Eindhoven University of Technology

MASTER

Data-driven innovation in healthcarea research on how to enable the development of disruptive data-driven innovation inhealthcare

van den Broek, L.

Award date:2017

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EINDHOVEN, AUGUST 2017

Data-driven innovation in healthcare

A research on how to enable the development of disruptive data-driven innovation in healthcare

By Laura van den Broek

BSc. Industrial Engineering for Healthcare TU/e 2015 Student identify number 0777869

In partial fulfillment of the requirements for the degree of

Master of Science in Innovation Management

Supervisors: Dr.ir. Bob Walrave Dr. Ksenia Podoynitsyna Company Supervisor: Dr. Fleur Hasaart

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TUE – School of Industrial Engineering Series Master thesis Innovation Management Keywords: Disruptive innovations, information asymmetry, data science, healthcare, ecosystem theory, proactive healthcare, prevention.

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Abstract The sustainability of the Dutch solidary healthcare system is under pressure because of the rising

healthcare cost. In order to sustain a solidary healthcare system, disruptive innovations are needed.

Such innovations have the potential to substantially decrease costs, make the healthcare system less

complex and improve the convenience of the healthcare system. Both in practice and in academic

literature, data science is seen as a promising technique to drive disruptive innovations in the

healthcare industry (Davenport & Harris, 2007; Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014).

However, compared to other industries the healthcare industry has adopted less data-driven

innovations in the recent years (Kayyali, Knott, & Kuiken, 2013).

This research shows that a main bottleneck for developing and implementing such data-driven

disruptive innovations, is the information asymmetry in the healthcare system. This information

asymmetry stems from a reluctance to share data between different stakeholders within the

healthcare industry – i.e., the patient, the healthcare practitioner, and the health insurer. Thus, an

important result of this research is that decreasing this information asymmetry appears vital for

enabling disruptive data-driven innovations.

To realize this, this research proposes that the current healthcare system should transform to an

aligned ecosystem in which all stakeholders share population health as the core mission of the

ecosystem. In such an aligned ecosystem, all stakeholders are incentivized to share their data.

In such an aligned ecosystem where the main barrier for innovation has been mitigated, data-driven

disruption can take place. This research then shows that the most promising application of data-driven

techniques is the effective prevention for high-risk members.

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Management summary The Dutch healthcare costs experienced a strong rise in the past twenty years and this rise is expected

to continue in the coming years (CBS, 2016; Ewijk, Horst, & Besseling, 2013). To sustain the Dutch

healthcare system, which is recognized for its solidarity, the rising healthcare cost must be curbed. To

do so, disruptive innovations are wanted (Christensen, Bohmer, & Kenagy, 2000; Hwang & Christensen,

2008; Robinson & Smith, 2008). Disruptive innovations namely have the potential to make healthcare

cheaper, simpler or more convenient (Christensen et al., 2000). Although disruptive innovations are

wanted, past studies show that it is hard to disruptively innovate in healthcare (Herzlinger, 2006;

Thakur, Hsu, & Fontenot, 2012; Christensen et al., 2000). Therefore, the healthcare system has not

been able yet to disruptively innovate in healthcare to curb its rising costs.

Recently, both academia and industry experts established hope for data science as a promising

technique to disruptively innovate to lower healthcare costs without compromising on quality

(Davenport & Harris, 2007; Bates et al., 2014).

Given the rising healthcare costs and data science as a possible technique to support disruptive

innovations, the following research question is addressed in this research:

To give an answer to this research question, the following three sub-questions are addressed

accordingly:

1) What are the barriers for such innovation in the healthcare system?

2) How to mitigate these barriers in the healthcare system?

3) Assuming the main barriers are mitigated, what is the most promising area of application for

data-driven innovation?

These questions are specifically addressed from the perspective of a health insurer. A focus on a health

insurer is adopted since they have a central role in the Dutch healthcare system and which gives them

the power to drive disruptive innovations throughout the healthcare system.

Method

To lay the basis for data-driven innovations in healthcare, a design science approach for business

problem-solving was adopted. To structure this design science research, the regulative cycle of Van

Strien (1997) is followed. This regulative cycle is visualized in Figure 1.

In this research, the problem investigation, solution design, and design validation phase were

performed. Each of these three phases served a different goal and therefore, different data sources

were collected.

The aim of the problem investigation phase was to give an answer to the first sub question ‘What are

the barriers for such innovations in the healthcare system?’. To do so, input from both a theoretical-

and practical problem investigation was collected. The theoretical problem investigation was done via

a systematic literature review to identify the barriers for disruptive innovations in healthcare and

How to enable the development of disruptive data-driven

innovation within healthcare?

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analyze the potential of data science in healthcare. The practical problem investigation tried to verify

the results of the literature review in a case study in the context of the Dutch healthcare. This was

done by analyzing the data of a diabetes type II data set obtained from CZ.

The solution design aimed to give an answer to the second sub question of this research, being ‘How

to mitigate these barriers in the healthcare system?’ and the third sub question, being ‘Assuming the

main barriers are mitigated, what is the most promising area of application for data-driven

innovation?’. In this phase, three different types of qualitative data sources were used, namely

participation, in-depth interviewing and conferences. Table 2 and Figure 2 give an overview of the wide

range of incorporated data sources.

A quantitative validation of the solution design on the most promising area of application for data

science was performed. For the quantitative analysis, the data of the Lifestyle Coach data set was

analyzed.

The following three sub chapter shortly elaborate on the results of this research, starting with the

problem investigation, followed by the solution design results and lastly, the design validation results

are discussed.

Problem investigation

In the theoretical problem investigation to answer sub question one, four barriers for disruptive

innovation in healthcare are identified, namely: the fragmentation of the healthcare system; fee-for-

service pricing model; overregulation; strategic rigidity. The first three of these barriers all relate to

the information asymmetry in healthcare (Castano, 2014; Nguyen, 2011; Sterman, 2000; Yip & Hsiao,

2009). Therefore, the information asymmetry between the stakeholders in healthcare is identified as

the main barrier to disruptively innovate in healthcare and subsequently, as a significant contributor

to the rising healthcare costs (Cruz & Kini, 2007).

The case study that has been conducted showed that the information asymmetry is also identified the

main barrier for data-driven innovation. The quantitative analysis namely showed that it is infeasible

to develop a data-driven innovation that improves the effectiveness of the care delivery for diabetes

type II because of a lack of data.

Therefore, as an answer to the sub question addressed in this research phase, the information

asymmetry is considered the overarching barrier for both disruptive and data-driven innovation within

the healthcare system. So to disruptively innovate in healthcare by the use of data science, first the

information asymmetry should be mitigated.

Note that data science is often noted as a direct driver of innovation in healthcare (Bates et al., 2014).

This research shows that without mitigating the information asymmetry, data science in itself cannot

effectively drive disruptive innovations.

Solution design

The second sub question is about how to mitigate the main barrier found in the problem investigation.

To mitigate the information asymmetry in healthcare, this research proposes that the different

stakeholders in the Dutch healthcare system should form an aligned ecosystem. In the current

healthcare system, misalignment exists between the main financial value driver of the pharmaceutical

companies and the health insurers as compared to the health centered value driver of other

stakeholders. This misalignment causes suboptimal results (Adner, 2012; Talmar, Walrave,

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Podoynitsyna, & Holmström, 2017). For the Dutch healthcare system, a suitable overall ecosystem

mission is proposed: population health. This shared mission would create an aligned ecosystem in

which disruptive data-driven innovation can take place.

Assuming this aligned ecosystem is in place, the third sub question is answered. Five different data-

driven value propositions are scored on their suitability to disruptively innovate in healthcare as a

health insurer, namely: 1) Dashboard to identify best physician; 2) Effective prevention for high-risk

members; 3) Effective prevention low-value care; 4) Effective prevention hospitalization; 5) Data

science team to help providers. Of these value propositions, the second value proposition, namely

effective prevention for high-risk members is identified most suitable to harvest the disruptive

potential of data science in healthcare.

Design validation

In the design validation the potential for disruptive innovation of the effective prevention for high-risk

members value proposition has been assessed. ‘The Lifestyle Coach’ data set is analyzed since it is was

identified as most comparable with the effective prevention for high-risk members value proposition.

Additionally, this chosen data set has a decreased information asymmetry compared to the standard

healthcare system. In the Lifestyle Coach, which is a doctoral research, different stakeholders

collaborate to improve the prevention for high-risk members. The data analysis validates that the

decrease in information asymmetry leads to better insights into the effectiveness of the preventative

treatment for a health insurer. Despite these improvements, to disruptively improve the prevention

for high-risk members, the information asymmetry should further decrease.

Discussion

To harvest the potential of data science in healthcare, CZ should align with the ecosystem’s core

mission. To do so, this research advises to let the data science team pioneer autonomously from CZ

with population health as their core mission. This research suggests that to maximize their impact,

their efforts should be focused on the effective prevention for high-risk members value proposition.

Via experimental collaborations within the industry, this team should experiment with the population

health value driver as being their core mission and as a result, it should try to form an aligned

ecosystem within this experimental collaboration in which information is shared. Via such

experimental aligned ecosystem, the disruptive data-driven innovation can mature in the strategic

niche of effective prevention for high-risk members. For CZ, the implementation of effective prevention

for high-risk members at the data science team should embody the start of an ongoing process in which

data science is applied to maximize the population health within the whole organization.

Besides these practical implications, this research also contributes to the literature. In the existing body

of research, a rather technology focused perspective on data science in healthcare is adopted. In this

research, a more strategical focus is adopted on how to harvest the data science potential in

healthcare. This shift from a technological to a strategical focus resulted in a shift in research from

dyadic relations towards a research in which the complexity of the Dutch healthcare system is taken

into account by reasoning from the polyadic interdependencies in healthcare.

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Acknowledgements This master thesis ends my journey of being a student at the Eindhoven University of Technology. In

September 2011, together with seven fellow students, this journey started at the bachelor Industrial

Engineering for Healthcare. The manageable size of this group resulted in a close collaboration with

the teachers and experts from practice. Every quartile, a project was performed for a healthcare

organization, covering most types of healthcare providers – hospitals, home care providers, and

mental healthcare institutions. From the beginning onwards, this close collaboration with practice gave

me the chance to really get a feeling of the real-world problems, as well as the opportunities that exist.

Many supply chain firms – which are broadly discussed at the general Industrial Engineering program

– are so advanced that they are constantly trying to realize marginal improvements. In stark contrast,

in healthcare there is still room for major improvements. These major improvements in healthcare

influence us all.

In my master thesis, I was driven to focus on such major improvements in healthcare. My experience

at the Jheronimus Academy of Data Science combined with some successful pioneers in healthcare

made me particularly interested in the disruptive innovation power of data science in healthcare.

Luckily, CZ, a Dutch health insurer, was also inspired by the potential of data science. With great

pleasure, we started a fruitful and inspiring collaboration to fulfill the last requirements for my master's

degree in the form of the master thesis project presented here.

For this inspiring collaboration, I especially want to thank some people who made this project such an

exceptionally interesting research.

I want to thank Bob Walrave, my first supervisor of the TU/e. First of all, I want to thank you for using

your network to realize this great place to graduate at. Even more, I want to thank you for being such

an enthusiastic and involved supervisor throughout my research. Secondly, I would like to thank Ksenia

Podoynitsyna for being my second supervisor. Your feedback helped me to take a helicopter view and

by that, improve my research.

I also would like to thank Fleur Hasaart and all other members of the Data Science Team. Fleur, thank

you for supporting me to make the best out of this collaboration. In your enthusiasm, you gave me the

chance to connect and learn from so many people, within CZ as well as far beyond the border of The

Netherlands. Throughout this project, you have been a great example for me. I greatly appreciate that

I got the chance to learn from you, both on the topic of this research as well as on a professional level.

I am looking forward to our further collaborations.

Finally, yet importantly, I would like to thank my family, friends, and boyfriend. Without having you

around me, my journey as a student, and particularly the last half a year, would have been far less

adventurous and exciting.

I hope you enjoy reading my master thesis as much as I enjoyed doing this research.

I wish you all the best!

