Knowledge management in data-driven business models during the digital transformation of healthcare organisations

ABSTRACT

With the rapid development of digital technologies and the outbreak of the COVID‐19, digital transformation (DT) has been accelerated. This appears to pose specific challenges to the medical field, leading to an inevitable trend towards DT in healthcare organisations. Determining how to develop strategies to master the substantial opportunities brought about by DT is a fundamental issue. Knowledge management (KM) is a key vehicle that can drive DT because it provides a solid foundation for organisational strategies and learning, and helping establish operational priorities. Using the operations of healthcare organisations in Taiwan as an example, this study discusses the challenges and opportunities faced by healthcare organisations related to DT based on data-driven business models, where the concept of KM, organisational agility (OA), and business models are integrated to develop a KM-OA-enabled DT conceptual framework intended to support DT implementation in healthcare organisations. This can serve as a foundation for future studies of DT

KEYWORDS:

• Digital transformation
• data-driven business model
• knowledge management
• digital technology
• healthcare organisation
• organisational agility

1. Introduction

The rapid development of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and the application of big data has further accelerated digital transformation (DT) (Kodama, 2020). In particular, the outbreak of the novel coronavirus disease in 2019 (COVID‐19), which is an infectious disease, has led to DT in work styles, such as telemedicine and remote home healthcare services, which are now progressing faster than ever in healthcare organisations at a global scale. However, this appears to advance various challenges to medical professionals, leading to an inevitable trend in the DT of healthcare organisations (Uslu et al., 2020). DT can facilitate effective healthcare solutions and enable new business models, which can create new possibilities for innovative services and better ways of achieving work-related purposes. DT-related practices can also create value and generate new knowledge for organisations. Therefore, healthcare organisations need to develop newer, faster, and more dynamic ways to mobilise and manage knowledge. This highlights the importance of knowledge management (KM), which is essential to the success of an organisation undergoing DT. Proper KM can greatly help healthcare organisations accelerate the required changes, create value, and generate new knowledge. This allows organisations to acquire a sustainable competitive advantage by effectively utilising, reconfiguring, and deepening their knowledge. Therefore, KM can be considered a crucial task of organisations (Huarng & Rey-Marti, 2019). Additionally, information systems (IS) and information technologies (IT) can facilitate the creation of knowledge through the rapid dissemination of ideas, comments, and revisions to designs (Marion & Fixson, 2021). Thus, to truly benefit from DT, healthcare organisations should improve their KM approaches to detect and manage meaningful information/knowledge and develop effective applications (Manesh et al., 2021).

Furthermore, to effectively navigate the ever-evolving healthcare landscape, healthcare organisations need to develop agile capabilities to respond swiftly, efficiently, and effectively. Organizational agility (OA) refers to an organisation’s ability to adapt and thrive in a constantly changing environment, facilitating rapid responses to uncertainty and changes (Bhatti et al., 2021; Saha et al., 2017a, 2017b). This capability is particularly critical for healthcare organisations that operate in a highly volatile medical environment (Berlin et al., 2017). By fusing KM and OA, healthcare organisations can enhance their organisational learning and improve their managerial and operational proficiency (Saha et al., 2017b).

However, studies exploring DT from the perspective of KM are scarce. This study thus bridges these theoretical and practical gaps by exploring how KM facilities DT in terms of improving performance and providing high-quality medical services to patients in a healthcare setting. Therefore, the purpose of this study is to investigate the role of KM in the processes of DT and propose a new data-driven business model to deal with DT in healthcare organisations. In summary, this study is intended to address the following research question: How can healthcare organisations build a data-driven business model leading to digital transformation by taking advantage of effective KM practices?

