual domains of health related quality of life and lifestyle, to provide personalized information on health related quality of life and lifestyle, and to support people with and beyond cancer in finding optimal supportive care, adjusted to their personal well-being and preferences. A description of Oncokompas is provided in text Box 1.

Description of Oncokompas

The web-based self-management application ‘Oncokompas’ was developed with the aim to support people living with and beyond cancer in self-management by monitoring health-related quality of life (HRQOL), cancer-generic and tumor-specific symptoms and life-style, providing feedback and information on their personal scores, as well as a personalized overview of supportive care options.

Oncokompas consists of three components: Measure, Learn and Act. Based on patient-reported outcome measures (PROMs) (Measure), users get tailored information on multiple HRQOL, symptoms and lifestyle topics (Learn), and a personalized overview of supportive care options (Act).

Users log in at the Oncokompas website, and first complete a short questionnaire on e.g. marital status, treatment type and time since treatment (before, during or after treatment), to determine which topics are relevant. An overview with the relevant topics is provided from which users can choose which they want to complete. There are over 100 topics in Oncokompas.

In the component ‘Measure’, users complete PROMs for each of the selected topics. Oncokompas is a dynamic system, i.e. based on users’ answers, follow-up questions or more in-depth questions are presented when necessary. Data from the Measure component is processed in real-time.

In the Learn component, users obtain an overview of their PROM scores. Feedback is provided by means of a 3-color system: green (no elevated well-being risks), orange (elevated well-being risks), and red (seriously elevated well-being risks). Users receive personalized information based on their PROM scores and background information on the topic, when they click on the topic. In case of (seriously) elevated well-being risks (orange or red scores), also tips and self-care advice is given, to support them in improving symptom burden themselves.

In the Act component, users obtain a personalized overview of supportive care options, tailored to their wellbeing risk and preferences. If the user has an orange score, self-help or low-intensive interventions are suggested, while contact with a medical specialist or their general practitioner or more intensive interventions are advised if the user has a red score.

Users can access Oncokompas at any time, from any place and Oncokompas can be used multiple times. When users login again, they can see the overview of PROMS scores of their previous visit and read the corresponding information in the components Learn and Act again, or they can complete Oncokompas once again and start with the component Measure again. When used repeatedly, users can see an overview of their scores over time. Repeated use is encouraged by sending reminders by e-mail every two months.

Oncokompas was developed using a stepwise, iterative and participatory design approach. People living with and beyond cancer, care providers and health care assurance companies were involved and several studies were conducted to optimally fit Oncokompas to patients’ and care providers’ preferences. The development consisted of five steps:

  1. (1)

    Selection of relevant topics,

  2. (2)

    Selection of validated questionnaires (Patient Reported Outcome Measures; PROMs),

  3. (3)

    Composing of algorithms connecting PROM scores with well-being profiles and advices,

  4. (4)

    Writing texts for well-being profiles and advices,

  5. (5)

    Composing of strategies for self-help or seeking supportive care.

Steps 1 till 5 were carried out by the research group together with a team of experts including health care providers (medical specialist, nurse specialist and paramedics) and people living with and beyond cancer (representatives of patients associations and patients/survivors from participating medical centers). The PROMs were selected based on the COSMIN criteria (Consensus-based Standards for the selection of health Measurement Instruments) (www.cosmin.nl). A literature search was carried out to identify PROMs as candidates for Oncokompas according to the COSMIN checklist. Meetings were organized in which the expert teams were consulted regarding the results of the literature search and COSMIN checklist. In case a PROM did not fulfil the necessary criteria, the expert team consented on selecting another PROM. Algorithms were developed that link the results of the PROMs of a user to personalized feedback on the symptoms (information and psychoeducation) and to advices on self-management and professional care. A national database with supportive care options was built in Oncokompas to allow personalized access to supportive care including self-help. In Oncokompas, users receive tailored information on their physical, psychological and social functioning, spiritual issues and lifestyle. Users with minor problems are informed on self-help interventions and on professional care in case of major problems (a stepped care approach). Based on the positive results of needs assessments among cancer patients and care professionals [41, 42], a plan of requirements for Oncokompas was formulated and clarified to the designer and programmers, who used their expertise to translate this plan into a prototype of Oncokompas. Usability tests identified some weaknesses in the user interface that resulted in adjustments, e.g. clearer user instructions. Studies among survivors of head and neck cancer and breast cancer showed that Oncokompas was feasible with an adoption grade of 64% and 75% respectively and a mean satisfaction score of 7.3 and 7.6 on a scale of 10 [43, 44].

