In the 21st century, the technological world is evolving with increasing rapidity. This is especially true in the field of artificial intelligence (AI), which is transforming markets in revolutionary ways. The aim of this article is to explore the impact of the development of these new AI new technologies on medical services, and products, and to classify them according to patient needs and benefits. We contribute to the literature by demonstrating the added value for the patient, for the healthcare system, and for the physicians (service providers), the interconnectedness of the factors influencing the development of new technologies, and the benefits for key stakeholders. We focus on demonstrating key innovative solutions that enable new functionalities, higher standards of service and improved clinician competence.

The article is both theoretical and practical in nature. Our primary research method is analysis of the literature and information we collected while managing the project “Implementation of a telemedicine model in the field of cardiology by ‘Polish Mother’s Memorial Hospital – Research Institute, 5/NMF/2066/00/62/2023/295, subsidized by the Norwegian Financial Mechanism and the state budget.”

First, we consider the theoretical aspects of the empowerment that emerges from new technologies, products and services, and then focus more on AI based technology for healthcare. Next, we propose an original classification of new e-health technologies according to their added value to the main healthcare stakeholders (patients, clinicians, and the healthcare system itself). Then we discuss some of the challenges faced by the implementation of new e-health technologies, products and services, and finally offer some conclusions.

Empowering new technologies, products and services — theoretical aspects

The process of harnessing new technologies and products in providing healthcare services is deeply embedded within the healthcare system as a whole. In particular, this involves healthcare providers and the services they provide, aimed at strengthening and improving the health of individuals and societies through disease prevention, early detection, treatment, and rehabilitation. Unfortunately, the healthcare situation in most European countries is expected to deteriorate due to population ageing, price increases, and the increasing complexity of healthcare technologies (Marmot et al., 2012). This entails a demand for a disproportionately high level of financial, material, and human resources.

Hence, meeting the health needs of citizens, and thus ensuring the availability of health-related services, depends primarily on a number of critical factors (Sobiech, 1990, p. 10): the volume of financial resources flowing into the health care system of a given country, the number and qualifications of medical staff, their spatial distribution and efficiency, the availability and application of medical technology and apparatus, and access to medical expertise (know-how) (Bukowska-Piestrzyńska, 2013, p. 66). With healthcare funding becoming more constrained every day, it is becoming increasingly important to look more closely at the processes of purchasing, distributing, and harnessing new technologies. Metrics such as stock coverage, urgent purchases, and non-standard purchases are particularly important in the management of healthcare facilities (Santosa et al., 2022, pp. 1-6).

The integration of AI systems in handling some aspects of communications during the diagnosis and treatment process could prove crucial for patient well-being and thus for the doctor-patient relationship. AI-based technologies, products and services will raise new questions about the limits of usability, cost-effectiveness and the ever-increasing cost of healthcare – above all, the issue of optimality in the creation of new products and services (Ayad et al., 2023).

A promising avenue of opportunities to apply new solutions to this challenge lies in digital healthcare, particularly through the implementation of artificial intelligence. These implementations involve a wide range of technologies. Digital tools are “fine-tuning” the capabilities of medical staff and facilitating a shift towards “consumerized” healthcare. This allows citizens to become more involved in managing their family’s healthcare. Digitalization, however, also brings risks, particularly if the challenges it presents are not adequately understood. If these wonderful technological advances are misused, they may expose society to the “dark side” of digital innovation. For example, if smart homes are not designed with the patient’s needs firmly in mind, but instead for the convenience of the “system,” they may give patients a prison-like experience, with robotic and sensor monitoring and control (Stahl & Cockelberg, 2016). Moreover, while AI can improve doctors’ technical skills to operate new technological solutions, it may also reduce their exposure to varied clinical experience (which in turn may make it more difficult to detect rare and atypical diseases).

Accountability in the health sector therefore raises critical questions: accountability for what, and to whom?

Ramachandran et al. (2015), for instance, reported that as many as 60% of patients with chronic diseases show interest in receiving healthcare via telephone, highlighting the a growing demand for e-health services in recent years. The management of chronic diseases is costly for individual patients and their families, as well as for the national health service. Therefore, there is great potential for developing new e-health technologies to improve the management of chronic diseases. Implementing these e-health technologies in healthcare systems can yield significant improvements and facilitate the integration of different aspects of healthcare (Hunt, 2015).

The responsible development of new technologies and bringing innovations to market require the active involvement of all stakeholders, from the very beginning of the innovation process. This helps to accurately identify the needs and priorities of innovation for society (Owen et al., 2012; Stahl et al., 2017). In health care, this means involving patients, carers and other stakeholders in the innovation process, anticipating the risks associated with new solutions, and ensuring that the solutions offered are implemented in a responsible and safe way, with the patient at the forefront (Pawelec, 2022).

