Conceptualizing the landscape of digital health entrepreneurship: a systematic review and research agenda

Weimar, Sascha Noel; Martjan, Rahel Sophie; Terzidis, Orestis Article — Published Version Conceptualizing the landscape of digital health entrepreneurship: a systematic review and research agenda Management Review Quarterly Provided in Cooperation with: Springer Nature Suggested Citation: Weimar, Sascha Noel; Martjan, Rahel Sophie; Terzidis, Orestis (2024) : Conceptualizing the landscape of digital health entrepreneurship: a systematic review and research agenda, Management Review Quarterly, ISSN 2198-1639, Springer International Publishing, Cham, Vol. 75, Iss. 2, pp. 1619-1667, https://doi.org/10.1007/s11301-024-00417-0 This Version is available at: https://hdl.handle.net/10419/323482 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Management Review Quarterly (2025) 75:1619–1667 https://doi.org/10.1007/s11301-024-00417-0 Conceptualizing thelandscape ofdigital health entrepreneurship: asystematic review andresearch agenda SaschaNoelWeimar1 · RahelSophieMartjan1· OrestisTerzidis1 Received: 26 July 2023 / Accepted: 2 March 2024 / Published online: 15 April 2024 © The Author(s) 2024 Abstract Digital health, which encompasses the use of digital technology to enhance and support healthcare, can potentially transform the healthcare industry. As healthcare systems struggle to provide high-quality care, entrepreneurs and startups have turned to digital health technology to create innovative products and services that aim at improving healthcare outcomes and reducing costs. This has made digital health entrepreneurship a major driving force for achieving good health and high-quality care. The study aims to analyze the current state of research in digital health entrepreneurship by identifying contributing disciplines and common research themes. A comprehensive literature review based on state-of-the-art definitions of digital health entrepreneurship was conducted to achieve this goal. A total of 164 articles met the final inclusion criteria, and the subsequent inductive analysis revealed the presence of three contributing disciplines and ten distinct themes: Academia Transfer and Education, Ecosystem and Stakeholder, Product and Business Development, Business Model, Classification, Management and Strategy, Regulation, Digital Technology, Implementation and Adoption, and Evaluation. These themes were organized into a conceptual framework depicting the internal and external building blocks of digital health entrepreneurship. The review highlights the importance of a structured understanding of the industry, including its business models and regulatory environment, as well as the role of the various healthcare stakeholders. This analysis can guide researchers and entrepreneurs seeking to navigate the digital health landscape and proposes further research avenues. Keywords Digital health· Telehealth· eHealth· Startup· Entrepreneurship· Innovation JEL Classification I100· K230· M130 * Sascha Noel Weimar sascha.w[email protected] 1 Institute forEntrepreneurship, Technology Management andInnovation (EnTechnon), Karlsruhe Institute ofTechnology (KIT), Karlsruhe, Germany 1620 S.N.Weimar et al. 1 Introduction andmotivation Worldwide healthcare systems face significant challenges, including rising healthcare costs (Wulfovich 2020), outcome problems (Herrmann etal. 2018), and aging populations with a high prevalence of multimorbidities (Bratan etal. 2022). To face these challenges, innovative healthcare solutions are a key social and economic priority (Bratan etal. 2022) and are crucial for achieving highquality healthcare (Friebe 2020). Accordingly, the United Nations has defined good health and well-being as one of the sustainable development goals (SDGs) (Bratan etal. 2022). Responding to these issues, healthcare systems are increasingly adopting precision medicine, disease prevention, and value-based healthcare, in which providers are rewarded for long-term health outcomes and the well-being of their patients (Friebe 2020). Digitalization has significantly impacted various industries, resulting in the emergence of previously unimaginable products and services (Sreenivasan and Suresh 2022). Several digital technologies drive this transformation, including computers, the internet, smartphones, and vast amounts of data. The healthcare industry has particularly felt the effects of digitalization with the adoption of health apps, virtual doctor appointments, and more (Denoo and Yli-Renko 2019). Furthermore, the recent COVID-19 pandemic has accelerated digitalization (Golinelli etal. 2020). Digital solutions have the potential to address the challenges mentioned above, empower patients, and improve access to care in rural communities (Wulfovich 2020; Tariq 2023). However, some hurdles prevent the success of digital health solutions (Ahmed etal. 2019). These include regulatory barriers in the healthcare sector, which are in place to ensure the safety and quality of medical products (Herrmann etal. 2018). Additionally, user acceptance has been low but is crucial, especially when collecting sensitive health data (Wilkowska and Ziefle 2012). The convergence of the need for innovation in healthcare and the potential offered by digitalization, together with other factors such as the COVID-19 pandemic, has given rise to the field of digital health entrepreneurship. It studies how digital health ventures pursue opportunities to deploy digital healthcare innovations in uncertain conditions (Wulfovich 2020). 1.1 Digital health andentrepreneurship evolution To understand the current state of digital health entrepreneurship and related challenges, it is helpful to primarily examine the evolution of the terms digital health and entrepreneurship. Digital health encompasses a range of concepts and technologies, including electronic health (eHealth), mobile health (mHealth), wearable devices, telehealth, telemedicine, health information technology, and artificial intelligence (WHO 2019; FDA 2022). 1621 Conceptualizing thelandscape ofdigital health… Over the past few decades, digital health terminology has evolved significantly, with many terms overlapping in meaning. However, the core goal of digital health remains the same: to use digital technology to improve patient care. In the 1970s, medical informatics and telemedicine were coined (Kazley etal. 2012; Chen etal. 2020). In the following decades, information and communication technology (ICT) for health and telehealth emerged, shifting the focus from disease-specific medicine to broader health issues. After 2000, eHealth and mHealth were established (Chen etal. 2020), with mHealth often defined as a subcategory of eHealth (Otto etal. 2020; Alenoghena etal. 2022). Although each term emerged based on the needs of its time, they often overlap and cannot always be distinguished. With the growing impact of digitalization in healthcare, the term digital health has emerged as an umbrella term encompassing a variety of related concepts (WHO 2019; FDA 2022). This development is illustrated in Fig.1, and an overview of the terms and their definitions are provided in Table1. These terms are predominantly utilized in the context of digital health. While digital health represents the broad concept of integrating digital technologies into healthcare and wellness, the term digital health technology (DHT) has emerged to specifically denote the digital technologies that are employed to achieve this integration. As of now, there is no international harmonized DHT framework. According to ISO (2023), DHT encompasses systems that use computing platforms, connectivity, software, and sensors for