A Disciplined Innovation Approach to Health Technology Solutions

Noel Carroll

and Ita Richardson

1,2

ARCH- Applied Research for Connected Health Technology Centre, University of Limerick, Limerick, Ireland

Lero- The Irish Software Reseach Centre, University of Limerick, Limerick, Ireland

Keywords: Connected Health, Innovation, Healthcare Technology, Software Solutions.

Abstract: Despite the potential of innovation-driven healthcare technology services to increase the quality,

accessibility and quality of care, the realization and success of such promise has yet to be achieved. This

prompted us to explore the barriers towards success for healthcare software companies and examine what

frameworks are employed across industry to support their growth in the digital healthcare market. As part of

a three-phase study, this article reports on the first phase – to synthesize the literature on the readiness

factors for healthcare technology companies. The findings of this research will guide our second phase of

this research in surveying industry healthcare software companies. In so doing, we can establish readiness

factors for healthcare software companies with a view to offering a more structured and disciplined

approach to healthcare innovation.

1 INTRODUCTION

We often learn how small and medium-sized

enterprises (SMEs) across the national and

international service sector must consistently and

continuously innovate and adapt to ensure their

survival (Gebauer et al. 2012). It is a matter of

‘survival of the fittest’ to evolve with the dynamic

external environment. To do so successfully largely

depends on the SME’s service innovation capability

and competences to deploy resources and improve

services. This is a challenge particularly in a

healthcare context since technology advancements

continue to rapidly grow while concerns around

healthcare device safety and regulation continue to

surface and challenge innovation (Carroll and

Richardson, 2016). Thus, pertinent questions need to

be asked such as, how can an organization

continuously evolve and offer a new service to meet

healthcare needs? Where does the added capability

and competencies come from to do so? From our

experience, two key factors here are to 1) identify the

unmet healthcare needs and 2) examine how or

where the capabilities will come from to address

those needs.

We often learn about the growing success of

companies breaking new ground in healthcare

innovation and dominating market leadership

(Carroll, 2016). While, this is very much welcome

across the healthcare sector, little is known about

why companies, particularly software companies,

fail to achieve their business objectives in reaching

new markets (Kellermann and Jones, 2013). Thus,

uncovering both why companies fail and what we

can do to reduce such occurrences, drew our

attention towards the concepts of evaluation,

organizational readiness and capability maturity to

establish a more disciplined view of healthcare

technology innovation. Technology has contributed

towards a shift within healthcare practice which

highlights the growing reliance and trust we now

place on software to support healthcare decisions.

However, unlike some sectors, for example business,

failure to correctly align healthcare needs with

software requirements can have devastating

consequences on people’s health – potentially fatal.

2 TOWARDS DISCIPLINED

INNOVATION

In recent years, the concept of ‘Disciplined

Entrepreneurship

’ was coined at MIT and offers a

comprehensive step-by-step approach to creating

solutions. It focuses on the iterative process towards

a final solution to meet users’ needs. Aulet (2013)

http://disciplinedentrepreneurship.com/

Carroll N. and Richardson I.

A Disciplined Innovation Approach to Health Technology Solutions.

DOI: 10.5220/0006228703890395

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 389-395

ISBN: 978-989-758-213-4

389

attempts to move away from an abstract view of

entrepreneurship and proposes a toolkit to guide

innovation. This provides a rich insight on

entrepreneurship as a skillset. The toolkit provides

24 steps that is described as disciplined

entrepreneurship and is a practical step-by-step

process to channel the innovation and maximize the

chances of success and ultimate impact. Such a

formal process is considered beneficial to focus the

innovation process. We previously explored how a

similar approach could be achieved in a software

engineering and healthcare innovation context

(Carroll and Richardson, 2016). In this research we

employed Design Thinking with a view to aligning

healthcare innovation and software requirements and

address customer pain points using the Connected

Health Innovation Framework to a) support software

developers in clearly identifying healthcare

requirements and b) extend and enrich traditional

software requirements gathering techniques.

However, we have identified that there is a need to

take this a step further and move towards

establishing measures of innovation in order to

assess risk and the capability to deliver an innovative

solution within a process flow. We describe this as

‘disciplined innovation’.

3 PROBLEM STATEMENT

By services, we often refer to an intangible product,

for example, banking, consultancy, healthcare,

education and software development. Thus, the

service economy is vital across the globe. For the

purposes of this paper, we focus on healthcare and

software development as an exemplar to support

SMEs, i.e. the ‘health-tech’ market. Yet, despite the

significance placed on the service sector, there is a

lack of practical measurement or management tools

for innovation. Such a gap in literature and practice

ensures that the promise of health-tech innovation is

never fully realized across SMEs (Kohler et al.

