A Configuration Approach to Multi-Sided Platforms in ...

The Configuration of an ALS Platform Case

Forty-First International Conference on Information Systems, India 2020 1

A Configuration Approach to Multi-Sided Platforms in Healthcare: An ALS Platform Case

Completed Research Paper

Introduction

In healthcare, digital multi-sided platforms (MSPs) promise to improve intersectoral and interprofessional collaboration among diverse stakeholder groups, with an emphasis on active participation and engagement of patients and patient communities (Irwin et al. 2014; Zenooz and Fox 2019). In general, MSPs are defined as systems that create value by enabling direct interactions between two (or more) otherwise distinct par-ties, such as suppliers and users (Hagiu and Wright 2015). By connecting these parties, they enable cross-side positive network effects, which describes a tendency of mutual preconditioning and reinforcement of the different sides (Parker et al. 2016; Song et al. 2018).

The market is split between highly visible examples (e.g., Apple Health) and a broader, differentiated, and less visible group of specialized platforms (and platform initiatives), which address an array of rare diseases including amyotrophic lateral scleroris (ALS) under discussion in the current paper. Procurement of care offerings is a complex administrative process between patients, general practitioners (GPs), specialized doctors, care centers, hospitals, therapists, providers of assistive technology devices (ATD), pharmacies, and even insurance companies. Patients and their immediate medical community are often overwhelmed by the complexity and fragmentation of the specialized care offerings, ATDs and medication when simulta-neously faced with quickly deteriorating health conditions (Bakker et al. 2015).

While some papers have applied a perspective of MSPs to digital healthcare platforms (Yaraghi et al. 2015; Otto and Jarke 2019), few have taken a multi-stakeholder, processual perspective on platform design and management (e.g., Vassilakopoulou et al. 2017; Fürstenau et al. 2019). Given the selective nature of the cases studied so far, we need more evidence to fully understand how MSPs in healthcare can work on a sustainable base as well as the trade-offs and unintended consequences to be considered, also in face of the changing conditions in which these platforms operate. Furthermore, most contributions have so far con-sidered platforms based in the United States and the Scandinavian countries, which each having relatively unique regulatory and social policy conditions. This leaves a void in our understanding of the dynamic con-ditions and mechanisms of platform establishment in other settings, especially with regard to different na-tional health systems.

The purpose of this paper is to shed light on a single case example of a specialized platform for ALS care and to reconstruct the platform’s organizing logic and network structure in a nuanced and detailed man-ner over time.

This paper follows an abductive approach (cf. Alvesson and Kärreman 2007; Locke et al. 2008). It starts with the puzzling fact that very few examples of successful MSPs can be observed especially in the German healthcare market, views this in the light of theoretical notions from MSPs, and aims to uncover conditions when platform establishment might occur nevertheless. To this end, the paper reconstructs the design and development of Ambulanzpartner.de (APST), a case and care management platform focused on ALS pa-tients. In line with the recommendations for multidisciplinary ALS care teams (Nagasaka and Takiyama 2015) and guidelines for care (Mitchell 2000), APST has developed a platform for case management or concerted care, linking patients and their helpers, medical care providers (therapists, doctors, pharmacists, and care institutions), supply partners (medical supply and equipment stores), and cost supporters (insur-ance companies). It thus operates as a multi-sided integration platform across multiple stakeholder groups. The platform is patient-centered and orchestrates customized care, which integrates care providers and supply partners. It represents a hybrid care management concept based on a combination of coordinated services with a digital management platform (Meyer and Münch 2016).

We are reconstructing the contribution and innovation of the APST platform by applying such a multi-stakeholder, processual perspective. The case discussed is an illustration of how a digital health platform



can facilitate inter-professional coordination and cooperation across multiple providers. It also shows areas of conflict and diverging perceptions of the platform’s value, which reinforces the need for a nuanced anal-ysis of the incentives and disincentives of various stakeholders to participate in a digital healthcare plat-form. We also aim to reveal how this organization’s visions, cognitive models on how the platform contrib-utes value to its various communities, have emerged over time.

Conceptual Background

Multi-Sided Platforms

Multi-sided platforms get two or more stakeholder groups on board and enable interactions between them (Hagiu and Wright 2015). The “theory” of MSPs is at the intersection of various discourses (Baldwin and Woodward 2009; Gawer 2014), within industry infrastructure (Gawer and Cusumano 2014), platform eco-systems (Ceccagnoli et al. 2012; Parker et al. 2017), modes of networking (Raivio and Luukkainen 2011), cross-side network effects (Anderson et al. 2014; Rochet and Tirole 2003; Song et al. 2018), business mod-eling (Muzellec et al. 2015), and (development) strategy (van Alstyne et al. 2016).

Our research utilizing this theory is embedded specifically in the discourse on platform business models. A platform business model can be defined as the way in which a platform creates, delivers, and captures value for its stakeholders (cf. Täuscher and Laudien 2018). We consider the platform business model by identifying potential customer (stakeholder) groups, which in return addresses the various value proposi-tions and relations between these stakeholders, and the next section will provide more information on how the approach of configuration analysis helps to relay this result. It is a ‘platform care model’, insofar as economic aspects are necessary constraints though not the drivers of the platform’s development.

