The healthcare sector faces a multitude of challenges such as, among others, an aging population, increasing demand for personalised care, a shortage of health professionals, suboptimal task allocation, and rising operational costs. While not a panacea per se, digitisation does offer substantial opportunities to help in many of the challenges faced by the healthcare sector [1,2,3]. Personal Health System (PHS) Technologies, including health data monitoring, intelligent processing, evidence-based interventions, and active patient engagement [4], can mitigate clinical errors, aid healthcare professionals, improve information management, and enhance patient access to health services [5]. However, the integration of digital technologies and data sharing into existing healthcare practices often does not fit well with established institutional norms, with data sharing in healthcare being perhaps one of the most prominent "headache" topics [6]. Data sharing in healthcare often conflicts with deeply rooted values like privacy, trust, and control [7], and with ethical codes and GDPR regulations regarding doctor-patient confidentiality and healthcare data classification. Actors in the healthcare domain encounter various challenges related to data-driven healthcare innovations, including a lack of trust to share data, concerns about personal health data re-identification, strict or unclear privacy regulations, and limited governance frameworks for data access and security [8].

In both academia and industry, secure and privacy-friendly digital data sharing solutions have been developed that are tailored to institutional health practices in a ‘by design’ [9,10,11,12,13] approach. Privacy-Enhancing Technologies (PETs), referring to a group of different technologies, play a significant role in this effort, aiming to enhance both privacy and security. This, in turn, facilitates data sharing within existing data technologies where privacy and security serve as limiting factors. PETs contain various techniques, including anonymisation, pseudonymisation, and data attribute obfuscation, often relying on mathematical algorithms and cryptographic methods for cross-organisational analysis while safeguarding privacy [14, 15]. For a comprehensive understanding of PETs, we encourage the readers to explore the resources provided by the Information Commissioner's Office [16]. Over the last decade, PETs have become more accessible and commonplace [11], moving from lab-and experimental settings towards the market [14, 17]. Yet, despite progress made on PET techniques, many PET developments seem to remain at the proof-of-concept stage [14, 18]. Resolving the privacy, trust, and control issues described above through technology design alone may not suffice for the adoption of digital solutions in the healthcare sector. Given that PETs offer a broad range of solutions that mitigate or resolve privacy and trust-related concerns in healthcare, it raises the question of why their adoption is not faster and more widespread.

The widespread adoption of PETs remains elusive, as indicated by Klymenko et al. [19]. Well-known challenges in technology adoption and diffusion persist [2, 6, 11, 16]. Achieving widespread adoption requires research into methods that extend beyond mere technical constraints [20, 21]. The Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS), based on a comprehensive review [22], describes seven key domains for assessing healthcare technologies: the condition or illness, the technology, the value proposition, the adopter system, the organisation(s), the wider context, and the interaction and mutual adaptation between all these domains over time. This comprehensive framework, including various domains and their dynamic evolution, is a lens to analyse non-adoption of technologies in specific contexts. We utilize the NASSS framework to evaluate an ongoing innovation and adoption project in the Netherlands focused on PETs within the healthcare domain. This framework enables us to contextualize the case study and frame discussions and perspectives from the various stakeholders involved.

Recent literature emphasises the importance of co-creation for technology adoption. The adoption of PETs in healthcare involves the collaboration of actors within various disciplines. It extends beyond health science and requires expertise in areas such as privacy, data science, and data infrastructure. Consequently, PETs are integrated into a broader multi-industry value system, often referred to as a 'value constellation' [23]. Due to the high interdependencies between disciplines, creating change within this constellation cannot be established with one discipline. Therefore, to ensure the integration of technology within the healthcare domain, several recent studies advocate for the incorporation of diverse perspectives during the developmental phase, through user engagement and application of interdisciplinary frameworks [24,25,26]. However, co-creation in a multidisciplinary setting also poses its own challenges, as different actors bring different perceptions of the problem, perspectives, interests and values that are not always congruent [27].

Transdisciplinary (TD) is an emerging field focused on co-creation in complex value-constellations. It stresses the need for interdisciplinary consciousness to enable change, which involves ‘a kind of know-how exhibited by individuals involved in a group process who can actively empathise with different disciplinary perspectives’ [28]. TD methods such as frame reflection and the dynamic learning agenda facilitate reflection-in-action, enhancing the ability to switch between different frames to solve unique problems [28]. Frame reflection helps to uncover patterns in first-order reflection (problem-solving) and second-order reflection (values and assumptions). The dynamic learning agenda promotes mutual learning by collectively identifying, analysing, and prioritising common challenges. It stimulates emerging designs, learning from experiences of others, participation of stakeholders, and facilitation of dialogue and reflection [29]. These methods prioritise collaborative framing and action, without the need for consensus, emphasising the collective deciphering of complex topics by connecting previously unrelated elements [30]. TD methods stand out from co-creation methodologies by including a wider range of disciplines, and integrating diverse knowledge and expertise beyond the academic field, thus addressing the challenges faced by PET adoption, particularly in bridging the academia-industry gap.

Most studies that have investigated the challenges of technology adoption in healthcare [3, 5, 6, 8, 19, 22, 31,32,33,34] primarily focused on identifying these obstacles. As two key examples, Witjas-Paalberends et al. [8] outlined key challenges faced by multidisciplinary teams adopting digital technologies in healthcare, including managing data variety, addressing data sharing and quality issues, implementing effective data management strategies, and overcoming a lack of big data skills. They proposed that adopting best practices, fostering trust, and reducing conservatism in the field could accelerate the integration of advanced big-data technologies and methods into collaborative healthcare innovations. Similarly, Klymenko et al. [19] emphasised the gap between academia and industry regarding PETs specifically, highlighting the need for tailored education on PETs to navigate privacy compliance complexities. Future steps involve creating learning materials and deploying e-learning platforms to bridge academia-industry gap. While these studies shed light on implementation hurdles and provide recommendations, the underlying value-driven reasons why current attempts to overcome these challenges fail remain unclear.

In this study we explore whether sustainable adoption of PETs in the health domain is impacted by conflicting logics and disciplinary disparities. By applying TD approaches to a case study around PET development in the Dutch health care sector, we explore how these approaches can facilitate collaboration and mutual learning to overcome these hurdles. The purpose of this study is to reveal, through the application of the aforementioned TD methods, the following objectives: a) underlying value misalignments and b) if such methods help in overcoming such misalignments to feed mutual learning and c) whether such mutual learning has an effect on alleviating barriers for adoption. It thereby aims to contribute to existing literature on barriers for PET adoption, as well as to literature of TD approaches for leading complex innovation processes. Diverging from previous studies on technology adoption in healthcare, this research adopts a collaborative perspective to delve into the persistence of these challenges and explore strategies for overcoming them.