New technologies and products in medicine mean smarter, safer, and more patient-centered healthcare services. By improving fit-for-purpose design, efficiency, and effectiveness, they help to reduce errors and shorten the length of hospital stays. The marketing management of healthcare services increasingly focuses on the individual purchaser – a shift emerging in many healthcare organizations thanks in part to new technologies. It should be recognized that there is an important difference between an “ordinary” customer, who can opt out of a purchase, and the patient-consumer of a healthcare service, who relies on medical consultations that directly affect his or her health or life (Białowolski et al., 2012).

Traditionally, patients have often been passive recipients at the endpoint of the service delivery system, rather than active stakeholders. One of the dangers of powerful new technologies is that patients may become even more marginalized, as healthcare is provided and delivered in an increasingly administrative, programmed manner. The doctor may also become more like a robot, carrying out programmed tasks in what could be described as “inhumane services.” The alternative approach places the patient at the center and puts technologies, products, and services at their disposal that allow them to design and control their healthcare based on their own needs. In this, it is important to shift away from seeing patients as a homogeneous group, instead categorizing them as distributed across a spectrum, including:

1)“Informed Users,” who are in a position to use technology with a better understanding;

2)“Engaged Users,” who play an activist role in the wider healthcare system, empowered by technology;

3)“Innovative Users,” who contribute their own ideas based on a deep understanding of healthcare problems.

Artificial Intelligence based technology for healthcare

Advancing safety in the organization of health technology use underscores that while consumers generally trust mature and complex technologies, advances in this area often obscure our understanding of the basics of how such technologies operate. We rely on them not because we are unaware of the potential risks, but because we believe that these risks are properly managed both by control procedures and by human oversight (by a physician). For example, we use increasingly advanced medicines without fear, often without fully grasping the complex clinical trial process that validates their safety. Similarly, we consent to robotic surgeries without fear that our health will be compromised (Turpin et. al., 2020).

The application of new technologies in healthcare should create new value, which may vary depending on the stakeholder. On the one hand, there are private companies that develop and market a technology, product or service, offering it to patients and hospitals in exchange for payment. This technology or product usually enables new functionality, a higher standard of healthcare, or a higher level of proficiency among doctors. On the other hand, there are hospitals that seek to generate maximum value, provided that does not exceed costs. Value is created when it increases revenue, enables more patients to access services, or allows diseases to be detected more quickly, improving quality of life. Value can also be derived from adhering to new global trends, such as the use of AI (Kulkov, 2021).

Artificial intelligence (AI) in healthcare involves the deployment of advanced mathematical algorithms and computer software to analyze complex medical data. The analysis of large datasets (“big data”) makes it possible to predict the probability of particular medical events. Programs that operate with the support of AI have the ability to learn autonomously (machine learning), by harnessing the collected data and the performed analyses.

Some of the first medical applications of artificial intelligence emerged in the field of radiology. AI systems are able to automatically assimilate X-ray data from databases containing thousands of images and then use this knowledge to assess a particular case and even evaluate a patient’s skeletal age (Jankowski, 2018). Physicians from the Department of Radiology, School of Medicine, Stanford University conducted a study in which 33 patients with nonspecific or common interstitial pneumonia were enrolled. Participants were selected by radiologists with 15-year experience. The same group of patients was qualified by n AI algorithm and two medics who had attended a one-year training course in the field. The AUC (area under the curve) obtained by the AI was 0.81, indicating its strong diagnostic ability. Interestingly, different diagnostic errors were found between the trained doctors and the algorithm, involving different patients. Such findings suggest the possibility of diminishing the risk associated with human error and the possibility of AI collaborating with physicians to further minimize incorrect diagnoses (Depeursinge et al, 2015).

Artificial intelligence in the field of radiology facilitates the search and analysis process for lesions, and is additionally able to detect the smallest lesions that may have been overlooked by experts (Arbabshirani et al., 2018). Recent studies also show that deep learning can adaptively improve image reconstruction during MRI examinations, leading to shorter scan times and increased quality of the obtained images, and thereby to a higher diagnostic value of the examination performed. Such improvements are particularly notable in images obtained with the FLAIR (fluid-attenuated inversion recovery) MRI sequence, which is commonly used for imaging specific brain structures (Hagiwara et. al., 2019).