Laura

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Table of Contents Abstract ................................................................................................................................................... iv

Management summary ............................................................................................................................ v

Acknowledgements ............................................................................................................................... viii

1. Introduction ..................................................................................................................................... 1

2. Method ............................................................................................................................................ 3

2.1 Problem investigation ................................................................................................................... 4

2.1.1 Theoretical problem investigation ......................................................................................... 4

2.1.2 Practical problem investigation .............................................................................................. 4

2.2 Solution design ........................................................................................................................ 6

2.2.1 Participation ........................................................................................................................... 7

2.2.2 In-depth interviews ................................................................................................................ 7

2.2.3 Conferences ............................................................................................................................ 7

2.2.4 Synthesis of data sources ....................................................................................................... 9

2.3 Design validation ................................................................................................................... 10

3. Problem investigation ................................................................................................................... 12

3.1 Theoretical problem investigation .............................................................................................. 12

3.1.1 Barriers for disruptive innovations in healthcare ................................................................. 12

3.1.2 Data science in healthcare ................................................................................................... 14

3.1.3 Patient-centered healthcare system .................................................................................... 15

3.2 Practical problem investigation ................................................................................................... 16

4. Solution design .............................................................................................................................. 21

4.1. Value drivers .......................................................................................................................... 21

4.2. Value propositions ................................................................................................................. 25

5. Design validation ........................................................................................................................... 28

6. Discussion ...................................................................................................................................... 31

6.1 Practical implications ............................................................................................................. 31

6.2 Theoretical implications ........................................................................................................ 33

6.3 Limitations & future research ............................................................................................... 34

7. Conclusion ..................................................................................................................................... 36

Bibliography ........................................................................................................................................... 37

Appendix I – Global financing system of Dutch healthcare .................................................................. 44

Appendix II – Systematic literature review overview ............................................................................ 45

Appendix III – Additional information on members in diabetes type II data set .................................. 49

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Appendix IV – Relation between research- and practice based problem investigation ....................... 52

Appendix V – Underpinning of Table 6 ................................................................................................. 54

Appendix VI – Additional information for lifestyle coach claims data set ............................................ 55

List of figures Figure 1: Regulative cycle ........................................................................................................................ 4

Figure 2: Organogram of CZ with corresponding data sources............................................................... 9

Figure 3: Total healthcare costs per year .............................................................................................. 17

Figure 4: Average total healthcare costs per year - dietary advice ....................................................... 17

Figure 5: Average total healthcare costs per year - exercise program ................................................. 18

Figure 6: Classification tree for with and without insulin use ............................................................... 19

Figure 7: Value drivers of healthcare ecosystem .................................................................................. 22

Figure 8: Average yearly healthcare costs per member per provider .................................................. 28

Figure 9: Distribution of 'decrease' and 'increase' member groups per provider ................................ 29

Figure 10: Ecosystem Value Proposition ............................................................................................... 32

List of tables Table 1: Available information per claim ................................................................................................ 5

Table 2: Overview of information sources within healthcare system .................................................... 8

Table 3: Number of members with and without intervention .............................................................. 16

Table 4: Barriers for data-driven innovation for diabetes type II members ......................................... 20

Table 5: Value driver analysis of healthcare stakeholders .................................................................... 23

Table 6: All value propositions evaluated on selected criteria ............................................................. 27

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1. Introduction Because of the improvements in healthcare, education, socio-economic conditions and lifestyle, the

life expectancy in the Netherlands increased from 78 to 82 years over the past 25 years (OECD/EU,

2016). The increased life expectancy was accompanied by a strong rise in healthcare costs over the

past twenty years (CBS, 2016). In 2000, the total healthcare expenditures of The Netherlands were

equal to 46,5 billion euro’s and in 2015 these costs more than doubled up to 95,3 billion euro, which

is equal to 14,05 percent of the Dutch GDP (CBS, 2016). Without radical changes, the rise in healthcare

costs is expected to continue the coming years with a total healthcare cost of 150 billion euro in 2030,

which will be around twenty percent of the Dutch GDP (Ewijk et al., 2013).

The Dutch healthcare system is recognized by its solidarity principle (Ewijk et al., 2013). Solidarity in

this system means that all individuals, irrespective of income and health status, have access to the

same care, both in the availability of care and in the quality of care (Ewijk et al., 2013). Such solidarity

is achieved via income-dependent taxes which account for about seventy percent of all healthcare

funding (Rijksoverheid, 2012). In Appendix I, a visual representation of the healthcare financing in The

Netherlands is given. This financing structure, in combination with the rising healthcare costs, creates

a higher tax pressure for the working generation. The aging of the population, which makes the

working generation weaker represented in the population, intensifies this tax pressure (CBS, 2007).

The increasing pressure on the healthcare funding creates a call for cost-saving innovations in the

healthcare industry. Hwang and Christensen (2008) identify two main types of innovations in

healthcare, namely sustaining innovations and disruptive innovations. Sustaining innovations are the

continuous improvements introduced by the caregiver or supporting organization itself. An important

aspect of these innovations is that they sustain and improve the existing working process (Hwang &

Christensen, 2008). Sustaining innovations can, therefore, be seen as an ‘upgrade’ of the existing

process (Christensen, Raynor, & McDonald, 2015). In this improvement process of sustaining

innovations, there is a point in time in which the quality of the product or process overshoots the needs

of the majority of the customers. When this happens, opportunities arise for innovations that create

new trajectories that can (partly) replace the existing trajectories. Those innovations are called

disruptive innovations, which result in products or services which are usually simpler, cheaper or more

convenient (Christensen et al., 2000). Therefore, to create cost-saving innovations in healthcare,

disruptive innovations are expedient (Christensen et al., 2000; Hwang & Christensen, 2008; Robinson

& Smith, 2008). Past studies show that it is hard to disruptively innovate in healthcare (Herzlinger,

2006; Thakur et al., 2012; Christensen et al., 2000). The healthcare industry did not yet manage to curb

the rising healthcare costs with disruptive innovations.

The healthcare costs account for about a quarter of the Dutch governmental budget (Rijksoverheid,

n.d.). The expected increase in healthcare costs to twenty percent of the Dutch GDP spend on

healthcare by 2030, will affect the governmental spending (Horst, Erp, & Jong, 2011). Without changing

the funding of healthcare, an increase in healthcare expenditures will lead to a higher total needed tax

income for the society. This, in combination with a smaller working age group, will lead to higher

health-related tax premiums for employees and employers. If these tax premiums grow faster than

the economy, this will result in lower purchasing power (Horst et al., 2011; Ministry of Health, Welfare

and Sports, 2012). Since the zero-sum game applies on the governmental budget, another effect of the

increase in healthcare costs is the suppress of other governmental tasks, such as social security and

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education (Horst et al., 2011). With respect to the latter, both the society and its individuals benefit

from cost-saving disruptive innovations.

Recently, both academia and industry experts established hope for data science as a powerful

technique to disruptively innovate to lower healthcare costs without compromising on quality

(Davenport & Harris, 2007; Bates et al., 2014). Healthcare is an information intensive industry and an

ever increasing amount of health (related) data is generated on a minute-to-minute basis, both in- and

outside the hospital. This, in combination with the recent technological improvements, makes it

possible to collect, store and analyze such large amount of data (Patil & Seshadri, 2014; Koumpouros,

2014; Kayyali et al., 2013).

Given the rising healthcare costs and data science as a possible technique to support disruptive

innovations, the following research question will be addressed in this research:

To answer the main research question, the following three sub-questions need to be answered

accordingly:

1) What are the barriers for such innovations in the healthcare system?

2) How to mitigate these barriers in the healthcare system?

3) Assuming the main barriers are mitigated, what is the most promising area of application for

data-driven innovation?

This research adopts a design science problem-solving strategy (Aken, Berends, & Bij, 2007) to improve

the performance of the current healthcare system. Specifically, this research is performed with a focus

on a health insurer.

This research shows that to disruptively innovate the healthcare system by the use of data science, the

healthcare system should transition towards an aligned ecosystem. In the current healthcare system,

different value drivers exist. These different value drivers create a misalignment between the

healthcare stakeholders which results in resistance to share data. Since information is not shared

sufficiently, an information asymmetry is created. The information asymmetry in healthcare appears

to be the underlying cause of the existing barriers to disruptively innovate in the healthcare. To

decrease this information asymmetry, the core value driver of the stakeholders in the healthcare

system should be aligned. This should be achieved by the transition from the current healthcare system

towards an aligned ecosystem with population health as the ecosystem’s core mission.

This report is structured as follows: The next chapter describes the method used to conduct this

research, followed by the problem investigation in Chapter 3. The problem investigation aims to

answer to the first sub question about the barriers disruptive data-driven innovation in healthcare is

facing. In Chapter 4, the solution design of this research is elaborated upon, explaining how the

identified barriers should be mitigated. In Chapter 5, a validation of this solution design is given making

use of a claims data set provided by CZ. In Chapter 6, both the practical and theoretical implications of

this research are discussed. Also the limitations and directions for further research are elaborated

upon. In the last chapter, being Chapter 7, the research ends with an all-embracing conclusion.

How to enable the development of disruptive data-driven

innovation in healthcare?

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2. Method To develop actionable knowledge regarding disruptive innovation in healthcare, a problem-solving

approach is adopted. The aim of this approach is to design a solution for a particular problem in an ill-

defined context in order to solve the problem of a business (Aken, et al., 2007).

More specifically, in this research, a design science approach for business problem-solving is adopted.

The design-science approach is developed to bridge the gap between the academic and the business

world, with the purpose to develop an actionable system that does not exist yet (Romme, 2003). Such

newly developed target system should improve the effectiveness of the current system (Denyer,

Tranfield, & Aken, 2008; Wieringa, 2009). In design-science, these target systems are developed mainly

for the professional in the field (Aken, 2004).

The nature of this study is exploratory and practice-oriented (Wieringa, 2009) and is performed in

collaboration with CZ. CZ is a nonprofit Dutch health insurer headquartered in Tilburg. With 2.7 million

customers, CZ has a market share of 20.5 percent in The Netherlands. Of CZ’s nine billion euro’s annual

turnover, 97 percent is spent on healthcare. The organizational costs account for the remaining three

percent (CZ, 2015). The research topic of this study is in line with the vision of CZ. Their management

is exploring opportunities for data-driven innovations that have the potential to disruptively innovate

the healthcare system. The central role of a large health insurer gives CZ the power to drive disruptive

innovations throughout the healthcare system. Therefore, this research takes the perspective of a

health insurer to lay the basis for data-drive innovations in healthcare. Notice that the solution is thus

designed from the perspective of a health insurer. This chosen perspective results in a solution with

unique features for this stakeholder (Wieringa, 2009).

To structure this research, the regulative cycle of Van Strien (1997) is adopted. Figure 1 visually

represents this regulative cycle. Three of the four main subtasks of the regulative cycle were executed,

being the problem investigation, the solution design, and the design validation. The problem

investigation aimed to answer the first sub-question of this research, namely ‘What are the barriers

for disruptive data-driven innovations in the healthcare system?’. Drawing upon these barriers, the

solution design phase aimed to give an answer to the second sub-question of this research, being ‘How

to mitigate these barriers in the healthcare system?’. Additionally, the solution design phase aimed to

give an answer on the third sub-question addressed in this research, being ‘Assuming the main barriers

are mitigated, what is the most promising area of application for data-driven innovation?’. In the third

phase, the proposed solution design was validated within the context of CZ. The implementation phase

is considered out of scope in this research (Aken et al., 2007).

In this report, each phase made use of different data sources and is discussed in separate chapters.

Therefore, also the method used per phase are separately discussed. Chapter 2.1 elaborates on the

method used for the problem investigation, followed by the solution design method in Chapter 2.2 and

finalizing with the method used in the design validation in Chapter 2.3.

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Figure 1: Regulative cycle (Source: Wieringa, 2009)

2.1 Problem investigation In this study, the problem investigation was performed with an emphasis on the identification of the

current barriers before developing a solution to mitigate these barriers (Wieringa, 2009). In doing so,

the problem investigation aimed to answer the following knowledge question addressed in this

research: ‘What are the barriers for disruptive data-driven innovation in the healthcare system?’

(Wieringa, 2009). To answer this question, the problem investigation draws on input from both theory

and practice.

2.1.1 Theoretical problem investigation

During the problem investigation, theory-based input is collected via a systematic literature review.