healthcare and related uses. Although some DHTs are part of the medical technology (MedTech) product family, have a medical purpose and thus are regulated as medical devices, others do not fall under the regulatory umbrella of medical devices. This is illustrated in Fig.2. The subset of DHT that is regulated is known as Software as a Medical Device (SaMD). SaMD is a subcategory of DHT and refers to software that is intended to be used for one or more medical purposes that are performed without being part of a hardware medical device (IMDRF 2014). A subcategory of SaMD are Digital Therapeutics (DTx)(Fürstenau etal.2023). A DTx product refers to software intended to treat or alleviate a disease, disorder, condition, or injury by generating and delivering a medical intervention that has a demonstrable positive therapeutic impact on a patient’s health (ISO 2023). Besides Digital Health eHealth mHealth Telehealth Medical Informatics Information and Communication Technology (ICT) Telemedicine 2019 1970 Health Informatics Year of Appearance Subclass of Fig. 1 The affiliation between the most important terminology used in the context of digital healthaccording to year of appearance 1622 S.N.Weimar et al. standalone applications, digital approaches are often also used in hardware medical devices (Gilbert etal. 2023). If the software is not standalone and serves as an integral component of a specified hardware medical device or is intended to drive a hardware medical device, it is referred to as Software in a Medical Device (SiMD). As shown in Fig.2, SiMD is not part of DHT (ISO 2023). Despite the fact that numerous definitions of entrepreneurship exist, it has often been framed as examining how, by whom, and with what effects opportunities to create future goods and services are discovered, evaluated, and exploited (Shane and Table 1 A summary of the terminology used to describe the field of digital health Term Description Digital Health Digital Health is a broad umbrella term encompassing electronic health (eHealth), mobile health (mHealth), wearable devices, telehealth, telemedicine, health information technology, and artificial intelligence applied in healthcare (WHO 2019; FDA 2022) Medical and health informatics Both terms encompass collecting, analyzing, and transmitting health data and information to support health care (Chen etal. 2020) ICT for health Information and communication technology (ICT) for health fulfills or enables information processing and communication by electronic means (Otto etal. 2020) Telemedicine and telehealth Both terms use ICT over distances to deliver healthcare services and medical education. Telemedicine focuses on service delivery through physicians, while telehealth includes all healthcare providers (Chen etal. 2020; Otto etal. 2020) eHealth Electronic Health (eHealth) refers to tools and services using information and communication technologies to improve prevention, diagnosis, treatment, monitoring and management (Otto etal. 2020) mHealth Mobile Health (mHealth) is the use of mobile communication for health information and services and is often defined as a subclass of eHealth (Alenoghena etal. 2022) Software as a Medical Device (SaMD) Software in a Medical Device (SiMD) Medical Devices Digital Health Technology (DHT) Digital Therapeutic (DTx) Fig. 2 Digital Health Technology (DHT) and the related terminology in the context of medical devices, adapted from ISO (2023) 1623 Conceptualizing thelandscape ofdigital health… Venkataraman 2000). Entrepreneurs exploit opportunities such as new technologies with different levels of innovation (Christensen 1997). Innovation is seen as the driving force for the entrepreneurial process (Drucker 1985). The transformative impact of digital technologies has greatly influenced the evolution of entrepreneurship. These advancements have given rise to many new business opportunities while fundamentally reshaping the nature of entrepreneurship (Denoo and Yli-Renko 2019). In this scope, digital entrepreneurship has been defined as creating new ventures by developing or using such digital technologies (Baierl etal. 2019). Building on these insights and the definitions of Wulfovich (2020), Shane and Venkataraman (2000), and Baierl etal. (2019), digital health entrepreneurship can be defined as a field that examines the pursuit of deploying digital health innovations by leveraging digital technologies to benefit patients or the wider health and social care system. 1.2 Research questions The field of digital health entrepreneurship is rapidly evolving, and further research is needed to fully understand its potential and challenges (Denoo and Yli-Renko 2019; Sreenivasan and Suresh 2022). To fill the gap of a structured understanding of the field in the literature, this work aims to comprehensively review digital health from an entrepreneurial perspective. The primary objective is to map the scientific evidence and to identify areas that necessitate further research. Accordingly, three research questions (RQs) are formulated to achieve this goal: • RQ1 Which disciplines contribute to the field of digital health entrepreneurship and which research methodologies are employed? • RQ2 What are the key thematic clusters within the field of digital health entrepreneurship? • RQ3 Which research gaps and fruitful directions can be identified? By identifying research opportunities, the goal is to contribute to developing a more robust evidence base in this critical area and support the ongoing growth and development of the digital health sector. The remainder of this work is structured as follows: The next section describes the scientific methods employed to investigate the research questions. Next, the findings from the literature review are presented through a citation network and a conceptual framework. The subsequent discussion section provides a detailed exploration of the results obtained from the literature. Finally, the work concludes with a summary of the essential findings and their implications. 1624 S.N.Weimar et al. 2 Methodology We conducted a systematic literature review according to Kraus etal. (2020) and vom Brocke et al. (2009). A systematic literature review is a rigorous research method that creates findings in a reproducible manner and, thus, helps to create an unbiased synthesis (Kraus etal. 2020). Additionally, it is a suitable method to explore research gaps fitting to the research scope of this work (Brocke etal. 2009). Kraus etal. (2020) define four steps of a systematic literature review which were followed in this work: Planning the review, identifying and evaluating studies, extracting and synthesizing data, and disseminating the review findings. 2.1 Planning thereview Following a preliminary examination of the literature by searching for ‘digital health entrepreneurship’ on Scopus, we have precisely delineated the research questions that will serve as the foundation for guiding the review planning process. The questions were formulated in response to the research gaps identified during the primary investigations. As an additional step of the review planning process, we decided to conduct the subsequent search on three databases to include most articles: Web of Science, Scopus, and PubMed. PubMed was selected to find relevant medical literature, whereas Web of Science and Scopus index relevant entrepreneurship literature as the central databases (Kraus etal. 2020). 