New emerging capabilities for managing data-driven innovation in healthcare: the role of digital platforms

2.1 Data-driven innovation

In 2014 the European Union anticipated the “new oil” trend–data, inviting firms to re-think key resources for competitive success. Data were already theorized by Perez (2002) as an inexpensive resource, apparently inexhaustible, applicable to various products, processes and sectors. With the convergence of many technologies such as sensors and devices, certain firms have leveraged digital tools to facilitate the capture, exchange and analysis of data (Klingenberg et al., 2019). Scholars show data improve efficiency and promoting significant change in terms of product development (Davenport et al., 2012; Porter and Heppelmann, 2014); industry (i.e. diagnoses of health problems in Chen et al. (2013) or precision agriculture in Porter and Heppelmann (2014) or manufacturing); and society (Brynjolfsson and McAfee, 2014). At the same time, data-driven smart sustainable cities are being instrumented, datafied and computerized so as to improve, advance and maintain their contribution to the goals of sustainable development through more optimized processes and enhanced practices (Bibri, 2019). The diffusion of digital technology has contributed to generating a tremendous amount of data not always sufficient in terms of quality and helpful format to exploit the potential of analytic tools. Furthermore, the use of collected data is often still limited to several problems related to the complexity of the data-based application and structural limitations of some organizations that are not able to leverage on data power.

Big data driven management practices have contributed to business innovations and performance improvement for industrial organizations. Chen et al. (2013) list a group of challenges links to data strictly related to the big data characteristics:

1. Volume: scarcity vs. big volume

2. Velocity: speed vs. delay in updating

3. Variety: various vs. single source

Moreover, the challenges vary in different application scenarios. Particularly, the healthcare scenario manages predominantly scientific data collected from data-intensive experiments or applications (Chen et al., 2013). These data are very application-dependent, ranging from structured data (e.g. time-series data) to semi-structured data (e.g. Extensible Markup Language (XML) data) and unstructured data (e.g. images) (Zhou et al., 2019). Industry challenge concerns the difficulty of handling multiple sources of data (Despotovic et al., 2018), small dataset typical of fragmented healthcare organizations (Cao et al., 2014); privacy and protection rules that obstacle the implementation of flexible and modular approaches to matching minimal data from different organizations (Kaushik and Raman, 2015).

2.2 Digital technology in industrial sector

A digital technology is defined as a set of information technology artifacts augmented by third-party peripheral derivatives such as hardware and software systems that facilitate the integration of business resources with those of the business ecosystems (De Reuver et al., 2018). It is a digital environment where actors of different natures exchange information, goods and services.

Digital technologies are mainly discussed in three fields of research - information systems, economies, and management – that promote various conceptualizations of digital platforms. However, none of each conceptualization can understand the phenomenon completely. Indeed, the convergence of different discipline studies is relevant to define digital platforms’ characteristics and evolution.

Information system studies are focused on digital infrastructures, architectures and technology. Information system scholars define digital platforms as technical artifacts where the platform is an extensible codebase offered at a third-party that can complement this codebase integrating modules (Tiwana et al., 2010; Boudreau, 2012). Pervasive digitization gives birth to a new type of product architecture: the layered modular architecture. This new architecture instigates profound changes in the ways that firms organize for innovation in the future (Yoo et al., 2010). German academic and industrial circles believe that the concept of Industry 4.0 is the fourth industrial revolution led by intelligent manufacturing. Digital technology plays a certain role in promoting industrial digital transformation. Such as digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. Many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields (Tao et al., 2019). In the extent of Industry 4.0, there are two recurring topics: individualization and integration (within the value chain and across the value system). The developments of Industry 4.0 within the literature and reviews were described by (Brettel et al., 2017), those in the four areas of individualized production, end-to-end engineering in a virtual process chain and production networks were analyzed in eight journals and the implementation of Industry 4.0 were introduced by (Ortt et al., 2020). Digital platforms are relevant as they impact the second topic. Digital platforms can stimulate knowledge sharing (Scuotto et al., 2017).The configuration of artifacts enables the interrelation among users, providers and third parties, inevitably impacting the style of control and governance of the platforms. For instance, blockchain technology is promoting decentralized platforms introducing a new paradigm of governance and control.