Appendix II gives an overview of the search queries executed in this systematic literature review. The

search queries were all entered in both Google Scholar and Focus. Articles were selected on content-

related criteria. In the first selection phase, the title and abstract were used to select the most relevant

articles. After this phase 85 articles were selected. Subsequently, these 85 articles were evaluated in a

manual scan of the entire article, resulting in 25 selected articles. In addition, this set of 25 articles is

complemented with articles based on references in the initial set of publications. Besides the scholarly

literature, four industry publications were used as an additional source of information (Aken et al.,

2007).

2.1.2 Practical problem investigation

A practice-based problem investigation was performed within CZ drawing upon the concepts defined

in the literature review. This practice-based investigation was carried out through the analysis of a data

set retrieved from the CZ claims data base. This analysis was conducted to validate the barriers for

disruptive innovation identified in the literature review in a practice-based situation of CZ specifically

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for data-driven innovations. Besides that, the practice-based analysis investigated if additional barriers

for data-driven innovation exist in practice. To identify these barriers for data-driven innovation that

exist within CZ, a case study was selected. This case study is in line with the in literature proposed

transition from a reactive towards a proactive patient-centered healthcare system (Patil & Seshadri,

2014) for high-cost members (Bates et al., 2014). In Chapter 3.1.3, the transition from a reactive

towards a proactive healthcare system is elaborated on. In short, this transition describes a change

from care delivery when malfunctions arise, towards the delivery of preventative care. An analysis was

performed to identify which barriers exist for a health insurer to support the proactive healthcare

delivery for diabetes type II patients.

A focus on diabetes type II was selected because it is a high-cost disease that accounts for 1.9 percent

of the total healthcare costs (www.volksgezondheidenzorg.info, 2013). In addition, diabetes type II

correlates strongly with behavioral factors resulting in proactive healthcare delivery opportunities

(Fernandez-Llatas, Martinez-Millana, Martinez-Romero, Benedi, & Traver, 2015).

The analyzed data set contained all healthcare claims of 488 randomly selected and anonymized

diabetes type II members from 2006-2016, resulting in a total of 500.000 claims. These 488 members

were randomly selected from all anonymized insurance members that met the following criteria:

The members were insured at CZ over the whole period (2006-2016).

In 2006 and 2007 no diabetes related claims were declared. In this research, both a pharmacy

declaration with the ATC-code A101 and the diabetes type II integrated care claim were

identified as diabetes related claims.

In 2008, a diabetes related claim (as identified in the previous bullet) was declared.

In this data set, per claim, the following information was available:

Table 1: Available information per claim

The diabetes type II claims data set was enriched with a demographic data set of the Central Bureau

of Statistics (CBS). Based on this CBS data set, the following information was added to the diabetes

type II claims data: Percentage immigrants; Of these immigrants percentage non-Western immigrants;

Percentage lower education; Percentage medium education; Percentage higher education. Per claim,

1 Based on the universal applied drugs classification system of the World Health Organization, A10 being drugs

prescribed for diabetes (World Health Organization, 2017)

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the demographic data was matched with the diabetes type II members based on the postal code of

the member.

Based on the one-sentence general claim description, two different claims were identified as proactive

interventions, namely dietary advice and exercise program. The effect of these proactive interventions

was analyzed via data visualization and statistical analyses. For the data visualization, bar charts were

made to visualize the difference in costs between members with a proactive intervention and without

the proactive intervention. A t-test was performed to statistically validate the differences between

these two groups. Additionally, a decision tree was made to assess if the proactive intervention

influenced the insulin use of the members. The insulin use namely appeared to be a proxy for the

health of a diabetes type II patient in which less insulin suggest an improved health and vice versa.

One should be careful with interpreting the results of all the diabetes type II analyses since limitations

to statistically support the results exist. An example of such limitation is the sample size. It should be

taken into account that the diabetes type II problem investigation did not aim to extend the body of

medical knowledge regarding diabetes type II research. Rather, the diabetes type II data analysis was

performed to identify the barriers that exist for data-driven innovation in healthcare. Therefore, based

on the experiences while analyzing the diabetes type II data set, the practical problem investigation

finalizes with an overview of the problems and corresponding barriers were identified.

The barriers identified in the problem investigation were discussed in two semi-structured interviews.

One interview was held with a general practitioner to discuss the results with a healthcare professional.

The other interview was conducted with the manager healthcare innovations of CZ. In the problem

investigation, the semi-structured interviews were recorded, transcripted and summarized. In

Appendix IV the concepts of both the theoretical- and practice-based problem investigation are placed

in relation to each other and used as input for the solution design phase, described in the next sub

chapter.

2.2 Solution design The solution design phase followed an exploratory design process, in which the barriers identified in

the problem investigation were used as input for the solution design (Aken et al., 2007). The solution

design aimed to answer sub question two and three, namely ‘How to mitigate these barriers in the

healthcare system?’ and ‘Assuming the main barriers are mitigated, what is the most promising area

of application for data-driven innovation?’.

To design a solution that gives an answer to these questions, the grounded-theory approach was

adopted. The grounded theory approach is ‘a structured approach for the exploration of unfamiliar

territory’ (Aken et al., 2007, p.138) in which the results emerge from the data (Strauss & Corbin, 1994).

This method is suitable for this study in a new and unfamiliar territory. For the data collection in this

phase, data source triangulation was applied to increase the validity and reliability of the solution

design (Easterby-Smith, Thorpe, & Lowe, 1991). In the solution design phase, the participation and in-

depth interviewing methods were applied in order to collect rich data (Marshall & Rossman, 1995).

Additionally, information from plenary presentations of three visited conferences was collected. These

different modes of data collection are further discussed in the following three sub chapters.

7

2.2.1 Participation

For the data collection via participation, the researcher participated in ten full days of ‘design-sessions’

at CZ. These design sessions aimed to lay the basis for a solution to answer this study’s focal research

question. In these design sessions, the author of this research was involved as one of the key agents,

together with two concept developers and one strategic IT-consultant, all employed by CZ.

Additionally, the researcher also participated in a four-hour long brainstorm session with multiple

stakeholders in the healthcare system – an IT company; pharmaceutical company; diabetes type I

patient; health insurer. The aim of this brainstorm session was to give an answer to the question ‘How

can we together improve the health of diabetes type II patients by the use of data science?’.

2.2.2 In-depth interviews

For the in-depth interviewing data collection, multiple interviews were held with different

stakeholders in the healthcare system. Because of the exploratory nature of this research, both the

unstructured and semi-structured interview approach was applied to gather a rich amount of data.

Prior to the interviews, an interview protocol was created which was checked by a CZ employee. The

interviews were not recorded in order to minimize hold on respondents’ responses (Harvey, 2011). For

the data collection during the interviews, the responses were written down and summarized directly

after the interview.

2.2.3 Conferences

The last mode of data collection during the solution design phase was the information collected during

three visited conferences. These three conferences were selected to get a comprehensive overview of

both the potential and the problems of data science (in healthcare). Of the three selected conferences,

one was focused on data science in the financial sector – both banking and insurance – in The

Netherlands. This conference was selected aimed to obtain industry specific knowledge in the Dutch

context. The second conference attended was in The United States of America and focused on data-

driven innovation in the healthcare industry. This conference was attended to gain healthcare specific

knowledge, identify best-practices and to learn from the experiences of innovative data-driven

healthcare providers, insurers and healthcare systems. During this conference, an international range

of speakers was present representing all stakeholders in the healthcare system. Lastly, a conference

about ‘Big data in healthcare’ organized by the Dutch Ministry of Health, Welfare and Sports was

attended. This conference was attended to gather policy-specific information around data science in

healthcare. During these conferences, all information obtained from relevant speakers was

summarized based on notes made during the presentations.

Table 2 gives an overview of all data that was collected via these three modes of data collection in the

design phase. For the conferences, only the cited speakers are included in this table not to make the

list too extensive. The organogram in Figure 2 gives an overview of all the different data source

collected in this phase within CZ.

8

Table 2: Overview of information sources within healthcare system

9

Figure 2: Organogram of CZ with corresponding data sources in yellow

2.2.4 Synthesis of data sources

In order to get from the raw data to the solution design, a synthesis process was performed. There was

a creative leap from the synthesis process of this raw data towards the solution design (Aken et al.,

2007). This creative leap was substantiated by logic and by the insights of open coding on the raw data

of this research. For the open coding, different codes were developed by answering the question ‘what

was this about?’ for each information source (Aken et al., 2007). The researcher grouped these

different low-level codes based on similarity (Willig, 2013). First, the most similar codes were grouped,

for example ‘focus on the high-cost patient’ and ‘focus on elderly’. After this first merge, again the

matching concepts were grouped, but now on a higher level (Willig, 2013). This stepwise merging

resulted in an overarching distinction between the concepts value drivers and value propositions. The

solution design chapter is therefore also divided in a value drivers section and value propositions

section. Both these sections aim to answer one of the two sub questions addressed in this phase.

The value driver section proposed a solution to the question ‘How to mitigate these barriers in the

healthcare system?’. The synthesis of the value drivers codes revealed the misalignment of value

drivers in the Dutch healthcare system. This misalignment appeared to be the underlying cause of the

barriers for disruptive innovation. To align the value drivers, a value driver analysis was performed. In

this analysis, the current value drivers in the healthcare system were compared with the desired value

drivers. This comparison identified the required changes. The desired situation was defined based on

an identified best-practice. This best-practice, namely Discovery Health, was identified a best practice

based on the input of two independent information sources of this research.

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Given the changed value drivers, the value proposition section of the solution design aimed to give an

answer to the last sub question of this research, namely ‘Assuming the main barriers are mitigated,

what is the most promising area of application for data-driven innovation?’. In doing so, the value

proposition section elaborated on five data-driven tactics that have the potential to disruptively

innovate in healthcare. These tactics are all value propositions from the perspective of a health insurer

that were presented during the attended data in healthcare conference in the United States of

America. For the selection which value proposition is most suitable within the Dutch healthcare system

each value propositions was scored on seven selection criteria for CZ. The seven criteria were

established based on the information gathered throughout this research. More specifically, from all

interviews held within CZ, the codes corresponding to the requirements for data-driven innovations

were used as input for the selection criteria. Appendix V gives for each value proposition an

underpinning of the scores on the seven selection criteria. The value proposition effective prevention

for high-risk members was identified most suitable for CZ to drive disruptively innovate.

2.3 Design validation After the design process, the effective prevention for high-risk members value proposition was

validated within CZ. Because this value proposition is not implemented yet, it was hard to validate the

suitability and success of this design by interviews (Aken et al., 2007; Wieringa, 2009). Therefore, a

quantitative validation was performed on a case study that was most comparable with this value

proposition. The selected case study was the Lifestyle Coach research. The Lifestyle Coach research is

a doctoral research funded by CZ that started in 2012 at Maastricht University. In this doctoral

research, 176 high-risk members, four healthcare providers and CZ collaborate with the purpose to

improve the effectiveness of preventions for high-risk members. This is not done via the use of data

science. Rather, this research aimed to improve the effectiveness via the collaboration between the

different stakeholders and delivering a new type of intervention, the Lifestyle Coach. Four different

type of Lifestyle Coach interventions were developed by the research of Maastricht University. In this

validation, the experimental design of the doctoral research is assessed to validate if this case study

suggests the disruptiveness of the effective prevention for high-risk members.

The analyzed data set contained all the healthcare claims from 2012 to 2016 of 176 anonymized CZ

members that participated in the Lifestyle Coach research. This resulted in a total of 100.000 separate

claims. Per claim, the same information was available as described in Table 1 for the diabetes type II

claims data. The claims data was analyzed via graphical analyses by using bar charts.

Based on the information gathered during the design validation, the solution design was adjusted and

complemented. The final solution design was identified valid when it gave an answer to the research

questions (Aken et al., 2007). In other words, a valid design should be a basis for a solution to enable

the development of disruptive data-driven innovation within the healthcare system. Besides these

practical implications, this research also contributes to the literature. In the existing body of research,

a rather technology focused perspective on data science in healthcare is adopted. In this research, a

more strategical focus is adopted on how to harvest the data science potential in healthcare. This shift

from a technological to a strategical focus resulted in a shift in research from dyadic relations towards

a research in which the complexity of the healthcare system is taken into account by reasoning from

the polyadic interdependencies in healthcare.