Configuration Analysis of Multi-Sided Platforms in Health Care

We have chosen configuration analysis (Lyytinen and Damsgaard 2011) as a holistic approach to recon-struct the organizing logic and network structure of a digital health platform. Configuration analysis is de-fined as a conceptual and methodological approach considering simultaneously organizing logic and (network) structure in economic units spanning more than a single organization. By organizing logic, we refer to managerial rationales for designing and evolving specific platform arrangements in response to environmental and strategic imperatives (cf. Yoo et al. 2010), including vision and functionality of the platform. By network structure, we refer to the scope, volume, and intensity of relationships among stake-holder groups, including revenue streams and modes of appropriation. Lyytinen and Damsgaard (2011) examined patterns of adoption units, which are the participating organizations in inter-organizational in-formation systems (not just individual organizations), and thereby suggest a multi-perspectival analysis. While previous research has used configuration analysis primarily as a framework for static analyses, we are using this form of analysis to illustrate the emergent and dynamic processual characteristics of MSPs; in other words, the transition between different configurations over time. By configuration, we refer to the way in which the different configuration elements introduced in the following are instantiated. Table 1 depicts how we have adopted Lyytinen and Damsgaard’s conceptualization for the analysis of MSPs in healthcare, specifically of the evolution of configuration patterns.

Research Design

Case Context and Selection

We will focus on the case of APST to illustrate how digital health platforms can contribute to the improve-ment of patient care. This platform aims to coordinate and enhance treatment of ALS patients. ALS is a severe, relentlessly progressive and fatal neurodegenerative disease. It is characterized by progressive weakness of voluntary muscles of movement as well as those for swallowing, speech and respiration (Soriani and Desnuelle 2017). Most patients suffering from this yet non-curable disease pass away within 2-4 years after the onset of symptoms due to respiratory failure. Only 5-10% of patients will go on to live for more than ten years (Seitzer et al. 2016). Given the dire prognosis and swift progression of the disease, patients



and their helpers are often overwhelmed by the challenges of organizing medical care and dedicated equip-ment. As one patient representative and affected person (#10) himself described: “The further the disease progresses, the greater the dependence on external help.” ALS care also faces profound coordination chal-lenges (for a review, see Seitzer et al. 2016): “The ideal way to deal with the disease is through interdiscipli-nary hospital teams, with the external support of primary care teams and patient and family associations. These teams generally aim to provide comprehensive, joint care by the various professionals involved in the care of ALS patients. There are various care models. The teams usually include neurologists and respiratory medicine specialists, as well as nurses, physiotherapists and social workers, but they can also be more or less extended, depending on the resources of each centre, to include other members including occupational therapists, cardiologists, psychologists, ear, nose and throat specialists, etc., or even in some cases by providing home care. … The key objectives of these teams are to optimize medical care, facilitate commu-nication between team members, and thus to improve the quality of care” (Güell et al. 2013, p. 529; the care network is depicted by Soriani and Desnuelle 2017, p. 289).

Figure 1. Conceptual Configuration Framework for Digital Healthcare Platform Analysis

Scope and content ofdata exchanges, con-tent of messages, choreography and coverage of relatedbusinessfunctionality.

Ke

y f

un

ctio

na

lity

Cognitive model for how the interorga-nizational informa-tion system improves interorganizational structures and pro-cesses.

Org

an

izin

gv

isio

n

Model for how the platformcontributes to improveinterorganizational struc-tures and processes, whichmay include an active role ofthe patient and/ or her immediate care takers.

Technical and organizational scope of the platform: how it functions, coordinates, orchestrates the participating individuals, role of information infrastructure, flows, technical standards and architecture, how technical issues tie into interorganizationalprocesses

(1) Overall pattern and organization logic.

(2) Actor constellation (who is linked by the platform - nodes).

(3) Interorganizational structures, i.e. adding / deleting / transforming roles of actors and linkages between them.

(1) Which functions in the actor constellation are affected (created, deleted, transformed) by the platform.

(2) What is the role of the platform (e.g. network effects) and the information exchanged in this?

(3) The orchestration (platform dynamics, platform mechanisms).

Nature of relation-ships between theparticipatingorganizations.

Mo

de

of

inte

ract

ion

Scope and volume of structural relationships among participating organizations.

Str

uct

ure

Composition of the network (ecosystem, stakeholder constellation), scope and structure of the relationships;governance structure.

Different types of relationships (and exchanges/ transactions/ interaction) between the participating organizations (partners, users, …) as facilitated or brokered by the platform.

(1) Scope and type of relationships facilitated by the platform

(2) Revenue model/ revenue streams (who pays whom for what and what is available for free)

(3) Governance model, levels of control(4) Structure and compatibility of incentives

(1) Types of linkages: a) brokered (platform as intermediary), b) facilitated: direct link between platform participants

(2) Types of interactions, e.g.: purchase of goods and services, information transparency, data for research.

(3) Volume of interactions.

Scope and content ofdata exchanges, con-tent of messages, choreography and coverage of relatedbusinessfunctionality.

Mo

de

of

ap

pro

pri

ati

on

Expected or demonstratedimpact on the participatingorganizations. Includingdifferent kinds of possible impactsas mentioned here.

(1) Coordination efficiency and matching quality for the individual patient.

(2) Automation of matching.(3) Network externalities.(4) Monitoring of platform transactions as

basis for performance review. (5) Information quality (transparency/

feedback) on the platform.

Original formulation(Lyytinen/Damsgaard

2011)

Reconceptualization fordigital healthcare platforms

Operationalization



Data Collection and Analysis

Our emphasis will be to examine and analyze how APST developed its organizing logic and network struc-ture over time to contribute to the improvement of patient care. To reconstruct the development of this platform, we follow an in-depth, qualitative case study approach (Eisenhardt 1989; Yin 2018). Through a dynamic configuration analysis, we focus on the state of the platform in different phases.

