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Open Digital Platforms in Health Care: Implementation and Scaling Strategies



We investigate strategies to implement and scale digital platforms in highly-regulated settings such as health care. Despite considerable research efforts, these scaling dynamics are still not well understood. Based on a qualitative, comparative study (U.S. and Germany) of six digital health care platforms, we suggest two feedback cycles that contribute to explaining the arising difficulties: First, we observe that openness on code and content layers fuels platform growth as suppliers, healthcare providers, insurances, and patients are more likely to use and contribute to the platform. In opposition, risks such as a lack of or uncertainty about regulations prompt the provider to close the platform to uphold control which in turn reduces the benefits for potential suppliers. We further discuss scaling strategies when multiple user groups are involved.
Open Digital Platforms in Health Care
Thirty Seventh International Conference on Information Systems, Dublin 2016 1
Open Digital Platforms in Health Care:
Implementation and Scaling Strategies
Daniel Furstenau
Freie Universität Berlin
Garystr. 22, 14195 Berlin
Carolin Auschra
Freie Universität Berlin
Boltzmannstr. 20, 14195 Berlin
We investigate strategies to implement and scale digital platforms in highly-regulated
settings such as health care. Despite considerable research efforts, these scaling
dynamics are still not well understood. Based on a qualitative, comparative study (U.S.
and Germany) of six digital health care platforms, we suggest two feedback cycles that
contribute to explaining the arising difficulties: First, we observe that openness on code
and content layers fuels platform growth as suppliers, healthcare providers, insurances,
and patients are more likely to use and contribute to the platform. In opposition, risks
such as a lack of or uncertainty about regulations prompt the provider to close the
platform to uphold control which in turn reduces the benefits for potential suppliers. We
further discuss scaling strategies when multiple user groups are involved.
Keywords: Digital health platform, implementation and scaling strategy, openness
Platforms leverage software code that other companies can build on and that consumers can use (Gawer
and Cusumano 2008). This has become the template for the establishment and long-term success of
software-based companies, startups and established businesses. Many digital platforms have emerged
over the last years in different industries. Some scaled exponentially like Uber. While Uber was
occasionally held back (Freier 2015), it has constantly grown its population of users and services.
Launched in 2010 in the U.S., Uber now attracts around eight million users in 60 countries and has added
multiple services like carpooling and food delivery. This is hardly plain luck. To achieve that growth,
network effects are required that attract sufficient numbers of users, both on the supply and demand side
(Gawer 2014). To attract both sides, platform provider’s may try to tune their implementation and scaling
strategy, i.e. by identifying the need for interoperability or openness (Eisenmann et al. 2009) and
understanding and caring for the needs of customers and suppliers (Hagiu and Rothman 2016; van
Alstyne et al. 2016). The peak in attention for platform strategies shows the importance of this topic.
Surprisingly, we can only observe first efforts to implement and scale digital platforms in health care.
While Apple Health and Google Fit have begun to resurrect the industry (Chen 2014), vivid examples of
platforms that affect the everyday, regular treatment processes of patients are still rare. As Google noted in
2011 when it shut down its ‘Health’ platform, many companies ‘haven’t found a way to translate that
limited usage into widespread adoption in the daily health routines of millions of people’ (Google 2011). In
addition, Google’s and Apple’s attempts can be described as at least partially closed in that they subtly
exclude many parties from owning, providing, using, or contributing to the platform (Eisenmann et al.
2009). Open platforms, defined by the absence of restriction for participation, offer a promising
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alternative. Their architecture builds on components that have well-defined, published interfaces (API’s)
allowing for interconnection and use in ways other than as originally implemented or intended (Estrin and
Sim 2010). At least theoretically, this makes them an engine for health care innovation (Estrin and Sim
2010). Despite their potentials, we are still awaiting such ‘open’ platforms that overcome the traditional
model with episodic care in clinic and hospital settings which is suboptimal for overcoming for instance
chronic diseases. Implementation rates of IT-based solutions fall short in many major countries such as
the U.S. and Germany (Lluch and Abadie 2013). These examples show the preliminary nature of and the
difficulties to implement and scale open digital platforms in health care.
From the literature in health information systems, we begin to understand that implementing and scaling
a digital platform especially in highly-regulated environments such as health care is a difficult task. It
requires to resolve tensions around autonomy-related benefits and control (Eaton et al. 2015). While
autonomy of users without centralized control may result in difficulties for the provider to enforce his or
her own vision, find a viable business model, and meet the regulatory requirements, too strict control can
discourage potential users and thus prevent the platform from scaling. The question of control is
particularly challenging in health care. At worst lives are at risk when a platform fails. There is a need for
rigid quality control (Thorseng and Jensen 2015), security and privacy (Huckman and Uppaluru 2015),
and certification (Diamond and Shirky 2008). In extension to other markets, a health care platform must
thus sensibly balance the interests and goals of multiple parties as well as underlying issues of
accountability and liability (Constantinides and Barrett 2014). Thus, it is necessary for platform providers
to carefully consider the type and degree of openness when implementing and scaling a digital health
The literature on health information systems has produced a rich contextual understanding of how and
why implementing and scaling IT-based solutions in health care is difficult and how adoption may be
spurred (Aanestad et al. 2014; Aanestad and Jensen 2011; Braa et al. 2007; Grisot et al. 2014; Hanseth and
Aanestad 2003; Hanseth and Bygstad 2015), yet it says relatively little about the particular characteristics
of and challenges in platform-based settings. Thus, it tends to underplay the growing complexity arising
from more and more differentiated activities and actor constellations in the health care value creation
process. The novelty of our study lies in our focus on platforms that are targeted toward companies that
develop medical applications (supply-side users) for usage by health service providers, insurances, and/or
patients (demand-side users). This presents one very recent and promising form of service delivery in
health care, enabled by the growing availability of easy accessible health care API’s (Huckman and
Uppaluru 2015)1. Taking the scant knowledge on these issues as our point of departure, we ask:
How and by which strategies do actors implement and scale open digital platforms in highly-
institutionalized environments such as health care?
To shed light on this issue, we subscribe to a sociotechnical view on platforms (Eaton et al. 2015). That
means we consider platforms as mutually shaped by social and technical elements (Winter et al. 2014).
Our perspective generally very close to a digital infrastructures viewpoint considers platforms as
evolving sociotechnical systems (Tilson et al. 2010). We focus on bottom-up efforts as they often leverage
existing knowledge better and are more responsive to local needs than large-scale implementation
projects, hence increasing their odds of success (Aanestad and Jensen 2011; Constantinides and Barrett
2014; Hanseth and Bygstad 2015). Based on these premises, we are currently conducting an exploratory
multiple case study (Eisenhardt 1989; Yin 2013). We have chosen the U.S. and German health care
industry for a comparative study (for reasons and details see below). By doing so, we aim to investigate
how to design and position digital health care platforms that scale despite challenging institutional
conditions. Our preliminary results suggest that: Firstly, platform providers are faced with both a positive
cycle of network effectsfueled by incentives to open up to suppliersthat adjusts the establishment of a
platform as well as a countervailing risk cycle caused by the highly regulated and institutionalized
environment. This risk circle favors platform’s closeness in the sense that it rejects the interference of non-
core suppliers in the platform creation process, thereby potentially impeding its establishment. Secondly,
conceivable scaling strategies vary by the participating groups that become directly targeted and can be
1 Albeit representing another promising direction for the study of digital health care platforms, our focus is not on
health information exchanges (HIE) and other regional or national data exchange efforts (see e.g. Demirezen et al.
2016; Thorseng and Jensen 2015; Winkler et al. 2014; Yaraghi et al. 2015)
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divided into push and pull strategies, i.e. strategies that address the primary target group (push) and those
addressing other groups that affect the primary target group (pull)a well-known concept from logistics,
supply chain management and marketing (see e.g. Hinkelman 2005).
To arrive at these contributions, this article proceeds as follows. Section 2 presents the theoretical
background on open digital health care platforms. In section 3, we introduce our research design. Section
4 shows preliminary results. Section 5 concludes, shows limitations of this study and introduces next steps
in exploring this research area.
Open Digital Platforms in Health Care!
Digital platforms can be distinguished from many traditional IT-based solutions which do not have built-
in, by-default capabilities for supply-side user extension and commercialization (Tilson et al. 2010, 2013).
Given the importance and ubiquity of the phenomenon in the digital era, both economics and engineering
have picked up on and theorized about it (Gawer 2014). The engineering view suggests that platforms
consist of a core and modules that interact via standardized interfaces (Tiwana et al. 2010). Accordingly, a
platform acts as a building block (Baldwin and Woodward 2008). From here we can see how standardized
interfaces allow for dynamics and scale in the periphery while the core can be optimized for stability and
reliability. This helps to explain innovation in platform ecosystems (Tiwana 2015; Tiwana et al. 2010).