11

In the following three chapters, the problem investigation, the solution design and the design

validation are further elaborated upon in respectively Chapter 3, Chapter 4 and Chapter 5.

12

3. Problem investigation To sustain the Dutch solidary healthcare system, disruptive innovations are required. Data science is

identified a powerful technique to disruptively innovate in healthcare (Davenport & Harris, 2007;

Bates et al., 2014). This research aims to lay the basis for a solution design to disruptively innovate in

the healthcare system by the use of data science. To do so, in this problem investigation chapter, the

barriers for disruptive innovation in healthcare are identified in the theoretical problem investigation

in Chapter 3.1, and respectively the barriers for data-driven innovation in the practical problem

investigation in Chapter 3.2. Additionally, the potential of data science is reviewed.

3.1 Theoretical problem investigation In healthcare there is an emphasis on sustaining innovations (Hwang & Christensen, 2008). Most

innovations are designed to improve the traditional working trajectory of the medical practitioner or

hospital (Hwang & Christensen, 2008). To curb the rising healthcare costs, new cost-saving innovative

trajectories are needed (Christensen et al., 2000; Hwang & Christensen, 2008; Robinson & Smith,

2008). In this research, these innovations are referred to as disruptive innovations.

Following the theory of Christensen et al. (2000), the new trajectories of disruptive innovation enter

the market with a lower performance than the current trajectory and therefore target the least-

demanding customer (Christensen et al., 2000). Initially, these new trajectories bring advantages, like

price or convenience, that outweigh the lower performance for the least-demanding customer. Over

time, the performance of the product or service of the disruptive innovation can improve and will then

at a certain point meet the needs of the vast majority of the market. In healthcare, this new trajectory

will result in a better match between the complexity of the treatment and the level of clinical skills

needed for that treatment (Christensen et al., 2000; Hwang & Christensen, 2008; Robinson & Smith,

2008).

3.1.1 Barriers for disruptive innovations in healthcare

Although such disruptive innovations are often beneficial or required, across industries, incumbents

have difficulties with developing disruptive innovations (Christensen & Raynor, 2003; McDermott &

O'Connor, 2002). In the healthcare system this seems particularly challenging (Herzlinger, 2006; Thakur

et al., 2012; Christensen et al., 2000). Four main barriers are identified that impede disruptive

innovations in the healthcare system. The first three barriers are grounded in the overarching

healthcare system. The fourth is a barrier that restricts the development of disruptive innovations on

the company level.

The first characteristic of the healthcare system that discourages disruptive innovation is the

fragmentation of the healthcare industry. In healthcare there are five main stakeholders, namely

patients, providers, researchers, pharmaceutical companies, health insurers and the government. All

these stakeholders have their own goals, incentives, and information (Feldman, Martin, & Skotnes,

2012). Additionally, the main process of healthcare, namely the delivery of care, is also highly

fragmented over different providers what complicates the coordination of care delivery.

The fragmentation barrier can be explained with the theory of dynamic complexity. All different

stakeholders are linked and interact together within the healthcare system (Atun, 2012). Within this

dynamic system, the actions of the different stakeholders influence the others, together determining

the behavior of the system (Atun, 2012; Plsek, 2003; Sterman, 2000). The consequences of an action

for one stakeholder can be different compared to the consequences for another stakeholder

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(Forrester, 1961). An example of this in healthcare is that the delivery of a new, high-quality, low-cost

treatment can lead to a better recovery for the patient but to a lower turnover for the medical

specialist who performed this treatment. In this example, the new treatment has a positive

consequence for the patient in contrast to a negative consequence for the specialist. Such complex

and fragmented system, with different and sometimes contradicting interests, complicates the

implementation of disruptive innovations (Atun, 2012; England, Stewart, & Walker, 2000; Hwang &

Christensen, 2008).

Secondly, the most common pricing method in healthcare, being the fee-for-service pricing model,

discourages disruptive innovation in the healthcare system. In this pricing method, the delivery of care

is encouraged due to the reimbursement of treatment volume (Robinson, 2001). Via this pricing

method, the healthcare provider is given an incentive to deliver more care because this results in more

turnover, and by that, this pricing method can drive up demand (Christensen, Grossman, & Hwang,

2009; Robinson & Smith, 2008).

This behavior can to a large extent be realized because of the principal-agent problem that exists in

the healthcare industry (Nguyen, 2011). In healthcare, the principal-agent problem manifests itself

between different relations (Smith, Stepan, Valdmanis, & Verheyen, 1997). Both the relation between

the healthcare practitioner and the patient and the relation between the practitioner and the health

insurer can be seen as principal-agent relations (Smith et al., 1997). The principal-agent problem exists

when a healthcare practitioner, the agent, maximizes his interest at the expense of the principal

(Nguyen, 2011). The agent is the informed individual and has no direct incentive to reveal all his

information and knowledge to the ill-informed principal (MacDonald, 1984). Under such

circumstances, the actions of the healthcare practitioner cannot be properly monitored, measured and

understood by the principal (Robinson, 2001). The fee-for-service model can reinforce the principal-

agent problem (Nguyen, 2011).

This fee-for-service pricing method gives healthcare providers little to no economic incentive to

implement disruptive cost-saving innovations (Luft, 2009). This little incentive for disruptive innovation

from the healthcare provider is reinforced by the lack of economic incentives from the patient's

perspective. In traditional brick-and-mortar stores, customers have a strong incentive to buy products

that best match their needs. In healthcare, this is different since the consumed care is financed via a

third-party payer. This third-party payment structure gives patients (almost) no economic incentive to

search for the treatment with the best price-quality ratio which in turn, gives healthcare providers very

little incentive to disruptively innovate their caregiving (Hwang & Christensen, 2008).

The third external barrier to disruptively innovate is the barrier of over-regulation (Curtis & Schulman,

2006). In the highly regulated healthcare industry (Conover, 2004), regulations are external rules that

are applied in a uniform way to both the providers and the patients (Curtis & Schulman, 2006).

In general, the healthcare regulations create a performance threshold (Curtis & Schulman, 2006), with

the goal to protect the patient and the public safety (Hwang & Christensen, 2008; Robinson & Smith,

2008). The performance threshold results in change-averse regulations that complicate the

implementation of new trajectories (Hwang & Christensen, 2008; Robinson & Smith, 2008; Curtis &

Schulman, 2006). The performance threshold requires that all treatment and healthcare products

meet at least the performance of the threshold defined in the regulations (Curtis & Schulman, 2006).

When the performance threshold exceeds the needs of the majority of the patients, the regulations

withhold the rise of disruptive innovations. With such a performance threshold, it is not possible to

create a product or treatment that better fits to the demand of the lower end and average patient

14

(Curtis & Schulman, 2006). Therefore, these regulations complicate the implementation of disruptive

changes and are expected to drive up costs (Bates, 2002; Christensen et al., 2000; Conover, 2004).

The fourth barrier for disruptive innovation is the internal barrier strategic rigidity. The path

dependency theory explains this internal barrier for disruptive innovation to a great extent. Path

dependency creates a system in which an organization is tied to its previous decisions and its existing

situation (Wilsford, 1994). The existing situation dominates in the policy and decision-making, and

therefore, the innovations are most likely to be incremental innovations. To disruptively innovate,

strong dissentient forces are needed to create a new trajectory (Wilsford, 1994).

Underlying this behavior is the resistance to change within organizations. In general, people, and by

that also healthcare practitioners and policy makers, resist change (Thakur et al., 2012). This individual

resistance to change can result in organizational resistance to change (Lorenzi & Riley, 2000).

Organizational change is defined as ‘some threat, real or perceived, of personal loss for those involved’

(Lorenzi & Riley, 2000, p.117). Examples of such threats are changes in the job routine or a threat to

job security (Lorenzi & Riley, 2000). The resistance to change creates a low level of interest in disruptive

innovations (Talmar, Walrave, Holmström, & Romme, n.d.).

When wanting to disruptively innovate in healthcare, one must be able to contend against the

organizational resistance to change and the system’s logic by giving a strong dissentient force to cope

with that resistance to change (Robinson & Smith, 2008; Wilsford, 1994).

Interestingly, the concepts behind the three external barriers described in this chapter, namely the

dynamic complexity, the principal-agent problem and the overregulation in healthcare, all relate to the

information asymmetry in healthcare (Castano, 2014; Nguyen, 2011; Sterman, 2000; Yip & Hsiao,

2009). As such, the information asymmetry between the stakeholders in healthcare is identified the

main barrier to disruptively innovate in healthcare. Subsequently, it is identified as a significant

contributor to the rising healthcare costs (Cruz & Kini, 2007). Cruz and Kini (2007) argue that the

information technology (IT) can help to mitigate the information asymmetry in healthcare. An example

of such information technology that can potentially help to mitigate the information asymmetry in

healthcare is the data science technology.

The remainder of this literature review will focus on data science in healthcare. Data science in

healthcare namely has the potential to propel innovations that reduce costs and improve care

(Raghupathi & Raghupathi, 2014; Nambiar, Sethi, Bhardwaj, & Vargheese, 2013; Patil & Seshadri,

2014).

3.1.2 Data science in healthcare

In the past few years both academia and industry experts established hope for data science as a

powerful technique for innovation towards a superior healthcare system (Bates et al., 2014; Groves,

Kayyali, Knott, & Kuiken, 2013; Feldman et al., 2012; Koumpouros, 2014; Wactar, Pavel, & Barkis,

2011). This hope is fueled by success stories in other industries and the unprecedented cost-saving

potential for healthcare (Davenport & Harris, 2007; Bates et al., 2014). The immaturity of the data

science research field creates an ambiguity of definitions. This research uses the term data science as

a collective term for a group of techniques, such as statistical modeling, machine learning and process

mining (Chen, Chiang, & Storey, 2012).

Most data science applications in healthcare are designed around predictive analytics. Predictive

analytics on itself are algorithms that predict the risk of a certain outcome (Bates et al., 2014). Both in

15

literature and practice, a good strategy to implement such predictive analytics is with a focus on high-

cost patients (Bates et al., 2014; IBM, 2013). The focus on high-cost patients is identified as a direction

for data science in healthcare with a high cost-saving potential (Bates et al., 2014; IBM, 2013). Such

focus is particularly interested since the Pareto principle applies to the distribution of the Dutch

healthcare costs (Kommer, Slobbe, & Polder, 2005). Decreasing costs in the high-cost patient group,

therefore, has a substantial impact on the total healthcare costs. Data science algorithms can reveal

information to improve the allocation of resources for these high-cost patients and by that, curb the

rising healthcare costs (Bates et al., 2014).

The focus on high-cost patients can be related to the theory of strategic niche management. This

theory advocates the creation of a protective space in a niche market for a disruptive innovation to

mature (Schlipzand, Raven, & Est, n.d.; Smith & Raven, 2012). Without such a protective space,

industry incumbents would reject new disruptive innovations which prevent the experimentation and

creation of such innovations (Schlipzand et al., n.d.). In the protective space, the disruptive innovation

gets the chance to experiment and mature, which appears crucial for the success of such disruptive

transition (Schlipzand et al., n.d.).

With its potential in healthcare, data science emerges as a plausible technique to disruptively innovate

the healthcare industry (Patil & Seshadri, 2014). The next paragraph describes the possible paradigm

shift that is being enabled via disruptive data-driven innovations in healthcare (Patil & Seshadri, 2014).

3.1.3 Patient-centered healthcare system

The current healthcare delivery model is considerably reactive and disease-centered, in which

healthcare is delivered after problems arise (Patil & Seshadri, 2014). Healthcare can innovate to a more

proactive healthcare system via a more patient-centered healthcare approach (Chawla & Davis, 2013;

Epstein, Fiscella, Lesser, & Strange, 2010). In such approach, patients actively participate in the care

they consume and this care is tailored to the needs and preferences of individuals (Chawla & Davis,

2013; Koumpouros, 2014). To create a shift to proactive patient-centered healthcare delivery, data is

needed (Abidi, 2001). By integrating different information sources and by analyzing this information,

data science tools can contribute to the shift from population-based healthcare to individual evidence-

based healthcare delivery (Chawla & Davis, 2013; Koumpouros, 2014).