Data Collection: For the basis of an unbiased analysis, we used several data sources for triangulation purposes (e.g. Lincoln and Guba 1985; Yin 2018) (see Table 1). First, we have collected primary interview data from APST (n=8). Building on an informal talk with one founder in January 2019, the primary author conducted two confirmatory interviews with the founders (first with one and a second with both founders). Through the founders, other employees of the platform firm could be snowballed, such as the care research manager, allowing us deep insights into the firm’s internal logic and processes. Further, we have attended an APST supplier workshop in November 2019 and interviewed suppliers to complement the platform own-ers’ perspective (n=9). The full-day event also allowed us to administer a survey and to collect structured responses from n=14 individuals. As a third perspective, we gathered formal interview data from patient representatives. These organizations were contacted individually and interviewed in April 2020 (n=6). Since one interview partner was not able to speak anymore due to his illness, a written conversation repla-ced for the interview. These voices helped us to relate the platform and supplier responses to concrete pa-tient needs and challenges. Further secondary and archival documents were collected to supplement, com-plete, and expand the analysis. One author witnessed two presentations from APST and was able to receive the slides afterwards. APST provided us further internal documents (e.g., outlining its governance struc-ture). Moreover, we collected the totality of news articles and facebook posts (including uploaded videos) of APST, academic articles on APST published in general outlets as well as in media coverage since its foun-dation in 2011 up until April 2020.

Data Analysis: We analyzed our data following an abductive approach (Alvesson and Kärreman 2007; Locke et al. 2008), questioning theoretical ideas based on data and constantly cycling back and forth be-tween one and the other. After storing our data in a case study database (Yin 2018) using the software MAXQDA, we conducted a qualitative content analysis (e.g., Mayring 2000; Schreier 2014) by systemati-cally assigning “successive parts or the material to the categories of a coding frame” (Schreier 2014, p. 170). We were thus able to reduce our data and to focus on selected meanings related to our research question. Coding started in a deductive way. The framework of Lyytinen and Damsgaard (2011) (see also Table 1) is the basis of our coding scheme. We thus started to code for (1) the organizing vision, (2) key functionalities, (3) structure, (4) modes of interaction, and (5) modes of appropriation, representing overarching configu-ration elements of APST at several points of time. We operationalized each element by sub-categories. For instance, regarding modes of interaction, we coded as a first sub-category for types of linkages (either bro-kered by the platform while acting as an intermediary or facilitated by the platform as direct linkage by its participants). We described each linkage in detail by naming the connected actors, taking on a multi-stake-holder perspective. A second sub-category to operationalize modes of interaction focuses on the type of interaction enabled by the platform (e.g., purchasing of goods and services, collecting data for research, providing information for platform participants). In addition to deductive coding, new inductive codes emerged during data analysis. For instance, different roles of the platform representing its key functions that were not foreseen by Lyytinen and Damsgaard became visible (e.g. offering offline-trainings for plat-form participants like medical professionals but also patients and their care givers). Our coding was there-fore both concept- and data-driven. In a following step of data analysis, we linked our codes to four phases of platform development, representing different configurations. With the help of this axial coding (Strauss and Corbin 1990), we are able to reconstruct the dynamic configurations of APST. All members of the re-search team coded the data and held regular discussion meetings on emergent findings. During each step of data analysis, we compared coding and upcoming interpretations, such as the platforms two modes of operation as a multi-sided platform and a research infrastructure. By following this approach of data anal-ysis, we are able to reconstruct the platform’s configuration and development.



Table 1. Data Sources

# Source, role and description Period Quantity

A - Primary interview data from platform owner firm (APST) n=8

1 Formal confirmatory interview with founders (#1.1-1.2) March / July 2019 2

2 Informal talk with founders (#2) January 2019 1

3 Informal talks with care research manager (#3.1-3.2) July / Sept. 2019 2

4 Informal talks with other employees at supplier work-shop (e.g., case management) (#4.1-4.3)

November 2019 3

B - Primary interview and observational data from suppliers n=9

5 Informal interviews at supplier workshop (#5.1, …, 5.5) November 2019 5

6 Formal interviews with suppliers (e.g., medical supply stores, assistive device firms) (#6.1 … 6.4)

April 2020 4

7 Attendance of / observation at supplier workshop (incl. field notes, gathered materials) (#7)

November 2019 Full 1-day workshop

8 Short survey administered at supplier workshop (#8) November 2019 14 valid responses

C - Primary interview data from patient representatives n=6

9 Formal interviews with patient representatives (#9.1-9.5) April 2020 5

10 Written conversation with patient representative (#10) April 2020 1

D - Internal data from platform n=6

11 Internal documents by APST (e.g., internal platform gov-ernance concept (2018), general terms and conditions for suppliers, patients, medical partners; presentation to stu-dents (2019), neurology congress presentation (2019))

2018 – 2020 6

E - Secondary and archival documents 220 sources

12 News articles 2012 – 2018 40

13 Facebook posts by APST 2011 – 2020 163

14 Videos uploaded by APST 2013 – 2014 5

15 Scientific publications on health services research of the platform

2013 – 2018 7

16 Media coverage (e.g., Spiegel, Ärztezeitung) 2011 – 2015 5

Configuration Analysis

Our configuration analysis of APST follows the conceptual model’s logic as proposed in Figure 1.

Organizing Vision

The overall vision for APST can be summarized by four guiding principles: Social entrepreneurship (Saebi et al. 2019), using the economic mechanisms of a digital multi-sided platform to improve the coor-dination efficiency of care, as well as providing complimentary access to the platform for patients, their relatives and doctors. As one patient representative interviewee (#9.1) noted “the patients and those af-fected simply need to be treated as soon as possible.” Empowering patients by providing more infor-mation, transparency, and informed choices to patients. The same representative (#9.1) indicated as a func-tion of APST and similar platforms “that those affected have more insight into their care.” Another patient representative remarked (#9.3), the aim is that “patients are independently informed, they are informed promptly, they are informed about all ongoing studies and treatment options, they are informed about



medications.” This helps patients to retain control over their data creating health care and medication files, with strict data security and data access policies are consciously implemented. This assessment was also shared by a patient representative (#9.1) who said: "The main challenge is of course data protection.” Like-wise, the founders claim (Meyer et al. 2013, p. 166): “Through AP … the patient can take a direct influence on their own care. The involvement of the patient in the decision-making and treatment process will deci-sively determine products and services in medicine. It corresponds to socio-medical and social progress, which focuses on the co-determination and self-determination of the patient.”