Economists have suggested a second view on platforms (Gawer 2014). Here, platforms are seen as
competing in two- or multi-sided markets (Rochet and Tirole 2003). The platform acts as an intermediary,
spanning boundaries between at least two groups of actors, ‘complementors’ (which we will call suppliers
within this article) and ‘users’ (van Alstyne et al. 2016). This view creates sensitivity for the importance of
“getting both sides on board” in platform establishment and scaling processes (Rochet and Tirole 2003).
However, we suggest that platform providers in the health care sector tend not to act as classical
intermediaries in multi-sided markets. Platform providers are often main contributors to the platforms as
well as their financiers, bringing further user and suppliers together for their own sake (e.g., to achieve a
competitive advantage by offering the platform).
Generally, scaling refers to expanding an IT-based solution to support a growing population of users and
services (Monteiro 1998). For digital platforms, scaling requires an individual level adoption process by
two or more groups of actors. This necessitates structures that can accommodate both continuously
increasing numbers of users and suppliers, i.e. growing within and out of existing target groups, and
offering new services. Hence, the startup problem, i.e. attracting early users or “bootstrapping” a solution
(Hanseth and Lyytinen 2010), becomes more complex. Existing approaches, i.e. designing for initial
usefulness, mutual learning, and complexity management (Hanseth and Aanestad 2003; Sanner et al.
2014), may provide some guidance but they need refinement. For instance, incremental stakeholder
mobilization, as suggested by Aanestad and Jensen (2011), may be difficult as multiple sides need to be
convinced simultaneously. Therefore, it may be necessary to “push” or “pull” particular groups of users in
non-obvious ways.
One important parameter in implementing and scaling a digital platform is its degree of openness.
Openness in platform-based contexts means access to at least three important layers: Code, content, and
physical infrastructure (Lessig 2001). We assume as known physical infrastructure and briefly discuss the
other two. On the code layer, openness refers firstly to questions of participation in the platform itself and
secondly to restrictions to innovate on top of the platform (Eisenmann et al. 2009). Complete openness on
all levels is rare. Linux may be mentioned as an example. A more common situation is some degree of
closeness of the platform core, i.e. ownership and provisioning may be restricted. Only a limited circle of
people have access. For Apple Health, the platform itself is for instance owned and provided by a single,
privately-owned entity. This is a potential source of controversy in health care given different opinions on
whether and to what extent health care is a private or public good (Constantinides and Barrett 2014). In
contrast, we also see more participatory approaches where the platform creation process itself is jointly
promoted by multiple parties, e.g. joint ventures, project-based organizations, or consortia. This can also
help to distribute the share of investment costs across multiple parties, integrate different knowledge, and
reduce the risk of failure (Berggren et al. 2011; Hanseth and Bygstad 2015; Sydow et al. 2012). Yet, it also
comes with a number of challenges such as ‘lowest common denominator’-solutions, slow progress, and
conflicts of interest (Eisenmann et al. 2009). The second set of code-related questions concerns
innovations on top of the platform, i.e. ‘how to handle complementors?’ (Eisenmann et al. 2009). As an
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example: In contrast to Apple, Google has historically provided suppliers a little more freedom in terms of
building apps (e.g., upload and certification is less regulated). Multiple boundary resourcesappear which
represent potential control points for negotiating and tuning the relationship between platform provider
and suppliers (Eaton et al. 2015; Ghazawneh and Henfridsson 2013). One question is the type of interfaces
for building new applications: Should they be public or private; free or for charge; what level of access is
provided (Davenport and Iyer 2013)? All of these areas potentially restrict access for some suppliers. On
the content layer, openness relates to who can access what content, add content (see e.g. Wikipedia) and
with what ease data can be transferred to other contexts. In a sense, this goes beyond questions of
technical interoperability as discussed above but also taps into questions of semantic and process
interoperability. Dictionaries, glossaries, and ontologies try to standardize terminology so that it can be
used for treatment and decision support in multiple contexts. Also patients may want to access and use
their personal data in multiple contexts and in multiple parts of the health care system (Estrin and Sim
2010). Altogether, access to code, content, and physical infrastructure may be open or restricted in
multiple ways, depending on the access granted to ownership and provisioning of the core as well as
degrees of freedoms to innovate on top of it and to use the platform in different ways.
Figure 1. Elements and demand-sided mechanisms of digital health care platforms
Depending on the number of stakeholders participating in the platform, platforms can be one-sided, two-
sided, or even multi-sided (Yaraghi et al. 2015). In the empirical analysis, we focus on two-sided platforms
(e.g., platforms providing apps by developers to patients). We hereby suggest that the demand side of a
health care platform can be segmented into three broad user groups (see Figure 1): Patients, health service
providers like hospitals and physicians, and payers. Typical payers are depending on the national health
care system statutory and private health insurances as well as employers. Stakeholders can also take a
dual role, for instance if an organization is responsible for both health service delivery and its financing
(see cases of health care organization in the U.S.). The platform connects these stakeholders with
companies on the supply side that provide medical services or apps (suppliers), e.g., genomics labs or
providers of clinical decision support. Not in all cases do all the mentioned stakeholders participate in a
single platform. We however assume that different user groups are in specific reciprocal relationships to
each other (e.g. providers offer treatments for patients), influencing conceivable scaling strategies by
fulfilling the needs of each group by the platform.
Altogether, our interest in this study is how platform providers implement and scale open digital health
platforms. To this end, we adopt a sociotechnical view on platforms (Eaton et al. 2015), acknowledging the
(e.g.%hospitals,% physicians)
Tra dit i ona l% model
Scali ng stra teg ies
Demand)for bettertreatment /)payment
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importance of actors, structures, technologies, processes, and their mutual interdependence (Winter et al.
2014). This allows considering tensions between openness and control in the process of implementing and
scaling digital platforms and debates over ‘control points’ (Eaton et al. 2015). Platform openness involves
challenging questions on multiple levels such as whether and to what extent the platform architecture
should allow user to interoperate. Platform openness in a technical and semantic sense is desirable for the
user and should also profit the entire health system (Estrin and Sim 2010). It may also be desirable for the
provider as long as it attracts users and creates network effects (Eisenmann et al. 2009). Yet, openness
may also create challenges as whether the user stays with the platform and whether the provider can
create a viable business model. Furthermore, it is important that legal, data security, privacy, and quality
control requirements are addressed. Therefore, openness becomes a strategic consideration. In a sense,
platforms can be understood as a centralized way of providing capabilities; they unify important control
rights in the hands of the platform provider. But, it is far from clear how health care actors organize the
process of creating a new digital platform: Who is influencing and sponsoring the newly emerging
platform and what role do users play in aligning the development of the platform with his/her own
interest? The empirical analysis should enable to gain some insights into the implementation and scaling
strategies that actors use to create digital health platforms.
Research Design
This empirical study focuses on multiple exemplary cases within the German and U.S. health care system,
applying a qualitative research design. While our review of the existing literature showed that there are
many considerable efforts to understand implementation and scaling in health care, they have rarely
focused on digital health platforms as conceived here as innovation-promoting platforms that can be
extended via publicly available API’s (Estrin and Sim 2010; Huckman and Uppaluru 2015). An exploratory
and comparative multiple case study approach is appropriate for our aim to add insights to this topic
because it allows analyzing the phenomenon in its real-world context and to advance mechanisms and
strategies of scaling (Eisenhardt 1989; Henfridsson and Bygstad 2013).
Research contexts and comparative design. Our aim is to compare efforts in Germany and the U.S.
Comparing two different health care systems is fruitful because the institutional conditions and
regulations differ. The German health care sector is based on the general model of solidary financing
(citizens are obliged by law to get health insurance; insurers are independent from service providers). It is
especially characterized by its traditional long-grown structures and strong regulation (Klöcker et al.
2015). Yet, for about 10 years, new entrants such as internet-based platforms and digital health startups
try to enter the market, providing innovative platform solutions and enriching the market of more
traditional, internal IT infrastructures like electronic health record (EHR) systems in hospitals. In
contrast, the U.S. health care sector provides a generally more market-oriented environment in which
different approaches compete on different levels (Winkler et al. 2014). In addition, efforts to establish
open digital health platforms are generally more progressed than in Germany. First platform battles are
starting to arise, compromising different standards such as HL7 v2, v3, and HL7 FHIR (Fast Healthcare
Interoperability Resources; see Bender and Sartipi 2013). In both systems, markets are highly regulated by
the state, but due to the different general approaches the U.S. system can be classified as less regulated
than the German one. Contrasting efforts in both countries potentially helps to better understand the
institutional conditions in which efforts to implement and scale digital health platforms nest and unfold.