2013). In an attempt to identify a tool to support

technology companies to guide SMEs to improve

their healthcare innovations, the authors are

continuously faced with the need to restart the

innovation evaluation process for each company.

In this paper, we propose a decision support tool

that will guide organizations to self-assess their

current organizational operations. Such a tool would

support organizational management practice. It

would provides us with a real opportunity to

establish a framework to guide organizations through

the evolutionary dimensions of healthcare

technology innovation.

In a recent article, Christensen et al. (2016)

suggest that, “business model innovation is crowded”

which is driving companies to mount both offensive

and defensive initiatives involving new business

models. Identifying innovation attributes allows us to

have greater control of the innovation flow process

and develop predictable business models to drive

decision-making tasks, measured performance and

accommodate for an efficient innovation process

flow. This becomes the primary motivation to

introduce a ‘disciplined innovation’ model.

4 HEALTHCARE INNOVATION

CAPABILITIES

Healthcare service environments become

increasingly complex when technology is

implemented to execute specific clinical, technical

and business processes to deliver care. This

ultimately adds to the complexity of a service

environment, making it one of the most difficult

environments in which to examine and manage

service capabilities. Capabilities are complex,

structured, and multi-dimensional. They may be

described as fundamental determinants resource

utilization to support and sustain organizational

performance (Teece, 2009). Managing process

maturity has been well documented throughout the

business and IT literature. Little research in this area

is carried out within a health-tech domain.

In IT management, maturity models play an

important and influential role in organizational

change (Becker et al., 2009). The availability of

service and innovation capabilities has motivated us

to review how we conceptualize the health-tech

service environment. The success of innovation often

relies on a number of contributing factors. For

example, according to Birkinshaw et al. (2011, p. 3)

the following “conditions” contribute towards

sustained innovation: (1) shared understanding:

cultural understanding of organizational behaviour;

(2) alignment: aligning systems and processes to

achieve desired performance metrics; (3) tools:

training, concepts, and techniques to innovate; (4)

diversity: optimizing external influences and insights

to offer solutions within a particular domain; (5)

interaction: establishing platforms to exchange ideas

and build networks; and (6) slack: providing

opportunities to access additional resources to

develop ideas. These conditions contribute towards

organizations overall competencies and capabilities.

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5 CAPABILITY MATURITY

MODELS

The ultimate goal of an organizational capability is

to contribute towards some form of value, e.g.

improved healthcare and increased profits. There are

a very large number of variables that are dependent

on the context and industry which determine the

important role capabilities play in value creation. At

an abstract level we may identify the attributes of a

capability to include (Carroll and Helfert, 2015)

value creation, outcome focused, measurable, and

maturity driven process. Within an innovation

environment, capabilities need to be dynamic.

Dynamic capabilities are considered the source of

competitive advantage. Teece and Pisano (1994)

identify two key aspects in harnessing competitive

advantage through dynamic capabilities that may

apply to a service innovation environment:

(1) The shifting character of the environment, e.g.

healthcare needs;

(2) The importance of strategic management in

agility, adaptability and reconfiguring internal

resources to meet external demands.

Winter (2003, pp. 4-5) suggests, “dynamic

capabilities typically involve long-term commitments

to specialised resources […] [and] […] there must

be an ecological demand for the costs of the

capability and the use that is actually made for it”.

Managing dynamic capabilities requires some form

of structure and models – for example, the capability

maturity model (CMM) (Paulk, 1995). The CMM

assumes progress is made in distinct stages and

capture capability maturity at a given time (moving

through five progressive stages - initial, repeatable,

defined, managed, optimized).

The results of this assessment process supports

the organization to position themselves against

defined best practices while identifying areas of

weakness to drive change (Becker et al., 2009;

Carroll and Helfert, 2015). However, existing

maturity models tend to focus on large organizations

(Blommerde and Lynch, 2016, p.2) and are “too

broad to account for the specificities of service SMEs

and fail to reflect their unique characteristics”. We

set out to address this gap in a health-tech context.

5.1 Health-tech Innovation

Focusing on singular innovation is considered a

thing of the past, i.e. developing one solution and

forever reaping the rewards. Organizations must

continuously innovate and demonstrate their

dynamic capabilities to execute effective innovation

capability (Blommerde and Lynch, 2016).

Blommerde and Lynch (2016, p.2) suggest, “SMEs

are unaware of their service innovation capability or

how to improve their innovative maturity mode”.

Thus, some form of innovation measurement is

required to support SMEs.