2. Literature review

2.1. Digital transformation

The rapid rise of the COVID-19 pandemic has not only changed the private lives of more than 100 million people around the world, but also has significantly affected the operational model of healthcare organisations. This ongoing pandemic creates a pressing need to take digitalisation into consideration to find effective solutions, such as reducing personal contact to contain the virus and lowering the infection rate. Coupled with the booming development of digital technologies, healthcare organisations are expected to synthesise innovative technologies through process automation and optimisation to provide quality medical services, thereby transforming them into smart hospitals (Frick et al., 2021). Under the impacts of the success of IT, an unprecedented wave of digitisation is currently driving innovation in various fields, and the healthcare industry is no exception (Legner et al., 2017). IT is influencing and transforming traditional medical services and pushing healthcare organisations into a DT process, in turn creating a smart medical model that will optimise healthcare management systems (Uslu et al., 2020). Faced with the advance of digital technologies and the need for organisations to remain competitive, DT has become a hot topic in recent years (Annosi et al., 2020).

2.1.1. The definition of digital transformation

The term “digital transformation”, given the different definitions of its scope and focus, is still in a grey area (Stephanie & Sharma, 2020). For example, from the DT functional-level perspective, DT encompasses organisational change, which is a process of improving organisational performance through the integration of digital technologies such as social media, mobile computing, analytics, cloud computing, big data, and AI into business processes (Frick et al., 2021; Legner et al., 2017). Likewise, Li et al. (2018) emphasised the impact of digital technologies and suggested that DT is accelerated by transformational IT. From a business-centric landscape, Bharadwaj et al. (2013) linked DT to business models and strategies. They argued that DT involves not only technologies, but also a set of strategic renewal and transformation processes intended to create value through digital resources. Matt et al. (2015) put forward similar views. They advocate that DT incorporates the products, services, and organisational changes brought about by new technologies, which go well beyond the process paradigm. Gurbaxani and Dunkle (2019) embraced DT as the reshaping of an organisation, involving vision and strategies, organisational structure, processes, capabilities, and culture. Synthesizing these arguments, we identified three attributes to guide our thinking on DT in healthcare organisations. These three attributes are (1) scope (the extent of the transformation), (2) means (the approach involved in the transformation), and (3) purpose (the expected outcome of DT). Therefore, in this study, DT is defined as an evolutionary process that leverages digital capabilities, technologies, and strategies to increase the efficiency of healthcare organisations and improve the health of patients, which transforms how medical services are provided and delivered and launches digital medical models intended to capture value. From this integrated perspective, we discuss the challenges and opportunities faced by healthcare organisations during DT based on a data-driven business model, and we suggest how to apply KM to respond to and implement DT, create value, and improve competitive advantage.

2.1.2. The DT journey in healthcare organisations

With the booming development of digital technologies, there have been several waves of DT in the healthcare field, from Healthcare 1.0 to the 4.0 revolution (Hathaliya & Tanwar, 2020), similar to the industrial revolution (industry 1.0 to industry 4.0) (Aggarwal et al., 2021). Healthcare 1.0 focused on public health solutions (Chen et al., 2020), whereas Healthcare 2.0 replaced manual records with electronic records (Hathaliya & Tanwar, 2020) and introduced medical imaging systems (Aggarwal et al., 2021). In Healthcare 3.0, patient healthcare records were delivered, which was an alternative to data charts for patients (Aggarwal et al., 2021; Tanwar et al., 2020). Healthcare 4.0 endorsed the era of smart healthcare (Chen et al., 2020), using cloud computing, big data, Internet of Medical Things (IoMT), telemedicine technologies, and AI, as well as providing personalised recommendations to achieve patient-centric, predictive healthcare (Aceto et al., 2020; Aggarwal et al., 2021; Hathaliya & Tanwar, 2020; Sharma et al., 2019). Nowadays, the new technological paradigms converging in Healthcare 4.0 are driving healthcare organisations towards an era of major revisions to previous technologies, which will lead human beings into completely new and transformative healthcare services (Aceto et al., 2020).

2.2. Organizational agility

The capacity of organisations to detect and react to changes in dynamic situations, known as organisational agility, is commonly considered to be the crucial capability for promptly and effectively adapting their services and innovative activities to capitalise on new opportunities (Cai et al., 2019; Cegarra-Navarro et al., 2016; Idrees et al., 2022). Agile organisations can endure and thrive when facing with challenges by adjusting the original business models or creating new ones (Bhatti et al., 2021; Rialti et al., 2019). Agility can equip organisations with the capability of transforming and renewing business models (Doz & Kosonen, 2010). If organisations is incapable of act swiftly, they might find it difficult to adapt their operations and procedures to the changes in the surrounding environment (Bhatti et al., 2021; Cegarra-Navarro et al., 2016).