From 2017 until 2021, three randomized controlled trials (RCTs) were conducted to investigate the reach, efficacy and cost-utility of Oncokompas among cancer survivors, among incurably ill patients and among their partners [45,46,47,48,49,50,51]. Main reasons for not reaching or using Oncokompas were no access to the internet, no symptom burden, no supportive care needs or lack of time [49,50,51]. Users selected many cancer-generic and tumor-specific topics to address, indicating added value of the wide range of available topics [50]. Oncokompas did not improve the amount of knowledge, skills and confidence for self-management. Among cancer survivors, the application improved health related quality of life and tumor-specific symptom burden [48] and was not more expensive than usual survivorship care [49]. Among incurably ill cancer patients and their partners, no significant effect of Oncokompas was found based on the RCTs (partly carried out during the COVID-10 pandemic) [51]; further publications are planned.

In 2015, we conducted a pilot study on the adoption and implementation of Oncokompas (at that time only available for cancer survivors) via care providers [52]. The study was carried out among 65 hospitals throughout the Netherlands. Health care providers filled out a questionnaire on the implementation of Oncokompas in their organization, consisting of study specific items and items based on the Measurement Instrument for Determinants of Innovations (MIDI) [53]. The MIDI comprises 29 determinants in 4 domains that predict the use of innovations: the innovation itself (Oncokompas), the user (healthcare professional), the organization (hospital) and socio-political context. In total, 20/65 eligible hospitals agreed to implement Oncokompas (adoption rate 31%). In these 20 adopting hospitals, the majority of the responding health care providers (44/61) indicated their patients were offered access to Oncokompas (implementation rate 72%). Comparing those health care providers who did and did not implement Oncokompas, the groups differed significantly on innovation-related (procedural clarity, complexity) and user-related determinants (importance of outcome expectations, professional obligation, social support and self-efficacy). After this study, we observed that maintenance was a problem and many hospitals stopped offering Oncokompas.

To better understand adoption and implementation, we investigated drivers of resistance among oncology nurses towards online self-management tools in cancer care [54]. Drawing from earlier research, combining clinical and marketing perspectives, the Resistance to Innovation model (RTI-model) was developed. The RTI-model distinguishes between passive and active resistance, which can be enhanced or reduced by functional drivers (incompatibility, complexity, lack of value, risk) and psychological drivers (role ambiguity, social pressure from the institute, peers and patients). Both types of drivers can be moderated by staff-, organization-, patient- and environment-related factors. In total, 2500 nurses were approached of which 285 responded (11%). In line with the RTI-model we found that passive and active resistance among oncology nurses towards (online) self-management tools were driven by both functional and psychological drivers. Passive resistance towards online self-management tools was enhanced by complexity, lack of value and role ambiguity and reduced by institutional social pressure. Active resistance was enhanced by complexity, lack of value and social pressure from peers and reduced by social pressure from the institute and patients. In contrast to what we expected, incompatibility with current routines was not a significant driver of either passive or active resistance. This study further showed that these drivers of resistance were moderated by expertise, managerial support and influence from external stakeholders (government). The conclusion was that passive and active resistance in oncology nurses towards online self-management tools for cancer patients are driven by functional and psychological drivers, which may depend on expertise, managerial support and governmental influence.

Several meetings were organized during the project with representatives of patient societies, health care professionals, researchers, health care assurance companies and the technology transfer office in Amsterdam to develop a dissemination and valorization plan assuring sustainability of Oncokompas, also beyond the timeframe of the project. This resulted in the current (2022) collaboration with Sananet, an eHealth provider which has taken Oncokompas in their portfolio. Dissemination of Oncokompas is, among others, promoted by contacts in hospitals, announcements of results and products in digital newsletters and through social media. However, the digital care market is difficult and further implementation and upscaling efforts need to be continued.

Conclusion

Digital technologies in supportive head and neck cancer care are not a promise but a fact. Research and development following a participatory design approach and the RE-AIM framework helps to deliver patient-centered, effective and efficient applications ready to be used either as adjunctive, guided or fully automated technology. Implementation and upscaling of evidence-based digital technologies in routine cancer care remains a challenge.