A third principle is improve care for ALS patients, i.e. creating an information platform (repository of care files etc.), which yields coordination efficiencies and provides the basis for feedback between the plat-form’s stakeholders. As one patient representative (#9.1) insisted: “That's what we want, patient care. That's what we want, that we get a little more involved and that we get a little more together. And just get more information to help and support those affected.” The patient community and platform-based-data provide unique opportunities for ALS research. For instance, one supplier (#7) envisioned that “biofeedback” rep-resents one such unique opportunity for research-based insights. Thus, APST is also a research platform or infrastructure for multiple functions in research, including epidemiological public health research (e.g. register studies, “the reality of care”), medical trials, and contract research.

Based on these principles, the platform supports two models or modes of operation: a multi-sided platform for ALS care management and a research infrastructure for secondary use of patient data and medical studies.

Primary Model - Multi-Sided Platform for ALS Care Management

Figure 2 illustrates main products and services coordinated via the platform (i.e., ATDs, therapeutics, med-ications, and care opportunities) as well as the main stakeholder groups for whom these products and ser-vices are targeted.

Figure 2. APST’s Multi-Sided Platform Model (adapted from Meyer and Münch 2016, p. 34)

Moreover, the platform has become a significant data repository - each patient’s electronic health record and additional medical and technical information required for the procurement and provision of ATD are documented on the APST platform. Digitized administrative information (prescriptions refills, etc.) are used for the procurement processes. Patients are consistently polled by the platform in order to provide feedback and to assess the services and product quality.

Secondary Model – Research Infrastructure for Secondary Use of Patient Data and Medical Studies

One of the overall aims of the platform is evidence-based improvements of the quality of provision of care and ATDs to ALS patients. Based on the data collected on the platform and access to a unique patient com-munity, the platform is used as a research infrastructure for (1) health services research and (2) ALS

Case

managementAPST

Assistive

technologies

Therapeutic

products

Medi-

cationsCare

Medical

doctorPatient Relatives Social

service

Internet

portal

Supply side

Demand side



and neurology-related medical and pharmaceutical research. Patient information, transaction data and systematically structured patient feedback regarding the quality of services and equipment have been combined to study the overall quality of care provision (e.g., a comparative study of the provision of ATD across four ALS centers, which showed that only 64% of indicated ATDs reached the patients) (Funke et al. 2015, p. 1010). Given that APST supports about 50% of the German ALS population, there is rich data on a representative sample, which can be used for multiple studies - amongst others for a registry study, analyz-ing results in the patient population. The two founders of APST state: “Data generated in the context of care management is used for a systematic analysis of care on the basis of informed patient consent. This results in a ‘double effect’: The digitization of care data on the APST Internet portal serves directly to coordinate care and at the same time research into care through the evaluation of ‘routine’ data (real world evidence)” (Meyer and Münch 2016, p. 35).

Access to APST’s patient population provides a unique opportunity for ALS research. The prediction of the progression of ALS based on voice data, medication trials or experimental research on assistive technologies being at the forefront.

Key Functionality

The two models support four functional areas: Case management aims at matching individual patient’s needs involving medical, therapeutic and pharmaceutical care providers as well as providers of ATD.

To support case management and to be able to scale it, APST functions as a transaction platform for efficient procurement of care services and equipment, i.e. process facilitation and automation. Conse-quently, the traditional process (patient – doctor – insurance – care or equipment provider) is transformed and APST has been established as an intermediary between patients (or prescribing doctors) and service providers.

Figure 3. Workflow for ATD Provisioning (adapted from Meyer and Münch 2016, p. 36)

Figure 3 depicts the workflow supporting the platform for the provisioning of ATDs, such as wheelchairs, walkers, and toilet seat raisers. APST coordinates the entire workflow, answering different questions such as “What kind of wheelchair?,” “Who could provide this needed product the fastest?”, “Is it suitable to this patient’s specific situation?,” as well as “Prescription support,” until “Cost estimations for their health in-surance.” This process did not yet function completely smoothly, and some supply partners complained about a lack of automation and interfaces, support of the platform in the approval process vis-à-vis the health insurance companies as well as support vis-à-vis the health insurance companies in case of objec-tions, which occur frequently. One supplier (#6.3) raised the concern “double entries are negative, we lack csv import/export interfaces to connect APST with our own warehouse management system.” Another com-menter (#7) wished, “to contact patients directly in the system in case of contradictions.” One care partner (#6.4) pointed out economic and ethical problems that fall together in cases where care is not delivered fast enough: “It is unfortunate if a patient passes away before the reimbursement is approved, because the ser-vice cannot then be billed.”

As part of process facilitation and coordination across care providers, APST functions as an information and communication platform. It hosts extensive documentation for case management, as evidenced by the data structure of the platform, which includes: Medical partners (doctors, pharmacists, caregivers

APST platform

Medicaldoctor

Patient Supplier Patient Supplier

pr ov ides in dication

r eceivesca ll

r eceivesor der

is v isited

su ggests care tr eatment

Medicaldoctor

SupplierHealth

insurance

n eedspr escription

r eceivespr escription

cov erage ofcosts

cr eatespr escription



etc.), picture galleries (e.g. with a profile picture of the patient, optional), diagnoses and guiding symptoms, a document center with the upload and download functionality of supply-relevant documents (therapy re-port, test report, doctor's letter, etc.), an overview of existing aids (type, product name, area of application, etc.), provision of aids (status of the regulation, payment and delivery), overview of the remedies (physio-therapy, speech therapy, occupational therapy, specific subdivision into measures catalog, prescription quantity, frequency recommendations, etc.), provision of remedies (status of the prescription), medication plan with overview of all current and completed medications, overview of all nursing services (care location, nursing care, type of care, etc.), evaluations of treatments, products and medications by the patient based on questionnaires, grading and comments (APST - Versorgungsnetzwerk n.d., p. 6).