Economies studies have focused on the theorization of digital platforms as of multi-sided markets, particularly exploring effects generated by multiple user groups in terms of network externalities (Rochet and Tirole, 2003; Boudreau and Hagiu, 2009; Evans and Schmalensee, 2013). The value-capture problem for innovators in the economy involves some different challenges from those in the industrial economy. It inevitably requires understanding the dynamics of platforms and ecosystems (Teece, 2018a, b). Network externalities were found to have a wide impact on the technology’s usefulness, increasing as its installed base of users increases (Katz and Shapiro, 1985; Arthur, 1989; Shapiro and Varian, 1998). Moreover, at the same time, the technology’s usefulness invites actors with different interests to share exigencies and interest in a win-win logic.

Managerial studies mainly focus on the platform’s ability to change firms’ business models, stimulating innovation and ecosystem development. Managerial studies on platforms contribute to considering digital platforms as socio-technical phenomena rather than technical phenomena due to their property in stimulating business networks (De Reuver et al., 2018) and ecosystem dynamics (Hein et al., 2020). For instance, Tiwana and Bush (2014), Evans and Schmalensee (2013) and Parker et al. (2016) provide multiple perspectives on platforms’ ability in shaping business, organizational models and entire economies. Industry 4.0 provides new paradigms for the industrial management of SMEs (small and medium enterprises). SMEs do not exploit all the resources for implementing Industry 4.0 and often limit themselves to the adoption of cloud computing and the internet of things (Moeuf et al., 2018). Digital platforms in fact, challenge traditional business propositions by offering hardware or software tools that simulate the definition of co-created value, inducing a set of new rules, standards and organizational processes to coordinate consumers, suppliers and partners (De Reuver et al., 2018; McIntyre and Srinivasan, 2017).

According to the managerial literature, digital platforms enable platform agents to share and use shared resources and knowledge while leveraging unique resources. In this respect, the recent advancements of digital technology in collecting, analyzing and interpreting information have boosted the adoption of digital platforms as facilitators of interactions and have placed information and network management at the center of many business models (McAfee et al., 2012; Van Alstyne et al., 2016). In other terms, platform configurations were able to develop multilateral links among business actors (e.g. customers, suppliers and partners) and stimulate network and ecosystem innovation (Ciborra, 1996; Gupta et al., 2007). Within these networks, digital platforms mediate interactions between actors (Parker et al., 2016) and leverage innovation (Yoo et al., 2010).

Several scholars have tried to classify platforms. De Reuver et al. (2018) distinguish platforms according to the industry connection:

1. Social media platforms based on people interaction and experience sharing (e.g. Facebook).

2. Operating system platforms based on the combination of operative systems that provide parallel or combined processes (e.g. Android and iOS in the Telco industry).

3. Payment platforms that aim to simplify payment processes (e.g. PayPal, Apple Pay in the financial industry).

4. Peer-to peer digital platforms to undercut the role of intermediaries in traditional transactions from mobility industry to tourism to finance (e.g. Uber, Airbnb).

Blaschke et al. (2019) execute a taxonomy of platforms identifying three archetypes classified on four-layered dimensions—namely, infrastructure, core, ecosystem and service dimensions (Kazan et al., 2018; Karhu et al., 2018). The infrastructure could be with a direct, indirect and open access (Constantinides et al., 2018; Henfridsson and Bygstad, 2013). Respectively the infrastructure owner allows access, and intermediaries allow access, infrastructure devoid of permission. Core dimension refers to core artifacts of software and hardware. Karhu et al. (2018) distinguish in access openness and resource openness, indicating the access of third parties respectively to use the core artifacts to create platform augmenting derivatives and access core resources by forfeiting related intellectual property rights for the advantage of platform improvement. The ecosystem dimension refers to the complex network of the platform – owner, partner, end-user and provider – (Wareham et al., 2014), differentiating the network in federated and private networks (De Reuver et al., 2018). Despite the abundance of research in open innovation, few contributions explore it at inter-organizational level, and particularly with a focus on healthcare ecosystem, characterized by a dense network of relationships among public and private organizations (hospitals, companies and universities) (Secundo et al., 2019). Respectively an open-loop ecosystem that mobilizes varied platform-augmenting third-party actors distributing value among federated; closed-loop ecosystems comprising an exclusive selection of private actors with the scope to protect services from unauthorized actors. Service dimension is intended as the benefits of one party (Williams et al., 2008). Platforms could have an exchange orientation or a design orientation. The exchange orientation (Bapna et al., 2017) corresponds to reducing transaction costs in direct actor-to actor exchanges. Design orientation (Karhu et al., 2018) aimed to enable third parties to design platform derivatives and disseminate them to a large audience.