2.3. Knowledge management

It is generally believed that digital technologies accelerate the development of DT and play an important role in operational resources, which makes them an effective ingredient to promote the implementation of DT (Chierici et al., 2021; Nambisan et al., 2019). Digital technologies can support the process of knowledge acquisition, dissemination, and utilisation (Chierici et al., 2021), which makes it possible to acquire and manage the knowledge assets of organisations. This highlights the importance of KM in the process of DT and implies that effective KM contributes to the success of DT. KM is a systematic discipline with a set of approaches, involving the identification, capture, storage, creation, sharing, application, and leverage of collective knowledge in order to improve performance and create value in organisations (Casey & Zehnder, 2021; de Souza et al., 2020; Giraldo et al., 2019; Wang & Wu, 2020). KM supports an environment of interaction and participation among employees (Alrahbi et al., 2020). Thus, successful DT is not limited to the utilisation of technical applications (Kodama, 2020), and determining how to conduct effective KM to manage organisational knowledge assets should also be taken into consideration. Taking KM as the management foundation for providing innovative medical services is essential for developing the high value-added business models necessary to create a competitive advantage in the DT process. With the rapid increase in information availability, big data provides a large amount of information, which can be transformed into knowledge in the DT process (Manesh et al., 2021). Leveraging KM to exert the huge advantages of managing big data can improve the efficiency of knowledge acquisition, sharing, and application, thereby increasing the potential for innovation and prompting digital innovation (Kronblad, 2020).

2.4. Data-driven business model

Big data is an information asset with high volume, velocity, variety characteristics, seeking to extract knowledge from data and transform it into business advantages (Hartmann et al., 2016). This requires cost-effective, innovative forms of information processing that enhance insight and decision-making (Hartmann et al., 2016; McAfee & Brynjolfsson, 2012). Big data can be an effective mechanism for developing competitive advantages and is a booster for successful business models (Sorescu, 2017). Data-driven organisations leverage insights extracted from data to guide and support business decisions, moving from intuitive and experience-based decision-making to more data-validated and predictive approaches (Gokalp et al., 2022; Kayabay et al., 2022). In healthcare organisations, a huge amount of data are collected in HISs during the delivery procedures of medical services, and the bulk of such data is constantly increasing from a variety of sources, such as IoMT machines and wearable devices. However, data must be analysed in an appropriate and effective manner to gain insights to create critical capabilities, optimise costs, and improve performance. Additionally, with the benefit of big data, the digital connection between patients and medical services not only can capture their experiences, but also becomes a new capital that can spur business model innovation in healthcare organisations (Cheah & Wang, 2017). Therefore, determining how to capture knowledge and wisdom through leveraging the processes of big data collection, processing, and analysis in order to streamline business operations and to enhance competitive advantage is a key issue for the success of DT.

A business model can be viewed as a conceptual tool (Mazzarol et al., 2018) that delineates the rationale of how an organisation creates, delivers, and captures value (Osterwalder & Pigneur, 2010). Therefore, healthcare business model can be considered as a strategic tool that maps how services, image, distribution, employees, and operational infrastructure are adequately integrated to create value for patients and organisations.

Johnson et al. (2008) advocate that a successful business model consists of four interlocking elements: a customer value proposition (create value for customers through the offering to meet their needs), a profit formula (a blueprint for how to create value for the company while offering value to customers), key resources (the assets required to deliver the value proposition to targeted customers), and key processes (the operational and managerial processes necessary to successfully deliver value to target customers). Osterwalder and Pigneur (2010) developed the business model canvas that can be used as a tool to depict the logic of how an organisation generates benefits and help shape the organisation and facilitate strategy formation in an effective manner. This business model consists of nine basic building blocks: customer segments, value propositions, channels, customer relationships, revenue streams, key resources, key activities, key partnerships, and cost structure. This study integrates the conceptual model proposed by Johnson et al. (2008) with the business model canvas comprising of those nine building blocks (Osterwalder & Pigneur, 2010) to construct a data-driven business model that can be used as a guideline for healthcare organisations to achieve successful DT initiatives.