Blommerde and Lynch (2016) present the key

dimensions of service innovation capability which

link to all five stages of capability maturity model

using a ‘Service Innovation Capability Maturity

Index’, namely focusing on 1) user involvement; 2)

knowledge management; 3) strategizing and 4)

networking. In addition, and with a view to focusing

on the measurement of innovation, Kohler et al.

(2013) introduce a Service Innovation Model that

comprises of four layers. From their description, they

explain that the top layer (innovation capabilities) is

connected with service innovation capability

indicators, which are captured in the second layer.

Each capability is associated with an indicator that

quantitatively captures the implementation of the

innovation capability in the company.

Performance is a key factor in innovation and

new terms have been introduced over the last decade

such as ‘disruptive innovation’. While the concept of

disruptive innovation stems new terms such as ‘value

network’ which may be described as “the context

within which a firm identifies and responds to

customers’ needs, solves problems, procures input,

reacts to competitors and strives for profit”

(Christensen, 1997; p. 31), we need a systematic

approach to manage the innovation process.

In the Service Innovation Model, the indicators

are a core focus for the assessment and monitoring of

the service innovation capabilities. The indicators are

described by Kohler et al. (2013; p. 1350) as being

quantitative representation of the innovation

capabilities. The indicators are connected to a set of

asset categories within sets of assets and assessed on

a numerical scale. These assets are categorized into

assets, i.e. human, financial, physical, intellectual

property rights, information and information

technology, and relationship assets.

There is also a

similar outlook on the dynamic nature of innovation.

For example, den Hertog et al. (2010) suggests there

are dynamic service innovation capabilities that

successful service innovators outperform their

competitors in some of the following:

1. Signaling user needs and technological

options;

2. Conceptualizing;

3. (Un)bundling;

4. Coproducing and orchestrating;

A Disciplined Innovation Approach to Health Technology Solutions

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5. Scaling and stretching.

Thus, we have identified that there is a natural

evolutionary process in the innovation process. This

process requires an organization to move between

specific maturity stages of innovation. Maturity

phases are well documented throughout the literature

in CMM but may need to be tailored within an

innovation context and more specifically, within a

health-tech context. For example, Carroll and Helfert

(2015) explain how the traditional view of the

organizational environment raises concerns

regarding the mismatch in the methods used to assess

business value and understanding service process

maturity. They demonstrate this by unpacking the

nature of service capabilities that allows us to

understand the primary components of value co-

creation and their contribution towards service

maturity within an innovation environment to access

organizational readiness. This offers a suitable lens

to view a disciplined approach to innovation that can

be easily adopted by SMEs in health-tech. We also

need to examine how organizational readiness aligns

with innovation capabilities.

5.2 Organizational Readiness

Throughout the literature, organizational readiness is

often associated with organizational change

management (OCM) (Armenakis et al. 1993;

Weiner, 2009). Change is a critical factor for

organizational readiness and is a multi-level, multi-

faceted construct which healthcare technologies

often face to introduce technology innovation. In

most cases, such change refers to organizational

members’ shared resolve to commit towards a

change in practice and a collective ability to improve

organizational performance. Thus, organizational

readiness for change varies as a function of how

much organizational members value the change, e.g.

within a hospital context.

Value of change must be weighted up against the

risk (e.g. cost and investment of resources)

associated with innovation. According to Weiner

(2009) there are three key determinants of change

implementation capability: task demands, resource

availability, and situational factors. We argue that

innovation capabilities are a fourth key determinant

of organizational readiness – which needs to be

calculated to assess the impact of innovation on

organizational readiness. We explain that innovation

is the process of introducing new ideas, devices, or

methods to bring about some change.

We can begin to uncover the key enablers of

innovation by taking a holistic view of change and

integrate this with business activity rather than

isolated processes. This enables us to develop an

innovation model and identify the guiding principles

that are grounded in organizational experience –

documented throughout literature (phase 1 of our

research, as presented in this paper) and captured by

surveying industry experiences (phase 2 of our

research, future work). Weiner (2009) describes how

organizational readiness is “considered a critical

precursor to the successful implementation of

complex changes in healthcare settings”. Weiner

also cautions “most publicly available instruments

for measuring organizational readiness for change

exhibit limited evidence of reliability or validity” –

hence the motivation for this research.

In the case of healthcare software companies,

innovation drives organizational changes to meet

new market demands. To ensure that innovation can

be successful, metrics must be established to drive

such change. Thus, OCM may be described as an

approach to transition an organization from their

current state to a new desired state. This involves the

integration and alignment of people, processes,

culture and strategy to innovate.

Before OCM can be successfully implemented,

managers must clearly evaluate readiness for change.