2.2 Identification ofliterature The subsequent phase of the systematic literature review involved the identification of relevant search terms. Synonyms and related terms were added to the two core concepts of digital health and entrepreneurship based on the terminology used in the field. Table2 lists the final keywords and their associated terms. A composed search string was created based on the keywords, combining the core concepts with the connector ‘AND’ and the respective synonyms with ‘OR’. Finally, the composed search query was applied to the search engines chosen as part of the planning. The search was limited to the title, abstract, and keywords of the publications. After removing duplicates, a total of 6993 articles remained. Table 2 The core concepts and related keywords making up the search query,with an asterisk denoting the inclusion of multiple word endings Core concept Related search terms Digital health digital health*, mobile health, mhealth*, m-health*, ehealth*, e-health*, telemedicine, tele-medicine*, telehealth*, tele-health*, software as a medical device*, digital therapeutic*, healthtech*, health-tech*, medtech*, med-tech* Entrepreneurship entrepreneur*, startup*, new venture, new firm, new business, innovation* 1625 Conceptualizing thelandscape ofdigital health… 2.3 Study selection strategy Title and abstracts were scanned according to the inclusion and exclusion criteria summarized in Table3. These criteria were thoughtfully chosen to maximize the outcomes. The search was limited to peer-reviewed publications to focus on high-quality work and adequately answer the research question. Additionally, entrepreneurship and business aspects should be significant in the papers. Due to the rapid evolution of digital technology, only literature with clear connections to modern digital technology was included. The full text was analyzed for the remaining 499 papers, and the inclusion and exclusion criteria were further applied. The ranking of the journal in which the paper was published and the number of citations it received were used as quality indicators to guide the selection, resulting in 141 articles. From the 141 publications included, 16 additional papers were found through a forward and backward search. As a result of some research articles appearing in monographs, an additional seven items were added based on a manual hand search. The literature selection resulted in the identification of 164publications, which were included in the final dataset. The entire review and selection process is visualized in Fig.3. 2.4 Extracting andsynthesizing data Based on the selected literature, themes were derived according to Braun and Clarke (2006). Themes are described as patterned responses within the dataset. Thematic analysis is a foundational method for qualitative analysis with benefits such as flexibility compared to other qualitative methods (Braun and Clarke 2006). Prominent features of textual data were coded and then collated into themes. In an ongoing analysis, the specifics of each theme were redefined to generate clear definitions and names for each theme. In addition, themes were discussed within the research team. Final themes were mapped into a concept matrix, as Webster and Watson (2002) and Brocke etal. (2009) proposed. 3 Results The connection of the publications to entrepreneurship and startup research varies, and not all papers explicitly mention entrepreneurship, new ventures, or startups. The papers do, however, all relate to some extent to pursuing opportunities to deploy Table 3 The inclusion and exclusion criteria that were applied throughout the search process Inclusion criteria Exclusion criteria • English and German language • Peer-reviewed articles • Connection to entrepreneurship research • Relevant in the scope of modern digital technology • Not enough theoretical contributions • Business and entrepreneurial aspects play only a minor role • No significant connection to digital health • No access to the full paper 1626 S.N.Weimar et al. digital health innovations and are, therefore, relevant to entrepreneurship in digital health. Figure4 illustrates the increasing trend in the number of digital health entrepreneurship publications over the years, indicating a growing research interest. Despite an unrestricted search period, the earliest paper identified was published in 2006. By applying the inclusion and exclusion criteria, papers published before 2006 were Total recordsidentified(n = 11313) Potentially relevant records (n = 6993) Duplicaterecords removed (n = 4320) Potentially relevant full text records (n = 499) Records excludedbased on title and abstract (n = 6494) Records after screening(n = 141) Records excludedbased on full text screening(n = 358) Forward and Backward Search (n = 16) Mergeofall records (n = 164) Hand Search (n = 7) Database search: Web of Science (n = 2752) Scopus (n = 5191) PubMed (n = 3370) Identification Screening Eligibility Included Fig. 3 An overview of the literature search and selection strategy 1633 Conceptualizing thelandscape ofdigital health… 4.2 Ecosystem andstakeholder (T2) In classical entrepreneurship, some startup stakeholders are customers, investors, and partners. For digital health startups, this stakeholder landscape can become significantly more complex. Stakeholders are typically patients, healthcare providers, governments, and insurance companies (Nilsen etal. 2020). Research has shown that it is essential for health startups to engage with stakeholders early in the development process (Barlow etal. 2006; Nilsen etal. 2020), as failing to do so can lead to conflicting perspectives and interests (Lyles etal. 2021). Early stakeholder analysis is an effective strategy for engaging stakeholders (Lee and Sheikh 2016). Another way to facilitate engagement is through living labs, which provide a more advanced platform for early stakeholder involvement (Bygholm and Kanstrup 2017;Swinkels etal. 2018). Scholars have developed guidelines on how to establish such living labs, drawing on practical insights (Fotis etal. 2023). Alliances between startups and other organizations are essential for the success of digital health products (Kikuchi etal. 2021; Capponi and Corrocher 2022). Even failed relationships can provide valuable learning experiences (Hasche and Linton 2018). Particular academia-industry alliances contribute to later success (Ford etal. 2019, 2021). Funding a digital health venture can be challenging. Academia-industry partnerships might be a basis for receiving grants or support through technology transfer programs (Ford etal. 2021). Another research path looks at entrepreneurial and innovation ecosystems and has built guidelines or models for implementing digital health ecosystems in which startups can thrive (Hermes etal. 2020). These systems involve interactions between startups and various institutions, including government agencies and independent organizations (Loebker etal. 2021; Bhattacharyya etal. 2022). Digital health accelerator programs are among the ecosystem actors, capable of supporting startups. However, a significant challenge is the lack of research evidence for the products, an area where accelerators currently do not provide sufficient support (Njoku etal. 2023). Further research could examine how startups shape their ecosystems and their roles within them (Denoo and Yli-Renko 2019). While some preliminary research on the roles of startups in the ecosystem exists (Mohammadparast Tabas et al. 2023), further research could focus on the evolving roles in digital health. Additionally, there is a need for practical strategies for managing alliance partners, which is a crucial factor for the success of digital health startups (Denoo and Yli-Renko 2019). More concepts on how to practically support digital health startups, particularly in areas like evidence generation, should be developed. 