2.4.1. Value proposition

The advanced analytics in big data accelerates the transformation from the current disease management model to a precise, preventive, and personalised strategy to keep people healthy. Digital technologies offer the potential to make the healthcare environment more open, flexible, and interactive and can provide rich data and information as well as value-added agency services, enabling patients to be more involved in managing their own health (Kostkova, 2015). Precision medicine is expected to be one of the biggest revolutions in the next few years (Denicolai & Previtali, 2020). Precision medicine aims to prevent and treat diseases based on a person’s unique genetic makeup, lifestyle habits, and environment in order to deliver tailored healthcare services and promote the development of a new healthcare paradigm (Denicolai & Previtali, 2020). Gathering, integrating, and analysing big data collected from multiple sources (e.g., personal digital tools, mobile devices, and wearable devices) enables healthcare teams to track and report results, identify side effects and adverse events and proactively take interventions and prevent the deterioration of health. Thus, in term of a value proposition, it integrates precision medicine and delivery of personalised healthcare.

2.4.2. Profit formula

In terms of a profit formula, big data analysis can become the new currency of healthcare organisations. It provides a great opportunity to streamline operational processes that will increase efficiency, performance, responsiveness, and access to care in an automated and integrated digital health ecosystem. Precision medicine supports the sustainable development of healthcare in terms of benefits, such as reducing the length of hospital stays and unnecessary testing, optimising workforce planning, and reducing medical management costs (Denicolai & Previtali, 2020). The profit formula dimension incorporates revenue streams and cost structure. In terms of revenue streams, precision medicine can increase income from out-of-pocket services. When evaluating a cost structure, it is necessary to consider professional manpower costs and equipment investments, including professional training costs and the costs associated with the development digital technologies.

2.4.3. Key resources

Key resources include knowledge, digital technologies, intelligent big data, big data analysts, and repositories since knowledge is the primary resource for obtaining and sustaining competitive advantage (Stenius et al., 2017). The key to effectively using digital technologies is to facilitate cross-functional collaboration and offer the right information/knowledge and expertise to the right people at the right time (de Souza et al., 2020). Big data analysts, who professionals who are great at handling large amounts of information and have the skills to clean and organise large data sets, are important in big data analytics. When leveraging big data, through the process of big data analysis and KM, useful information and knowledge are extracted and generated from big data and then are transformed and accumulated into organisational assets that are stored in knowledge-based repositories in order to refine the use of available resources and help organisations make more informed decisions (Durst et al., 2019; Wang & Wu, 2020).

Additionally, the key resources dimension also incorporates customer relationships, channels, and key partnerships. For example, customer relationships have a profound effect on the overall customer experience. Offering self-services include self-service payment machines and mobile payment applications operated by patients themselves can save waiting time at the counter. Offering personalised self-paid services for remote home healthcare intended to provide medical care services to patients at home involves the use of technologies such as blood-pressure monitors, wireless or wired weight scales, wireless blood glucose metres, and wireless pulse oximeters (Bowles & Baugh, 2007). The vital signs and health information of patients can be collected into HISs, and a range of healthcare delivery services can be provided in the form of information dissemination, self-care assistance, and suggestions. This allows patients to better access services, and healthcare professionals can learn to better manage customer expectations, leading to promoting patient relationships. Also, inviting patients to participate in their own care helps establish a good medical-patient relationship through the process of shared decision making (SDM). SDM is a communication process through which patients and clinicians collaborate in making optimal healthcare decisions that align with what matters most to patients (Elwyn et al., 2012). Clinicians explain treatments and alternatives to patients and help them choose the treatment option that best suits their preferences as well as their unique cultural and personal beliefs (Elwyn et al., 2012). In terms of channels, providing different types of channels can help create a great patient experience. For example, mobile application functions (such as payment, registration, drug use information and reminders, interactive healthcare systems for patients with chronic diseases), intelligent robots, and self-service operation equipment provide channels to provide options for how patients want to be reached. In terms of key partnerships, drawing support from the professional talents and capabilities of information vendors and equipment manufacturers, big data analytics and AI-embedded KM platforms can be jointly developed to optimise resource allocation and reduce costs and risks.