Based on the patient community and the extensive data collection, APST functions as a research plat-form, first of all to identify evidence-based improvements for the care of ALS patients and thus engender ambidextrous learning dynamics, i.e. transaction-based identification of improvements and community-based feedback for care providers. The platform provides opportunities for medical research (medication trials etc.) and contract research, e.g. for pharmaceutical companies or equipment manufacturers.

On a 5-point Likert scale with 5 as the highest value, suppliers (n=14) rated the coordination support for ATD transactions as 3.69, patient-related information provision as 3.71, service improvement possibilities as 2.85, and use of contract research possibilities as 2.71. This shows that while the trans-actional functions of the platform formed the core of the platform, more advanced functions around data-driven research were still being established.

Structure

The key stakeholders of the APST platform – except for the research partners - are depicted in Figure 2. The structure of the relationships follows the main functions of the platform, in summation:

• Information, communication and coordination

• Care provision including ATD

• Payments (transaction fees, payments for research contracts)

The platform facilitates exchanges between its various stakeholders in a hub-spoke model, which empha-sizes direct exchanges between the different “customer” segments of the platform. This is in line with the definition of an MSP, as well as coordination and exchanges within the respective segments, most notably interprofessional care coordination among the care providers. Thereby, our data suggested that how orders were distributed among suppliers remained somewhat “elusive” to them. As one supplier (#6.4) noted: “Af-ter all, we are not the only provider, which means there are other providers who are all part of the APST network. And it is not necessarily obvious by which criteria sometimes one or the other is selected.” It was also apparent that some of the suppliers were also in a strong competitive relationship, which in some cases made overall coordination more difficult. As one voice acknowledged (#7), “processes run sideways or in parallel”, which showed that transactional support for suppliers could still be improved further.

The platform is governed by its owners, the platform providers, who have a dual role as entrepreneurs and ALS medical specialists and researchers. This is highly beneficial in terms of professional medical knowledge, but problematic in terms of the strict separation between the responsibilities of medical doctors on the one side and providers of medication or ATD on the other side. At the supplier conference (#7), the founders noted that this challenge is partly circumvented because money is passed on to ALS ambulances and thus supports research into ALS, and that the platform plans to collect even more data in the future to conduct its own health care research, which will benefit patients. APST can be described as a provider-driven competence network, in contrast to (patient) community-driven, or vendor-driven approaches.

The revenue model has emerged over time: In which (1) the platform was initially funded by a research grant, (2) this funding was complemented and increasingly substituted by transaction fees of care and ATD providers on the platform and gradually (3) funding based on contract research has been solicited. The transaction-based revenue has clearly benefited from the scaling of the platform and the increasing number of patients (from 200 in 2011 to exceeding 8,000 in 2017) as well as care providers (e.g., from 4 to 15 ALS



ambulances). Moreover, in line with many MSPs, one segment - the patients - is subsidized, which has been a driver of membership growth on all sides. The founders note that,

“APST is financed via a multi-sided platform model ... For the medical partners (patients, rela-tives, doctors), the services and the Internet portal are provided free of charge. The service archi-tecture and technology platform are financed from licenses of the supply partners (aid and remedy providers, pharmacies and other licensees)… The willingness of the supply partners to pay results from efficiency advantages of their own service provision, an increase in quality and improved resource management” (Meyer und Münch 2016, p. 38)

This ties back to the patient who is offered the service of the platform without charges, thus contributing to facilitating positive cross-sided network externalities.

Mode of Interaction

To reiterate, the interaction structure between patients, the platform, care providers and research partners is divided in line with the two models of the platform. A. Facilitated, which provides direct links between platform segments, while the other links are B. brokered and thus intermediated by the platform. The pri-mary types of platform-based exchanges include: information, (sensor, log) data, communication and co-ordination, transactions (service provision, ATDs, prescriptions, etc.), and payments.

Mode of Appropriation

APST improves the provision of patient care in four distinct ways: First, individual case-management, i.e. the platform links individual patients with a network of care providers. The “individual coordination of the care package“ (Merl and Stöger 2014, p. 112) is supported through the process structures of the platform. Thus, it reduces coordination efforts (costs) and accelerates the set-up of a care network, so that distribution of crucial data can be securely shared via the platform. Second, the patient feedback func-tions as peer review for other patients and customer feedback to the care providers to increase transpar-ency about perceived service and equipment quality. For example, the patients can rate the platform, sup-pliers, assistive technologies, and medications. It is thus meant to yield a learning process and increase quality of care and ATDs. As one founder suggested, “We want to make the processes for ALS patients still better, also through new assistance technologies”. Third, the monitoring and documentation of ALS care management across a large sample of patients with aggregated feedback to the care service providers con-tributes to evidence-based insights into ALS care and metrics for the furthest reaching impact of the platform. Finally, the platform reduces data collection costs for ALS research based on a large patient sample (e.g., register study).

The platform has successfully scaled and yields positive cross-sided – with respect to the patient community - and same side network externalities, which the Dynamic Analysis section will show in more detail. The facilitation of quality improvements, learning and research opportunities have become a key driver for the platform development, as the implemented rating system demonstrates.