4.3 Product andbusiness development (T3) Medical device development is often guided by roadmaps, such as the stage-gate process, which defines various quality gates throughout the product life cycle (Pietzsch etal. 2009), or the Biodesign process developed at Stanford (Balamurugan and Naveen 2021). These frameworks provide a structured approach to the product or 1634 S.N.Weimar et al. startup life cycle. Recently, there has been a shift towards methods that guide entrepreneurs in designing digital health solutions. This marks the transition from classical product development as conducted in hardware-heavy medical technology companies (waterfall method) towards agile methods needed to develop digital solutions. One of the most cited methods developed for digital health is the Centre for eHealth Research Roadmap (CeHRes roadmap) (van Gemert-Pijnen etal. 2011). Other stateof-the-art methods often incorporate lean (Eppley etal. 2021), design thinking (Eckman etal. 2016; Mummah etal. 2016; Chokshi and Mann 2018), human-centered design (Levander etal. 2023), design science research (Gregório etal. 2021) or the lifecycle itself (Bhavnani etal. 2017) to further involve stakeholders in the development process. Much of the literature discusses patient-centered approaches and value cocreation that might serve as the basis for patient empowerment through digital technologies and safe products (Kraus etal. 2021; Rassi-Cruz etal. 2022). A recent study attempted to incorporate requirements related to risk management into the co-design process (Sternini etal. 2023). Future research might build upon these findings to create co-design methods for regulated digital health products. Some research suggests that a more holistic approach, which considers both human-centered design and environmental sustainability, may be necessary to develop digital health products (van Velsen etal. 2022). There is limited literature on methods for developing solutions for specific user groups, such as older adults, who face unique challenges (Matthew-Maich etal. 2016). Various toolkits and methods have been created to assist with the practical application of these methods, providing more detailed instructions for the different design phases of digital health products (Hughes etal. 2021; Kayser etal. 2022; Kip etal. 2022; Marvel etal. 2018). Another emerging approach is focusing on responsibility during the early stages of business development (Oftedal etal. 2019; Naughton etal. 2023, Thapa and Iakovleva 2023). While the literature on these methods is extensive, few case studies demonstrate their practical application or incorporate startup specifics. 4.4 Business model (T4) Creating a viable business model is the key element of entrepreneurship (Wulfovich 2020), and it also seems crucial for digital health entrepreneurship (Limburg etal. 2011). A digital health business model might involve a significant redesign compared to traditional or medical device business models (Steinberg etal. 2015). With their flexible structure, startups are particularly well-suited to make this shift (Herrmann etal. 2018). Tariq (2023) states that six forces impact business model innovation: funding, accountability, industry players, technology, public policy, and customer. Recent literature reviews have identified various business models developed for DHTs and concluded a lack of evaluation regarding their usefulness (Kelley etal. 2020; Velayati etal. 2022). Key components of digital health business models are the value proposition, key processes, profit formula, and critical resources (Sterling and LeRouge 2019; Velayati etal. 2021). The CompBizMod framework for complex healthcare services is one example of the few business models validated by 1635 Conceptualizing thelandscape ofdigital health… experts (Peters etal. 2015). Another example tries to include sustainability aspects in the business model (Oderanti etal. 2021). Some studies describe digital health business modeling based on use cases (Kijl and Nieuwenhuis 2011; Sprenger 2016; Alnahdi 2023). Sprenger (2016) investigated the utility of a pattern-based business model design. Alnahdi (2023) applied Osterwalder’s Business Model Canvas and Value Proposition Canvas to the domain of digital health, suggesting innovative approaches for structuring and articulating the value of digital health initiatives. Reimbursement through health insurances is one strategy to achieve a sustainable revenue model in healthcare (Herberz etal. 2018). Nevertheless, the path to reimbursement can be tedious and involves engaging healthcare insurers and complying with regulations (Herberz et al. 2018). Governments worldwide are passing new laws to encourage startups and create new reimbursement pathways. Van Kessel etal. (2023) analyzed the digital health reimbursement landscape in nine countries, finding that diversity among these systems creates barriers for entrepreneurs. An example of a reimbursement pathway is Germany’s digital health application (DiGA) framework. Under this framework, digital health products that provide direct patient care must meet a complex set of regulations and demonstrate a positive impact on healthcare. Once they fulfill these requirements, physicians can prescribe these products, and all public German health insurances reimburse the costs (Loebker etal. 2021). Although these frameworks require improvements, they have demonstrated the potential to facilitate new, digital health-specific reimbursement pathways for startups (Schlieter etal. 2023). Another element that is essential for digital health business models is the value proposition (Chen etal. 2013; Shaw etal. 2018; Lentferink etal. 2020). One study identified nine types of value propositions commonly used by digital health companies in the developing world (Gorski etal. 2016). Apart from the value proposition, securing funding plays an important role in the early stages of a business model. Due to potential delays in market access caused by regulations and mandatory clinical evaluations in the digital health industry, digital health entrepreneurs must ensure their business model is well-supported by investments to overcome the challenging ‘valley of death’ (Manbachi etal. 2018). In addition, many conventional sources of capital are not familiar with healthcare (Tariq 2023). For this, it is essential to understand early-stage funding environments and to present a clear differentiation from competition (Wulfovich 2020). Future research might explore business models of existing ventures (Chakraborty etal. 2021), describe business model design (van Meeuwen etal. 2015), and models that allow for effective value capture (Denoo and Yli-Renko 2019). It is crucial to address the specific digital health business model challenges that include the complex stakeholder landscape (Nilsen etal. 2020), the revenue model and reimbursement challenges startups face (Hagen and Lauer 2018; Chakraborty etal. 2023a), regulations (Berensmann and Gratzfeld 2018; Chakraborty etal. 2023a), and funding (Joseph etal. 2011). By understanding the various tools, key components, and business models in digital health, entrepreneurs can design and implement solutions that meet the needs of patients and healthcare providers. A preliminary study has explored digital platform business model changes over time (Essen etal. 2023). Digital platforms offer new avenues for value creation and emerging digital platform 1636 S.N.Weimar et al. business models in healthcare could be explored further (Lehoux et al. 2014; Konopik 2023). 