Introduction
Business intelligence has become a significant aspect of the modern world. It includes various techniques, tools, methods, and strategies that help business practitioners convert raw data into meaningful pieces of information, improving the performance of companies that apply business intelligence to their activities (Business Integrity Services, 2021). The concept can be exceptionally effective in the healthcare industry because of the major amounts of data it has to work with and the significance of medical activities for the human world.
The mentioned facts can explain why researchers anticipate that the healthcare business intelligence market will expand from 2020 to 2030 by 16.10% as per the compound annual growth rate (GlobeNewswire, 2022). Thereby, the primary research question of this paper is how new digital technologies are changing the business models that firms use. This is important because these ideas can be used to develop radical new types of business models. In addition, the paper discusses the benefits that business intelligence provides to the healthcare industry. It appears that the usage of business intelligence digital models such as customer decision journey, product-service system, and online analytical processing can be effective in the healthcare sector.
The Theory Models Utilized
Theory Model 1: Customer Decision Journey
The first theory chosen for this research is called the customer decision journey (CDJ) model, which primary feature is associated with utilizing customer journey maps to understand how customers make choices when acquiring various products and services. An essential process involved in this model is customer journey mapping (CJM), and its popularity is growing nowadays among business practitioners and academics due to its usefulness in terms of understanding the experiences of customers of a particular company (Rosenbaum, Otalora, and Ramirez, 2017). Accurate modeling of customer decision journeys can be an effective tool for identifying customers’ interests, which, in turn, can lead a company to find the best possible solutions, improving customer satisfaction and general organizational performance. The CDJ theory has been chosen for this research since it is customer-oriented. Although the healthcare industry contains business elements as any other professional field, its main priority is to take care of people’s health and lives, which is why a customer-oriented business model is one of the most appropriate decisions for this research.
As mentioned previously, the customer journey mapping process is the core of CDJ theory, which is why it is essential to explain the process to demonstrate how the model works as an aspect of business intelligence. Rosenbaum, Otalora and Ramirez (2017, p. 2) define customer journey mapping as “a visual depiction of the sequence of events through which customers may interact with a service organization during an entire purchase process.” In other words, in order to use the CDJ model appropriately, a company’s leaders must thoroughly analyze their customers and the nature of their products or services to visualize the process of customer decision-making. The main result – a customer journey map – must include every possible touchpoint that customers might encounter while being a part of the service exchange process (Rosenbaum, Otalora, and Ramirez). CJM can be an efficient tool to foster innovative decisions within a company, allowing senior managers who understand the mentioned touchpoints to employ the corresponding tactics working with cross-functional teams.
For example, the traditional patient journey may begin when they start experiencing new symptoms that they find uncomfortable or disturbing enough to see a doctor. The following stages may include preparation for a doctor’s visit, diagnosis, treatment selection, condition management, and treatment switching in case of necessity. Recognizing the symptoms is the first phase of the customer journey, which, depicted in a simple way, would include experiencing the symptoms themselves and making an appointment with a doctor. However, a thorough and detailed map should include more touchpoints, such as checking the provider network’s connection, doing own research on the symptoms to understand them, and only then making an appointment with a doctor. The more touchpoints healthcare leaders consider when mapping customer journeys, the more knowledge they can acquire regarding the external factors that might influence the customer decision process, such as the Internet connection quality.
Customer journey maps in the CDJ theory are usually divided into three major periods. The first of them is pre-service, referring to the customer experience before the provision of services. For example, the mapping process for choosing a doctor may include seeing advertisements on healthcare-related websites or receiving positive recommendations from friends and colleagues. The second period, service, is related to the time customers actually receive services: during a visit to a hospital or when interacting with doctors and nurses. Finally, the period of post-service refers to the service-related activities customers engage in after the exchange process, such as leaving reviews on the hospital’s website or giving feedback on the doctor’s competence to friends. As it can be seen, a customer journey map provides a highly detailed description of the customer decision-making process, depicting every single step.
Theory 2: Product-Service System
The next model chosen for this research is Product-Service System (PSS), which is a promising theory in product development nowadays as it represents win-win solutions for customers and companies simultaneously, being a significant servitization component. According to Fargnoli et al. (2018, p. 387), servitization is “the evolution of product identity based on material features, to a position where the physical component is joined to the service system indissolubly.” In other words, it is a process associated with changing a particular business’s focus, transferring it from the development of physical products only to the creation of an entire set of various products and services, and that process is the main source of win-win solutions for companies and customers. The PSS approach corresponds with the concept of servitization, contributing to the development and improvement of services associated with products’ life cycles (Fargnoli et al., 2018). The theory has been chosen for this research because of its effectiveness in the healthcare industry, which is the primary field analyzed in this study.