Summary

In essence, the APST configuration (Table 2) combines an MSP (hub-spoke), with two types of sub-net-works, the care network (Primary Model) and the research network (Secondary Model) with three stake-holder groups (or market sides): the patient community, the care provider community, and the medical research community.



Table 2. Summary Configuration of the APST Platform

Configuration elements

APST Characteristics

Organizing vision

Four guiding principles: social entrepreneurship, empowering patients, reinventing ALS care and platform as a research infrastructure. The principles are manifested in two interdependent models: (1) A multi-sided transaction platform for ALS care management, (2) the platform as research infrastructure.

Key functional-ity

(1) Case management: Facilitating matching of individual patient and ATD, care providers. Care management: Platform based coordination (process facilitation and automation).

(2) Transaction platform: Efficient procurement of care services and equipment and coordi-nation of care providers. Traditional process is transformed: APST as new intermediary.

(3) Information and communication platform: Extensive documentation of and for case management: Patient health, care, and medication record. Consolidated patient feedback, which functions as peer review or other patients and customer feedback to the care providers.

(4) Research platform: Research is based on a. monitoring of transactions evidence-based care research, b. patient feedback and participation in surveys, c. patient sensor data, d. patient participation in (medication) trials.

Structure (1) Direct and indirect exchanges among the platform segments. (2) Platform provider-based governance poses regulatory challenges. (3) ‘Shared value’ model: healthcare providers pay transaction fees, the service is free for the

patients. Research funds and contract research as additional funding sources.

Mode of interac-tion

Interaction structure between patients, the platform, care providers and research, differentiated by the basic models. Types of exchanges:

• Information, • (sensor, log) data,

• communication and coordination,

• transactions (service provision, ATDs, prescriptions etc.),

• payments.

Mode of appro-priation

Four main contributions and areas of impact: (1) Reduced coordination cost and increase allocation efficiency (matching). (2) Increased transparency and learning based on patient feedback. (3) Evidenced based insights about ALS care. (4) Reduced data collection costs for research.

Dynamic Analysis

Scaling the Platform

The APST platform started in 2011 with 200 registered users of almost exclusively ALS patients. Within a period of eight years, the user base exceeded 8,000 patients (50% suffer from ALS, the other users come from different complex neurological diseases such as stroke, multiple sclerosis, Parkinson's disease or mus-cle diseases) and amounts to approximately 50% of the ALS patient population in Germany1. Further, the number of providers on the platform grew consistently as demonstrated that in 2017, 1,273 providers took part in the APST network, up from 1,061 providers in 2015. Similarly, the number of medical partners has increased; Initially the ALS center at the Charité had focused coordinating ATD supply (67% or 941 ATDs between June 2011 and October 2014; Funke et al. 2015). While APST started with 4 ALS centers, today it

1 While up-to-date data on prevalence of ALS is lacking (Longinetti and Fang 2019), it has been reported that ~8,000 patients in Germany suffer from ALS (ALS-hilfe.org 2020), of which more than 4,000 are using APST



includes 15 ALS centers all over Germany. 155 medical doctors took part in patient care through APST in 2014 and their number has increased since then. Taken together, these scaling dynamics suggest a super-linear growth of patients enrolled in the platform, which represents a critical patient sample for real-world research and evidence (of use beyond the platform). Our data on providers and medical partners is less robust and does not yet allow us to derive a specific trend; yet, initial data indicates it is also increasing.

Extending the Scope of the Platform

Over time, the scope of the platform (gradually) moved from ALS to other complex neurological diseases, such as stroke, multiple sclerosis, Parkinson's disease or muscle diseases (Meyer and Münch 2016). The prerequisite was an extendable portal structure and service architecture. According to the founders, “The extension to other indication areas is technologically possible and scalable, as the Internet portal has al-ready been financed and confirmed with a revenue model.” (Meyer and Münch 2016). Neurological diseases have in common that they cause high organizational expenses in the coordination of patient care, that they require a high level of specialization to ensure care quality, and that there is a high need for coordination between different medical partners and providers. They differ in their severity, chronic versus non-chronic, and may be of more rare diseases (such as ALS). Therefore, the platform indicated at the supplier workshop (#5) that it hoped to scale its model to those indications within the next years.

The functionality of the platform has gradually been extended as well. The portal started in its first version with a focus on ATD and therapeutic product management, emphasizing the platform’s main focus as a case management and transaction platform. In 2012, the care management module went live. It accounted for a high degree of complexity in nursing care, since dynamic forms of care are implemented in the spectrum of outpatient, day-care or inpatient care during ALS disease (Meyer et al. 2013). In 2013, a module to coordi-nate medications across care sectors went live. As the founder noted within the supplier workshop (#7), “in the field of pharmaco-therapy, prescriptions are already put online as PDF files at APST at the time of ap-plication and before assessment by the health insurance.” This also strengthened the platform’s core as a care management and transaction platform. In 2014, a survey module was integrated into the platform. This allowed one to rate ATP, therapeutic products, medications, delivered care, as well as providers, med-ical partners, and the platform itself. It enabled constant feedback and learning and presented an important step in the direction of an information platform. Suppliers (n=14) indicated on a 5-point scale, at 2.86 that feedback from the platform had already helped them to improve their services, showing potential further room for improvement. It also laid the foundation for the research platform, which became one of the plat-form’s main focuses today. In 2018, a new therapeutic product module went live.

Configuration Dynamics

Our data illustrate four development stages of the platform’s logic, which often affected several aspects of the platform configuration.

From a manually to digitally-mediated platform (2011 – 2013): A first shift that took place early on in the logic of the platform’s development was from manual to digital support of care management. Manual processes of care management supported by telephone and personal contact have been replaced by digitally mediated transactions. This became possible by the provision of new functionality on the platform, in particular the ATD and Therapheutic Product Management module. This phase ended in 2013 with the key functionality of the platform in place. The logic of this phase is characterized by a professionalization of the processes for all stakeholders.