Based on the described variables, the taxonomy distinguishes in:

1. orchestration platform;

2. amalgamation platform;

3. innovation platforms.

Orchestration platforms assemble federated networks with high levels of openness—both access or resource openness—derivatives. They rely on indirect access to existing digital infrastructures and are orientated to design.

Amalgamation platform assembles a private network of few private actors that cultivate and grow private businesses without the intervention of third parties. However, they are contingent on specific resources and capabilities to make the platform self-sustainable. They rely prevalently on direct access to existing digital infrastructures and are orientated to exchange.

Innovation platforms are a hybrid form of platform. They are prevalently open in including actors, in sharing resources and in adopting infrastructure. They aim to embrace process innovation to deliver digital service distinctively different from traditional industry’s dominant process logic.

2.3 Digital platform driven innovation management model

Recent researchers state that one of the most important factors for achieving innovation performance in the digital economy are digital platforms (Jacobides et al., 2019; Yudina and Geliskhanov, 2019). Trabucchi et al. (2021) propose a framework to conceptualize the platform-driven innovation phenomenon, describing different platform roles in the innovation processes. Authors classify platforms as simplifiers, catalysts and enablers of innovation.

1. Simplifiers homogenize pre-existing transactions, for example, re-value existing assets (Dell’Era et al., 2021).

2. Catalysts reveal to potential providers/buyers the existence of an unexploited market, facilitating matchmaking and the removal of frictions.

3. Enablers create new forms of content and interactions. Usually, this innovation happens outside the platform owner, however allowing others to adopt innovation.

Previous scholars had developed more in-depth research of platform dynamics that lead innovation, exploring platform capability prevalently in influencing platforms users such as end-users, providers, owners or the entire industries. More precisely, a group of scholars refers to digital platform capability for describing the platform’s role in capturing innovation for multiple platform stakeholders (Alegre and Chiva, 2008; Rai and Tang, 2010; Gawer and Cusumano, 2002; Jun et al., 2021).

Yoo et al. (2012) affirmed the innovation of digital platform technology at the user and provider levels，arguing its relationship was based on the capabilities of digital platform due to the platform’s ability to enable networking, data diffusion and knowledge absorption (Constantinides et al., 2018). For instance, Cenamor et al. (2019) assess the indirect but positive effect of platforms on SMEs’ performance via network capability. Digital platforms turn the focus of value creation toward the network, entailing a shift toward an externally oriented organization (Parker et al., 2016).

In a similar vein, Rai and Tang (2010), Teece (2017, 2018a, b), Nambisan et al. (2017) sustain that digital platform capabilities enable and promote innovation performance in participant organizations, integrating key knowledge and using internal and external resources of the organization. Authors hint at an innovative effect also at the industry level since platforms help organizations face the rapid changes of the market efficiently and respond better to highly changeable market needs (Helfat and Raubitschek, 2018; Teece, 2018a, b). De Reuver et al. (2018) provided a similar intuition, sustaining the platform’s ability to transform and innovate entire industries.

Helfat and Raubitschek (2018) analyze the dynamic capabilities of platform leaders/owners. According to Kroh et al. (2018) and Ravichandran (2018) digital platform capability may not improve firm performance directly but rather through dynamic capabilities. Helfat and Raubitschek (2018) provide a theoretical analysis that explains three dynamic capabilities of platform leaders – innovation, environmental scanning/sensing and integrative, explaining how these capabilities impact on innovation performance of the platform owner. Howells (2006) defined the innovation intermediary agency, “organization or body, which acted as an agent or intermediary between two or more parts in any aspect of the innovation process.” Particularly innovation capability refers to the organizational or individual capability to set routines and practices that stimulate product, services and process innovation. This capability improves the ability of platform leaders to take advantage of innovation opportunities and threats. The environmental scanning/capability refers to the organizational or individual capacity to recognize emerging patterns in the environment and interpret these data accurately and quickly (Helfat and Peteraf, 2015). This means adequate product and services to environmental opportunities. Integrative capability refers to designing and transforming their business models, products, and ecosystems as a consequence of cross-side network effects. As an intermediary, digital platform carries out digital innovation in a dynamic ecosystem, and the three complement each other and operate together. This capability supports interactions and relationships with external parties, enabling firms to align activities and products, resources and capabilities, investments and objectives with their partners, in addition to facilitating internal coordination within firms (Chen et al., 2017). Most of the studies of these scholars focused on the role of dynamic capabilities of digital platforms, but in the evolving enterprises (such as healthcare), digital platforms were not enough to manage their data innovation research.