The primary goal of utilizing the PSS model is usually to retain and account for the responsibility of the product’s life cycle. Thereby, the model can be highly effective when applied to production-related business companies since the manufacturing nature of the activity may impact the environment, which is why considerations on the life cycle are of high importance. Fargnoli et al. (2018, p. 387) report that manufacturers utilize PSS “to optimize the product’s life cycle, especially in the case of products with a high environmental impact during their use phase.” The model implementation can help organizations maintain or even augment the product’s value during its entire life cycle and simultaneously achieve positive environmental outcomes. Additionally, businesses can integrate services with products to increase customer satisfaction, which is another reason why the usage of the PSS model is appropriate in the research grounded in the customer-oriented healthcare industry. The influence on the environmental aspect, however, remains a priority for the production of medical devices and preparations.
Almost any manufacturing company can utilize the PSS theory, integrating services with products to achieve better business outcomes. For example, suppose a construction company signs a contract to build a particular industrial facility near a forest for a city – that facility would be the product. The organization, in addition, officially promises that the forest will not suffer any damage during the building process, meaning that the company takes responsibility for the environmental impact that might be caused by its activity (an integrated service). One more example: if the firm creates smartphone software which would be the product, in this case; it can provide customers with a contract stating that any upcoming updates for that specific software will be provided to those who have purchased the software without any additional charge (an integrated service). That example relates to the aspect of customer satisfaction discussed previously. Overall, the implementation of the PSS model can be effective in entirely different business sectors.
Theory 3: Online Analytical Processing
The last theory utilized for this research is online analytical processing, or OLAP for short, which is the result of the transformation of the preceding model, namely traditional online transactional processing (OLTP). The significance and effectiveness of this theory for healthcare overall and this particular research can be explained by the rapid growth of health informatics as an aspect of the medical field (Ali-Ozkan, Nassif, and Capretz, 2013). Digital technologies are becoming more and more popular nowadays, integrating into almost every field of people’s lives, and the healthcare industry is not an exception, considering the active usage of computer sciences and information technologies for medical purposes (Dinh-Le et al., 2019). OLAP, as a model of analysis, can be an efficient instrument in the industry under discussion, especially for information management purposes.
The analytical nature of the OLAP theory makes it useful in cases involving high amounts of data. According to Ali-Ozkan, Nassif, and Capretz (2013, p. 210), the model represents “techniques including variety of functionalities such as aggregation, summarization, and consolidation as well as the ability to view information from different angles.” The main tool utilized within the theory is the OLAP cube, which can help managers, practitioners, and academics view various data sets in a three-dimensional format. Thereby, OLAP cubes can be exceptionally efficient in environments where business intelligence solutions are used for setting targets, financial simulations, analyses based on the “what-if” principle, budgeting, and much more (Ali-Ozkan, Nassif, and Capretz, 2013). The multidimensional approach can be helpful when two data dimensions are not enough to make definitive conclusions or achieve the desired results.
For example, a police department aims to collect information on youth crimes to see the correlation between the type of crime, the age of offenders, and the time period when the crime was committed. Various analytical instruments can show how two different aspects are related, while the three-dimensional OLAP cubes can depict the correlation between all three data sets simultaneously, making it easier for professionals to trace the pattern. Considering the amounts of data involved in the healthcare industry, the OLAP theory appears to be an appropriate model for this study.
Methodology
I applied three different theories to understand how new digital technologies can impact the business models used by various companies, specifically referring to the healthcare industry. These theories included the customer decision journey (CDJ) model, product-service system (PSS) model, and online analytical processing (OLAP) model. Thus, this research utilized those models to analyze the business intelligence solutions that can be implemented in the healthcare industry to achieve better outcomes and improve general performance within the field.
Analysis
Customer Decision Journey in the Healthcare Industry
Firstly, the CDJ model is applied to analyze the usage of business intelligence in the healthcare industry. According to Zheng, Wu, and Chen (2018), patient-centered (or customer-oriented, in other words) business intelligence applications have been growing recently, focusing on specific conditions related to patient-centeredness. That fact is one of the reasons why the CDJ model is appropriate for the medical field. For example, the benefits of utilizing customer-oriented business theories include improved health outcomes and increased patient reach (Viswanadham, 2021). This section of the paper analyzes how the CDJ model can be implemented within the healthcare industry.
For example, applying the business intelligence concept to the CDJ model can make it more efficient and powerful in terms of business solutions in the medical field. The figure above shows the difference between a traditional patient journey and a digitally-powered patient journey map. With the additional digital touchpoints, the map is more detailed, becoming an effective analytical tool with predictive nature (El Morr and Ali-Hassan, 2019). Thus, the phase of discovering new symptoms is supplemented with going online to do the symptom identification, seeking information on what to tell the doctor, and making an appointment through the hospital’s website. The stage of preparation for the doctor’s visit can additionally include researching reviews on the doctor’s competence online, and the diagnosed disease or condition can be examined on the web after the diagnosis phase to obtain more available information.