Sampling strategy. Case sampling represents a challenge as the phenomenon of open digital health
platforms is still infant. Many efforts are in their beginning and large private players (e.g., Google and
Apple) who recently entered the market are reluctant to share first-hand information on their strategy. As
we believe in the potential of bottom-up initiatives (Hanseth and Bygstad 2015), we have selected six
promising bottom-up initiatives, representing forerunners in the U.S. and Germany. Within our focus on
digital health care platforms, we have selected cases based on two additional criteria: (1) whether the
platform provider is an established player or a new entrant, and (2) the country (reasoning see above). We
did so because of an interesting tension that is (almost) exclusive for health care organizations: Whereas
established players (incumbents) often face considerable inertia in overcoming existing routines and
structures (Leonard-Barton 1992; Sydow et al. 2009; Tripsas and Gavetti 2000), the knowledge of existing
institutional structures and processes may help them to navigate through the complex regulated landscape
of health care. In contrast, new entrants may be less burdened with legacy processes and structures and
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may thus be abler to disrupt the rigid institutional environment. In addition, incumbents have the
advantage of the existing user network while probably facing greater difficulties to attract new users.
In Germany, we have selected three initiatives that are both different and extreme: Athena, a large hospital
chain, currently establishes a cloud-based health services platform for patients and physicians in
collaboration with startups. St. Mary’s, a large research hospital, plans to create a cloud-based healthcare
platform to connect users in its clinics and beyond. The startup Skyaid has launched a pilot for a cloud-
based Platform-as-a-Service (PaaS) platform. The German examples show an early, but promising stage of
health care platform development. In the U.S., we have selected three more advanced initiatives which we
consider as relevant (for an overview see Table 1). HSPC, the Healthcare Services Platform Consortium, is
health care provider-led organization striving for the delivery of a platform that supports innovative
healthcare apps to improve health and health care (HSPC 2016). SMART, a Boston-based initiative, has
created a promising app platform that enables applications to run on systems which have implemented the
SMART on FHIR specification accessing existing EHR systems or the like (Mandel et al. 2016). Carebox is
a U.S.-based PaaS-offering to create health care applications that run in a cloud environment. A more
detailed description of each of these cases is included in the findings section.
1. Athena
2. St. Mary’s University
5. Smart Health IT
New entrant
3. Skyaid
6. Carebox
Different interviewees
Different interviewees
We disguised the names of these cases as interviewees did only agree to provide access to information, which are
partly gauged as relevant for competition, if anonymity was guaranteed.
Table 1. Case sampling and interviews
Data collection. Our data collection started in 2015. For triangulation purposes, we rely on three main
data sources: First, we conducted first-hand interviews with different actors (platform providers, health
service providers, payers, other context-specific actors such as representatives of industry associations),
participating in emerging innovative health care platforms. Second, we analyzed different written
materials such as press releases, newspaper articles, blogs and position papers from and about the selected
platforms and, third, we participated in events (e.g., entrepreneurship summits, startup pitches, fairs,
meet-ups, discussions). At each event, various ad hoc interviews took place. So far we have conducted 29
formal interviews (see Table 1 for case split), took part in around 30 field events focusing on the
digitalization of health care and analyzed around 50 documents. Further interviews are scheduled.
Our data analysis follows an abductive approach (Locke et al. 2008). We have used both open and axial
coding to induce categories from the documents, transcribed interviews, and field notes, screening our
data for practices, drivers and challenges related to platform implementation and scaling. As a first step,
we coded the interviews with paraphrases close to the original data (e.g., ‘sensing conflicting interests’,
‘building coalitions’, ‘opening up to users’, ‘re-focusing’, ‘exerting control’, ‘taking advantage of
regulations’). For instance, we coded the statement “I try to hook up the right people to make decisions on
these draft standards” by a sponsor of HSPC (case 4) as ‘building coalitions’. Then, we started to aggregate
categories to a more abstract level (e.g., ‘fueling the positive cycle of network effects’, ‘fueling the risk
cycle’) (Gioia et al. 2012). By various iterations we strengthened our preliminary findings about
contributors to successful platform establishment and scaling strategies.
Case Description and Preliminary Findings
Table 2 characterizes the platforms in terms of their sides, platform openness on the code, content, and
physical infrastructure layer, as well as their scaling status. The analysis shows that while all of these
platforms are similar in that they bring together suppliers and demand-side users, the demand side varies
considerably regarding which user groups’ needs are or will be addressed. Also, while most of these cases
are committed to “openness” on many important layers, they have different approaches to platform
ownership, reflecting different levels of openness to participate in the platform development. To compare
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these cases regarding their implementation and scaling strategy, we must critically reflect their scaling
situation against the background of the individual case (country, established/entrant, sides, openness). In
this research-in-progress we can based on the sample only present relatively high-level observations
but our future strategy is to narrow down the sample (i.e., one-by-one) to limit the variance across cases.
Athena (Case 1, GER)
Sides: Athena aims to establish a two-sided platform which is designed for future expansion on the demand
side. The primary target groups on the demand side are patients and secondary collaborating physicians in
outpatient care. Startups are the main supplier. The platform aims to foster innovation and to promote the
long term goal of establishing an openly documented data exchange standard between hospitals and suppliers,
potentially usable also by other hospitals. Athena acts as the platform provider and also as its financier.
Code: Athena draws on IHE / HL7-based middleware and plans to open up to suppliers via a FHIR gateway.
Content: Easy access to content for patients is one of the platform’s main goals.
Infrastructure: No new physical infrastructure; usage of the German Telematics infrastructure is intended.
Scaling: The hospital chain has already begun to gather startups developing relevant apps in an accelerator
program. The platform is now in a development stage and will offer different services that are extended step-
by-step (first mainly in-house information to patients, later including services by suppliers). First services are
patient information display, appointments, and potentially medication plans.
St. Mary’s (Case 2, GER)
Sides: The platform is supposed to connect two sides. Healthcare providers, i.e. clinical users, represent the
demand side. Decision support vendors etc. will represent the supply side. The aim is firstly to automate
patient documentation and secondly to enable further value-creating processes such as better diagnoses.
Code: Commitment has been signaled to support HL7 and other international, non-proprietary standards.
Content: Content-wise standardization is planned to consistently track quality indicators”.
Infrastructure: As for Athena, no separate physical infrastructure is planned.
Scaling: The platform is currently in a conceptual stage. Later, a step-wise roll out is planned. The first
clinical users will be intensive care units, anesthesia, surgery, and other counseling units (e.g., cardiology).
The potential future user network also includes outpatient clinics, family doctors, physiotherapists,
nutritionists, nurses, and case managers, among others. So far this has been done within a closed, clinic-
internal network of participants and no suppliers are on-boarded so far.
Skyaid (Case 3, GER)
Sides: The platform is two-sided. It intends to attract suppliers and companies to create apps which are then
deployed on the Skyaid platform for usage by clinical users or patients.
Code: The code of the platform is based on web technology and HL7 FHIR interfaces. It aims to translate
conflicting standards into a single one, therefore providing open interfaces to suppliers on the code level.
Content: The platform strives for content-wise standardization by the usage of FHIR-based messaging. Easy-
to-use web technology is another aspect to make the content accessible.
Infrastructure: The platform is hosted in a secure, trusted environment; other hosting options are planned.
Scaling: Skyaid is currently in pilot stage and has completed several use cases funded by a government grant.
As one of the first use cases, the platform has enabled a cloud-based data exchange between different
healthcare providers. Skyaid currently refines its platform services in a network of initial users and suppliers.
During this phase, the platform is closed due to control issues with further potential suppliers, but opening it
up to developers with the aim to generate network effects is planned in the near future.
HSPC (Case 4, U.S.)
Sides: There are currently two sides involved in the platform. Suppliers (e.g., startups) provide apps and
services. Clinical users run them on the HSPC platform (i.e., using the SMART standard) to enrich their EHR
system data, run them in a standalone fashion, or advice a clinical decision support mechanism.
Code: The code of the platform is open source. It builds strongly on available web technology and FHIR.
Suppliers can access it via a code repository. New apps that shall be displayed in the HSPC gallery undergo a
review process. A full-blown app storewith a sophisticated certification process is planned.