4.5 Classification (T5) Several studies have attempted to classify digital health startups based on various dimensions, such as customer need (Herrmann etal. 2018), strategy (Labrique etal. 2013), value proposition (Gorski etal. 2016), other elements of the business model (Steinberg etal. 2015; Peters etal. 2015; Denoo and Yli-Renko 2019), or innovation routes (Janssen et al. 2013). By differentiating between a consumer, a provider, an insurer, and a government route, Janssen etal. (2013) describe four ways digital health startups can handle market access. Some studies in theme five have focused on specific regions, such as North America (Steinberg etal. 2015), Europe, or developing countries (Gorski etal. 2016), while others have taken a more global approach. Gehdeet al. (2022) sorted German digital health startups based on their area of activity and digital technologies used and identified five overarching configurations. However, current research has not yet provided a comprehensive, holistic classification of startups, which could help describe, evaluate (Botha etal. 2018), and guide (Janssen etal. 2013) new ventures. Future research could address this gap by developing a comprehensive description and classification of digital health startups. 4.6 Management andstrategy (T6) While this theme is related to the business model, it focuses on entrepreneurial and strategic management guidelines needed to build successful digital health startups. Norris etal. (2009) developed a sustainable mobile health strategy framework consisting of three phases: identifying applications, channeling development activities, and confirming activities. Other frameworks summarize best practices for creating and managing digital health innovations (Urueña etal. 2016; Muhos etal. 2019; Barbazzeni etal. 2022; Schee Genannt Halfmann etal. 2022; Biancone etal. 2023). Boonstra etal. (2011) focus on designing and managing digital health innovations by addressing the value chain, value shop, and value network theories, which have connections to the business model. It appears that the frameworks are often not practically applied. This is supported by recent research that concludes that startups often lack a strategic guide for successful market access and reimbursement (Hagen and Lauer 2018). Furthermore, research has been conducted on the entrepreneurial capabilities needed in the digital health sector, including digital technology knowledge, strategic abilities, and business model capabilities. Challenges entrepreneurs face are network management (Muhos etal. 2019) and the management of sensitive patient information (Gauthier etal. 2018). Other elements for successful digital health innovation projects include the implementation of rigorous evaluation methods and the cultivation of organizational agility (Urueña etal. 2016). Future research could use quantitative methods in digital health management (Angerer etal. 2022) or investigate the 1637 Conceptualizing thelandscape ofdigital health… strategic approach of skating the line between a wellness product and a regulated device (Simon etal. 2022). By understanding the management decisions and strategies that have been successful in the digital health space, researchers and practitioners can gain valuable insights into the effective management and growth of their digital health ventures. 4.7 Regulation (T7) Regulations such as the medical device regulation (MDR) and the invitro diagnostic medical device regulation (IVDR) in Europe aim to ensure the safety of medical devices. Nevertheless, they can pose challenges for small companies seeking to bring their products to market (Baines etal. 2022). Only certain types of digital health products fall under medical regulations. In Europe, primarily products intended to diagnose, detect, prevent, monitor, treat, or alleviate a disease or disability are subject to the MDR and are categorized into risk classes according to their potential impact on patients (Berensmann and Gratzfeld 2018). Subsequently, a robust quality management system and technical documentation must be established to guide and document product development. Furthermore, the safety and effectiveness of a digital health innovation must be demonstrated through a clinical evaluation (Berensmann and Gratzfeld 2018). These evaluations may include clinical trials which present a significant challenge due to the risk of potential failure (Olivier etal. 2021). Overall, numerous legal requirements govern the entry of a regulated product into the market, and the product can only enter the market once it undergoes a final conformity assessment (Berensmann and Gratzfeld 2018). Entrepreneurs must be aware of this regulatory environment. Scholars have attempted to clarify whether regulations apply to a digital health product by creating simplified decision trees (Lukas etal. 2021; Seifert etal. 2022). Garell etal. (2016) proposed a legal framework to support entrepreneurs navigating regulatory challenges. Another framework focuses on providing guidance for the digital health application (DiGA) process in Germany (Ataiy etal. 2023). Furthermore, suggestions have been made to incorporate regulatory information into university education, provide better tools and resources, and create standardized templates of regulatory documents (Baines etal. 2022). Case studies of startups, as conducted by Herberz etal. (2018), that have successfully navigated the certification process may help others achieve compliance (Baines etal. 2022). A further study proposes different combinations ofDHTs and offers regulatory perspectives on each case (Colloud etal. 2023). Kheir etal. (2021) interviewed entrepreneurs and outlined the structure of a quality management system implemented by a medical startup. By adapting the intended use of a product, a startup can control whether a product falls into the lifestyle and wellness product category (not targeting a specific medical condition and therefore not subject to regulation) or whether it is a regulated device (intended to treat a particular medical condition and subject to regulatory oversight). Simon etal. (2022) describe this as the possibility of skating the line between the two worlds and give first guidance for companies on benefits and associated risks. Another study proposes that the United Kingdom may become a highly 1638 S.N.Weimar et al. attractive location for initially developing digital health solutions, as UK regulators select innovative and well-designed regulatory features (Gilbert etal. 2023). Digital health products require strong data security and privacy measures as part of the regulations and to ensure user acceptance (Wilkowska and Ziefle 2012). In Europe, the general data protection regulation (GDPR) sets requirements for protecting personal data (Frielitz etal. 2019), and the MDR sets further conditions. Current solutions for ensuring security and privacy in digital health products are insufficient (Wicks and Chiauzzi 2015). To ensure compliance with regulations and user needs, startups must develop a data security concept early on (Frielitz etal. 2019). Li etal. (2023) provide initial insights into the experience of health startups with cybersecurity. Still, little research focuses on how startups should handle data security privacy and which tools or certificates are recommended to fulfill regulatory and user requirements. While data protection is essential for medical products, wellness, and fitness apps may be subject to less regulation. Future research could explore business models that prioritize data protection while generating revenue for the company (Denoo and Yli-Renko 2019). Although several contributions exist, regulatory paths are often unclear or have not been defined yet (Rassi-Cruzet al. 2022), and more research is needed to understand how startups can cope with the challenges. 