2.4.4. Key processes

In terms of key processes, the development of data-driven business models is expected to make a significant contribution to DT in healthcare organisations, facilitating the discovery of new diagnostics and treatments, and accelerating patient access to personalised healthcare data (Uslu et al., 2020). When data has been interpreted, it is transformed into information/knowledge that in turn supports decision-making (Zaki, 2019). Therefore, the process of transforming data into knowledge by relying on powerful intelligent data processing and analytical methods is a key process. The fusion of AI technologies and big data analysis methods (e.g., machine learning and optimisation algorithms) can contribute to making timely decisions related to medical needs, as well as making working methods easier and simpler.

Additionally, other key activities that occur during DT include AI-powered healthcare, KM, and telemedicine. An example of AI-powered healthcare is leveraging medical robots to provide patients with interactive health education services in an innovative, intelligent way or offering mobile applications for patients with chronic kidney disease (CKD) to manage and track their health. This can help them track diet and nutrition, check drug interactions, and record personal medication history. They can also estimate kidney function using the estimated glomerular filtration rate (eGFR), provide information on their disease symptoms, and help manage the progression of CKD (Siddique et al., 2019). These key activities create value through personalised services and enrich the patient experience. KM provides a solid foundation for DT based on data-driven business models, as well as organisational strategies and learning. The COVID-19 pandemic created new opportunities for telemedicine because telemedicine revealed the benefits of reducing the risk of infection and the value of remote detection and monitoring, which also sparked people’s interest in digital tools to develop new ways of improving care and saving lives and brought tremendous changes to the medical industry. We argue that telemedicine may become a reliable universal service in a post-pandemic world, with the potential to provide safe, efficient, and cost-saving healthcare to remote and underserved communities in transformative ways.

3. Research model and propositions

This study explores how KM can support DT based on the data-driven business model to improve efficiency and deliver excellent medical services to patients within a healthcare environment.

3.1. Research method

We have developed a research model and the associated research propositions to offer guidelines of the best practices for achieving the success of DT in healthcare organisations through a literature review and expert interviews to integrate the collective knowledge and perspectives of experts in the medical field.

First, based on the review of theoretical underpinnings related to the driving force of DT, OA, KM, a data-driven business model, and DT, and building on these theoretical premises, the interrelationships among the key constructs of interest in our proposed research model are identified. This shaped the building blocks in our initial research model.

Then, semi-structured personal interviews were conducted with eight experts to obtain the information required for the refinement of KM and the data-driven business model of the DT model. These experts included three academics and five professionals who held management positions in healthcare organisations and were involved in the implementation of digital technologies, operations management, and the development of organisational strategies. The interviews were carried out by focusing on identifying KM strategies and measures, the practices and applications of digital technologies, and the configuration and implementation of data-driven business modelling processes. All experts were assured of anonymity and confidentiality.

Consequently, this study clarifies the definition of DT and integrates the concept of OA and KM, and the development of a data-driven business model, where a KM-OA-enabled DT conceptual framework is developed to support DT implementation in the field of healthcare, as shown in Figure 1.

Figure 1. The conceptual framework for digital transformation.