Content: One important goal of the HSPC initiative is "true semantic interoperability“. The content shall be
standardized by using LOINC and SNOMED. HPSC is also collaborating with the Open CIMI initiative (CIMI
2016), which has created more than 5,000 clinical data models.
Infrastructure: The HSPC platform runs on public internet infrastructure or uses the clinics’ own networks.
Scaling: Several demo applications have been presented at HIMSS 2015 and 2016 conference (e.g., Bilirubin
risk chart, pediatric growth chart). We could identify around ten startups that are currently working with
HSPC to create apps. Some of these apps are already in use at client sites. The user side is leaning toward large
health service providers. The platform creation process is organized as a consortium; it is generally open to
everybody but discriminatory membership fees apply to engage in the consortium.
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SMART (Case 5, U.S.)
Sides: The platform is two-sided; the demand side is split into two customer segments. Suppliers create apps
(e.g., for clinical care, patient education, genomics). These apps are targeted either to health care providers or
to patients.
Code: A code repository allows suppliers to create apps, which are included in the app gallery after a review.
Content: Content and data models shall be standardized by using FHIR resource profiles. One important
premise of the initiative is that content should be easily accessible via web interfaces.
Infrastructures: The platform runs on public web and/or clinic-internal infrastructure.
Scaling: SMART has demonstrated the first six prototypical apps at HIMSS conference in 2014 (Mandel et al.
2016). E.g., for personalized medication, patient information visualization, cardiac risk, and some other use
cases. In 2016, the app gallery has grown to 22 apps. Some of these apps are already in regular use at Boston
Children’s hospital and other sites. The platform creation process is run out of Boston Children’s Hospital and
Harvard Medical School. Several healthcare providers, vendors, and other firms sponsor the development.
Carebox (Case 6, U.S.)
Sides: The platform is two-sided. It allows suppliers to create apps which are then deployed on the platform
and can be used by clients as a backend, clinical data repository, or in integration scenarios.
Code: The platform code is based on FHIR for internal and external data model as well as messaging. It
provides relational storage for FHIR resources with use of open source FHIRbase project and supports HL7
v2 messaging, CDA/CCD documents, etc. Basic usage is free; enterprises must acquire a license.
Content: Data models are based on FHIR profiles, a relatively new way of representing medical data.
Infrastructure: The Carebox platform is hosted in a cloud environment.
Scaling: The platform is currently in its launch. The private firm collaborates heavily with open initiatives
such as the HL7 FHIR work group, project Argonaut, and HSPC. Opening the platform up on the code level
and collaborating with other initiatives is a way to attract potential customers (e.g., clinical users). It
demonstrates the firm’s commitment, credibility, and capability. This is particularly important as the firm
cannot yet build on an existing customer base (although several pilots are underway).
Table 2. Platform design, openness, and scaling efforts of the different platform initiatives
Initial Characteristics of Scaling Strategies for Health Care Platforms
Building on the insight that incremental stakeholder mobilization is important for large-scale IT imple-
mentations (Aanestad and Jensen 2011), our analysis suggests that platform providers can target potential
groups participating in a platform via a push- or pull-strategy (see also Figure 1). By a push-strategy, the
platform provider addresses one of the platform-user groups (payers, health service providers, or patients
depending on the platform) directly as the primary target group. For instance, St. Mary’s, the German
research hospital, aims to scale within a closed set of hospitals while targeting the clinics themselves, but
initially no other user groups like patients. Secondly, by a pull strategy, a platform provider aims to attract
a certain platform-user group by addressing other groups which influence the targeted group in the sense
of the provider by a spillover effect. For instance, Athena (case 1) both tries to offer patients a better
service and to improve the quality of their stationary treatment to animate referring ambulatory
physicians to consider Athena’s hospitals in the highly competitive German hospital market. On the other
hand, they also plan to directly address referrers with the aim to attract more stationary patients (German
patients have the right to choose their hospitals freely). Pull strategies are multidimensional and address
by definition at least one user group less than the number of groups participating in the platform.
Two Feedback Cycles Influencing Platform Establishment and Scaling
To summarize major scaling dynamics in the cases, we suggest two feedback cycles which are important
for the establishment of health care platforms: First, the platform’s openness or closeness, which also
represents the level of control exerted by the platform provider, influences a positive cycle of network
effects. Second, the need to comply with regulatory requirements triggers a counterbalancing risk cycle.
First, the degree of platform openness and closeness mirrors the tension between autonomy and control
of each platform. Especially the more advanced U.S. cases show that the more open a platform is, the
higher is the probability that other organizations will build services on it which increases usage and thus
enables network effects to establish the platform (see also Eaton et al. 2015). For instance, SMART (case
5), has been fairly successful in attracting suppliers to its platform by drawing on openly available web
technology. This in turn resulted in a number of easy-to-use and useful medical apps, which has increased
the acceptance of the platform and directed further attention to it. The German hospital Athena (case 1)
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Thirty Seventh International Conference on Information Systems, Dublin 2016 9
attempts to pursue a similar path, i.e. drawing on open standards and interfaces. This in turn has already
helped them to recruit several startups that aim to test their services in Athena’s hospitals. Due to the use
of open standards, suppliers do not fear lock-ins and high switching costs, adding to their willingness to
contribute to the platform. We call this the positive cycle of network effects.
Second, a platform needs to cope with a lack of or conform to existing regulations in the health care field.
This includes compliance to national and professional laws. For instance, all German cases stated that they
profit from their long-standing experience (e.g., Skyaid’s CEO works in health IT for 15 years) with
German regulatory authorities, e.g. in handling data security or certification issues. Nevertheless, handling
these regulatory issues is time-consuming and costly for the platform providers. Therefore, a lack of or
uncertainty about regulations can trigger a countervailing tendency: Due to the overly strict interpretation
of existing rules, e.g. regarding data security which is not adhered by all potential suppliers, and rigid
habits within highly-regulated environments, we observed that platform providers tend to establish
platforms if at all that ensure them to stay in control, especially in the pilot- and rollout phase
(German cases 1 and 3). That makes it unattractive for companies to engage in the platform as it favors
slow movement and risk aversion. While this tendency was less pronounced in the U.S. with some crucial
regulations in place, here the problems had already shifted toward more specific strategic ones, e.g. the
risky decision whether to focus on simple or more complex use cases. For instance, one startup that has
previously worked with HSPC noted, “where I’m disagreeing is … how they prioritize what, how fast they
are moving” (case 4). In all these cases, network effects slow down or come to a halt. Also, the rise of
different initiatives fragments national platform landscapes. This prevents stakeholders from engaging in
a particular platform as they fear losing their investment (sunk costs) if they chose one that does not scale.
We label this as the counteracting cycle of risks for as well platform providers as for stakeholders.
Concluding Remarks and Next Steps
Our aim in this paper was to start a debate on how and by which strategies actors implement and scale
open digital platforms in highly-institutionalized environments. In contrast to other sectors, so far neither
in the U.S. nor in Germany a platform provider was able to establish a field-wide digital health care
platform. Whereas the establishment of a platform requires a positive cycle of network effects, this cycle is
countervailed in health care: Opening platforms up to suppliers comes with losses of control for the
platform provider. This creates a difficult challenge to align with institutional requirements (e.g., in regard
of national standards and laws). Moreover, the institutional setting often prohibits offering entirely open
platforms (that do not in some ways restrict the who, how, and on what level access is granted). In
consequence, a vicious risk cycle emerges that prevents platforms from scaling and slows down or hinders
their establishment.
So far, the challenge of integrating data across multiple sources like care providers and patients stays
unsolved, though initiatives that aim for more open platforms are coming up both in the U.S. and in
Germany. Our study indicates the significance of a platform design approach that is sensitive to the
preferences of different user groups and that builds on deep knowledge about the institutionalized laws,
regulations, and habits of the field.
Although our analysis revealed preliminary drivers and challenges of platform implementation and sca-
ling, our research is ongoing and provisional. First, despite our efforts to triangulate the data by using
different sources, our method of data collection and analysis may be biased by the inclinations of
interviewees and interviewers. We also only considered a limited number of cases in two specific national
contexts. Second, we do not claim that our findings are exhaustive. Further drivers (e.g., impulses by
politics, other national drivers) and challenges (e.g., resource shortages, power dynamics, and path
dependencies) must be considered as additional or even alternative explanations. Earmarked for future re-
search are multi-stakeholder, processual, real-time observations of successful and failed attempts to create
platforms (Langley 1999; Langley et al. 2013). We hope that this article animates and guides such work.
We thank Stefan Klein and Martin Gersch as well as the two anonymous reviewers and the associate editor
of the 2016 International Conference on Information Systems for their very helpful comments on earlier
versions of this paper. Daniel Furstenau is grateful for his support by Freie Universität Berlin within the
Excellence Initiative of the German Research Foundation.