4.8 Digital technology (T8) Digital health products rely on various digital technologies to function, with artificial intelligence (AI) being one of the most important (Wulfovich 2020). AI has many potential uses in healthcare, including decision support, image analysis, and chatbots (Arora 2020). However, there are numerous challenges to adopting AI in healthcare (Singh etal. 2020), which is why practical frameworks have been developed to provide guidance on best practices for founders (Arora 2020; Higgins and Madai 2020). One of the frameworks consists of a regulatory, clinical, data, and machine-learning model strategy (Arora 2020). Large amounts of data are becoming increasingly available through electronic patient records and real-time patient data (Arora 2020) from wearable devices, which entrepreneurs should consider when developing digital health products (Dinh-Le etal. 2019). Closely related to the AI applications, promising uses of virtual reality, augmented reality and mixed reality in digital health are personalized occupational, educational, and home healthcare applications (Kim etal. 2023). Another technology with potential healthcare applications is blockchain, a distributed ledger technology with decentralized principles that could improve accessibility and security in healthcare. Opportunities lie in improved patient record management (Badri etal. 2023), clinical trial trustworthiness, and protection of telehealth systems (Yaqoob etal. 2022). However, practical blockchain applications in healthcare are currently limited (Chen etal. 2019). In a first study, Russo-Spena etal. (2023) identified three impacts of blockchain for 1639 Conceptualizing thelandscape ofdigital health… value co-creation in healthcare: improving service interaction, impacting actors’ engagement, and fostering ecosystem transparency. To increase their chances of success, entrepreneurs in the digital health field should have strong technological skills and be aware of the need for a large amount of data for AI applications (Wulfovich 2020). 4.9 Implementation andadoption (T9) Although the COVID-19 pandemic has favored the implementation of digital health tools (Golinelli etal. 2020), they may not be adopted due to system-imposed (Hobeck etal. 2021), technological and organizational barriers (Renukappa etal. 2022). Some examples are a lack of information technology infrastructure, the impact of regulatory pressures (Lim and Anderson 2016), and resistance to change (Scott Kruse et al. 2018; Van Velthoven and Cordon 2019). Further research is needed to assess the efficacy of financial incentives to promote adoption, as the current evidence is limited (Zanaboni and Wootton 2012). There is a significant amount of literature on effectively implementing and adopting digital health solutions. Various frameworks have been developed to aid in their successful implementation (MacFarlane etal. 2011; Joseph etal. 2011; van Dyk 2014; Ross etal. 2016; L’Engle etal. 2017; Lundin and Dumont 2017; Greenhalgh etal. 2017; Hobeck etal. 2021; Quanbeck etal. 2021; Verweij etal. 2022; Talwar etal. 2023). One example is a framework to push health app prescriptions by physicians (Gordon etal. 2020), while another focuses on the consumer and user role in adopting digital solutions (Talwar etal. 2023). Nevertheless, there is no one-size-fits-all approach, and it is recommended to take a multidisciplinary approach (Hochmuth etal. 2020). There is a lack of research on organizational issues related to digital health adoption (Cresswell and Sheikh 2013). Some initial work on barriers and enablers for adoption has been published recently (Zhao etal. 2023; Olaye etal. 2023). Success factors for the adoption and market entry of medical startups have been identified as similar to general startup success factors (Lee etal. 2019). Nevertheless, specific to digital health are access to distribution channels, financing, health insurance cooperation, partnerships (Hanneken 2018; Lux and Kempf 2021), a robust technological infrastructure, revenue generation ability (Chakraborty etal. 2023b), and early attention to regulations (Bengtson etal. 2022). Some research focuses on the decision-maker perspective for success factors (Prodan etal. 2022). From the customer’s perspective, quality, personalization, and data risk are the main predictors of DHT adoption (Saheb 2020). Healthcare staff may hesitate to risk potential changes to valued existing services (Sanders etal. 2012). A perceived positive impact on the interaction between healthcare staff is another success factor (Murray etal. 2011). Only a few authors mention scale-up as part of adopting digital health ventures (Greenhalgh etal. 2017; L’Engle etal. 2017; Proctor etal. 2021), although scale-up is a significant challenge. 1640 S.N.Weimar et al. 4.10 Evaluation (T10) Digital health evaluation involves health technology assessment (HTA) from an entrepreneurial perspective and the evaluation of digital health startup characteristics. The origin of HTA lies in evidence-based healthcare. It advises decision-makers on the potential impact of introducing new health technologies (Wang etal. 2021) for establishing high-quality health systems (Ming etal. 2022). The areas of focus in HTA for digital health include evaluation of safety, clinical effectiveness, usability, economic aspects, and interoperability (Kolasa and Kozinski 2020). Using HTA tools early in product and business development can be helpful for entrepreneurs (Wang etal. 2021). Some approaches promise the rapid assessment of health technologies (Cai etal. 2023) or dynamic HTAs with real-world evidence (Brönneke etal. 2023). More evidence is needed for future sustainability evaluations of digital health (Degavre etal. 2022). Within this theme, one research stream evaluates different characteristics of digital health startups. A study on SaMD companies in the United States found that incumbents predominate, while startups seem to contribute more disruptive innovations. The study concludes that disruptive innovations in the field are only possible with access to sufficiently large data sets (Yu etal. 2023). One study proposed a tool to assess the responsibility of digital health solutions (Lehoux etal. 2023). Another study measured the clinical validity of digital health startups and found evidence for a low level of clinical robustness (Day etal. 2022). A separate investigation concurred with these results, highlighting that various healthcare unicorns lack scientific evidence (Cristea etal. 2019). It may be advisable for healthcare startups to invest in clinical validation efforts to increase trust in their products, as relying solely on internal data may not be sufficient to establish their validity (Cristea etal. 2019). Nevertheless, clinical trials might fail (Olivier etal. 2021), which imposes new risks. 