The following areas of the configuration were affected: The organizing vision of a multi-sided platform was enacted and in this phase APST began to emerge as a new intermediary, coordinating and matching be-tween patients, medical partners, and providers. The key functionality of case management and the trans-action platform was prominent in this phase. In terms of structure the platform began to coordinate be-tween the different stakeholder groups. In this phase APST was still mainly financed from research project funding, but its “shared value” model began to emerge (as described in the Structure section). Its govern-ance began to become more separated from research institutions, as the new organizational form (limited liability company) had been established. Modes of interaction followed the primary model, including case



management and care coordination, as well as digitally-mediated payments. The mode of appropriation followed the patient-centric model, while the other aspects such as network effects, feedback, learning, and so forth only came to the fore later.

Beyond transactions: information and documentation platform (2013 – 2015): A second shift took place when new mechanisms for patient feedback and document exchanges were established. The platform aimed to bring together the different stakeholders (providers, patients, and medical partners) to create an individualized “personal private network” for the patients’ needs. To achieve this, the possibility of data exchange between parties involved had to be created. This included the storage and exchange of documents via the platform within secure and private data spaces. As an example, the reimbursement pro-cess for a wheelchair can be complex and requires lots of information exchange. In addition, a survey mod-ule was established to provide feedback from the patient, thus gearing to close the loop between transaction and demand. This phase ended around 2015 when all areas of the platform were open to assessment: ATD, therapeutics, care products, providers, medical partners, as well as the platform itself participated.

This shift affected the platform in multiple ways: Regarding the organizing vision, the shift laid the foun-dation for the secondary model involving research infrastructure for secondary use of patient data and med-ical studies. Key functionalities enabling this shift were the survey module as well as data and information spaces, bringing together providers, medical partners, and patients. In terms of structure, the shift contributed to the empowerment of patients by providing options for feedback and assessment. It also laid the foundation for new stakeholder networks to emerge. Especially during the secondary use of the platform and interactions with research partners which only became possible in this phase. This also created the base for new revenue models, which were not however the focus at that point. Community-based assessment and learning was one important mode of interaction emerging in this phase, as was structured and consolidated quality assessment. In terms of modes of appropriation, this shift brought about increased transparency for providers and patients regarding perceived service and equipment quality based on patient feedback.

From transaction and information toward a research platform (2015 – 2017): This shift was characterized by the emergence of new uses and stakeholder networks, especially the emergence of second-ary uses of the platform as a research infrastructure for secondary use of patient data. While care manage-ment research has been a part of the platform from its beginning, only when more data accumulated on the platform, were these uses ignited.

This shift affected the platform profoundly. The organizing vision for the secondary model now began to materialize. More studies were conducted. Implications tended to be richer since the enrolled sample grew. The key functionality was not affected as much during this phase. Earlier functionality such as the survey module were drawn upon. Structurally, the networks of the platform extended considerably, as the scaling dynamics indicated. New networks emerged with new research partners from academia and industry. Modes of interaction became more data-based, using transaction data for research purposes as well as scru-pulously collected data from patients. Finally, regarding appropriation modes, a growing number of pa-tients and care providers linked to the platform yielded positive cross-side externalities in this phase. Re-search foci allowed insights and learning for public healthcare research.

From B2B to P2B2B platform (2018 – today): A last shift in the platform configuration has just begun to emerge as we collected the data. Initially, the patient received help through the platform mainly due to his medical condition (coordinated through his/her care coordinator). This last phase indicated a change towards a more active involvement of the patient on the platform. Instruments for this purpose are surveys and assessment procedures to assess the quality of life of patients as well as extended feedback and assessment mechanisms. The vision is an even more individualized matching between patients and medical providers and ATD/therapeutic providers. As one supplier (#7) envisioned, “start the care process before the patient identifies his or her needs.” For this purpose, sensor and movement data of the patient are also increasingly included. Analogously to the “internet of things,” using increasingly available sensor and track-ing data to match between patients and medical providers becomes a focus of attention. One of the founders (#2) noted that, today, APST functions mostly as a business-to-business-platform between specialized phy-sicians and providers. What is still problematic is to integrate patients in the first place and to match them to specialized physicians. To fill this gap, he proposed to copy the principles of the “internet of things.”



This shift is supposed to affect configuration in several ways. The organizing vision regarding the primary model becomes more detailed and fine-grained. New secondary and tertiary uses emerge. This results in a stronger integration between primary, secondary and tertiary uses of the platform, with an emphasized research component. In terms of key functionality, new research uses emerge. They include patient sensor data and more detailed tracking. This enables more individualized diagnoses based on a large sample, from which the individual potentially benefits. Structure-wise, the shift enables new networks including mone-tizing data and conducting research as a contract research organization. It also strengthens the existing partnerships as partners receive better and more individualized feedback. In terms of modes of interaction on the transaction side, algorithmic analyses of patient data come to the fore. Research-wise, survey data become more individualized and offer real-time higher prognostic values. Finally, in terms of modes of appropriation, algorithmic assessments of patient needs come to the fore, as do benefits from monitoring and tracking, contributing to an evidence-based discourse about healthcare quality and outcomes.