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Thirty Seventh International Conference on Information Systems, Dublin 2016 10
Aanestad, M., and Jensen, T. B. 2011. “Building Nation-wide Information Infrastructures in Healthcare
through Modular Implementation Strategies,” Journal of Strategic Information Systems (20:2), pp.
Aanestad, M., Jolliffe, B., Mukherjee, A., and Sahay, S. 2014. “Infrastructuring Work: Building a State-
Wide Hospital Information Infrastructure in India,Information Systems Research (25:4), pp. 834
van Alstyne, M. W., Parker, G. G., and Choudary, S. P. 2016. “Pipelines, Platforms, and the New Rules of
Strategy,” Harvard Business Review (94:4), pp. 5463.
Baldwin, C. Y., and Woodward, J. 2008. “The Architecture of Platforms: A Unified View,” Working Paper,
HBS Finance Working Paper No. 09-034, (doi: 10.2139/ssrn.1265155).
Bender, D., and Sartipi, K. 2013. “HL7 FHIR: An Agile and RESTful Approach to Healthcare Information
Exchange,” in 2013 IEEE 26th International Symposium on Computer-Based Medical Systems
(CBMS), pp. 326331.
Berggren, C., Bergek, A., Bengtsson, L., Hobay, M., and Söderlund, J. 2011. Knowledge integration and
innovation: Critical challenges facing international technology-based firms, Oxford: Oxford
University Press.
Braa, J., Hanseth, O., Heywood, A., Woinshet, M., and Shaw, V. 2007. “Developing Health Information
Systems in Developing Countries: The Flexible Standards Strategy,” MIS Quarterly (31:SI), pp. 381
Chen, B. X. 2014. “Apple Unveils New iOS and Mac Software at Conference,” New York Times, New York,
p. B3.
CIMI. 2016. “Clinical Information Modeling Initiative,” (available from:;
retrieved September 7, 2016).
Constantinides, P., and Barrett, M. 2014. “Information Infrastructure Development and Governance as
Collective Action,” Information Systems Research (26:1), pp. 4056.
Davenport, T. H., and Iyer, B. 2013. “Move Beyond Enterprise IT to an API Strategy,” Harvard Business
Review, (available from:; retrieved
September 6, 2016).
Demirezen, E. M., Kumar, S., and Sen, A. 2016. “Sustainability of Healthcare Information Exchanges: A
Game-Theoretic Approach,” Information Systems Research (Article in Advance).
Diamond, C. C., and Shirky, C. 2008. “Health Information Technology: A Few Years of Magical
Thinking?,” Health Affairs (27:5), pp. 383390.
Eaton, B., Elaluf-Calderwood, S., Soerensen, C., and Yoo, Y. 2015. “Distributed Tuning of Boundary
Resources: The Case of Apple’s iOS Service System,” MIS Quarterly (39:1), pp. 217243.
Eisenhardt, K. M. 1989. “Building Theories from Case Study Research,” Academy of Management Review
(14:4), pp. 532550.
Eisenmann, T. R., Parker, G., and van Alstyne, M. 2009. “Opening Platforms: How, When and Why?,” in
Platforms, Markets and Innovation, A. Gawer (ed.), Cheltenham, UK: Edward Elgar, pp. 131161.
Estrin, D., and Sim, I. 2010. “Open mHealth Architecture: An Engine for Health Care Innovation,” Science
(330:6005), pp. 759760.
Freier, A. 2015. “Uber Usage Statistics and Revenue,” (available from:; retrieved February 9, 2016).
Gawer, A. 2014. “Bridging Differing Perspectives on Technological Platforms: Toward an Integrative
Framework,” Research Policy (43:7), pp. 12391249.
Gawer, A., and Cusumano, M. A. 2008. “How Companies Become Platform Leaders,” MIT Sloan
Management Review (49:2), pp. 2835.
Ghazawneh, A., and Henfridsson, O. 2013. “Balancing Platform Control and External Contribution in
Third-Party Development: The Boundary Resources Model,” Information Systems Journal (23:2),
pp. 173192.
Gioia, D. A., Corley, K. G., and Hamilton, A. L. 2012. “Seeking Qualitative Rigor in Inductive Research:
Notes on the Gioia methodology,” Organizational Research Methods (16:1), pp. 1531.
Google. 2011. “An update on Google Health and Google PowerMeter,” (available from:; retrieved
September 6, 2016).
Grisot, M., Hanseth, O., and Thorseng, A. A. 2014. “Innovation of, in, on Infrastructures: Articulating the
Page 10 of 12
Open Digital Platforms in Health Care
Thirty Seventh International Conference on Information Systems, Dublin 2016 11
Role of Architecture in Information Infrastructure Evolution,” Journal of the Association of
Information Systems (15:4), pp. 197219.
Hagiu, A., and Rothman, S. 2016. “Network Effects Aren’t Enough,” Harvard Business Review (94:4), pp.
Hanseth, O., and Aanestad, M. 2003. “Design as Bootstrapping. On the Evolution of ICT Networks in
Health Care,” Methods of Information in Medicine (42:4), pp. 385391.
Hanseth, O., and Bygstad, B. 2015. “Flexible Generification: ICT Standardization Strategies and Service
Innovation in Health Care,” European Journal of Information Systems (24:6), pp. 645663.
Hanseth, O., and Lyytinen, K. 2010. “Design Theory for Dynamic Complexity in Information
Infrastructures: The Case of Building Internet,” Journal of Information Technology (25:1), pp. 119.
Henfridsson, O., and Bygstad, B. 2013. “The Generative Mechanisms of Digital Infrastructure Evolution,”
MIS Quarterly (37:3), pp. 907931.
Hinkelman, E. G. 2005. Dictonary of International Trade - Handbook of the Global Trade Community,
Novato: World Trade Press.
HSPC. 2016. “The Healthcare Services Platform Consortium,” (available from:; retrieved September 7, 2016).
Huckman, R., and Uppaluru, M. 2015. “The Untapped Potential of Health Care APIs,” Harvard Business
Review (93:12), pp. 17.
Klöcker, P. N., Bernnat, R., and Veit, D. J. 2015. “Stakeholder Behavior in National EHealth
Implementation Programs,” Health Policy and Technology (4:2), pp. 113120.
Langley, A. 1999. “Strategies for Theorizing from Process Data,” Academy of Management Review (24:4),
Academy of Management, pp. 691710.
Langley, A., Smallman, C., Tsoukas, H., and van De Ven, A. H. 2013. “Process Studies of Change in
Organization and Management: Unveiling Temporality, Activity, and Flow,” Academy of
Management Journal (56:1), pp. 113.
Leonard-Barton, D. 1992. “Core Capabilities and Core Rigidities: A Paradox in Managing New Product
Development,” Strategic Management Journal (13:S1), pp. 111125.
Lessig, L. 2001. The Future of Ideas (1st ed.), New York: Random House.
Lluch, M., and Abadie, F. 2013. “Exploring the Role of ICT in the Provision of Integrated Care - Evidence
from Eight Countries.,” Health Policy (111:1), pp. 113.
Locke, K., Golden-Biddle, K., and Feldman, M. S. 2008. “Perspective--Making Doubt Generative:
Rethinking the Role of Doubt in the Research Process,” Organization Science (19:6), pp. 907918.
Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., and Ramoni, R. B. 2016. “SMART on FHIR: A
Standards-Based, Interoperable Apps Platform for Electronic Health Records,” Journal of the
American Medical Informatics Association (2016:1), pp. 110.
Monteiro, E. 1998. “Scaling Information Infrastructure: The Case of Next-Generation IP in the Internet,”
The Information Society (14:1998), pp. 229245.
Rochet, J.-C., and Tirole, J. 2003. “Platform Competition in Two-Sided Markets,” Journal of the
European Economic Association (1:4), pp. 9901029.
Sanner, T. A., Manda, T. D., and Nielsen, P. 2014. “Grafting: Balancing Control and Cultivation in
Information Infrastructure Innovation,” Journal of the Association for Information Systems (15:4),
pp. 220243.
Sydow, J., Schreyögg, G., and Koch, J. 2009. “Organizational Path Dependence: Opening the Black Box,”
Academy of Management Review (34:4), pp. 689709.
Sydow, J., Windeler, A., Schubert, C., and Möllering, G. 2012. “Organizing R&D Consortia for Path
Creation and Extension: The Case of Semiconductor Manufacturing Technologies,” Organization
Studies (33:7), pp. 907936.