5 Conclusion With the goal of this work to map the scientific evidence of digital health entrepreneurship, a systematic literature review combining digital health and entrepreneurship was conducted to understand the current state of research in this area. After reviewing 6993 papers from multidisciplinary databases, 164 articles related to digital health entrepreneurship were identified. Health and medicine journals, along with digital health-specific journals, were found to be the primary sources of publications. Additionally, mainly business and a few engineering journals revealed relevant publications. These results show that the digital health entrepreneurship literature is based on the interplay of different disciplines as it is a combination of ‘digital’, which is driven by engineering and computer science disciplines, ‘health’, which is part of medical studies, and ‘entrepreneurship’, belonging to management sciences. This observation leads to the question, of why digital health entrepreneurship literature is not only published through 1641 Conceptualizing thelandscape ofdigital health… classical entrepreneurship journals. An explanation might be that the primary audience for health innovations includes healthcare professionals and health researchers, who tend to refer to digital health and medicine journals. Additionally, business journals may exhibit specific methodological preferences that do not always align with the interdisciplinary nature prevalent in digital health entrepreneurship studies. Lastly, while entrepreneurship as a discipline is well-established, digital health entrepreneurship is still emerging. As a result, business journals are gradually adapting to include more studies related to this area. While empirical and conceptual research methodologies are balanced for the examined dataset, qualitative methods dominate over quantitative methods. Although qualitative methods have their advantages, the digital health entrepreneurship literature could benefit from more quantitative work. (RQ1). Through an iterative and inductive research process, we derived ten themes relevant to digital health entrepreneurship: Academia Transfer and Education (T1), Ecosystem and Stakeholder (T2), Product and Business Development (T3), Business Model (T4), Classification (T5), Management and Strategy (T6), Regulation (T7), Digital Technology (T8), Implementation and Adoption (T9), and Evaluation (T10). These themes were then organized into a conceptual framework depicting the internal and external building blocks of digital health entrepreneurship. It became clear that the content of these themes was particular to digital health entrepreneurship. The requirements of medical device regulations and data security laws are unique to digital health ventures. Additionally, the acceptance of the technology plays an important role. Another crucial element is the exchange with the ecosystem, stakeholders, and academia, as digital health startups often need to survive clinical evaluations before entering the market successfully. Strong partnerships are required due to costly certifications and to overcome the valley of death. The analysis of the research themes showed an unequal distribution of scientific evidence. The theme with the most robust body of evidence was Implementation and Adoption. In contrast, the themes with the least evidence were Classification, Evaluation, and Digital Technology. The remaining themes had a moderate level of evidence. (RQ2). The proliferation of digital technologies, such as smartphones, artificial intelligence, and distributed ledger technology, has opened up opportunities for digital health entrepreneurs. Further research is needed to understand how to effectively create and utilize these opportunities, particularly in the case of distributed ledger technology, which has significant potential but many unresolved questions. While early evidence suggests that there are various approaches to structuring the digital health industry, they are not always based on real-world data. Tools and methods such as human-centered design and lean startup have been proposed for digital health ventures, but there is limited empirical evidence of their effectiveness. The business model, a central aspect of any entrepreneurial activity, has received increasing attention in digital health research. Primary research indicates how to develop a business model in digital health. Still, there is no consensus about which models to use, and they often have little practical use. The revenue model and possible reimbursement pathways have seen little attention. Descriptions of digital health venture strategies, such as skating the line between wellness products and medical devices, 1642 S.N.Weimar et al. exist, but startups often still miss applicable guidelines. While the implementation, adoption, and evaluation of digital health solutions have been studied on a larger scale, there is still a need for research on the clinical robustness of digital health startups and the incorporation of health technology assessment early in the business formation process. Digital health entrepreneurs often operate in a highly regulated industry where various stakeholders must be satisfied. The literature highlights the importance of Table 4 Key challenges in digital health entrepreneurship based on the ten identified themes Theme Future research agenda Academia transfer and education • Which success factors can be identified for digital health academia transfer? • How can digital health entrepreneurship be taught as a discipline in universities? Ecosystem and stakeholder • How can accelerators best support digital health startups especially in demonstrating clinical evidence? • How can business partnerships contribute to the success of digital health startups? Product and business development • How valuable are entrepreneurial design methods in practice? • How can medical device regulations be incorporated into product and business development? • How can customers with special needs be best addressed by entrepreneurial design methods? Business model • How can a business model be best adapted to the complex healthcare stakeholder network? • Which reimbursement pathways exist and when should they be used? • How to design value propositions to address multiple stakeholders? • What implications do medical device regulations have for the business model? • How can funding help digital health startups to overcome the valley of death? • Which types of business model innovation emerge from DHT? Classification • Which types of startups emerge in the digital health field and how can they be systematically described? Management and strategy • How to apply digital health strategy guides in practice? Regulation • Which regulatory requirements exist for digital health startups? • How and why do digital health startups skate the line between regulated and wellness/lifestyle products? • How can entrepreneurs be supported in developing a regulatory strategy? • How can entrepreneurs have access to open regulatory knowledge? Digital technology • Which business opportunities in healthcare exist for emerging digital technologies like blockchain technology? Implementation and adoption • How can digital health ventures be scaled up successfully? • What are the key factors for the acceptance of DHT by users? Evaluation • How can health technology assessment be incorporated early into the venture building? • How can digital health startups improve their clinical robustness? 