A significant advantage of AI in healthcare is its potential to relieve doctors of many of their duties, allowing for more patients to be examined. An example of such an application is a study conducted on 154 diabetic patients, which investigated the efficacy of diabetic retinopathy detection based on ocular fundus examinations by the Remidio NM FOP 10, an AI-based device. Results showed concurrence in 85 cases between the device’s assessments and those of ophthalmologists. There were four instances where diabetic retinopathy lesions were identified and 81 cases with no lesions detected. Discrepancies arose in 21 cases, involving poor-quality images. The study revealed that the Remidio NM FOP 10 has a detection accuracy of 80.2%. Additionally, the device can be operated by a trained individual without an ophthalmologist’s direct involvement, potentially increasing the accessibility of preventive measures for individuals with diabetes (Kaczmarek, 2021). Deep learning holds promise for the automatic detection of diabetic retinopathy, offering consistency and precision due to its methodological approach and detailed analysis capabilities.

Another example of the application of intelligent algorithms is their use in supporting Czech medical unit doctors during appointments with specialists. Here, the AI system listens to the patient and the doctor during the appointment at the medical facility and then files a transcription of their dialog. After a few seconds, the AI generates a report from the visit, capturing the most important information provided by the patient as well as the diagnosis, recommendations, and treatment suggested by the doctor. The specialist can edit the report, add or remove specific information that the algorithm has generated. This process not only improves the visit but also allows for detailed review of previous visits, increasing the potential for seeing more patients and reducing their waiting time.

The methodologies described above have not yet been implemented in standard use. Many systems are still in the testing and observation stage in order to verify their correct functioning. Nevertheless, intelligent algorithms often yield results that are on par with, or sometimes even better than, those achieved by medical experts. The cooperation of AI systems and medical experts can minimize the risk of human error when making a diagnosis. Nevertheless, despite the attractive solutions that AI offers, there are several challenges that cannot be overlooked. It is crucial to collect, store and share medical data correctly, in accordance with current regulations. Intelligent algorithms are trained based on huge databases, with content of quality that can be difficult to access. The more information AI assimilates, the more precise the final results and diagnoses will be. Ultimately, it is crucial for results generated by AI to be verified and approved by experts in the relevant medical field (Char et al., 2018).

During the COVID-19 pandemic, new technology played an important role in allowing health services to function through increased Internet capabilities. Telemedicine, in particular, has seen significant advancements, catalyzing dynamic changes in the medical field. In addition, a variety of applications have been developed to facilitate the monitoring of patient health, as well as websites providing necessary information for those interested in such innovations. Some of the solutions are developing globally, making it possible not only to treat, but also to improve procedures or save patients’ lives, thus raising the standard of medical care in the healthcare sector.

A classification of new e-health technologies according to benefits to the main healthcare stakeholders

This section of the article explores the emerging importance of these and other cutting-edge technologies and products in healthcare. The multifaceted nature of such technologies, exhibiting high complexity, mean that a broad range of traditional health care stakeholders must be taken into consideration in the analysis of their implementation. We evaluate the benefits and added value for various groups, including medical institutions, physicians, nurses, medical technicians, distributors, e-health providers, e-health systems managers, and patients. The needs of these stakeholders vary, necessitating tailored solutions that cater to specific requirements.

While some stakeholders are involved in R&D on new technology and products, others function primarily as distributors or supporters, while still others are end-users. The literature on this topic offers various classifications of new technologies and products – notably including Herrmann et al.’s (2018) classification of over 400 different digital health projects and solutions. These were categorized according to their purpose into ten different types: software as a medical device, advanced analytics, artificial intelligence, cloud services, cybersecurity, interoperability, medical devices data systems, mobile medical applications, wireless technologies, and novel digital health solutions. However, this classification primarily focuses on products aimed at healthcare professionals, mitting those designed for the industry, the insurance companies and other stakeholders.

Severika and Ceranic (2020), in contrast, offer a broader classification of new technologies pertaining to healthcare professionals, industries, insurance companies and other stakeholders. Their proposed categories include: lifestyle intervention tools, diagnostics and prevention tools, research and development & production optimization tools, remote tracing tools, clinical decision support tools, telemedicine tools, and workflow tools. The World Health Organization (2018) emphasizes that digital and mobile technologies are increasingly crucial in supporting the needs of health systems.

From a market perspective, new technologies and products that are implemented in medical units should first and foremost add value for patients and physicians. (The economic value of new technologies cannot be overlooked, of course, but it is not the focus of this article.) From this perspective of the added value for clinicians, we propose to segment the new technologies and products into seven categories: wearable devices, mobile applications, remote monitoring systems, technologies based on artificial intelligence algorithms, telemedicine platforms, electronic health records, and 3D printing technologies. These categories underscore the specialized development and implementation needs within medicine and their potential to offer significant value to clinicians and patients. In many cases, technologies and products span multiple categories.