Thorseng, A., and Jensen, T. B. 2015. “Building National Infrastructures for Patient-centred Digital
Services,” in ECIS 2015 Proceedings.
Tilson, D., Lyytinen, K., and Sørensen, C. 2010. “Digital Infrastructures: The Missing IS Research
Agenda,” Information Systems Research (21:4), pp. 748759.
Tilson, D., Sørensen, C., and Lyytinen, K. 2013. “Platform Complexity: Lessons from the Music Industry,”
in Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 46254634.
Tiwana, A. 2015. “Evolutionary Competition in Platform Ecosystems,” Information Systems Research
(26:2), pp. 266281.
Tiwana, A., Konsynski, B., and Bush, A. A. 2010. “Research Commentary - Platform Evolution:
Coevolution of Platform Architecture, Governance, and Environmental Dynamics,” Information
Page 11 of 12
Open Digital Platforms in Health Care
Thirty Seventh International Conference on Information Systems, Dublin 2016 12
Systems Research (21:4), pp. 675687.
Tripsas, M., and Gavetti, G. 2000. “Capabilities, Cognition, and Inertia: Evidence from Digital Imaging,”
Strategic Management Journal (21:10/11), pp. 11471161.
Winkler, T. J., Brown, C. V., and Ozturk, P. 2014. “The Interplay of Top-Down and Bottom-Up:
Approaches for Achieving Sustainable Health Information Exchange,” in ECIS 2014 Proceedings.
Winter, S., Berente, N., Howison, J., and Butler, B. 2014. “Beyond the Organizational ‘Container’:
Conceptualizing 21st Century Sociotechnical Work,” Information and Organization (24:4), pp. 250
Yaraghi, N., Du, A. Y., Sharman, R., Gopal, R. D., and Ramesh, R. 2015. “Health Information Exchange as
a Multisided Platform: Adoption, Usage, and Practice Involvement in Service Co-Production,”
Information Systems Research (26:1), pp. 118.
Yin, R. K. 2013. Case study research: Design and methods (5th ed.), Los Angeles: Sage.
Page 12 of 12
... The payer usually defrays these expenses from insurance premiums from the shared-risk community. Thus, we basically find a triangle relationship between the patient (as service recipient), the medical care SP (as service provider), and the payer, which is common in many developed countries (Fürstenau and Auschra 2016). ...
... At last, the user-centric perspective concerns platform government mechanisms and actor relations.From a market-based perspective, the demand side of healthcare platforms can be broadly subdivided into three user groups, i.e., patients, healthcare service providers, and payers. Platforms do not only interconnect these three parties with each other, but also with companies on the supply side which offer products, services, or digital applications(Fürstenau and Auschra 2016). Upon this,Fürstenau et al. (2019) developed a platform management framework to understand the interdependencies of a healthcare provider-led platform. ...
Digital transformation (DT) has not only been a major challenge in recent years, it is also supposed to continue to enormously impact our society and economy in the forthcoming decade. On the one hand, digital technologies have emerged, diffusing and determining our private and professional lives. On the other hand, digital platforms have leveraged the potentials of digital technologies to provide new business models. These dynamics have a massive effect on individuals, companies, and entire ecosystems. Digital technologies and platforms have changed the way persons consume or interact with each other. Moreover, they offer companies new opportunities to conduct their business in terms of value creation (e.g., business processes), value proposition (e.g., business models), or customer interaction (e.g., communication channels), i.e., the three dimensions of DT. However, they also can become a threat for a company's competitiveness or even survival. Eventually, the emergence, diffusion, and employment of digital technologies and platforms bear the potential to transform entire markets and ecosystems. Against this background, IS research has explored and theorized the phenomena in the context of DT in the past decade, but not to its full extent. This is not surprising, given the complexity and pervasiveness of DT, which still requires far more research to further understand DT with its interdependencies in its entirety and in greater detail, particularly through the IS perspective at the confluence of technology, economy, and society. Consequently, the IS research discipline has determined and emphasized several relevant research gaps for exploring and understanding DT, including empirical data, theories as well as knowledge of the dynamic and transformative capabilities of digital technologies and platforms for both organizations and entire industries. Hence, this thesis aims to address these research gaps on the IS research agenda and consists of two streams. The first stream of this thesis includes four papers that investigate the impact of digital technologies on organizations. In particular, these papers study the effects of new technologies on firms (paper II.1) and their innovative capabilities (II.2), the nature and characteristics of data-driven business models (II.3), and current developments in research and practice regarding on-demand healthcare (II.4). Consequently, the papers provide novel insights on the dynamic capabilities of digital technologies along the three dimensions of DT. Furthermore, they offer companies some opportunities to systematically explore, employ, and evaluate digital technologies to modify or redesign their organizations or business models. The second stream comprises three papers that explore and theorize the impact of digital platforms on traditional companies, markets, and the economy and society at large. At this, paper III.1 examines the implications for the business of traditional insurance companies through the emergence and diffusion of multi-sided platforms, particularly in terms of value creation, value proposition, and customer interaction. Paper III.2 approaches the platform impact more holistically and investigates how the ongoing digital transformation and "platformization" in healthcare lastingly transform value creation in the healthcare market. Paper III.3 moves on from the level of single businesses or markets to the regulatory problems that result from the platform economy for economy and society, and proposes appropriate regulatory approaches for addressing these problems. Hence, these papers bring new insights on the table about the transformative capabilities of digital platforms for incumbent companies in particular and entire ecosystems in general. Altogether, this thesis contributes to the understanding of the impact of DT on organizations and markets through the conduction of multiple-case study analyses that are systematically reflected with the current state of the art in research. On this empirical basis, the thesis also provides conceptual models, taxonomies, and frameworks that help describing, explaining, or predicting the impact of digital technologies and digital platforms on companies, markets and the economy or society at large from an interdisciplinary viewpoint.
... Recently, digital platforms have surfaced as a powerful way to organise public health care (Aue et al. 2016;Benedict et al. 2018) a foundation upon which functional capabilities, data, and processes are enabled and exchanged. The IS literature on digital platforms has focused more on private organisations with commercial platforms for product innovation and economic transactions (Saarikko 2015;Vassilakopoulou et al. 2017) and health care within the general health system (Furstenau and Auschra 2016). ...
... Vassilakopoulou et al. (2016) investigated the development and interaction between healthcare providers and patients involving national e-health platform. Generally, the literature on digital platforms for the health sector has focused more on health care (Furstenau and Auschra 2016). Less research, therefore, exists on health insurance as an important sector for providing health care financing, especially in relation to national health insurance in developing country context. ...
Conference Paper
This study aims to understand how institutionalisation of health insurance digital claims platform gets facilitated or constrained. The study is situated in a developing country context of Ghana. A growing body of information systems research on digital platforms to organise public health care exists and continues to evolve; however, the facilitators and constraints to institutionalisation of digital platform in health insurance have received little attention. This paper, therefore, applies a sociotechnical approach using institutional theory as the analytical lens and qualitative interpretive case study as the methodology. The findings show that institutionalisation of digital platforms is not linear and incremental, but goes through several iterations, sudden and non-linear disruptions. The critical barriers identified limiting institutionalisation of digital platform include; (1) Heterogeneous health care provider environment. (2) political change and leadership; and, (3) Management of the innovation process.
... B. je nach Entwicklungsstand der Plattform und der Abhängigkeit des Betreibers von einzelnen Komplementoren variieren (Parker und van Alstyne 2014). Darüber hinaus stellt sich die Frage, inwiefern Plattformen externer Regulierung unterliegen oder sie umgehen können.Am Beispiel von Plattformen im Gesundheitswesen wird der Gestaltungsanspruch von externen Regulatoren insbesondere dann deutlich, wenn eine Plattform ins Vergütungssystem der gesetzlichen Krankenversicherungen eingebunden ist(Furstenau und Auschra 2016). Davon unabhängige Plattformen, wie z. ...
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Zusammenfassung Im Zuge der zunehmenden Digitalisierung gewinnen interorganisationale Netzwerke, Plattformen und Ökosysteme zunehmend an Bedeutung. Jedoch bleibt oft unklar, was mit diesen Konzepten gemeint ist und in welchem Verhältnis sie zueinander stehen. Dieser Beitrag hat daher das Ziel, diese Konzepte genauer zu fassen und die jeweiligen Verhältnisse zueinander zu klären. Dies geschieht mithilfe der in der Organisationsforschung prominenten Praxis- und Institutionentheorie. Anhand der Dimensionen theoretische Wurzeln, zentrale Analyseebenen, Ziele, Governance/Steuerung, Dynamiken und Grenzen, Kooperation und Wettbewerb sowie Offenheit/Selektion von Mitgliedern werden die einzelnen Phänomene voneinander abgrenzt. Zudem werden die dyadischen und das triadische Verhältnis zueinander diskutiert und weitere Forschungsperspektiven aufgezeigt.