This shift from a disease-centered to a patient-centered healthcare model roots in the possible shift

from the current reactive healthcare system to a proactive system (Patil & Seshadri, 2014; Bates et al.,

2014). The shift from a reactive system to a proactive system originates from the maintenance

industry. Traditionally, companies had a reactive strategy in which maintenance was carried out when

visible malfunctions arose. Later, with the increase in technological possibilities, companies started to

shift to a proactive maintenance strategy in which preventive and tailored maintenance was

performed based on predictions. Research shows that this proactive maintenance strategy is positively

related to performance (Swanson, 2001). Because of successes in other industries, it is expected that

a proactive healthcare system will also lead to improved performances, both in the (perceived) quality

of care as on restraining the growth of healthcare costs (Chen et al., 2012; Patil & Seshadri, 2014;

Wactar et al., 2011).

16

Given the potential of data science to change towards a healthcare system with proactive healthcare

delivery, the next chapter elaborates on the practice-based problem investigation to identify the

barriers that exist for data-driven innovation.

3.2 Practical problem investigation This practice-based investigation draws upon the results of the systematic literature review. In other

words, the aim of this investigation is to identify if the information asymmetry is also considered the

overarching barrier specifically for data-driven innovation or if other or additional barriers exist in this

context. To assess this, a data analysis is performed to experience which barriers exist for data-driven

innovation in healthcare. In this investigation, a case study is defined based on the knowledge from

the systematic literature review. Namely, a case study to investigate towards which extent the current

healthcare delivery operates proactively for high-cost patients. For this investigation, a data analysis is

performed on a data set of diabetes type II members of CZ. The analysis is performed with the currently

available information for a health insurer. In this chapter, first the results of the analysis are described.

After that, the barriers for data-driven innovation experienced during this analysis are described.

From all claims data of the 488 diabetes type II members over the period from 2006 to 2016, two

proactive interventions can be extracted, namely dietary advice and exercise program. These proactive

treatments are carried out by respectively a dietician and a physiotherapist. A health insurer has no

insights into someone’s health status – for example the Hemoglobin A1c level, body mass index, weight

or blood pressure (McKenzie et al., 2017; General Practitioner, 7th of July 2017). Therefore, to measure

the effectiveness of the interventions, costs are taken as a target variable (General Practitioner, 7th of

July 2017). In this investigation, the assumption is made that costs are a derivate of health (General

Practitioner, 7th of July 2017). Under this believe it is assumed that more healthcare problems result in

more healthcare costs.

Of all 500.000 claims in the data set, 1700 claims are declared as dietary advice and 114 as exercise

program. This means that, from the available information of a health insurer, 0.36 percent of all claims

can be identified proactive claims. Table 3 shows per intervention the distribution of the 488 members

that receive the proactive intervention. These results show that around fifty percent of the diabetes

type II members in the data set receive a proactive intervention.

Table 3: Number of members with and without intervention

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Figure 3 visualizes the yearly sum of healthcare costs of all 488 diabetes type II members together2. As

can be seen in this figure, the total healthcare costs for these members follows an upward slope.

Because of the assumption that costs are a derivative of health, this upward slope indicates a

deteriorating health status.

Figure 3: Total healthcare costs per year

To measure the effectiveness of the two proactive interventions, the average total healthcare costs

with and without the intervention are analyzed. These results are shown in Figure 4 and Figure 5.

Figure 4: Average total healthcare costs per year - dietary advice

2 This figure shows the absolute costs, no correction for inflation is applied.

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Figure 5: Average total healthcare costs per year - exercise program

An analysis of the average healthcare costs of all members with and without the intervention

suggests no clear relation to the effectiveness of the proactive intervention on the healthcare cost of

a member. This is confirmed by an independent sample t-test. The null hypothesis cannot be rejected

for both interventions under an alpha of 0.05 percent (pdietary advice=0.88; pexercise program=0.09).

An interview with a general practitioner regarding these results supports the lack of proof for the

effectiveness of the current preventative treatments.

‘I want to prescribe these interventions, but they come and go. Sometimes such programs are being

reimbursed by a health insurer but always, after some times, the budget for such programs is finished

so we cannot send the patient again or send any new patients’ (General practitioner, 7th of July 2017).

‘Comparing the results of the dietary advice group with the non-dietary advice group is questionable.

Because at the practice of the GP also dietary advice is given, and also in the ‘ketenzorg’ program.

Therefore it is not that the sample without a dietary advice claim does not get any dietary advice. The

insurer just doesn’t know it. Testing the results of the dietary advice program is more a test if the

dietician is more effective than the GP practice’ (General practitioner, 7th of July 2017).

‘You miss crucial information as a health insurer to demonstrate the effectiveness of such lifestyle

programs. As a medical specialist, the status of a diabetes type II patient is being determined via the

Hba1c test. This test is performed every three months, together with the blood pressure and weight.

These values, together with what the patient tells, are used to determine the status of the patient’

(General practitioner, 7th of July 2017).

Another possible method to measure the effectiveness of the two intervention programs is via an

analysis of the insulin use of a patient. Such analysis can be done under the assumption that a decrease

in insulin use indicates better health.

‘On average, it is positive when the insulin intake decreases for diabetes type II patients, but this is

not always the case. If patients are terminal and eat very little, the insulin dose will also decrease, but

this does not mean the patient is healthier in that case’ (General practitioner, 7th of July 2017).

19

In analyzing the effectiveness of the two interventions via the cost of insulin claims, it is important to

study the effectiveness beyond short-term results because short term results are relatively easy to

obtain.

‘Short term results are very easy in diabetes research. If you eat fewer carbohydrates you need a

smaller insulin dose, so if you just eat healthy for ten weeks a research can show outstanding results,

but this is no long term effect’ (General practitioner, 7th of July 2017).

‘It is relatively easy to get short term results for diabetes type II patients’ (Manager Healthcare

Innovation CZ, 22nd of June 2017).

To analyze if the interventions influence the insulin use, a supervised learning technique is applied. In

this research, a classification decision tree algorithm is used. In supervised learning, both the in- and

output variables are defined prior to the analysis. In a classification decision tree, the algorithm creates

a mapping (i.e. a ‘tree’) based on all available input variables from how to come from the input to a

set of decisions that best match with the predefined output. Later, this set of decisions can be used on

new data of which the input variables are known to predict the expected output variable (Kotsiantis,

2007). In this research, the aim of the decision tree is to investigate if the variable ‘with/without

intervention’ is incorporated in the classification ‘with/without insulin use’. An analysis called

‘complexity parameter’ is performed to investigate which variables are included in the classification

tree. This analysis showed that the ‘with/without intervention’ variable is not included in the

classification tree and therefore is not considered to add sufficient prediction power to the insulin use

prediction. In Figure 6 one can see all variables that are included. The decision tree should be read

from top to bottom. An example as visualized in Figure 6 is that when ‘Age >=46’ not holds, the member

is classified as using insulin. In all further splits, the left branch always corresponds with ‘yes’ and the

right branch with ‘no’. In Appendix III a more detailed version of this classification tree is given.

Figure 6: Classification tree for with (1) and without (0) insulin use

The classification tree in Figure 6, as well as the other results discussed in this problem investigation,

show that from the perspective of a health insurer there is too little information to assess the

effectiveness of the preventative intervention. The preventative intervention is namely neither

positive nor negative on the insulin use of the diabetes type II members. Based on the in this chapter

described practice-based problem investigation, seven barriers were identified that were considered

to complicate the delivery of proactive healthcare from an insurers perspective. In Table 4 an overview

of these barriers is given together with a description how this barrier was experienced.

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Table 4: Barriers for data-driven innovation for diabetes type II members

These seven identified problems complicate the delivery of proactive healthcare. As one can see in

Table 4, all the barriers are a result of the information asymmetry that is present in healthcare.

Therefore, at least for this specific analysis, the practice-based problem investigation ratifies that the

information asymmetry is a barrier for disruptive data-driven innovation in healthcare. Since all

barriers identified in Table 4 are not necessarily specific for diabetes type II members, it is assumed

that this generally holds. Given the situation that a health insurer wants to stimulate proactive

healthcare delivery, as proposed in the literature study, the information asymmetry in healthcare

needs to decrease.

Based on the theoretical and practice-based problem investigation an answer to the first sub question

of this research can be given. The information asymmetry is considered the overarching barrier for

disruptive data-driven innovation within the healthcare system. So, to disruptively innovate in

healthcare by the use of data science, first the information asymmetry should decrease. Based on the

results of the problem investigation, in this research, data science is thus not considered a means to

decrease the information asymmetry in healthcare. Rather it is considered a means to disruptively

innovate in a healthcare system given that the information asymmetry already sufficiently decreased.

The next chapter of this research will draw on these results and focus on the remaining two sub

question.

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4. Solution design The problem investigation shows that the information asymmetry is the main barrier for disruptive

data-driven innovation within healthcare. Given this barrier, the solution design section of this

research aims to give an answer to the other two sub questions of this study, namely ‘How to mitigate

the information asymmetry in the healthcare system?’ and lastly ‘Assuming the main barriers are

mitigated, what is the most promising area of application for data-driven innovation?’. To do so, a

distinction is made between the value drivers in Chapter 4.1 that provide an answer to the first sub

question and the value propositions in Chapter 4.2 providing an answer to the last sub question.

4.1. Value drivers In healthcare, among others, the patient collects valuable information about its lifestyle and wellness.

The medical specialist has general medical data, and patient specific medical data and the health

insurer has a long history of claims data for most of its members. This information is all fragmented

over the different stakeholders and only little information is shared among stakeholders. For data

science to become disruptive within the healthcare system, the information asymmetry should

decrease by connecting its various information sources (Raghupathi & Raghupathi, 2014;

Knowledgent, 2013; Fernandes, O'Connor, & Weaver, 2012).

‘To improve healthcare, we need to connect the knowledge of all who are involved’ (Data Scientist

University Medical Centre, 19th of July 2017).

To do so, the different stakeholders should be both willing and able to share more information with

the other parties in healthcare (Fernandes et al., 2012).

‘If I am willing to share my information depends on the aim of the third party. If this party really exists

to keep my healthy, I believe it is of added value’ (Insurance Member, 29th of June 2017).

For the healthcare ecosystem to harvest its potential, the healthcare stakeholders should share one

overall mission (Adner, 2012; Talmar et al., 2017; Ouden, 2012; Walrave, Talmar, Podoynitsyna,

Romme, & Verbong, 2017). In line with the quote above, to make the stakeholders willing to share

their data within the healthcare system as proposed by Fernandes et al. (2012), all stakeholders should

be aligned. To align all stakeholders in healthcare, they need to have a shared goal. As a shared goal,

this research proposes to form an aligned ecosystem with population health as its core mission. In this

research an ecosystem is defined according to the definition of Adner (2017, p.40), in which an

ecosystem is ‘the alignment structure of the multilateral set of partners that need to interact in order

for a focal value proposition to materialize’. In an aligned ecosystem the integrated efforts of the

stakeholders in healthcare all focus on the overall mission, and add value to the needs of the end

customer (Clarysse, Wright, Bruneel, & Mahajan, 2014).

Discovery Health is a health insurer that operates from such aligned ecosystem perspective. In their

healthcare ecosystem, Discovery Health succeeded in decreasing the information asymmetry by

enriching their claims data with additional data they receive from their members. The ecosystem in

which Discovery Health is active operates from the ecosystem’s mission ‘Vitality’. In doing so, Discovery

Health placed the health of their members as the core mission of their business (Gore, 2015). In this

mission, Gore (2015), the CEO of Discovery, believes that financial success is a derivative of this

approach. Discovery Health is identified a best-practice, and the following paragraph elaborates

further on the insurance model of Discovery Health.