A detailed table of benefits for patients, doctors and the healthcare system is presented in Table 1.

We will illustrate our classification further by providing a few examples of the first product group in it: wearable devices, where the use of the product by the customer has medical applications. Several solutions will be discussed to illustrate their importance for patients.

HigoSense has created a device with 5 interchangeable tips that capture images and measurements of given areas of the patient’s body and is equipped with a module for listening to breathing, etc. This allows anyone, anywhere, to collect detailed medical data of similar quality to that obtained by a doctor during direct contact with the patient in the office or during a home visit. Data collection, delivery and sharing are carried out by means of the Higo app, which also supports medical interviews, management of patient’ history, scheduling examinations and communications with the doctor. (https://higosense.com/pl/produkt/) MedApp’s CarnaLife Holo solution employs a revolutionary technology for the three-dimensional visualization of diagnostic data to assist in the planning and execution of medical procedures. The HoloLens 2 goggles, developed by Microsoft, provide the ability to view 3D holograms of anatomical structures in a real-world environment. The doctor is able to interact with the holograms, such as rotating, scaling, moving and even entering inside the anatomical structures using gestures and voice commands. The entire process is carried out without the risk of compromising sterility and without the need to cooperate with additional technical staff. The goggles are an interactive screen that can be used for procedure planning and anywhere in the operating room, or even during the procedure. (https://medapp.pl/carnalife-holo/)

telDoc presents an innovative solution related to the Virtual Medical Assistant, which, during the patient’s contact with the medical facility, provides initial, ad hoc assistance and then refers the patient to the appropriate tests and specialist. During the visit, the doctor receives the test results and the initial medical history, which has been conducted by the Virtual Assistant via voice or chat, reducing the time spent on the administrative part of the visit. In addition, the company has created a Virtual Nurse Assistant, which regularly calls patients to ask how they are feeling, collects basic information about the patient’s vital functions, asks or reminds them to take prescribed medication, suggests contacting a medic in an alarming situation, and notifies relatives of the patient’s situation. (https://www.teldoc.eu/projekty)

Nestmedic’s Pregnabit medical device is designed for remote/hybrid KTG monitoring for women from 32 weeks of pregnancy, with indications for examination or hospitalization. The system consists of a mobile KTG device and a Medical Telemonitoring Centre service, where the test results are analyzed by medical experts. The use of specially developed medical algorithms aids doctors and midwives in monitoring and decision-making. (https://nestmedic.com/pregnabit/)

For patients, the main advantages of using modern technology of this sort include reduced access time to the doctor, increased intensity of treatment, a higher level of care resulting in better treatment outcomes, a better standard of living with chronic diseases with all-day health monitoring. Doctors, in turn, can optimize medical care and have faster, often immediate access to data in the form of epics or images. Artificial intelligence is also entering the operating theatre to assist the doctor, making procedures easier and reducing the number of repeat operations. For the healthcare system, however, it is the cost implications that are important. Estimation of the real costs of new implementations, reduction of unit costs with an increase in the number of interventions, possibility of detection of new diseases (including rare diseases).

Challenges for the implementation of new e-health technologies, products and services

Some of the key challenges include:

• Data accuracy and reliability: The accuracy and reliability of data collected by medical devices incorporating intelligent technologies is critical to the effective and efficient management of healthcare services. Patient accountability must ensure the timeliness, accuracy, relevance, appropriateness and consistency of measurements provided by AI devices (Etemadi & Khashei, 2020).
• Data security and privacy: Smart technologies generate and transmit sensitive health data, raising concerns about data security and privacy. Protecting personal data and health information from unauthorized access, breaches and misuse is paramount in the development of cyberhealth. Security measures must be implemented to protect patient data. These must be in line with data protection regulations and encryption techniques (Fatima & Colombo-Palacios, 2018).
• Integration: Integration of different smart healthcare technologies and systems is essential for seamless data exchange and collaboration (Shah et al., 2021). However, the challenges of integrating different devices, digital platforms and electronic health record systems can hinder effective data sharing and communication between patients and their doctors or healthcare organizations. One solution to this involves standardization.
• Adaptability: Devices, systems and platforms must be adapted to the type of patient, the level of health care reference, and the level of technological development of the organization implementing the new solutions (Chronaki et al., 2004).
• User acceptance and involvement: The success of the implementation of intelligent technologies depends on user acceptance and involvement. Patients need to be motivated to make consistent use of these technologies and to take an active part in their own care (Jankowska-Polańska et al., 2014). Clinicians need to follow protocols and monitor the activity and accuracy of patients’ use of the technologies. Overcoming barriers such as technology familiarity, usability concerns and resistance to change is key to widespread use of digital technologies.
• Legislation and regulation: Regulatory changes need to keep pace with the rapid development of smart technologies. Legislation needs to be put in place to ensure the safe, effective and ethical use of technology in healthcare, particularly artificial intelligence. In addition, reimbursement policies should take into account the value and cost-effectiveness of smart technologies, as this may have an impact on their availability and adoption (Orędziak, 2018).
• Accessibility: Ensuring equal access to smart technologies is essential to address inequalities in healthcare. Price, usability and accessibility of new technologies need to be considered (Bokolo, 2021).
• Validation: Rigorous clinical trials are needed for smart technologies, especially those based on artificial intelligence. Rigorous scientific research, randomized controlled trials and analysis of real-world data are needed to demonstrate the clinical value and safety of using smart technologies in healthcare. The margin for error in the use of new technologies in medicine, for example, is very small or may not exist at all. This has to do not only with protecting health, but also with protecting life (ICH Guidelines, 2016).
• Cost-effectiveness: Cost-effectiveness is an important factor in the introduction of new medical technologies. However, its role in improving quality of life and standards of care should also be emphasized (Trzmielak, 2014).