... The final view of observed algorithm development in is a process reconstruction for software platforms in the health sector. As pointed out by Fürstenau [309],such platforms are founded by start-ups or governments. This process of establishing platforms is also referred to as the platformisation process, which is defined as the gradual accumulation of additional layers that expand the functionality and scope of existing systems while reinforcing their entrenchment (anchoring), i.e. social and technical elements become the basis for new initiatives that in turn further stabilise these social and technical elements [192]. ...
Full-text available
This work has two main objectives: (1) to improve the understanding of semantic interoperability issues in healthcare and (2) to find possible solutions to these issues. Several research projects focusing on semantic interoperability support the work to achieve these goals. Semantic interoperability problems are caused by value conflicts between different stakeholders in the health care system over semantic resources that define the meaning of data. These conflicting values cause containment, which is the dominant business model in healthcare. For this purpose, data is not available where it is needed. In turn, this results in a significant power asymmetry with regard to semantic resources that are locked up in different systems. Furthermore, this power asymmetry causes problems in the management of health data. This thesis proposes a solution for this chain of effects of semantic interoperability in the form of a work practice for productive work on semantic resources. Ideas from participatory design and co-design support the work practice—specifically, technology-enhanced activity spaces as an approach to solving different value concepts of the participants. In addition, the work practice uses OpenEHR's detailed clinical modelling approach to create semantic resources. The theory from the Commons studies supports a governance model of the work practice that is independent of state and the market. The specification of such a work practice is considered a formalization innovation. This work leads to an interesting innovation that has the potential to help solve semantic interoperability problems or at least provide enough knowledge to improve their understanding. Thus, both goals of the work have been achieved.
... While they have a pivotal role in the livelihood of II, innovations are complex to manage. They are often slow to achieve and end up consuming resources beyond those anticipated (Bygstad & Hanseth, 2016;Furstenau & Auschra, 2016;Jensen, 2013;Modol & Chekanov, 2014). Often, this results in delays in achieving the intended innovation, undesired outcomes, and incurring unnecessary costs. ...
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It is through innovations that information infrastructures keep abreast of advancements in technologies and address changes in requirements from existing and new user groups. Innovations, therefore, help to sustain the value of an information infrastructure. But the processes leading to these innovations are hard to manage and often result in delays in achieving the intended objectives of the innovation, undesired outcomes, or unnecessarily high costs. Focusing on the healthcare sector, this study attempted to answer the research question “how can the processes of innovations on health information infrastructures be managed to achieve their intended objectives?” The study employed a qualitative, case study design that involved three innovation initiatives on the health information infrastructure in Tanzania; the development of a mobile-based data management application, tuberculosis and leprosy case-based system, and a data analysis application dubbed scorecard. A total of 18 interviews were conducted, as well as 20 documents reviewed. Further, 2 focus group discussions were conducted, one in the middle and another after the preliminary analyses. Theoretically, this thesis advances our understanding of managing innovation processes in information infrastructures through generification and generativity concepts. Generification addresses the design of innovations to suit multiple different installed bases while generativity addresses the preparation of the installed base for innovations. The thesis argues that in developing innovations, the dictates of these concepts are interdependent and occur simultaneously. Further, the thesis proposes twelve (12) approaches to managing innovation processes on information infrastructures. It presents these approaches in three maturity-based stages; structural setup which guides on the technical and organisational environment for innovation, implementation which guides the process of development, and grafting, which describes the process of binding the innovation to the installed base. Compared to generativity and generification dictates, four of the twelve approaches indirectly inherit from these concepts while others are newly proposed by this study.
... For example, aside from common technical requirements, complements must comply with existing regulations (e.g. national and professional laws), and thus confirm with regulatory requirements (Furstenau & Auschra, 2016). Consequently, in the context of digital platforms, we expect an overall increase of PIC resulting from increase in the Complementor-PIC and Complement-PIC. ...
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Complementary products and services of third-party complementors have become one of the cornerstones for the success and sustainability of digital platforms. To understand how and why these complementors and their complements decide to contribute to digital platforms, Information Systems (IS) research has paid considerable attention to the effects of control modes on shaping platform governance. However, there is still a lack of understanding of the causal effects of a widely applied, yet under-examined control mode, namely input control (i.e. the set of mechanisms that screen and sort out complementors and their complements before entering the digital platform’s ecosystem). In particular, extant literature has largely ignored the distinction between complementor-related and complement-related input control. Using a sequential mixed-methods approach, this paper first provides results of a quantitative study from a survey with 114 web-browser extension developers to investigate hypothesised relationships, then provides a qualitative study based on semi-structured interviews with 22 developers to confirm and complement the formerly found relationships. Both studies provide consistent support for the assertion that both complementor-related and complement-related input control negatively affects complementors’ continuance intentions and that perceived usefulness and satisfaction mediate these effects. As such, our paper contributes to IS governance research primarily by (1) conceptually distinguishing between complementor-related and complement-related input control and (2) uncovering their distinct effects on critical complementor beliefs, attitudes and behavioural intentions. Moreover, our paper offers insights that can help platform providers to effectively manage their screening and gatekeeping processes for the success and sustainability of their digital platforms.
... In contrast to other methods for analysis, such as content analysis, the use of the Gioia method approach allowed for alternative theoretical explanations to emerge from the data. The method has been used effectively to understand phenomenon in other health-care studies (Furstenau and Auschra, 2016;Schö lmerich et al., 2016). Furthermore, Maas et al. (2016) also applied the method to a study of the development of new practice resources. ...
Full-text available
Purpose Knowledge is a key success factor in achieving competitive advantage. The purpose of this paper is to examine how mobile health technology facilitates knowledge management (KM) practices to enhance a public health service in an emerging economies context. Specifically, the acceptance of a knowledge-resource application by community health workers (CHWs) to deliver breast cancer health care in India, where resources are depleted, is explored. Design/methodology/approach Fieldwork activity conducted 20 semi-structured interviews with frontline CHWs, which were analysed using an interpretive inductive approach. Findings The application generates knowledge as a resource that signals quality health care and yields a positive reputation for the public health service. The CHW’s acceptance of technology enables knowledge generation and knowledge capture. The design facilitates knowledge codification and knowledge transfer of breast cancer information to standardise quality patient care. Practical implications KM insights are provided for the implementation of mobile health technology for frontline health-care professionals in an emerging economies context. The knowledge-resource application can deliver breast cancer care, in localised areas with the potential for wider contexts. The outcomes are valuable for policymakers, health service managers and KM practitioners in an emerging economies context. Social implications The legacy of the mobile heath technology is the normalisation of breast cancer discourse and the technical up-skilling of CHWs. Originality/value First, this paper contributes three propositions to KM scholarship, in a public health care, emerging economies context. Second, via an interdisciplinary theoretical lens (signalling theory and technology acceptance model), this paper offers a novel conceptualisation to illustrate how a knowledge-resource application can shape an organisation’s KM to form a resource-based competitive advantage.
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The healthcare industry has been slow to adopt new technologies and practices. However, digital and data-enabled innovations diffuse the market, and the COVID-19 pandemic has recently emphasized the necessity of a fundamental digital transformation. Available research indicates the relevance of digital platforms in this process but has not studied their economic impact to date. In view of this research gap and the social and economic relevance of healthcare, we explore how digital platforms might affect value creation in this market with a particular focus on Google, Apple, Facebook, Amazon, and Microsoft (GAFAM). We rely on value network analyses to examine how GAFAM platforms introduce new value-creating roles and mechanisms in healthcare through their manifold products and services. Hereupon, we examine the GAFAM-impact on healthcare by scrutinizing the facilitators, activities, and effects. Our analyses show how GAFAM platforms multifacetedly untie conventional relationships and transform value creation structures in the healthcare market. Supplementary information: The online version contains supplementary material available at 10.1007/s12525-021-00467-2.
Technical Report
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Standards for Telehealth Services
Anhand von Experteninterviews sowie Markt- und Fallstudien-Analysen untersucht der Beitrag digitale Plattformen des Gesundheitswesens. Ihr breites Spektrum automatisierter Dienstleistungen weist ein hohes ökonomisches Nutzenpotential auf, ist jedoch nicht unstrittig. Kritisiert werden z.B. das Monopolstreben und die potenzielle Diskriminierung durch Plattformalgorithmen. An dieser Diskussion ansetzend greift der Artikel systematisch auf Merkmale und Kategorien digitaler Plattformen zurück, um deren Bedeutung und Effekte im Gesundheitswesen zu diskutieren.