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Discovery Health operates in South Africa, a country with comparable regulations for health insurers

as The Netherlands – everyone must be accepted for health insurance against the same premium

(Malcolm, Roehrig, & Pring, 2017). The management of Discovery Health realized that more data is

needed than traditionally available for a health insurer to effectively improve the vitality of their

members. Among others, Discovery Health needs to know about the BMI, blood pressure and lifestyle

behavior of their members. To track this data, Discovery Health designed the so-called ‘Vitality’

program (Malcolm et al., 2017). This is a supporting system with apps, linked to fitness devices and

eating habits, resulting in a decrease in information asymmetry between Discovery Health and their

customers. Through this interface, Discovery Health shares its knowledge about a healthy lifestyle with

their members. This knowledge is obtained through their research institute and supported by their

health claims, resulting in personalized pathways to improve their members’ health. Figure 7 gives a

visual representation of the value drivers within the healthcare ecosystem of the members of

Discovery Health, Discovery Health itself and the South African Society, all sharing the one mission,

Vitality.

As can be seen in Figure 7, it is no necessity that the different stakeholders in the ecosystem share all

value drivers. This figure shows that in an aligned ecosystem each stakeholder can have its value

drivers when they align with the one shared mission in the healthcare ecosystem (Ouden, 2012).

In line with the example of Discovery Health, the different stakeholders in the Dutch healthcare system

should form an aligned ecosystem to decrease the information asymmetry in healthcare. For the Dutch

healthcare system, a suitable overall ecosystem mission is defined as population health. The following

paragraphs describe the analysis towards which extent there is alignment between the current value

drivers of the different stakeholders and the ecosystem value driver population health. The two or

Figure 7: Value drivers of healthcare ecosystem Discovery Health – Its members – South

African Society (Source: Discovery.co.za)

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three most important value drivers for each stakeholder are given in Table 3. These value drivers are

presented in descending order of perceived importance.

Table 5: Value driver analysis of healthcare stakeholders

First of all, both the Dutch citizen in health and the patient have ‘health’ as the most important value

driver of the healthcare system. For a citizen in health, the financial security is another important value

driver as well as convenience. For a patient, access to the best quality of care is important, which is a

derivative of ‘health’, the most important mission of the patient. Below, some quotes from the

interviews are stated that underpin the value drivers for both the Dutch citizen in health and the

patients.

‘I prefer to get information that makes me healthier instead of a discount on my healthcare insurance

premium’ (Insurance member, 30th of June 2017).

‘Definitely we will need a health insurer, also in a data-driven healthcare system. People will keep the

risk of becoming ill but we will not be able to bear all related costs’ (Insurance member, 30th of June

2017).

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Secondly, the value drivers of the healthcare providers are shortly explained. The most important value

driver for a healthcare provider is the health of their patients (Freidson, 2001). This is in line with the

current version of the oath of Hippocrates medical specialists still need to swear. An additional value

driver for healthcare providers is that treating patients is their work and it generates a source of

income. A third value driver, which is in line with the income motivation, is the ease of a treatment.

The three quotes below validate the importance of these value drivers for healthcare professionals.

‘We sometimes say medical specialist are greedy, but I really believe that in 99.9 percent of the time

doctors want the best for their patients’ (Strategic Consultant Kaiser Permanente, 17th of May 2017).

‘We want to link different data sources in healthcare in order to realize health optimization for our

patients. The goal of linking this data is to be able to exactly say what effectiveness we have on

certain exercise programs or medication prescriptions. This effectiveness should be defined based on

both medical and emotional quality measures. To really make this a success, we want to change our

finance model into funding based on this effectiveness’ (Director 1st line healthcare group, 25th of July

2017).

‘Time is money’ (General Practitioner, 7th of July 2017).

‘I do not want to walk through a whole lot of questions for each patient. I only want use such support

tools in case of doubt’ (General Practitioner, 7th of July 2017).

The main value driver of the pharmaceutical company is profitability. Most pharmaceutical companies

are stock listed or are partly owned by investors which makes profitability their most important value

driver. In order to be profitable, a pharmaceutical company develops medicines to treat patients.

‘Focus on the pharma industry, they are so overpriced. When three pharma companies sell a certain

medicine in the Dutch market and two of these companies stop with the production of this medicine,

the remaining company increases the price like crazy’ (General Practitioner, 7th of July 2017).

‘We want to eradicate diabetes. When we succeed in that, our business is over but our people are

close to being gods’ (Pharmaceutical Company, 10th of July 2017).

The value drivers of the Dutch politics are expected to be in line with the value drivers for the ‘society’

visualized in Figure 7. For politics, it is of great importance to have a healthy society. In line with this,

the productivity of the society is an important value driver. In addition, because the zero-sum game

applies in the governmental spendings, curb the rising healthcare costs is also a value driver for the

Dutch government.

Lastly, the value drivers for Dutch health insurers have been analyzed. The main role of a health

insurer, and following from that, also the first to mention value driver is to restrain the healthcare

costs. The second value driver of a health insurer is the quality of care. These two value drivers

establish a continuous trade-off between cost and quality. The general public generally perceives this

balancing act as negative and solely focused towards cost reduction, which creates little trust.

‘To keep our healthcare, for now and in the future, accessible, good and affordable, we influence the

healthcare system by purchasing care for our members based on cost management, quality, and

innovation’ (Annual report CZ, 2016).

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‘The main goal of an insurer is earning money’ (Insurance member, 30th of June 2017)

‘There is little trust in health insurers within the Dutch healthcare system’ (Director Healthcare Group,

25th of July 2017).

‘If it wouldn’t be against the rules, I expect a health insurer to increase the premium for high-risk

patients, which I think is absolutely unfair’ (General Practitioner, 7th of July 2017).

The value driver analysis exposes the onerous position of both the pharmaceutical industry and the

health insurer within the Dutch healthcare system. The main value driver of the other stakeholders in

the healthcare system are all in line with the ecosystem’s mission, population health but the financial

value driver of the health insurer and pharmaceutical company are not aligned. This misalignment

causes suboptimal results (Adner, 2012; Talmar et al., 2017). To create an aligned healthcare

ecosystem in which data flows between the different stakeholders so that the barrier for disruptive

innovation decreases, both the health insurer and pharmaceutical companies should share the

ecosystems mission, population health as a core value.

Thus, to mitigate the information asymmetry that exists in healthcare, the healthcare system should

create an aligned ecosystem with population health as its core mission. Given this strategy on how to

mitigate the barriers for disruptive data-driven innovations, Chapter 4.2 elaborates on the selection of

the most suitable value propositions for a health insurer in such aligned ecosystem.

4.2. Value propositions In line with the value drivers analysis described in Chapter 4.1, traditionally, the value propositions of

a health insurer were mainly focused on delivering financial security and administrative relief (Gupta

Strategists, 2015). In the past decade, CZ, as well as other Dutch health insurers, have started to

implement more service oriented value propositions (Senior Communication Advisor, 2017). As

described in the previous section, to disruptively innovate in healthcare, the next required shift is the

creation of value propositions aligned with population health.

In line with the shift towards population health, a wide range of value propositions can be suitable to

perform as a health insurer. This sub chapter expounds five of such possible data-driven value

propositions from the perspective of a health insurer. All these value propositions aim, to a greater or

lesser extent, to improve the population health. Note that these value propositions do not aim to

mitigate the information asymmetry for disruptive data-driven innovation in healthcare. Rather, these

value propositions aim to improve the population health in such aligned ecosystem. The final

paragraphs of this sub chapter elaborate on which value proposition has the most potential to

disruptively innovate in healthcare by the use of data science.

(1)-A first value proposition discussed during the data in healthcare conference is a dashboard to

identify the best physician per treatment (Barnes, 28th of April 2017). In this benchmarking tactic, the

first challenge is to identify what is considered good for a specific specialism, which is done based on

follow-up data. The best performing medical specialists are asked why they outperform their peers.

Subsequently, this information is shared with all others in that specialism.

‘We benchmark everything on this dashboard’ (Barnes, 28th of April 2017).

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(2)-A second tactic is the effective prevention of high-risk members (Gale, 27th of April 2017). In this

tactic, data science tools are in place to predict the risk of getting high-impact diseases like diabetes

type II, chronic obstructive pulmonary disease (COPD) and congestive heart failures (Gale, 28th of April

2017). These chronical – often lifestyle related (American Diabetes Association, 2005; Fernandez-Llatas

et al., 2015; Galobardes, Constanza, Bernstein, Delhumeau, & Morabia, 2003; Sharma & Majumdar,

2009)– diseases are high-impact diseases from the patient, society and insurers perspective

(Blumenthal, Chernof, Fulmer, Lumpkin, & Selberg, 2016). If a member is identified as being at high-

risk for adverse health events, other algorithms predict which intervention has the highest chance of

success for that particular member. Subsequently, these interventions are implemented in a patient’s

clinical pathway. Below, a quote is given of a possible intervention from a health insurer who applies

this tactic.

‘A possible intervention is that when such chronical diseased member arrives home after

hospitalization, we give him or her a daily visit the first days after surgery to help with their

medication and to signal complications early. By doing so, we are trying to prevent hospital

readmissions and we try to prevent making the member’s situation even more complex’ (Interview

employee Clover Health, 23th of November 2016).

‘We step away from efficient care. Instead of efficient care, we want effective care. We aim to

prevent that our insurance members become patients’ (Gale, 28th of April 2017).

(3)-Thirdly, a tactic is a solution aimed to identify low-value care (Rosenthal, 27th of April 2017). All

care that does not add any value to the health of the society is considered low-value care, for instance

fraudulent claims and supply-induced demand (Parker, 27-04-2017). A considerable percentage of care

is considered low-value care (Parker, 27-04-2017; Pomp & Hasaart, 2009; Hasaart, 2011). The specific

percentage in the Dutch healthcare system is currently undefined. This tactic gives a health insurer

insight in wasteful expenses and wasteful referral patterns in order to prevent low-value care.

‘From every dollar spent on healthcare, thirty cents is wasted. With data, we can identify this thirty

cents’ (Rosenthal, 27th of April 2017).

(4)-The fourth value proposition to elaborate on is the effective prevention of hospitalization (Monsen,

27th of April 2017). This is a tactic to identify all insurance members who are at the greatest risk for

hospitalization in the coming four months. These members with high-risk on hospitalization are given

a preventative interventions (Monson, 27-04-2017; Brower, Monsen, Dvorkis, Pal, & Larish, 2017). In

the algorithm of this value proposition discussed at the conference, age > 76, the diagnosis of cystic

fibrosis and the diagnosis of muscular dystrophy appear to be good predictors (Monson, 27th of April

2017; Brower et al., 2017). In this algorithm, the chance on hospitalization is predicted on a weekly-

basis via a logistic regression model (Monson, 27th of April 2017).

‘When someone is predicted as a high-risk for hospitalization member, the office person of a medical

specialist is called so that this person can contact the high-risk person’ (Monsen, 27th of April 2017).

(5)-The last tactic is to deploy a data science team of the health insurer as consultants within healthcare

organizations (Elmer, 27th of April 2017). This is particularly interesting in a vertically integrated

healthcare system. In this tactic, the health insurer helps healthcare providers to develop analytical

support to improve the quality and effectiveness of their care.

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‘As a consultant at different hospitals, you really get a feeling of what is going on around there’

(Elmer, 27th of April 2017).

To identify which value proposition is considered the most effective tactic for disruptive innovation

initiated and implemented at CZ, the five value propositions above are being rated based on seven

selection criteria. These selection criteria are developed based on the information gathered during the

interviews at CZ. All tactics are evaluated on an ordinal, low (L) –medium (M) –high (H) scale. In

Appendix V, an underpinning of all scores is given.

Selection criteria:

1. The solution should improve the health of CZ members

2. The solution should not create extra administrative pressure for the healthcare provider

3. The solution should decrease the overall healthcare costs of CZ

4. The solution should support the decrease of the information asymmetry in healthcare

5. The data science component must be understandable for the stakeholders

6. The data should be available within CZ to already start developing the algorithms

7. The solution should fit within the boundaries of the GDPR

In Table 6 observe that effective prevention for high-risk members scores highest on the seven criteria

for CZ. Therefore, this value proposition is assumed most suitable for CZ. Additionally, the effective

prevention value proposition appears in line with the vision of CZ. In 2012, CZ funded the Lifestyle

Coach research. This was a doctoral research on how to better manage the prevention for their high-

risk members. The funding of the doctoral research supports the interest of CZ in the value proposition

proposed in this master thesis research. Chapter 5.3 elaborates on the preliminary validation of the

effective prevention value proposition by analyzing the Lifestyle Coach claims data. The claims data

resembles all claims from participants in the doctoral research.