Conclusions

In the coming years, medical professionals can expect to be able to access more advanced and highly specialized tools will be available to medical professionals, increasing their competence and capabilities. Continued advances in artificial intelligence (AI) research in medicine are also likely contribute to the thorough validation of both existing and future systems, which could lead to their widespread adoption. However, the integration of advanced technologies, particularly AI, into healthcare practices represents a significant paradigm shift towards improving patient care and enhancing healthcare delivery systems. Throughout this article, we have explored the multifaceted impact of these technologies, demonstrating how they not only augment clinical practices but also empower patients by offering more personalized and accessible healthcare solutions. The bibliographic analysis and examples discussed herein offer a certain overview of the practical applications and theoretical implications of AI in healthcare, emphasizing the dual benefit to both clinicians and patients.

Our findings illustrate that AI-driven tools can significantly relieve the workload of healthcare professionals, allowing for the expansion of healthcare services and specializations that cater more directly to patient needs. This not only improves the efficiency of healthcare delivery but also enhances the quality of patient care by enabling more accurate diagnoses and tailored treatment plans. The classification of new e-health technologies that we have proposed herein may serve as a clear framework for understanding the various ways in which these innovations can be implemented to maximize their benefits across different sectors of the healthcare industry.

Moving forward, the continuous advancement and deployment of these technologies necessitates a committed approach to research and validation, ensuring that they meet the highest standards of efficacy and safety. The collaborative acceptance by healthcare professionals and patients is crucial for these technological innovations to be successfully integrated into everyday medical practices. Such acceptance is dependent on clear demonstrations of the improvements these technologies bring to patient outcomes and healthcare workflows.

In conclusion, the successful deployment of AI and other innovative technologies in medicine requires ongoing analysis and adaptation to the evolving needs of the healthcare sector. By aligning these technological advancements with the real-world requirements of both healthcare providers and recipients, we can ensure that they lead to more effective, efficient, and empathetic healthcare services. The promising developments discussed in this article not only highlight the current achievements but also pave the way for future innovations that will continue to transform healthcare.

Aknowledgements

The article was funded by the project entitled “Implementation of a telemedicine model in the field of cardiology by Polish Mother’s Memorial Hospital – Research Institute subsidized by the Norwegian Financial Mechanism and the state budget,” under contract no. 5/NMF/2066/00/62/2023/295.

References

Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018). Advanced machine learning in action: Identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Medicine, 1, 9. https://doi.org/10.1038/s41746-017-0015-z

Auwal, F. I., Copeland, C., Clark, E. J., Naraynassamy, C., & McClelland, G. R. (2023, September). A systematic review of models of patient engagement in the development and life cycle management of medicines. Drug Discovery Today, 28(9), 103702. https://doi.org/10.1016/j.drudis.2023.103702

Ayad, N., Schwendicke, F., Krois, J., S. van den Bosch, Bergé, S., Bohner, L., Hanisch, M., & Vinayahalingam, S. (2023, December). Patients’ perspectives on the use of artificial intelligence in dentistry: A regional survey. Head & Face Medicine, 19, Article 23. https://doi.org/10.1186/s13005-023-00368-z

Białowolski, P., Grabowska, I., Kotowska, I., Strzelecki, P. &Węziak-Białowolska, D. (2012). Social Diagnosis 2011: The objective and subjective quality of life in Poland. Ed. Czapiński, J., & Panek, T. The Council for Social Monitoring Warsaw.