Recent research shows that digital platform capability can also positively affect an organization’s other internal capabilities and transform industries. We propose a theoretical model (Figure 1) that considers in an overall manner the impact on platform dynamic capability on the user side and industry side. The efficient management of internal and external information flows facilitates opportunity discovery at the industry level and accelerates innovation at the user level (Mikalef and Pateli, 2017; Shu and Steinwender, 2019).

3.1 The case study approach

We conducted a qualitative and explorative study in order to answer our research question on how digital platforms support firms in managing data innovation challenges. According to Yin (2003), the research method of the pilot case study is used as the first step of exploratory research to support studies in which the authors have not already defined specific propositions and hypotheses – as in this instance. This case adopts the abduction research scheme. Furthermore, other authors in the literature argue that a single case study can be useful to validate theoretical developments (Eisenhardt, 1989; Easton, 1998) by focusing on a special unit (Jacobsen and Sandin, 2002) to explore a new phenomenon in order to understand it (Cousin, 2005). In particular, there are studies that focus on a single case study, which can validate the development of new theories (Eisenhardt, 1989; Yin, 1994). In agreement with the statement, we focused on a special unit – a data-driven platform – validating the theoretical development of the effect of platform dynamic capabilities and trying to understand the role of the platform in managing innovation challenges.

We select a case of relevant importance, unique in its nature, to conspicuously explain a phenomenon still under investigation. We have defined this company as unique in that they have developed a data-driven platform operating in the health sector as we will describe in detail below. The selected case was Apheris AI GmbH which well represents the innovative role of data-driven platforms. The platform operated across a wide range of industries, such as Manufacturing, Material Sciences, Chemistry and Automotive, but the healthcare industry was the main focus of the platform. The choice was based on the fact that we were looking for a data-driven platform, operating in the health sector with the aim to propose innovative solutions for data management. In particular, the selection of this case was based on the fact that there are no other data-driven platforms capable of managing data in the health sector while protecting patient privacy. Apheris AI GmbH in fact, combines AI and innovation in data management, dealing with data challenges linked to privacy and protection. It acts as a bridge in the adoption and management of innovation in the complex European healthcare system (the General Data Protection Regulation), where data are hardly shared due to endogenous factors (i.e. Privacy, Intellectual property), while it proposes solutions to improve and speed up research and knowledge sharing. The European context, with respect to the American context, is more complex concerning privacy, protection and security aspects, thus offering a wide casuistry of data-driven innovation barriers and opportunities. For instance, healthcare privacy laws in the USA allow hospitals to share information with contractors and allow researchers to analyze patient data without express permission from those patients. Healthcare companies can use that information in any way they see fit, including to boost profits. In Europe, this is not the case.

Multiple sources of data collection were adopted to gain an in-depth understanding of the Apheris’s dynamics. The documentation involved consisted of semi-structured, indirect interviews, company documents taken from the official company website, the official page of the social network (e.g. LinkedIn, Twitter) and online pitches on the YouTube channel. Based on this qualitative research approach conducted using primary and secondary data, we used multiple sources of evidence to triangulate data (Yin, 2003).

Semi-structured interviews were addressed to the founder and the marketing director. Using this sort of research instrument, we also gained the opportunity for the discussion to include areas that had not been planned by the interviewer. We wanted to provide a theoretical model and enrich them with a case study.This meant that we adopted an abductive research protocol. Distinctions between abduction and induction, and between phronesis and theory, were often elided in methodological discussion about case study. Making these distinctions clear offered a pathway for the better conduct of case study and for a less apologetic stance in its use. Case study can more unselfconsciously look to the anatomy of narrative for the justification of its processes and its conclusions (Thomas, 2010).

The data collected from the interview was compared with data collected from other secondary sources thus improving the validity of the data coding sources (Patton, 1999). First of all, we carried out a text analysis on the documents related to the case study. Secondly, we compared them with other information collected from the official social pages of the brand. In this way we were able to ask more specific questions during the interviews in order to get more details and information. Finally, we triangulated the data to strengthen the contextuality of our analysis model.