Table 6: All value propositions evaluated on selected criteria

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5. Design validation In consonance with the proposed change towards a value proposition for effective prevention, at

Maastricht University a doctoral research on prevention of high-risk members is performed. This

doctoral research was performed to improve the prevention for these members. An additional effect

of the design of the doctoral research was a decrease in information asymmetry between the

collaborating stakeholders – i.e., the insurance member, healthcare provider, and insurer. In this

master thesis research, an analysis on the related claims data is conducted. The goal of this analysis is

to validate if the decrease in information asymmetry results in a greater ability for a health insurer to

assign and improve the delivery of preventative interventions. So, in other words, validate if the

effective prevention for high-risk members in the current design of the doctoral research has the

potential to disruptively innovate the prevention for high-risk members.

In the doctoral research, four selected healthcare providers collaborated with CZ and 176 CZ members.

These members were all at high-risk for developing chronical diseases or they were already diagnosed

with a chronical disease. By this collaboration, the researchers aimed to improve the delivery of

preventative interventions. The intervention is considered successful if it prevents a participant from

developing a chronical disease or if it stabilizes those who are already diagnosed with a chronical

disease (Program Manager Lifestyle Coach, 22nd of June 2017).

The Lifestyle Coach intervention was carried out in 2014, 2015 and 2016 by four providers. In total four

different types of Lifestyle Coach interventions were performed. Figure 8 shows the average yearly

healthcare costs per member per lifestyle coach provider. In these graphs, the costs of the Lifestyle

Coach intervention are not included.

Figure 8: Average yearly healthcare costs per member (without lifestyle coach costs) per provider

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Again as in the diabetes type II analysis, it is assumed that healthcare costs are negatively correlated

with someone’s health status. Given this assumption, Lifestyle Coach provider B in Figure 8 shows the

greatest effectiveness. Despite, the limitations of the data set3 make it impossible to identify if this

decrease is statistically significant and if so, to determine the reason for the decrease in costs. The

same limitations hold for the differences in healthcare cost per Lifestyle Coach intervention type (see

Appendix VI).

Since above graphs do not statistically proof differences in effectiveness per provider, further analyses

are performed based on each member’s healthcare costs. All members have been classified based on

the pattern of their healthcare costs from 2012 to 2016. 32 Members are classified as ‘ascending

healthcare costs’, and 21 members are classified as ‘descending healthcare costs’. The differences

between these two groups are analyzed. The distribution over the lifestyle coach providers of both the

‘descending’ and ‘ascending’ group is given in Figure 9.

.

Figure 9: Distribution of 'decrease' and 'increase' member groups per provider

These results suggest a difference in effectiveness over the different providers. Among others, these

results indicate that the Lifestyle Coach interventions executed by provider ‘D’, demonstrates lowest

effectiveness. This provider solely performed the Lifestyle Coach intervention for children. This result,

combined with the average increase for this specific age group, suggest a lower effectiveness of the

Lifestyle Coach intervention for children compared to adults.

The analysis of the descending member group vs. the ascending member group also suggests a

potential difference in the effectiveness of the Lifestyle Coach caused by the complexity of the

members’ disease profile. The graphs supporting this suggestion are given in Appendix VI. The

3 Being the small sample size and the unequal distribution of the type of intervention over the four providers

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members of the descending cost group showed a more complex disease profile in the first three years

resulting in higher average healthcare costs. Around 2014, a decrease in complexity decreases and

consequently, the total healthcare cost decreases, especially for the specialisms cardiology, mental

healthcare and nervous system medication. Contrary, the claims for the ‘ascending’ group showed a

less complex disease profile in the first years but an increase in complexity and consultations over time.

More research on a larger data set is needed to prove if the Lifestyle Coach intervention is indeed less

effective for children and more effective for complex members.

This validation on the lifestyle coach data set shows a decrease in information asymmetry between

the three involved stakeholders, namely the patient, the healthcare provider and the health insurer.

In the current healthcare system, it is namely not possible for a health insurer to specifically provide

preventive interventions to high-risk members since they are not aware of the risk profile of their

members. In the research of Maastricht University, a collaboration is designed in which the health

insurer can specifically stimulate preventive interventions for its high-risk members. Additionally, CZ

also gets more insights in the preventive intervention that is delivered compared to the earlier

discussed diabetes type II case. In the lifestyle coach data set, the health insurer can analyze the

effectiveness of each particular intervention. With an increased sample size, more advanced data

science techniques can be developed to predict the cost effectiveness per type of intervention for a

particular high-risk member.

Although improvements can be made, to disruptively improve the prevention for high-risk members,

the information asymmetry should further decrease. From the perspective of a health insurer, still

essential information is missing. Among others, the health insurer is only possible to predict or

measure the effectiveness of a specific Lifestyle Coach intervention based on costs.

‘Measuring healthcare by costs is wrong’ (Avi Israeli, 27th of April 2017).

Measuring the effectiveness of the preventive intervention solely on costs gives a too simplified view

and collides with the suggested change towards a health insurer whose primary value driver is

population health. To conclude, the results suggest an improved situation regarding the information

asymmetry of effective prevention for high-risk members. But despite the improvements, to

disruptively innovate the prevention for high-risk members by using data science techniques, the

information asymmetry should further decrease.

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6. Discussion To curb the rising healthcare costs, disruptive innovations are highly needed (Christensen et al., 2000;

Hwang & Christensen, 2008; Robinson & Smith, 2008). Data science is identified as a technique with

the potential to disruptively innovate in healthcare (Davenport & Harris, 2007; Bates et al., 2014). This

research aimed to lay the basis for how to disruptively innovate in healthcare by the use of data

science. In this chapter, both the practical and theoretical implications are discussed, followed by the

limitations of this research and directions for further research.

6.1 Practical implications The value driver chapter of this research improved the understanding of how to mitigate the

information asymmetry in healthcare in order to mitigate the main barrier for disruptive innovation.

The value proposition chapter elaborates on five potential data-driven applications from a health

insurer’s perspective. In this chapter, the practical implications of these results for CZ will be discussed.

The theoretical problem investigation revealed that the information asymmetry present in healthcare

is the main barrier for disruptive innovation in the healthcare system. The practice-based problem

investigation at CZ shows that specifically for data-driven innovations, the information asymmetry also

occurs as the main barrier for innovation. In the current system, all healthcare stakeholders lack

necessary information to disruptively innovate in healthcare by applying data science techniques. From

the viewpoint of a health insurer, specifically medical and lifestyle-related information is missing.

Other healthcare stakeholders are reluctant to share this data with the health insurer, which makes it

impossible for health insurers to develop disruptive data-driven innovations. These stakeholders

believe that a health insurer will always, in one way or another, use the information against the patient

or healthcare provider.

For a health insurer to support this data sharing to disruptively innovate in healthcare, it should align

its core value driver with the ecosystem’s mission. This means that CZ should focus its efforts on

population health. Adopting this value driver as being their core value driver does not mean CZ cannot

restrain the healthcare cost at all. Adrian Gore, the CEO and founder of Discovery Health, states that a

truly health focused insurer will have a healthier population which in turn results in lower healthcare

costs.

Reasoning from the strategic niche management theory, the researcher advises CZ to select a specific

data-driven value proposition to materialize disruptive data-driven innovation in a population health

ecosystem (Schlipzand, et al., n.d.; Smith & Raven, 2012). Effective prevention for high-risk members is

identified the most suitable value proposition for the strategic niche from the perspective of a health

insurer. This value proposition is in line with the ecosystem’s core mission and it has the potential to

disruptively innovate the healthcare industry. The results of this research do not limit the

materialization of other value propositions in the future. After the maturing of this value proposition

and the data science technology in the Dutch healthcare ecosystem, other value propositions can

further support the disruptiveness of data science in healthcare.

The effective prevention for high-risk members value proposition is validated in the Lifestyle Coach

research performed at the university of Maastricht. In this doctoral research, the information

asymmetry between the insurance members, the healthcare provider and the health insurer

decreased. With this decreased information asymmetry, CZ was able to target the Lifestyle Coach

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interventions solely to high-risk members. Despite the decreased information asymmetry, from the

available data of a health insurer the effectiveness of the preventative intervention could only be

assessed based on costs. This creates a too simplified version of the reality. Besides that, measuring

the effectiveness solely on cost can create counterproductive incentives within the healthcare

industry, like adverse selection.

For applying data science to disruptively innovate the prevention for high-risk members, the decrease

in information asymmetry of the Lifestyle Coach research is considered not sufficient. In the desired

situation, the information of the three collaborating stakeholders – insurance member, healthcare

provider, and the health insurer – should be integrated to maximize the population health within the

ecosystem. Based on this combined knowledge, prediction models could then identify high-risk

members and predict which intervention is expected to be most effective. In Figure 10, the Ecosystem

Pie Model of Talmar et al., (2017) illustrates how the desired ecosystem for this value proposition

should ideally look like. In this applied Ecosystem Pie Model, all relevant stakeholders for this value

proposition are included, namely the insurance member, the healthcare practitioner, the health

insurer, and politics. The patient and the pharmaceutical company are not included in the Ecosystem

Pie Model since no focused is placed upon these two stakeholders in the effective prevention value

proposition.

Figure 10: Ecosystem Value Proposition - Effective Prevention high-risk members

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A closer look on the Ecosystem Pie Model shows that the three data-rich stakeholders (the citizen, the

healthcare provider and the health insurer) should share its stakeholder-specific data within the

healthcare ecosystem. To realize this, all stakeholders in the healthcare system should adopt the

ecosystem’s core mission population health. In Figure 10, for the four stakeholders, the proposed

resources, activities, value addition and value capturing are given in line with the population health

value driver in the effective prevention value proposition.

Thus, to harvest the potential of data science in healthcare, CZ should align with the ecosystem’s core

mission. To do so, this research advises to let the data science team pioneer autonomously from CZ

with population health as their core mission. This research suggests that to maximize their impact,

their efforts should be focused on the effective prevention for high-risk members value proposition.

Via experimental collaborations within the industry, this team should experiment with the population

health value driver as being their core mission and as a result, it should try to form an aligned

ecosystem within this experimental collaboration in which information is shared. Via such

experimental aligned ecosystem, the disruptive data-driven innovation can mature in the strategic

niche of effective prevention for high-risk members. For CZ, the implementation of effective prevention

for high-risk members at the data science team should embody the start of an ongoing process in which

data science is applied to maximize the population health within the whole organization.

6.2 Theoretical implications As proposed in the literature review of this study, the healthcare system is in need for a transition from

the standard reactive healthcare delivery model towards a proactive healthcare delivery model

(Chawla & Davis, 2013; Epstein et al., 2010). Data science is recognized as a technique that can support

this transition (Chawla & Davis, 2013; Koumpouros, 2014).

In analyzing this potential shift, previous researchers mostly adopted a technological focus in which

the potential of data science in healthcare is discussed (Monsen et al., 2017; Spruit, Vroon, &

Batenburg, 2014; Duan, Street, & Xu, 2011; Palaniappan & Awang, 2008; Srinivas, Kavihta Rani, &

Govrdhan, 2010; Wilbik, Keller, & Alexander, 2011; Chawla & Davis, 2013; Raghupathi & Raghupathi,

2014). In this focus, a dyadic relationship is assumed between the patient and the healthcare

organization, being a health insurer (Monsen et al., 2017), the healthcare provider (Spruit et al., 2014;

Duan et al., 2011; Palaniappan & Awang, 2008) or a research institute (Srinivas et al., 2010; Wilbik et

al., 2011). In reality, such partial solution will hardly be able to disruptively innovate in healthcare

(Ouden, 2012). In the existing literature, the challenges and calls for further research are therefore

also the availability of data and how to integrate data from different stakeholders (Koh & Tan, 2010;

Nambiar et al., 2013; Raghupathi & Raghupathi, 2014).

‘Possible directions include the standardization of clinical vocabulary and the sharing of data across

organizations to enhance the benefits of healthcare data mining applications’ (Koh & Tan, 2010,

p.70).