Then, the phase of treatment selection can be complemented with the digital touchpoint related to online research aimed at comparing generic medications with branded ones. In addition to the main purpose of the healthcare industry companies – taking care of people’s health – the indication of such comparison can help medical organizations predict the usage of different preparations, identifying how they can impact the gross domestic product of the country and improving customer experience and care quality (Jayaraman et al., 2020). The usage of digital resources to analyze the costs of treatment coverage can complement the following stage of condition management, assisting patients in planning their financials.
Product-Service Systems in the Healthcare Industry
The second model applied to analyze the application of business intelligence in the healthcare industry is a product-service system (PSS). Kruk et al. (2018) state that the nature of the medical field requires broad systematic actions to improve the quality of care, which is why the healthcare product itself cannot be fully effective without the integration of additional services. Evidently, the medical industry deals with people’s health and conditions, and such an important and challenging task cannot be performed effectively without a complex system of services integrated with the product itself. Thereby, the implementation of the PSS model and the related digital technologies as the business intelligence solution can positively impact the industry under discussion.
For example, the practical usage of devices and preparations within the healthcare industry is one of the essential aspects determining its performance. As it is known, health status significantly depends on the environment in which a particular population lives, and healthcare corporations should consider the environmental impact of their activity to ensure the positive health status of people, preventing various conditions and diseases from ever occurring. In addition, the integration techniques of the PSS model can enhance productivity in the medical field and reduce operational costs, increasing the industry’s overall performance (OECD, 2014). Thereby, considering the products’ life cycle involved in different healthcare activities is crucial.
The figure above illustrates how the PSS model can be implemented to improve life cycle management in the medical field. The first stage, as described in the theory, is the preliminary analysis, aiming to identify system characteristics and priorities with the usage of market analysis and patient surveys. This phase can help business leaders, particularly healthcare managers, determine the priorities for improvement related to enhancing the life cycle of medical devices and preparations to restrain or even completely eliminate their negative impact on the environment. Fargnoli et al. (2018, p. 389) state that, during the preliminary analysis, “customers’ priorities are identified in order to bring to light critical customer perception attributes of the PSS.” In addition to the environmental impact, customer satisfaction remains a significant aspect of the PSS theory, as mentioned previously, which is another reason why the corresponding analyses are essential in the medical devices sector, where the regulated market rules guide most of the customers’ requirements at a normative level.
Then, the following stage is the detailed analysis, which includes the evaluation of the current situation and the assessment of the life cycle itself and the correlated costing. That phase is necessary to identify the intervention measures that need to be implemented to improve the products’ life cycle and reduce their negative environmental impact. Quantitative data is required to identify critical components of the current situation and conduct a detailed analysis of the system, including both the initial product and all the integrated services as parts of a major unified system (Fargnoli et al., 2018). This stage is also highly important since it involves the identification of weaknesses in the current life cycle management system from the viewpoint of environmental and economic concerns. Should these concerns be adequately addressed during the detailed analysis phase, it can positively impact the life cycle described by the PSS theory and the maintenance management and highly increase the medical products’ reliability.
Finally, the feasibility analysis stage enhances the measures identified during the second phase by improving the proposed solutions, considering alternative scenarios, and screening life cycle modelling to turn the proposed interventions into optimal measures. This stage utilizes the quality function deployment tool and conducts new market analysis to reach the best possible business intelligence solutions. The information collected during the previous phases can be further utilized for better identification of customers’ desires and needs, environmental concerns related to the healthcare industry’s activities, and the cost priorities that should be considered to enhance the industry’s operational functions and productivity (Fargnoli et al., 2018). The two major goals that can be accomplished with the completion of the feasibility analysis (and, correspondingly, the completion of the PSS analysis overall) are the increase in the medical corporations’ ability to fulfil the patients’ demands and needs and to improve the current system with the consideration of the product’s life cycle.
Online Analytical Processing in the Healthcare Industry
The last theory applied to business intelligence in the healthcare industry for the purposes of this research is online analytical processing (OLAP). The tools provided by the OLAP model can be impactful within the digital component of the medical field since it works with significant amounts of information, and databases, where it is stored and analyzed, are often not user-friendly, undermining the analytical aspects of the healthcare activities (Ali-Ozkan, Nassif, and Capretz, 2013). Thereby, the automatization of the processes related to working with data would be helpful in the medical industry. One of the possible appropriate business intelligence solutions is data mining, which researchers define as “the automated process of discovering previously unknown useful patterns within structured data” (Ali-Ozkan, Nassif, and Capretz, 2013, p. 211). However, proper information structuring is required in order to make data mining effective, as described in the process’s definition, which is where the OLAP model can be helpful.