Discussion

We have provided a detailed analysis of a specialized healthcare platform, which reflects the contingencies of ALS care in Germany, and are attempting to reconstruct its organizing logic and network structure in a nuanced and detailed manner. For this purpose, we have adapted and extended configuration analysis as proposed by Lyytinen and Damsgaard (2011), which had been developed with the purpose of studying in-terorganizational information system adoption. Our analysis has revealed a pattern of (1) starting with in-dividualized case management to address immediate patient needs, as well as supporting the case manage-ment by a MSP. This is to mobilize economies of scale and improve transaction efficiencies. As the platform grew, network effects and allocation efficiencies in a small and highly specialized segment of healthcare became visible. (2) Professionalizing the exchanges by building an information platform to document in-formation about the platform participants (from patient care records to technical specifications of ATDs). (3) Initiating several feedback loops for learning across multiple stakeholders and research goals, to in-crease the quality of care services and the value of the platform for its participants. The insights contribute to care research for the benefits of the patients, the care providers and public health. (4) Extending the platform towards a patient-to-business-to-business (P2B2B) model, using sensor and tracking data for more individualized matching of patients and medical research partners.

Next to linking three groups of actors – patients, care providers, research funders and researchers – (cf. Figure 2), APST also facilitates inter-professional coordination and collaboration among specialized care providers. The design and the development of the platform is an encouraging example of how care models can be developed and improved. This especially applies to a country such as Germany where intersectoral care as well as digitalization of health care (e.g., no nationwide digital health infrastructure exists) is still lacking. The platform has evolved its existing workflows, which were initially insufficient for the needs of ALS patients – and funded, while recognizing and protecting the patients’ interests.

By taking a multiple stakeholder perspective, the platform design and development shows not only how an incentive compatible configuration can be achieved, but also how a dynamic of ongoing learning and im-provement can be facilitated across and between different stakeholders without violating the patients’ rights and interests. We accomplished this through voluntary and structured feedback which provides valuable information for care providers and other patients, and through monitoring platform exchanges which pro-vides unique insights in care research. The patient community constitutes a unique sample for ALS-related research. APST’s respect for the patients and the quality of its service to the patients is a strong motivator for the patients to volunteer as participants in research initiatives and projects, and donate their data for research purposes (APST 2020). However, we still need to mention that APST is – despite its strong focus on patients’ needs – still a provider-driven MSP.

The use and impact of technology has moved from minimal support during the initial phase of case man-agement via state-of-the-art process and documentation support (information platform) towards cutting edge development of algorithmic analysis of sensor-based patient data for diagnosis, forecast, and thera-peutic recommendations. Data collection, analysis, research and monetization are key drivers for the de-velopment of the platform. However, the founders took great care that the patients would be the sovereign net beneficiaries rather than the pawns of surveillance and exploitation (Zuboff 2019).



While APST is a single case study, we clearly see the merits of an in depth, dynamic, multi-stakeholder, configuration analysis (cf. also Flyvbjerg 2006). The research design affords to understand the contingen-cies of ALS care as a ‘hard case’, which puts any healthcare system to its limits: Providing the appropriate care in a timely manner for patients with a quickly deteriorating condition, who need a highly diverse spec-trum of care and assistance, is a daunting challenge.

We hope that our illustration of the strengths, adaptability and scalability of configuration analysis will yield more configuration research. We aim to extend our case study and to broaden our research design to more cases of health care platforms to yield more insights into configuration patterns and dynamics.

Conclusion

Our study makes three main contributions. Our first contribution is to the literature on platform business models (cf. Täuscher and Laudien 2018). We have adapted and transferred the configuration analysis ap-proach from Lyytinen and Damsgaard (2011) for the reconstruction and analysis of an emerging healthcare platform. Using an example from coordinating complex care processes with respect to neurological pa-tients, we demonstrate the productivity of the approach, extending previous accounts of MSPs in healthcare (Yaraghi et al. 2015; Fürstenau et al. 2019; Otto and Jarke 2019). The dynamic configuration analysis pro-vides insights into a rich case of social entrepreneurship, which incorporates a model for individualized patient-centric coordination of care (transaction platform). It is the beginning of creating of a digital infor-mation platform to facilitate inter-professional cooperation in the provision of care, and a research platform for evidenced-based research on the provision of ALS care, ALS medication and ATD, as well as algorithmic analysis of ALS patient data to improve diagnosis, prediction and care.

Our second contribution is in social entrepreneurship. We show how a platform uses economic mecha-nisms to achieve social effects (Saebi et al. 2019). In stark contrast to “platform capitalism” (Srnicek 2016), APST is an example of social entrepreneurship, using technology and economic principles to improve the quality of medical care. A strict data policy ensures that the patient data generated and collected on the platform are not used nor monetized without the patients’ consent. Instead, patients are invited to donate their data for epidemiological studies or to voluntarily participate in studies, aimed at improving medica-tion and ATD. APST is thus a digital example for a social entrepreneur that combines public health and economic value creation with the help of a digital platform that enables a two-sided value model (as pro-posed by Saebi et al. 2019). The case also sheds light on one trajectory of a social entrepreneur’s business model development; APST started with research grants, later income shifted towards transaction fees from providers (multi-sided model), while finally today it combines research grants, transaction fees, and con-tract research incomes. This approach, it turns out, enabled the growth and scalability of the APST platform.

From a practical perspective, highlighting the example of APST, we show how and under which conditions business model innovation in health care delivery is possible. The platform-based case management focus-ses on the optimal, customized care for individual patients. It aims at empowering the patients by providing information and creating transparency about care options. Moreover, it aims to facilitate feedback on the quality of care, medication and equipment, to improve the matching, inform learning and innovation of the care providers and thereby increasing benefits to the patient community. This case is a prime example for how a multi-sided platform model can be used to achieve and coordinate care in a better way. It also demon-strates the power of research platforms as an additional organizing vision for health care platforms. In its configuration, it exemplifies a European model of patient-centric health care platforms. While APST miti-gates some of the shortcomings of the current ALS care system in Germany, it also sets an example of how care could be organized within the organizational and regulatory structures of public health care. The paper thus contributes one answer to the question of how inter-organizational configurations can contribute to patient-centric innovation in healthcare management.

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