In reality, the healthcare system is more complex with a polyadic number of interdependencies and

relations. This research shows that to disruptively innovate within the healthcare system, the

complexity of the healthcare system cannot be simplified into a dyadic relation. Therefore, the

ecosystem theory is used to design a solution to decrease the information asymmetry in healthcare

and lower the barriers for disruptive data-driven innovations in healthcare. The ecosystem reasons

from the system as a whole rather than from one or two of the ecosystem’s stakeholders. In this

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research a more pragmatic, higher-level approach is adopted in which data science is not considered

the goal, but as a means to disruptively innovate in healthcare. This research, therefore, does not aim

to extend the body of literature with a new technological proven algorithm that, from a technological

perspective, has the power to positively influence population health. Rather, this research draws upon

the theoretical body of research that data science indeed has the potential to disruptively improve the

population health. Given this potential, the solution design of this research lays a basis for a change

towards a healthcare system in which the potential of data science can be harvested in order to

improve the population health.

6.3 Limitations & future research Although this research contributes to both practice and theory, this study is also subject to limitations.

In this chapter, these limitations are discussed, as well as the directions for further research originating

from this research.

The first limitation of this research is the context in which it is developed. The results are specifically

designed towards the features of the Dutch healthcare ecosystem, which makes the solution design

industry- and country-specific. This focus limits the generalizability towards other industries and

countries. For this solution design to be developed in a mature theory, future research can apply the

concept of this research in other industries and within the healthcare industry in other countries.

Additionally, also the focus on a health insurer for which an N=1 approach is adopted (Wieringa, 2009)

creates a design that is potentially biased towards this stakeholder in the healthcare industry. To

mitigate this bias, the researcher collected data from all main stakeholders in the healthcare industry.

A second limitation is the size of both the diabetes type II data set and the Lifestyle Coach data set. In

both analyses, the size of the data set resulted in barriers for statistical support. The researcher tried

to incorporate more advanced techniques like decision trees and regression models but the size of the

data sets, combined with the subtle differences of the interventions, resulted in a low performance of

these techniques. For further research, to develop models with an acceptable performance and to

uncover hidden patterns in the data regarding the effectiveness of an intervention, a larger data set is

needed.

Another limitation, and respectively a call of further research, is the exclusion of the regulative context

in which the solution design should operate. When different data sources will be integrated and

processed, which raises privacy and other regulative concerns. From May 2018 onwards a new data

protection law will apply in Europe, namely the General Data Protection Regulation (General Data

Protection Regulation, 2016). This new law will affect the implementation of the proposed solution

design. This new regulation will apply to organizations that are processing personal data. Within this

law, stricter rules will apply when processing health data, which limits the possibilities for data science

in healthcare (Article 9 General Data Protection Regulation, 2016). The results of this study request

further research on how this law limits the potential of the solution design and how to adapt the

solution design in order to comply with the General Data Protection Regulation and all other

regulations that apply.

A fourth limitation of this research is that no answer is given on the actor that should integrate and

process the shared data. The integrated information can be accessible for all stakeholders, or be

assigned to an existing stakeholder or a new organization within the healthcare ecosystem. The

orchestrating role of a health insurer makes them a potentially good stakeholder to be the

35

organizational body that integrates all information and processes this information. Contrary to the

healthcare provider market, the grand majority of the health insurers market is namely divided by only

four major players. On the other hand, the already trusted alignment with population health can make

a (consortium of) healthcare providers also the most suitable stakeholder to lead this initiative. Also

the government can potentially be a stakeholder at the center of such information sharing initiative. It

can also be that a new organization, separate from all current stakeholders, is the best stakeholder to

become the central informational body in healthcare. A new organization can completely be designed

for the purpose of this information hub. Further research is needed to give a conclusive answer on

who should be the administrator of all integrated information.

A last limitation of this research is the focus on the disruptive innovation theory. Both the advantages

and disadvantages of this theory within the context of this research are shortly elaborated on in the

following two paragraphs.

Firstly, the disruptive innovation theory assumes that initially, the innovation underperforms the

established practice (Christensen, et al., 2009). For data-driven innovations this does not necessarily

need to hold. Some of such innovations can outperform the current practice from the start. An

example of such innovation that can immediately outperform the current practice is the effective

prevention for hospitalization. Currently, in most of the cases no prediction is made regarding the risk

of hospitalization. An algorithm, although with no optimal performance in the initial phase, can

outperform the no prediction at all from the start. On the other hand, innumerable examples can be

thought of under which this assumption holds. A great example are all decision-support systems used

for diagnosing purposes. Currently, these support systems cannot replace the human knowledge for

all diagnose but in some niche markets, like image recognition for radiology, they already compete or

outperform human judgment (Wang et al., 2017).

Secondly, the disruptive innovation theory of Christensen et al. (2009) is not developed for ex-ante

predictions of success, rather for post hoc case analysis (Danneels, 2004). In the context of this

research, ex-ante the disruptive potential of data science in healthcare is assumed but this cannot be

proven yet. The disruptiveness of data science in other industries like marketing and supply chain

(McAfee & Brynjolfsson, 2012) and in some niche markets in healthcare (Kayyali et al., 2013) made the

researcher assume the disruptiveness of data science in healthcare ex-ante. A call for further research

is to further investigate and assess the disruptiveness of this technology within healthcare (Danneels,

2004).

36

7. Conclusion In the healthcare context, best practices show that the data science technology has the ability to

transform the rather reactive healthcare delivery model towards a more proactive model. Academic

literature as well as best practices show that such proactive models have the ability to improve the

quality of care and decrease the healthcare burden. Given the potential of data science in healthcare,

this study attempted to answer a so-called ‘how’ question, namely ‘How to enable the development

of disruptive data-driven innovations in healthcare?’.

Guided by design science, an exploratory research approach is adopted in which theoretical- and a

wide range of practice-based sources are included. In this research, the information asymmetry is

identified as the main barrier for disruptive innovation in healthcare. Among others, an analysis of the

claims data of five hundred diabetes type II patients showed that this is also a major bottleneck for

data-driven innovations in healthcare. By integrating the data sources of different healthcare

stakeholders this barrier for disruptive innovation in healthcare can be mitigated. In order to make this

integration possible, all healthcare stakeholders should work from an aligned ecosystem perspective,

being population health. In line with this ecosystem’s core mission, effective prevention for high-risk

members is considered the most suitable value proposition for a health insurer to disruptively innovate

in healthcare. The implementation of effective prevention for high-risk members should embody the

start of an ongoing process in which data science is used to maximize the population health.

37

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44

Appendix I – Global financing system of Dutch healthcare

45

Appendix II – Systematic literature review overview Search query # Selected

articles first

phase

# Final selected

articles

Authors & names of final

selected articles

Innovation in healthcare 10 6 Christensen et al. (2000)

Christensen et al. (2009)

Groves et al. (2013)

Hwang & Christensen

(2008)

Luft (2009)

Thakur et al. (2012)

Disruptive innovation in

healthcare

8 5 Christensen et al. (2009)

Groves et al. (2013)

Herzlinger (2006)

Hwang & Christensen

(2008)

Thakur et al. (2012)

Technological innovation in

healthcare

9 6 Christensen et al. (2009)

Herzlinger (2006)

Hwang & Christensen

(2008)

Groves et al. (2013)

Patil & Seshadri (2014)

Thakur et al. (2012)

Technology in healthcare 2 1 Bates (2002)

Reactive to proactive

healthcare

1 1 Patil & Seshadri (2014)

Cost-saving innovation

healthcare

4 0 -

46

Cost saving innovation

healthcare

6 3 Groves et al. (2013)

Hwang & Christensen

(2008)

Kayyali et al. (2013)

Cost reducing innovation

healthcare

11 6 Groves et al. (2013)

Herzlinger (2006)

Hwang & Christensen

(2008)

Kayyali et al. (2013)

Robinson & Smith (2008)

Business model innovation

healthcare

5 3 Christensen et al. (2009)

Herzlinger (2006)

Hwang & Christensen

(2008)

Increase efficiency

healthcare big data

6 4 Bates et al. (2014)

Feldman et al. (2012)

Nambiar et al. (2013)

Raghupathi & Raghupathi

(2014)

Radical Innovation in

healthcare

2 0 -

Major innovation in

healthcare

8 2 Thakur et al. (2012)

Herzlinger (2006)

Groves et al. (2013)

Openness to adopt

innovation healthcare

5 3 England et al. (2000)

Plsek (2003)

Thakur et al. (2012)

Willingness to adopt

innovation healthcare

2 1 Plsek (2003)

47

Strategic rigidity healthcare 0 0 -

Overregulation of healthcare 1 1 Curtis et al. (2006)

Agency theory healthcare

reimbursement innovation

1 1 Robinson (2001)

Agency theory healthcare

insurance

1 1 Mooney (1993)

Principal-agent relation

health insurer

1 1 Nguyen (2011)

Principal-agent problem

healthcare

1 1 Smith et al. (1997)

Reimbursement healthcare

theory

2 0 -

Path dependency barrier for

innovation health

1 1 Atun (2012)

path dependency healthcare 1 1 Wilsford (1994)

Resistance to change

healthcare

3 1 Lorenzi & Riley (2000)

Information asymmetry

healthcare

4 0 -

Big data in healthcare 11 8 Chawla & Davis (2013)

Chen et al. (2012)

Groves et al. (2013)

Feldman et al. (2012)

Kayyali et al. (2013)

Koumpouros (2014)

Nambiar et al. (2013)

Patil & Seshadri (2014)

48

Big data evidence based

healthcare

8 6 Bates (2002)

Chawla & Davis (2013)

Chen et al. (2012)

Groves et al. (2013)

Kayyali et al. (2013)

Nambiar et al. (2013)

Proactive data-driven

healthcare

5 3 Chen et al. (2013)

Feldman et al. (2012)

Groves et al. (2013)

data analytics in healthcare 8 3 Chawla & Davis (2013)

Groves et al. (2013)

Nambiar et al. (2013)

49

Appendix III – Additional information on members in diabetes type II

data set

50

51

52

Appendix IV – Relation between research- and practice based problem investigation

Aspects of Practices Theoretical foundation Synthesis of problem investigation.

The rising healthcare costs must be curbed in order to

sustain the Dutch solidary healthcare system

Of the total annual costs of CZ, 97% is spend on

healthcare.

Given the technological advances, the current value

proposition of a health insurer is not sustainable

Disruptive innovation

– Christensen et al.

(2000)

The Dutch healthcare costs have experienced a strong rise and this

rise is expected to continue in the coming years. This increase in

healthcare costs puts a pressure on its funding. To curb these rising

healthcare costs, the healthcare system needs disruptive

innovations to be implemented. Disruptive innovations must create

a better match between the needed complexity of a treatment and

the delivered complexity of the treatment. This will result in a more

expedient and convenience healthcare delivery.

Data-driven innovation in the Dutch healthcare system is

difficult because of the complexity of the healthcare

system and all different incentives.

Dynamic complexity –

Sterman (2000)

Although disruptive innovations are wanted to curb the rising

healthcare costs, this appears to be challenging because of different

barriers that exist in the healthcare industry. The fragmentation of

the healthcare system, with the underlying dynamic complexity, is a

barrier that discourages disruptive innovation in the healthcare

industry.

A health insurer has no access to the medical information

of its members. The only data a health insurer has is

claims data and some personal details (name, address

and date of birth)

Principal-agent

problem – Nguyen

(2011)

Although disruptive innovations are wanted to curb the rising

healthcare costs, this appears to be challenging because of different

barriers that exist in the healthcare industry. The principal-agent

problem, combined with the fee-for-service payment model,

stimulates a demand pull and a barrier for disruptive innovation in

healthcare.

53

With the available data within a health insurer, privacy

and other regulations limit the possibilities for data-

driven innovations.

Medical specialists highly value academic proof and

demand data-driven innovations to be academically

validated

Overregulation –

Curtis & Schulman

(2006)

Although disruptive innovations are wanted to curb the rising

healthcare costs, this appears to be challenging because of different

barriers that exist in the healthcare industry. Overregulation of the

healthcare system is a barrier to disruptive innovation in the

healthcare industry.

50% of all healthcare costs is made by 5% of the members

Focus on high-cost

patients – Bates

(2014)

The rising healthcare cost creates demand for effective ways to use

the information generated from predictive analytics to curb the

rising healthcare costs.

54

Appendix V – Underpinning of Table 6

Appendix VI – Additional information for lifestyle coach claims data

set

56

57

58