1649 Conceptualizing thelandscape ofdigital health… Table 5 (continued) Publication Classification Academia transfer Management and strategy Business and product development Business model Implementation and adoption Ecosystem and stakeholder Regulation Digital technology Evaluation Konopik (2023) × Kraus etal. (2021) × × Thapa and Iakovleva (2023) × Labrique etal. (2013) × Lee and Sheikh (2016) × Lee etal. (2019) × Lehoux etal. (2014) × × Lehoux etal. (2023) × L’Engle etal. (2017) × Lentferink etal. (2020) × Letourneur etal. (2021) × Levander etal. (2023) × Li etal. (2023) × Lim and Anderson (2016) × Loebker etal. (2021) × Lopez etal. (2019) × Lukas etal. (2021) × Lundin and Dumont (2017) × Lux and Kempf (2021) × Lyles etal. (2021) × 1650 S.N.Weimar et al. Table 5 (continued) Publication Classification Academia transfer Management and strategy Business and product development Business model Implementation and adoption Ecosystem and stakeholder Regulation Digital technology Evaluation MacFarlane etal. (2011) × Matthew-Maich etal. (2016) × × Mair etal. (2012) × Manbachi etal. (2018) × Martens etal. (2023) × Marvel etal (2018) × Mayrink etal. (2022) × Merkel and Huth (2022) Ming etal. (2022) × Mohammadparast Tabas etal. (2023) × Moore etal. (2022) × Muhos etal. (2019) × Mummah etal. (2016) × Murray etal. (2011) × Naughton etal. (2023) × Nilsen etal. (2020) × Njoku etal. (2023) × × Norris etal. (2009) × Oderanti etal. (2021) × Oftedal etal. (2019) × 1651 Conceptualizing thelandscape ofdigital health… Table 5 (continued) Publication Classification Academia transfer Management and strategy Business and product development Business model Implementation and adoption Ecosystem and stakeholder Regulation Digital technology Evaluation Olaye etal. (2023) × Olivier etal. (2021) × Peters etal. (2015) × × Pietzsch etal. (2009) × Prodan etal. (2022) × × Rassi-Cruz etal. (2022) × Renukappa etal. (2022) × × Ribeiro etal. (2019) × × Robinson (2021) × × Ross etal. (2016) × Russo-Spenaet al. (2023) × Saheb (2020) × Sanders etal. (2012) × ScheeGenannt Halfmann etal. (2022) × Schlieter etal. (2023) × × Scott Kruse etal. (2018) × Seifert etal. (2022) × Simon etal. (2022) × × Singh etal. (2020) × 1652 S.N.Weimar et al. Table 5 (continued) Publication Classification Academia transfer Management and strategy Business and product development Business model Implementation and adoption Ecosystem and stakeholder Regulation Digital technology Evaluation Sprenger (2016) × Sreenivasan and Suresh (2022) × × × Steinberg etal. (2015) × × Sterling and Lerouge (2019) × Sternini etal. (2023) × Suryavanshi etal. (2020) × Swinkels etal. (2018) × Talwar etal. (2023) × × Tariq (2023) Urueña etal. (2016) × van Dyk (2014) × van Gemert-Pijnen etal. (2011) × Van Kessel etal. (2023) × × van Limburg etal. (2011) × van Meeuwen etal. (2015) × van Velsen etal (2022) × 1653 Conceptualizing thelandscape ofdigital health… Table 5 (continued) Publication Classification Academia transfer Management and strategy Business and product development Business model Implementation and adoption Ecosystem and stakeholder Regulation Digital technology Evaluation VanVelthoven and Cordon (2019) × Velayati etal. (2021) × Velayati etal. (2022) × Verweij etal. (2022) × Wang etal. (2021) × Wicks and Chiauzzi (2015) × Wilkowska and Ziefle (2012) × × Wulfovich (2020) × × × × × Yaqoob etal. (2022) × Yu etal. (2023) × Zanaboni and Wootton (2012) × Zhao etal. (2023) × 1654 S.N.Weimar et al. Appendix B: Journal ranking See Table6. Table 6 Journals ranked by number of publications Journal Name Discipline Article Count Journal of medical internet research Digital Health 19 Federal health bulletin General Health and Medicine 7 Frontiers in public health General Health and Medicine 4 International journal of environmental research and public health General Health and Medicine 4 JMIR mHealth and uHealth Digital Health 4 BMC health services research General Health and Medicine 3 BMC medical informatics and decision making Digital Health 3 Implementation science General Health and Medicine 3 International journal of medical informatics Digital Health 3 Journal of business research Business 3 Journal of telemedicine and telecare Digital Health 3 Sustainability Other 3 Technological forecasting and social change Business 3 Technovation Business 3 Current directions in biomedical engineering Engineering 2 Frontiers in digital health Digital Health 2 Frontiers in medicine General Health and Medicine 2 Health informatics journal Digital Health 2 International journal of technology assessment in health care General Health and Medicine 2 Journal of commercial biotechnology Business 2 1655 Conceptualizing thelandscape ofdigital health… Table 6 (continued) Journal Name Discipline Article Count Journal of medical engineering & technology Engineering 2 Journal of responsible innovation Business 2 Medical devices: evidence and research Engineering 2 mHealth Digital Health 2 NPJ digital medicine Digital Health 2 Studies in health technology and informatics Digital Health 2 A journal of integrative biology Other 1 Advanced healthcare materials General Health and Medicine 1 Advanced intelligent systems Engineering 1 Advanced science Engineering 1 Annals of biomedical engineering Engineering 1 BMC medicine General Health and Medicine 1 Bulletin of the world health organization General Health and Medicine 1 Business research Business 1 Cardiovascular digital health journal Digital Health 1 Childhood Obesity General Health and Medicine 1 Cost effectiveness and resource allocation Business 1 Diabetology & metabolic syndrome General Health and Medicine 1 Digital health Digital Health 1 European journal of clinical investigation General Health and Medicine 1 Frontiers in pharmacology General Health and Medicine 1 Global health action General Health and Medicine 1 Global health, science and practice General Health and Medicine 1 Health and technology General Health and Medicine 1 1656 S.N.Weimar et al. Table 6 (continued) Journal Name Discipline Article Count Health policy and technology General Health and Medicine 1 Industrial marketing management Business 1 Informatics Engineering 1 International entrepreneurship and management journal Business 1 International journal of entrepreneurial behaviour & research Business 1 International journal of healthcare technology and management Business 1 International journal of industrial engineering and operations management Business 1 International journal of innovation management Business 1 International journal of network management Business 1 International journal on advances in life sciences General Health and Medicine 1 JMIR cardio General Health and Medicine 1 JMIR diabetes General Health and Medicine 1 JMIR human factors Other 1 JMIR medical informatics Digital Health 1 JMIR public health and surveillance General Health and Medicine 1 JMIR research protocols Other 1 Journal of business venturing insights Business 1 Journal of evaluation in clinical practice General Health and Medicine 1 Journal of general internal medicine General Health and Medicine 1 Journal of law and the biosciences Other 1 Journal of management information systems Business 1 Journal of medical devices Engineering 1 Journal of re attach therapy and developmental diversities Other 1 Journal of small business & entrepreneurship Business 1 1657 Conceptualizing thelandscape ofdigital health… Table 6 (continued) Journal Name Discipline Article Count Journal of the American college of cardiology General Health and Medicine 1 Medical science educator General Health and Medicine 1 Nature biotechnology General Health and Medicine 1 Neural computing and applications Engineering 1 Participatory design & health information technology Digital Health 1 Research in social & administrative pharmacy Other 1 Small enterprise research Business 1 Smart health Digital Health 1 Social science & medicine General Health and Medicine 1 Sociology of health & illness General Health and Medicine 1 Strategic Change Business 1 Telemedicine and e-health Digital Health 1 The journal of strategic information systems Business 1 Therapeutic innovation & regulatory science Business 1 Translational vision & technology Business 1 1658 S.N.Weimar et al. Author contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis as well as the first draft of the manuscript was written by SNW. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding Open Access funding enabled and organized by Projekt DEAL. Data availabilty Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. Declarations Conflict of interest The authors have no relevant financial or nonfinancial interests to disclose. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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