Bokolo, A. Jnr. (2020). Application of telemedicine and eHealth technology for clinical services in response to the COVID-19 pandemic. Health and Technology, 11, 359–366. https://doi.org/10.1007/s12553-020-00516-4

Bukowska-Piestrzyńska, A. (2013). Zmiany w systemie opieki zdrowotnej a dostępność usług stomatologicznych w Polsce w XXI wieku [Changes in the healthcare system and the availability of dental services in Poland in the 21st century]. Vol. XIV(10), pt. I. [in Polish]

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — Addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229

Chronaki, C. E., Lelis, P., Chiarugi, F., Trypakis, D., Moumouris, K., Stavrakis, H., Kavlentakis, G., Stathiakis, N., Tsiknakis, M., & Orphanoudakis, S. C. (2004). An open eHealth platform for health management using adaptable service profiles. International Congress Series, 1268. https://doi.org/10.1016/j.ics.2004.03.201

Depeursinge, A., Chin, A. S., Leung, A. N., Terrone, D., Bristow, M., Rosen, G., & Rubin, D. L. (2015). Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution computed tomography. Investigative Radiology 50(4), 261–267. https://doi.org/10.1097/RLI. 0000000000000127

Etemadi, S., & Khashei, M. (2020). Data accuracy and reliability. Computers in Biology and Medicine, 141. https://doi.org/10.1016/j.compbiomed.2021.105138

Fatima, A., & Colomo-Palacios, R. (2018). Security aspects in healthcare information systems: A systematic mapping. Procedia Computer Science, 138, 12–19. https://doi.org/10.1016/j.procs.2018.10.003

Hagiwara, A., Otsuka, Y., Hori, M., Tachibana, Y., Yokoyama, K., Fujita, S., Andica, C., Kamagata, K., Irie, R., Koshino, S., Maekawa, T., Chougar, L., Wada, A., Takemura, M., Hattori, N., & Aoki, S. (2019). Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation. American Journal of Neuroradiology, 40(2), 224–230.

Herrmann, M., Boehme, P., Mondritzki, T., Ehlers, J. P., Kavadias, S., Truebel, H. (2018). Digital transformation and disruption of the health care sector: Internet-based observational study. Journal of Medical Internet Research, 20, 104–112. https://doi.org/10.2196/jmir.9498 Hunt, C. W. (2015). Technology and diabetes self-management: An integrative review. World Journal of Diabetes, 6(2), 225–33. https://doi.org/10.4239/wjd.v6.i2.225

ICH Guidelines. (2016). Harmonized ICH Guidelines, Integrated Addendum to ICH E6(R1): Good Clinical Practice, E6(R2), version 4. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). https://database.ich.org/sites/default/files/E6_R2_Addendum.pdf

Jankowski M., (2018). Sztuczna inteligencja jako narzędzie wspomagające proces diagnostyczno-terapeutyczny, Menadżer Zdrowia, październik – listopad 8-9, 66–67.

Jankowska-Polańska, B., Ilko, A., & Wleklik, M. (2014). Wpływ akceptacji choroby na jakość życia chorych z nadciśnieniem tętniczym [Influence of the acceptance of the disease on quality of life of patients with hypertension]. Nadciśnienie Tętnicze, 18(3), 143–150. [in Polish]

Kaczmarek, E. (2021). Sztuczna inteligencja – pomoc w wykryciu retinopatii cukrzycowej [Artificial Intelligence – Assistance in Detecting Diabetic Retinopathy]. Optyka, 6(73), 48–49. [in Polish]

Kulkov, I. (2023). Next-generation business models for artificial intelligence start-ups in the healthcare. International Journal of Entrepreneurial Behavior & Research, 29(4).

Marmot M., Allen J., Bell R., Bloomer E., Goldblatt P., (2012). WHO European review of social determinants of health and the health divide, Lancet 2012; 380, 1011–29. 10.1016/S0140-6736(12)61228-8

Orędziak, B. (2018). Telemedycyna a konstytucyjne prawo do opieki zdrowotnej w kontekście wykluczenia cyfrowego [Telemedicine and the Constitutional Right to Healthcare in the Context of Digital Exclusion]. Zeszyty Prawnicze, 18(1). https://doi.org/10.21697/zp.2018.18.1.06 [in Polish]

Owen, R., Macnaghten, Ph., Stilgoe, J., (2012). Responsible Research and Innovation: From Science in Society to Science for Society, with Society. Science and Public Policy, 39(6), 751–760. https://doi.org/10.1093/scipol/scs093.