The data analysis was developed in a very precise and outlined order, using NVivo© 12 software to transcribe interviews and video pitches and to code and label data. The NVivo© 12 tool was also useful to discard data, not of interest with the reference literature (e.g. automotive data, chemistry data) and to identify key constructs from data. More specifically, we have followed the below process.

Interviews and pitches were transcribed, while other documents were directly uploaded on Nvivo as qualitative notes. After this phase, we coded data and then proceeded toward reducing codes into categories and concepts. Categories have given rise to enabled capabilities and innovation impacts, while concepts have given platform dynamic capabilities. The Nvivo model tools have supported us in creating a whole vision of platform capabilities, enable capabilities, level of action and impacts.

To discuss the role of the digital platform we firstly analyze platforms’ characteristics according to Blaschke et al. (2019), secondly, we explore the platform’s capabilities according to Helfat and Raubitschek (2018). In addition, we adopt the theoretical model proposed in Figure 1 to investigate the platform capability effect in terms of enabled capability and innovation impact on users, providers and the industry.

3.2 The case finding

Apheris AI Gmbh founded in Berlin, Germany by Robin Röhm and Michael Höh in 2019 presents itself as a start-up that enables companies to collaborate and analyze data without compromising privacy. The platform enables secure data analysis between organizations while keeping proprietary information private. With Apheris, organizations can build new data solutions and unlock entirely new insights from multi-party distributed data without compromising security and ownership. The company’s mission is “fundamentally change how companies collaborate securely and extract value from data”. In fact, very often large amounts of very important data remain “locked” in the hands of the data owner due to factors such as privacy, legal or security, slowing down technological development, innovation and denying valuable progress. This is especially true in the health sector in Europe, where it is almost unthinkable to share data from one hospital to another due to very restrictive privacy regulations that treat such data as sensitive.

The team of Apheris AI GmsbH is composed of highly educated, very technical and scientific people with 60% having a Ph.D. The startup given the innovative idea has managed to achieve excellent results and be awarded among the best startups in numerous competitions and rankings. In fact, among the various rankings, it is reported that in 2020 it manages to place 18th out of 50 according to the ranking top 50 startups (https://www.top50startups.de/start-ups/ranking/2020/apheris) and in 2021 it is placed as 25th out of 30 startups according to the Forbes ranking (Forbes, 2021). Furthermore, CEO Robin Röhm stated that, given the surprising innovation in such a delicate sector, he managed to obtain financial support from leading European and US investors including LocalGlobe, MuleSoft founder Ross Mason’s Dig Ventures and well-known faces such as Patrick Pichette, former CFO of Google and current board chairman for Twitter.

The Apheris AI platform has established several partnerships to innovatively propose solutions to existing problems. For example, on the occasion of the COVID-19 pandemic, it established a partnership with Openmined. Using specific technologies (e.g. Cryptography, Private Set Intersection), it was possible to transform the tracking data of people affected by the virus, preserving their privacy and generating an increase in the speed of information and response to reduce contagion. Another important partnership was made with Gaia-x, to propose a “Semantic Platform for Intelligent Decision Making and Operational Support in Control Centres and Situation Management’’ enabling resource planning, emergency communication, resource overload prediction, scenario simulation, advanced situation management and mitigation of critical supply chains with the privacy and data protection guaranteed by Apheris AI that will provide secure and privacy-compliant machine learning on distributed data.