One of the most promising analytical tools utilized in the OLAP theory is the OLAP table, which has been mentioned in the previous sections of the paper. For example, the table can be used to analyze data related to the risk of heart disease among people of different age groups and cities in different time periods. The table above illustrates three separate dimensions, including four age groups (from 13 to 18, from 18 to 30, from 30 to 63, and from 63 and up). The other two groups are the time in years (from 2018 to 2020, inclusively) and city locations, namely Los Angeles, Chicago, New York, and Detroit. The measured display in the table’s columns refers to the number of patients with mean total cholesterol units, excluding the correlation between Detroit and age groups. As it can be seen in the figure, the OLAP model can provide significant assistance to the healthcare industry because of its ability to view major amounts of information in multiple dimensions simultaneously, significantly simplifying the data-related analytical processes.
Improving Patient Experiences in the Healthcare Industry
The first group of ideas that occurred during the analysis refers to the impact that business intelligence solutions in the medical field can have on patient experience and general customer satisfaction. Business intelligence and the corresponding models discussed can add significant value to the quality of patient services during their treatment (Gaardboe, Sandalgaard, and Nyvang, 2017). In this case, the customer journey map and product-services system models can be highly effective since they are both customer-oriented, which implies that the application of those theories in the healthcare sector would mean the corresponding improvements in services because of the so-called patient-centeredness. Considering other benefits that the mentioned models can bring to the medical industry, they both appear appropriate for utilizing as business intelligence solutions with additional digital technologies.
External Positive Influences of Business Intelligence in the Healthcare Sector
The ideas of the second group relate to the external impacts that the application of business intelligence can have on various aspects of people’s lives. Specifically, there is a pattern connecting the healthcare industry with environmental sustainability as the production of medical devices and preparations can significantly affect the environment. In addition, healthcare facilities store and utilize major amounts of data, including patient personal information, meaning the leaks of such data could bring significant harm to people who are not currently patients, making data leaks another external factor associated with healthcare activity (Kulkarni et al., 2017). The usage of business intelligence can help enhance data storage, reducing the chances of potential information leaks.
Analytical Tools as the Measure of Improving Operational Functionality in Healthcare
Finally, the last group of ideas that occurred during the research is associated with the necessity for reliable and efficient analytical tools to work with loads of information involved in the healthcare industry. As mentioned in the previous paragraphs, healthcare practitioners work with significant amounts of data, which requires much time and effort to collect, analyze, and process (Dihn-Le et al., 2019). Using various analytical tools such as the OLAP cubes can simplify that process, making it easier for the healthcare sector employees to work with information and, thereby, improve the overall operational functionality of the industry. In addition, the medical systems nowadays become increasingly dependent on electronic health records (EHR) capabilities, which further increases the significance of digital technologies and the related business intelligence solutions in healthcare.
Conclusions
Business intelligence is an essential aspect of any business activity nowadays due to the transformations that have occurred in recent years requiring organizations and corporations to maximize the usage of business intelligence to maintain competitiveness and effectiveness in the market.
Business intelligence can be exceptionally helpful in the healthcare sector, especially with additional digital technologies that can boost the operational functionality of medical organizations and improve their general performance levels.
Successful application of digital technologies and business intelligence solutions in the healthcare sector can be achieved by implementing different enhanced business intelligence models, such as customer journey maps, product-service systems, and online analytical processing.
The theories mentioned above can provide medical organizations and the industry overall with various tools and techniques to reach better organizational outcomes, contributing to customer satisfaction, positive environmental influence, and operational functionality of healthcare facilities.
Proper usage of various models can enhance the general performance of the healthcare industry, which, in turn, can increase patient reach, positive treatment outcomes, and other factors improving the population’s health status.
Recommendations
Healthcare companies should consider utilizing customer journey maps to understand their patients’ ways of thinking and their basic desires, needs, and demands. Healthcare companies that are customer-oriented (or patient-centered) and implement business intelligence within their activities have higher chances of acquiring more clients and contributing to the improvement of the population’s health.
Medical organizations should understand the concepts of product-service systems and implement them to enhance the patient experience.
The appropriate usage of OLAP cubes is recommended for medical departments responsible for collecting, storing, analyzing, and processing data.
Overall, the companies working in the healthcare sector should constantly improve their business intelligence, seeking various ways to implement new methods and techniques and promoting the utilization of digital tools amongst other medical companies. Such an attitude can significantly contribute to the companies’ own benefits and the overall health of the population.
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