Armenakis et al. (1993) describes readiness in terms

of the organizational members’ beliefs, attitudes, and

intentions. Thus, there are critical elements of

change agents and social dynamics that influence

organizational readiness process which may

contribute towards the success of healthcare software

innovation. Identifying and measuring these

elements provides a benchmark on the current

organizational state compares with their ideal state to

derisk healthcare software innovation. We capture all

of these factors of CMM and OCM to present our

Disciplined Innovation Model for health-tech SMEs.

6 DISCIPLINED INNOVATION

Migrating from the current state to the future state of

an organization requires a number of key stages to

embrace an innovation culture to drive a specific

strategy and improve their competitiveness. This

enables organizational performance to achieve the

desired business goals. Therefore, innovation is not a

soft or vague construct, but rather, a critical process

to drive organizational performance. Why then is the

process of innovation less defined in terms of

organizational readiness and process flow

measurement? Where are the formal process models

to guide SMEs through the innovation process to

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Figure 1: Disciplined Innovation Model.

derisk health-tech initiatives? To begin to address

such questions, we need to ask: Why does a company

need to build a new solution, evolve an existing

solution to maximize performance? How does a

strategy cater for such change? What specific

function(s) of the organization must change to

ensure innovation is successful? Are there any

specific guiding principles to derisk the innovation

process?

Innovation is often linked with creativity and the

ability to design solutions for unmet needs in the

marketplace. However, it remains unclear whether

we can measure innovativeness within organizational

readiness. Some attempts were made to measure

innovation, open innovation and technological

diffusion. For example, Jalles (2010) examines

alternative variables such as technological progress

(using patents and a Intellectual Property Rights

Index) to explain different growth rates of income. In

addition, Narayana (2005) suggests the need to

measures innovation using a CMM to determine a

particular strategic route and whether organizations

need to learn of the innovation management process.

6.1 Disciplined Innovation Model

Figure 1 illustrates the initial Disciplined Innovation

Model. It is influenced by:

1. The key phases of innovation: knowledge,

persuasion, decision, and conformation;

2. Design Thinking stages: empathy, define,

ideate, prototype, and test;

3. CMM stages: initial, repeatable, defined,

managed, and optimized.

We also include the need to benchmark each

phase to measure the capability maturity as a

solution matures through each stage. This captures

the essence of our initial development of the

Disciplined Innovation Model and we have identified

the need to establish specific metrics for each stage

of the model. As the performance demanded by the

customers of a value network increases over time so

does the performance provided within a

technological paradigm. Within a healthcare

technology market, this could include a new set of

performance value attributes that are now more

relevant than the current paradigm to address

healthcare needs.

While there is a strong body of knowledge on

innovation as a method of competitive differentiation

and as a way to create customer value, less attention

has been devoted to developing a measure of

innovation (Dobni, 2008). Dobni (2008) identifies

innovation culture as an important factor to measure

and identifies seven factors: innovation propensity,

organizational constituency, organizational learning,

creativity and empowerment, market orientation,

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value orientation, and implementation context.

However, more emphasis needs to be placed on the

innovation flow process to support how we can

support the innovation process.

7 DISCUSSION & CONCLUSION

By embedding data analytics into innovation,

organizations can unlock new opportunities if guided

through a disciplined process. In healthcare, this can

build empathy for users and pave the way to

improved experiences to deliver truly user-centered

services and improved connectivity of services. We

identify that despite the potential of innovation-

driven healthcare technology services to increase the

quality, accessibility and quality of care, the

realization and success of such promise has yet to be

achieved.

To address this, we present the initial Disciplined

Innovation Model as a means to establish a self-

assessment toolkit for SMEs to support the

advancement of healthcare technology innovations

and determine whether they are ready for scaling up

their services and targeting innovation opportunities.

We also identify the need to evaluate healthcare

innovation from a healthcare practitioners

perspective (O’Leary et al. 2014) as part of our

future research.

While we introduce the initial version of this

model, as part of our future research we plan to build

on this by identifying specific metrics through

industry collaboration and piloting the model

through an iterative proves across a number of

health-tech SMEs. We anticipate that this model

could be tailored to fit other sectors to support SMEs

though a disciplined innovation process. We will

firstly focus on validating this work with health-tech

SME’s.

ACKNOWLEDGEMENTS

This work was supported, in part, by ARCH -

Applied Research for Connected Health Technology

Centre (www.arch.ie), an initiative jointly funded by

Enterprise Ireland and the IDA and Science

Foundation Ireland (SFI) Industry Fellowship Grant

Number 14/IF/2530.

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