3.2. The driving force of digital transformation and data-driven business models

In healthcare organisations affected by the COVID-19 pandemic and driven by digitalisation, fundamental changes have taken place in business models, strategies, processes, and medical services. However, digital technologies are the foundations and key to the tremendous changes happening within organisations (Zaki, 2019). Digital technologies are creating opportunities for innovation in healthcare organisations and can reshape the nature of service delivery through innovative changes in communication and collaboration methods (Kronblad, 2020). In healthcare organisations, health information systems (HISs) provide critical support to daily operations by serving as a shared information repository and facilitating communication among various healthcare professionals (e.g., physicians, nurses, pharmacists, medical technologists, social workers, dieticians, and administrative managers) (Fichman et al., 2011). With the development of digital technologies, HISs are also evolving. For example, an automatic dispensing cabinet can be integrated with HISs to update the latest prescriptions, which in turn will improve the safety of medicine use and will provide intelligent digital solutions for controlling high-risk or high-priced drugs. Digital tools make electronic forms easy to design and dynamically generate simply by dragging and dropping to set the position of the fields. This promotes the automation of processes and easy integration with HISs, in turn providing an effective data source for big data analysis. Furthermore, digital tools improve healthcare quality and performance by replacing the traditional handwritten whiteboard at the nurse station with a digital whiteboard (integrated with the HIS to collect information needed for inpatients and nursing care), which allows nurses to clearly understand important reminders and the treatment schedules for each patient.

Digital technologies offer the potential for greater connectivity to HISs and thrive on HIS applications. Data are being generated at an ever-increasing rate, so the need for fast, centralised data access has become critical. A data-centric architecture is the key to providing HISs with good quality that can meet patient expectations, reduce costs, and improve medical care outcomes. Digital technology plays a critical role in enabling data-driven business models. Cloud computing, big data analytics, IoMT, and AI are some of the key digital technologies that power data-driven business models. These technologies allow organisations to collect, process, and analyse a vast amount of data effectively on a real-time basis, and interpret the results of the analysis processes appropriately to gain insights and make informed decisions that lead to better medical outcomes (Hartmann et al., 2016; McAfee & Brynjolfsson, 2012). An empirical study by McAfee and Brynjolfsson (2012) found that organisations that depend on data-driven decision-making tend to achieve higher efficiency and profitable outcomes. Therefore, the following proposition is presented:

P1. The driving forces of digital transformation drive healthcare organizations to develop data-driven business models that leverage the power of data to enhance the quality of care, optimize operations, and create value for organizations, leading to successful DT.

3.3. Organizational agility and data-driven business models

Previous research has identified the existence of the correlation between OA and innovation in business models, and has indicated a positive relationship between the two. For example, Bhatti et al. (2021) investigated the causes and effects of business model innovation, and argued that OA has a substantial influence on business model innovation. In a similar vein, Clauss et al. (2021) found that there is a positive correlation between strategic agility and business model innovation, and this connection is further reinforced in highly turbulent environments. Therefore, this study argues that the data-driven business model can be viewed as a form of innovation in business models. OA allows the organisation to embrace a data-driven business model that offers the necessary flexibility and responsiveness to help healthcare organisations make effective decisions. By being agile, organisations can quickly adjust their data-driven strategies in response to the changes in the medical environment or to the shifts in patients’ needs. This results in improved decision-making quality and desirable outcomes of healthcare efforts, thus contributing to the success of DT. Therefore, the following proposition is developed:

P2: Organizational agility motivates healthcare organizations to embrace data-driven business models, which contributes to successful DT initiatives, by offering the required flexibility and responsiveness to recognize and respond to changes in a more effective manner.

3.4. The role of knowledge management for data-driven business models in DT

A data-driven business model provides a wide array of opportunities for value creation in healthcare organisations. However, the processes of capturing, distributing, and effectively leveraging knowledge, as well as the interaction with key partners and patients, require the development of a new KM approach to effectively manage DT and enable organisations to stay ahead and gain a competitive advantage. Alvarenga et al. (2020) have presented empirical evidence of the critical roles that KM plays in driving the success of DT initiatives in the Portuguese public sectors. Therefore, we argue that KM is a key assisting vehicle for DT because it supports the transformation of information and knowledge assets into enduring value for patients and employees in healthcare organisations and promotes the creation of new skills, the acquisition of new capabilities, the enhancement of capabilities, and the sharing of knowledge (Alrahbi et al., 2020). Data-driven technologies assist in interpreting information and knowledge from big data, provide personalised decision support, and help find suitable partners to jointly create and share knowledge. Promising digital technologies, such as big data, cloud computing, IoT, business intelligence and analytical capabilities, as well as advances in human-computer interaction, have great potential for creating new knowledge. Thus, KM is required to manage technologies and processes, analyse data, and create the culture that organisations need to improve their competitive advantage. We argue that the challenge for healthcare organisations to capitalise on the potential of DT is to develop a data-driven business model with integrated strategies and a holistic approach towards KM, building promising HISs and processes to simplify information exchange and data analysis, and cultivating a powerful culture of data-driven decision-making.