Pawelec, G. (2022). Rola nowych technologii w podnoszeniu jakości usług zdrowotnych w dobie pandemii COVID-19. [The role of new technologies in improving the quality of health services in the era of the COVID-19 pandemic]. Marketing i Rynek, XXIX(2). https://doi.org/10.33226/1231-7853.2022.2.2 [in Polish]

Ramachandran, N., Srinivasan, M., Thekkur, P., Johnson, P., Chinnakali, P., & Naik, B. N. (2015). Mobile phone usage and willingness to receive health-related information among patients attending a chronic disease clinic in rural Puducherry, India. Journal of Diabetes Science and Technology, 9(6), 1350–1. https://doi.org/10.1177/1932296815599005

Santosa, E. S., Fariab, S. C. M., Carvalhob, M. I. S., Molc, M. P. G., Silvab, M. N., & Silva, K. R. (2022, December). Management of unused healthcare materials and medicines discarded in a Brazilian hospital from 2015 to 2019. Cleaner Waste Systems, 3. https://doi.org/10.1016/j.clwas.2022.100046

Severika, B., & Ceranic, K. (2020, April). Digital Health Classification Systems. Statistics & Science. https://www.5-ht.com/en/media/blog/digital-health-classification-systems

Stahl, B.C. and Coeckelbergh, M. (2016) Ethics of Healthcare Robotics: Towards Responsible Research and Innovation. Robotics and Autonomous Systems, 86, 152–161. https://doi.org/10.1016/j.robot.2016.08.018

Shah, J. L., Bhat, H. F., & Khan, A. I. (2021). Integration of Cloud and IoT for smart e-healthcare. In V.E. Balas, S. Pal (Eds.). Healthcare Paradigms in the Internet of Things Ecosystem (pp. 101–136). Elsevier. https://doi.org/10.1016/B978-0-12-819664-9.00006-5.

Stahl, S., Morrissette, D. A., Faedda, G. L., Fava, M., Goldberg, J., Keck, P., Lee, Y., Malhi, G., Marangoni, C., Mcelroy, S., Ostacher, M., Rosenblat, J., Solé, E. Suppes, T. Takeshima, M. Thase, M., Vieta, E., Young, A. Zimmerman, M. McIntyre, R. (2017). Guidelines for the recognition and management of mixed depression. CNS Spectrums. 22(2), 203–219. https://doi.org/10.1017/S1092852917000165.

Sobiech, J. (1990). Warunki wyboru ekonomiczno-finansowych mechanizmów kierowania opieką zdrowotną. Zeszyty Naukowe, 109. Wydawnictwo Akademii Ekonomicznej w Poznaniu.

Trzmielak, D. M. (2013). Komercjalizacja wiedzy i technologii – determinanty i strategia [Commercialization of knowledge and technology – determinants and strategy]. Łódź: Wydawnictwa Uniwersytetu Łódzkiego. [in Polish]

Turpin, R., Hoefer, E., Lewelling, J., & Baird, P. (2020). Machine Learning AI in Medical Devices, Adapting Regulatory Frameworks and Standards to Ensure Safety and Performance. AAMI, BSI. https://www.medical-device-regulation.eu/wp-content/uploads/2020/09/machine_learning_ai_in_medical_devices.pdf

Ullah, M., Hamayun, S., Wahab, A., Khan, S. U., Rehman, M. U., Haq, Z. U., Rehman, K. U., Ullah, A., Mehreen, A., Awan, U. A., Qayum, M., & Naeem, M. (2023, November). Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective. Current Problems in Cardiology, 48. https://doi.org/10.1016/j.cpcardiol.2023.101922

Verbraecken, J. (2021, September). Telemedicine in Sleep-Disordered Breathing: Expanding the Horizons. Sleep Medicine Clinics, 16(3), 418–445.

World Health Organization. (2018). Classification of Digital Health Interventions v 1.0: A shared language to describe the uses of digital technology for health. https://apps.who.int/iris/bitstream/handle/10665/260480/WHO-RHR-18.06-eng.pdf

Online sources:

https://higosense.com/pl/produkt/
https://medapp.pl/carnalife-holo/
https://nestmedic.com/pregnabit/
https://www.teldoc.eu/projekty

Digital transformation is a critical phenomenon in today’s global economy. Through its activities, it is forcing customer orientation and a focus on customer needs and expectations. Marketing, also undergoing profound transformations, plays a considerable role in the transformation processes (Mazurek, 2019). In the case of marketing medical services, it is essential to point out its social-creative role in the context of the digitalisation of the health sector. Marketing is now more strongly associated with creating value for the general public. The manifestation of this is the change in the approach to value in marketing, which is increasingly associated with the resultant customer experience, thus personalising it (Baran, 2013).