6.1 Theoretical implications

This research holds several contributions to the prevailing theory. It enriches the existing literature on innovation in healthcare. Schiavone et al. (2021) developed a multi-level framework for the healthcare ecosystem rearranging, extending the value co-creation process beyond coordination between different types of stakeholders and emphasize the results of digital networks. This paper contributes to the theory by adopting a new lens of analysis for coping with innovation challenges within the digital healthcare network. In fact, the investigation of Apheris case study has provided evidence for formulating a new theoretical perspective. By analyzing the dynamic capabilities of digital platforms, a healthcare ecosystem covering healthcare enterprises, participants and digital healthcare networks is constructed, so as to coordinate the entire ecosystem with data-driven innovation, which is conducive to the healthcare industry to actively respond to the challenges of data innovation management to a large extent. The digital technologies transition should be carried out, starting from owner capabilities, at all network levels of action. Such interpretation enables exploiting new additional and useful capabilities, specifically learning, organizational and network capabilities. This evidence might be supportive for new additional studies which pretend to zoom each effect per enabled capabilities. Secondly, this paper also provides a different interpretation of the innovation challenges of healthcare network. In the context of rapid digital development, healthcare, as an important livelihood industry, plays a self-evident role, but at the same time, it is also experiencing some innovative challenges. Among them, the most important is the management of medical data and information. Medical data and information are characterized by various forms, wide distribution and complex sources. How to deal with these data and information is the focus of medical care industry. Furthermore, this paper can support digital transition management studies in complex environments. In other words, the healthcare sector is characterized by a great level of complexity and a high quality degree of knowledge. Under these circumstances we can draw evidence on digital platforms in complex environments. We provide a new point of view to tackle the study of digital platforms and value creation under the circumstances of knowledge intensive industry, sensitive data production and high level technologies. This article through to the digital platform used in medical care show the dynamic capability of multi-faceted analysis, from the website and interview to collect the data of information collecting and analysis, it is concluded that health care network innovation challenge is implemented based on the management of the whole system, and this kind of data management is needed to drive. In other words, this paper seeks to expand the literature on healthcare digital platforms by driving the innovation process through data-driven management refinement. The paper provides a new theoretical interpretation of innovation challenges for healthcare networks. In short, the paper moves the attention on data-driven innovations. This approach is full of novelty, the novelty comes from adopting digital platform dynamic capabilities for managing the healthcare data rather than technological or infrastructural points of view.

6.2 Managerial implications

This paper also advocates various managerial implications. Starting from the point of view of a single actor healthcare network, the investigation has expanded the perimeter of analysis beyond the lens of analysis of platform owners in favor of an overall overview of actors’ networks. That kind of approach provides practical implications in supporting several healthcare players. First of all, the hospitals should harness the idea that new capabilities might come from the platform owner for filling the lack of expertise in data-driven innovation management. The scarcity of data, the heterogeneity of data or the data access are only a part of a tricky and complex scenario around the transition of hospitals toward digital platforms. The paper seeks to provide new helpful guidelines for structuring effective and coordinated networks of healthcare players. Furthermore, this kind of consideration concerns also the pharmaceutical companies that might be providers and consumers of data. The incidence of management innovation in the health care system is also a category to be considered. To a large extent, the incidence of management innovation in the health care system provides a reference. The case study remarks support this evidence in fact the interview overscores the needs of leveraging the full potential of the company’s most valuable asset data. Results present data-driven platforms as intermediaries able to trigger actors’ capabilities in overcoming industrial challenges linked to a data-driven scenario and in enabling innovation practice. Over mentioned capabilities unlock in fact participants in terms of networking, learning and organizing capability respect to innovation. Furthermore, their role is reflected also on the industry capability to face innovation issues. The results generate several implications in favor of new business model developers. In fact, the study of capabilities put the attention on specific value creation activities that might be considered for starting a new business model. Our work might be supportive in clarify what area of healthcare digital platform are ready to be more innovative and disruptive. That kind of phenomena is not full of novelty, although our paper put the attention on the possible driver tied to the capabilities for business model generation within healthcare systems.

The paper conceptualizes potential capabilities emerging from the healthcare network. This result is not only useful for the single actors but most of all for the management of public or private healthcare systems. In fact, the entire system gets higher and higher performance by providing fluidity to data-driven flow by fostering the data sharing or by enhancing the standardization of data. The performance improvement concerns patient care, financial stability and innovation development.

6.3 Limitations and future research direction

Several limitations of the current research should be taken into consideration. Firstly, in this research, the data are collected from the perspective of a single actor network. On one hand, this is the most consistent point of strength, because Apheris is a health care start-up, it can collect data while keeping the company private. Although, on the other hand, this restricted point of view could not be effective for investigation on other players such as hospitals. This issue is just in part addressed in this paper by Apheris interview that albeit indirectly takes into consideration its partners’ comments, opinions and remarks. Another limitation might affect the generalizability of finding beyond healthcare digital platforms. The paper must be positioned in the vein of study of healthcare innovation management. Although several findings might be extended to digital platforms of other industries, the healthcare scenario is full of specific features such as the higher level and number of specialists, or the heterogeneity of needed knowledge that suggests avoiding generalization beyond the healthcare innovation processes. Although that limitation might be a starting point for new future research concerning other industries. Furthermore, this paper is an explorative study for digging new evidence about data-driven innovation within healthcare systems. New further studies seem to be necessary, especially for investigating the impact of emergent new capabilities per each challenge. This paper opens the avenue to new quantitative studies that intend to measure and assess the performance of new digital platform configuration within healthcare systems.