For big data analysis to work, it is very important to create efficient data management models (e.g., data warehouses) and to develop online analytical queries and process databases. An appropriate data warehouse design will convert all types of healthcare data into a common format for integration with other warehouses, and will have the ability to explore large data sets (Uslu et al., 2020). Harnessing the potential of KM and making good use of digital technology tools (e.g., advanced analytics, machine learning, AI) by gathering meaningful insights from data and developing search and retrieval functions will make it possible to discover the availability of contextual knowledge with predictive and prescriptive suggestions derived from knowledge-based repositories. This will help effectively solve problems at work and allow collaboration with others.

In order to create promising HISs and processes that simplify information exchange and data analysis, it is indispensable to construct and develop processes to contact and align the daily work processes of each job role within organisations, which can lead to change management strategies and communication plans.

Talent cultivation and management is a necessary component of a data-driven strategy. Using big data enables managers to make decisions based on evidence rather than intuition (McAfee & Brynjolfsson, 2012). However, with the tremendous influence of digital technologies, working methods are changing in multiple ways, so it is necessary to consider the learning and skills of employees. An integrated approach to the development and implementation of knowledge management systems and processes will be beneficial in data analytics. Training employees to learn how to filter and retrieve data and to draw actionable inferences through the KM platform will help them understand trends and patterns and derive insights from the available data necessary to find solutions. KM involves evaluating operational processes, employees, and technologies, where information systems are developed that leverage the relationships between these components to get the right information/knowledge to the right persons at the right time to facilitate cross-functional collaboration.

Furthermore, it is important to charter a cross-functional steering committee, which can facilitate and leverage the knowledge assets of healthcare organisations and align data-driven decision-making with organisational goals and strategies, leading to success during DT.

Drawing on the aforementioned discussions, the following proposition is presented:

P3: KM can support the development of data-driven business models, leading to successful DT for healthcare organizations by facilitating the capture, analysis, and dissemination of critical knowledge and insights, thus enabling data-driven decision-making, optimizing resource allocation, and facilitating continuous operational improvement.

In practice, we proposed a set of KM strategies and measures to serve as guidelines for healthcare practitioners developing KM initiatives based on a data-driven business model applied to the DT process. The business model dimensions, building blocks, strategies, OA, and KM support are summarised in Table 1.

Table 1. Summary of key elements for data-driven business models, KM, and OA support.

BM dimensions	Building blocks	Strategies	KM support	OA support
Value proposition	Value proposition	Precision medicine Personalised care delivery	Harnessing the full potential of digital technology by identifying and leveraging knowledge assets Building a knowledge repository of best practices helps to inform change management strategies and communication plans Talent cultivation and management Streamline knowledge workflows and foster collaboration to solve work problems effectively Charter a cross-functional steering committee	Respond quickly to the changing needs of patients and tailoring medical services to meet the specific requirements of different patient segments
	Customer Segments	Patients with personalised care needs
Profit formula	Revenue Streams	Income from out-of-pocket services		Optimise costs and streamline operations through agile processes and efficient resource allocation
	Cost Structure	Professional manpower costs Professional training costs Digital technologies development costs
Key resources	Key Resources	Knowledge Digital technologies Intelligent big data Big data analysts Repositories		quickly acquire or reallocate key resources as needed
	Customer Relationships	Personalised services with remote home care Shared Decision Making (SDM)
	Channels	Self-Service Technologies Mobile payments Medical robots Mobile applications		Establishing diversified channels quickly to cope with the evolving circumstances
	Key Partnerships	Information Vendors Equipment manufacturers
Key processes	Key Activities	Data analytics KM Interactive health education services AI-Powered Healthcare Telemedicine		rapidly adjust or prioritise key activities

NOTE: BM: business model; KM: knowledge management; OA: organisational agility.