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Increased processing power and higher transmission and storage capacity have made it possible to build increasingly integrated and versatile Information Technology (IT) solutions whose complexity has grown dramatically (BCS/RAE, 2004; Hanseth and Ciborra, 2007; Kallinikos, 2007). Complexity can be defined here as the dramatic increase in the number and heterogeneity of included components, relations, and their dynamic and unexpected interactions in IT solutions. Unfortunately, software engineering principles and design methodologies have not scaled up creating a demand for new approaches to better cope with this increased complexity (BCS/RAE, 2004). The growth in complexity has brought to researchers’ attention novel mechanisms to cope with it like architectures, modularity or standards (Parnas, 1972; Schmidt and Werle, 1998; Baldwin and Clark, 2000). Another, more recent stream of research has adopted a more holistic, socio-technical and evolutionary approach putting the growth in the combined social and technical complexity at the center of an empirical scrutiny (see, e.g., Edwards et al., 2007). These scholars view these complex systems as new types of IT artifacts and denote them with a generic label of Information Infrastructures (IIs). So far, empirical studies have garnered significant insights into the evolution of IIs of varying scale, functionality and scope including Internet (Abbate, 1999; Tuomi, 2002), electronic market places and EDI networks (Damsgaard and Lyytinen, 2001; Wigand et al., 2006), wireless service infrastructures (Funk, 2002; Yoo et al., 2005) or ERP systems (Ciborra et al., 2000).
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The digital age has seen the rise of service systems involving highly distributed, heterogeneous, and resource-integrating actors whose relationships are governed by shared institutional logics, standards, and digital technology. The cocreation of service within these service systems takes place in the context of a paradoxical tension between the logic of generative and democratic innovations and the logic of infrastructural control. Boundary resources play a critical role in managing the tension as a firm that owns the infrastructure can secure its control over the service system while independent firms can participate in the service system. In this study, we explore the evolution of boundary resources. Drawing on Pickering's (1993) and Barrett et al.'s (2012) conceptualizations of tuning, the paper seeks to forward our understanding of how heterogeneous actors engage in the tuning of boundary resources within Apple's iOS service system. We conduct an embedded case study of Apple's iOS service system with an in-depth analysis of 4,664 blog articles concerned with 30 boundary resources covering 6 distinct themes. Our analysis reveals that boundary resources of service systems enabled by digital technology are shaped and reshaped through distributed tuning, which involves cascading actions of accommodations and rejections of a network of heterogeneous actors and artifacts. Our study also shows the dualistic role of power in the distributed tuning process.
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Objective: In early 2010, Harvard Medical School and Boston Children's Hospital began an interoperability project with the distinctive goal of developing a platform to enable medical applications to be written once and run unmodified across different healthcare IT systems. The project was called Substitutable Medical Applications and Reusable Technologies (SMART). Methods: We adopted contemporary web standards for application programming interface transport, authorization, and user interface, and standard medical terminologies for coded data. In our initial design, we created our own openly licensed clinical data models to enforce consistency and simplicity. During the second half of 2013, we updated SMART to take advantage of the clinical data models and the application-programming interface described in a new, openly licensed Health Level Seven draft standard called Fast Health Interoperability Resources (FHIR). Signaling our adoption of the emerging FHIR standard, we called the new platform SMART on FHIR. Results: We introduced the SMART on FHIR platform with a demonstration that included several commercial healthcare IT vendors and app developers showcasing prototypes at the Health Information Management Systems Society conference in February 2014. This established the feasibility of SMART on FHIR, while highlighting the need for commonly accepted pragmatic constraints on the base FHIR specification. Conclusion: In this paper, we describe the creation of SMART on FHIR, relate the experience of the vendors and developers who built SMART on FHIR prototypes, and discuss some challenges in going from early industry prototyping to industry-wide production use.
Conference Paper
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The exchange of patient health information across different organizations involved in healthcare delivery has potential benefits for a wide range of stakeholders. However, many governments in Europe and in the U.S. have, despite both top-down and bottom-up initiatives, experienced major barriers in achieving sustainable models for implementing health information exchange (HIE) throughout their healthcare systems. In the case of the U.S., three years after stimulus funding allocated as part of the 2009 HITECH Act, the extent to which government funding will be needed to sustain health information organizations (HIOs) that facilitate HIE across regional stakeholders remains an unanswered question. This research investigates the impacts of top-down and bottom-up initiatives on the evolutionary paths of HIOs in two contingent states in the U.S. (New Jersey and New York) which had different starting positions before the HITECH funding. Based on our analyses of interview data collected from 34 leaders at the state, HIO, and provider level, our objective is to develop a model of contextual and operational factors that influence the sustainability of HIOs. The implications of our findings for other networks of heterogeneous healthcare systems, such as in the European landscape, will also be explored.
To enable a better understanding of the underlying logic of path dependence, we set forth a theoretical framework explaining how organizations become path dependent. At its core are the dynamics of self-reinforcing mechanisms, which are likely to lead an organization into a lock-in. By drawing on studies of technological paths, we conceptualize the emergent process of path dependence along three distinct stages. We also use the model to explore breakouts from organizational path dependence and discuss implications for managing and researching organizational paths.
Objectives: This paper assumes that in addressing major challenges related to telemedicine as networks enabling huge improvements of heath services we need to move beyond complexity and rather focus on the very nature of such networks. Methods: The results of this paper are based on an interpretive analysis of three case studies involving telemedicine, i.e. broadband networks in minimal invasive surgery, EDI infrastructures and telemedicine in ambulances. Results and Conclusion: The well-known concept of “critical mass” focuses on the number of users as a significant factor of network growth. We argue however, that we should not only consider the size of the network, but also the heterogeneity of its elements. In order to discuss heterogeneity along several dimensions, we find Granovetter’s and Schelling’s models of diversity in individual preferences helpful. In addition to the heterogeneity of the individual users, we discuss heterogeneity related to use areas and situation, to technologies, etc. The interdependencies and possible conflicts between these dimensions are discussed, and we suggest “bootstrapping” as a concept to guide the navigation/exploitation in/of these dimensions.
- This paper describes the process of inducting theory using case studies from specifying the research questions to reaching closure. Some features of the process, such as problem definition and construct validation, are similar to hypothesis-testing research. Others, such as within-case analysis and replication logic, are unique to the inductive, case-oriented process. Overall, the process described here is highly iterative and tightly linked to data. This research approach is especially appropriate in new topic areas. The resultant theory is often novel, testable, and empirically valid. Finally, framebreaking insights, the tests of good theory (e.g., parsimony, logical coherence), and convincing grounding in the evidence are the key criteria for evaluating this type of research.
Based on our interactions with the key personnel of three different healthcare information exchange (HIE) providers in Texas, we develop models to study the sustainability of HIEs and participation levels in these networks. We first examine how heterogeneity among healthcare practitioners (HPs) (in terms of their expected benefit from the HIE membership) affects participation of HPs in HIEs. We find that, under certain conditions low-gain HPs choose not to join HIEs. Hence, we explore several measures that can encourage more participation in these networks and find that it might be beneficial to (i) establish a second HIE in the region, (ii) propose more value to the low-gain HPs, or (iii) offer or incentivize value-added services. We present several other interesting and useful results that are somewhat counterintuitive. For example, increasing the highest benefit the HPs can get from the HIE might decrease the number of HPs that want to join the HIE. Furthermore, since the amount of funds from the government and the other agencies often changes (and will eventually cease), we analyze how the changes in the benefit HPs obtain from the HIE affect (i) participation in the network, (ii) the HIE subscription fee and the fee for value-added service, (iii) the number of HPs that request value-added service, and (iv) the net values of the HIE provider and HPs. We also provide guidelines for policy makers and HIE providers that may help them improve the sustainability of HIEs and increase the participation levels in these networks.
This paper examines the nature of the core capabilities of a firm, focusing in particular on their interaction with new product and process development projects. Two new concepts about core capabilities are explored here. First, while core capabilities are traditionally treated as clusters of distinct technical systems, skills, and managerial systems, these dimensions of capabilities are deeply rooted in values, which constitute an often overlooked but critical fourth dimension. Second, traditional core capabilities have a down side that inhibits innovation, here called core rigidities. Managers of new product and process development projects thus face a paradox: how to take advantage of core capabilities without being hampered by their dysfunctional flip side. Such projects play an important role in emerging strategies by highlighting the need for change and leading the way. Twenty case studies of new product and process development projects in five firms provide illustrative data.