4. Discussion and implications

4.1. Implications for research

This study makes some contributions to the fields of KM, business models, and DT. First, although digital transformation (DT) has become a top priority for most organisations in the current rapidly changing environment, the term “digital transformation” is still in a grey area due to different definitions of its scope and focus. We offer a clear definition of DT in healthcare organisations and help clarify some ambiguous DT-related concepts, which will help researchers and practitioners advance the theory and practice related to this subject. Second, this study puts forward a dominant link between big data and business model innovations and contributes to the theoretical perspective of DT by contextualising the key elements of business models. Finally, this study integrates the concept of KM, OA, business models, and big data as they relate to building a data-driven business model. Also, a KM-OA-enabled conceptual framework for DT is developed, which helps to bridge the research gap around DT strategies in healthcare organisations.

4.2. Implications for practice

Regarding the implications for practice, this study provides practitioners with examples of how healthcare organisations can leverage big data to generate new business models that help academics and practitioners understand the link between big data and business model innovations and derive measures for best practices. To be specific, this study develops a data-driven business model by referring to the operations of the healthcare organisations in Taiwan. This model can serve as a useful reference to create value and competitive advantages for healthcare organisations. Furthermore, KM is another pillar of the success towards DT. It plays a pivotal role and cannot be ignored. This study offers KM solutions and applications to help healthcare practitioners cope with highly changing and uncertain working environments to improve the efficiency and overall quality of medical services. Integrating the concepts and practices of KM with the innovative business model, leading healthcare organisations to be successful on the path of DT.

5. Conclusions and future research

The rapid influx of the COVID-19 pandemic has significantly affected the operating model of healthcare organisations and has in turn created a pressing need to take DT into consideration to find effective solutions. Coupled with the success of digital technologies, the urgent need for DT to cope with the ever-changing environmental challenges is even stronger. However, when attempting to determine how to derive value from the growing opportunities associated with DT, organisations must develop the organisational capabilities necessary to benefit from data-driven insights and must also find new ways to exploit digital technologies (Nasiri et al., 2020). Effective KM is one way to achieve this goal. A data-driven business model presents multiple prospects for healthcare organisations to create value. With the support of effective KM activities, including those related to capturing, distributing, and leveraging knowledge, organisations can identify trends and patterns in their business environments, and make informed decisions on resource allocation, process optimisation, and service delivery. This approach is essential for healthcare organisations to achieve successful DT and remain competitive in the industry. However, few studies have explored DT from the perspective of KM. This research bridges the theoretical and practical gaps in the literature by providing an understanding of how KM facilities DT to improve performance and provide high-quality medical services to patients in healthcare organisations. Using the operations of the healthcare organisations in Taiwan as an example, a strategic blueprint of a data-driven business model is proposed that can be used to present the various interactions among organisational structure, operating procedures, resource composition, and external customers (e.g., patients), which will help lead to an understanding of the dynamic changes taking place in business models and serve as an approach by which healthcare organisations can construct new business models. Furthermore, OA reflects an organisation’s ability to detect and react quickly to shifting circumstances, enabling the successful implementation of new innovative initiatives (Cai et al., 2019; Idrees et al., 2022). Integrating the concept of KM, OA, and the Canvas business model, a KM-OA-enabled DT conceptual framework was developed based on a data-driven business model intended to support DT implementation in healthcare organisations, which can serve as a foundation for future DT research.

Although this study has contributed to theory and practice, there are still some limitations that suggest possible directions for further research. This study focuses on the role of KM and the development of data-driven business models for DT in healthcare organisations, which may have limited the scope of the discussion of DT. Further studies are suggested to explore the nature and the implications of DT in other organisations and expand the scope to other dimensions, such as risk management and assessment of DT. Additionally, even though the framework was developed based on empirical evidence from previous studies and expert viewpoints, there is a need for empirical examination of the proposed framework.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Ministry of Science and Technology, Taiwan [MOST 109-2410-H-006 -043 -MY2]