The phenomenon of co-creating value with the customer also manifests itself in the healthcare market in the context of digital innovation. E-health platforms and tools respond to the needs and expectations of the main stakeholders in the healthcare system-patients. The coronavirus pandemic has significantly accelerated the process of digital transformation (Baudier et al., 2022; Li, 2021; Park et al., 2022; Pauzi & Juhari, 2020; Schiliro, 2020, 2021), including the health sector (Marx & Padmanabhan, 2020; Wahab & Saad, 2022), which has translated into an increase in innovative technical and technological solutions in medical records, medical services and preventive health care. In reaching the audience for these solutions, marketing communication is essential. The market for medical services is changing, and the marketing product is evolving. The digital maturity of patient customers is increasing, and the requirements for quality medical services are changing. The synergy of medicine, technology and telecommunications should translate into new medical services available to all. The role of marketing here is vast-from informing patients about new products/services to allowing them to learn about new features to getting feedback on digital solutions.

The article aims to present issues on digital transformation in the health sector with attention to its marketing dimension.

Research Methodology

The author used the desk research method. He reviewed the literature treating digital transformation in health care in terms of marketing. The bibliography includes 82 items, including scientific articles, reports, books, chapters from monographs and electronic sources-mainly from 2020–2022. The following scientific databases were used in the desk research analysis: Google Scholar, ResearchGate, Taylor and Francis Online and ScienceDirect. In searching the literature in the mentioned databases, the author used the following combination of words using Boolean operators (AND, OR): 'marketing’ AND 'digital transformation’ AND (’healthcare’ OR 'health care’ OR 'health service’ OR 'healthcare sector’ OR 'health sector’ OR 'healthcare industry’ OR 'health industry’ OR 'health industry’ OR 'medicine’). Searches supplemented the collected literature in the databases above for the following keywords: 'blockchain’, 'value’, 'co-creation’, '4P medicine’, 'artificial intelligence’ and 'machine learning’. The aforementioned scientific databases were used because of the possibility of collecting literature for this article about its purpose.

Digital Transformation-Essence and Significance

Digital transformation looks and runs differently for every organisation or company. Hence it is not easy to point to a single universal definition. At the same time, it signifies a cultural change manifested in constant questioning of the status quo, frequent experimentation and dealing with failure. The process of digital transformation can sometimes also mean moving away from existing, proven business processes to relatively new, still-developing practices (Nius, 2022). Therefore, digital transformation can be understood as a change in an organisation’s people, processes, technology and data components, creating an organisation’s evolution (McCarthy et al., 2022).

In general, digital transformation refers to a process aimed at improving an entity by inducing significant changes in its operation through the interplay of information, computing, communication and connectivity technologies (Kraus et al., 2021; Vial, 2019). Digital transformation introduces strategy — and customer-focussed changes through innovative information and communication technologies. This process aims to implement improved or new processes in modern organisations (Pihir et al., 2019). Thus, the digital transformation process represents the innovative use of digital technologies to provide better offerings to customers, design efficient operations or create new revenue streams for the business. The technologies used in the transformation process may not be new, but their innovative combination here matters. Hence, strategy, not just technology, is at the core of digital transformation (Chawla & Goyal, 2022; Kane et al., 2015; Vallero, 2019).

The transformation process is aided by digital platforms that create a socio-technical environment that mediates interactions between actors and uses data streams to create value-individual and community value by inducing business users and suppliers to innovate their existing business models (Pietronudo et al., 2022). As mentioned, digital platforms create value. This situation happens in two ways. First, they facilitate transactions and offer technological building blocks to create new products and services (Darius & Maticiuc, 2022; Shan & John, 2022). Transaction facilitation platforms are exchange platforms that create value for at least two different types of users who can benefit from interacting with each other. In contrast, platforms that offer technological building blocks aim to orchestrate industry innovation by co-creating value with external general partners (Hermes et al., 2020).

The importance of digital transformation is immense because it first forces companies to rethink the role and values that guide their business models. Second, it represents a significant change in companies’ fundamental pattern of value creation. Third, the transformation process causes a fundamental change in how an organisation thinks and uses legacy systems and tools to reposition part or all of the organisation in terms of value creation (Mugge et al., 2020). Finally, digital transformation helps organisations engage customers in the conception and product development phases, supporting the co-creation (co-innovation) process, which increases customer centricity (Hauke-Lopes et al., 2022; Imran et al., 2021). As one of the critical elements of digital transformation, customer centricity manifests itself in anticipating and shaping customer expectations, managing the customer journey and creating customer communities that communicate market value. Customer centricity focuses on empathy mapping to gain the benefits of reaching the right stakeholders (Pileggi, 2021; Tomièić-Pupek et al., 2021).

The importance of digital transformation should also be considered in reducing the impact of the COVID-19 pandemic, as it forced the rapid and unexpected implementation of digital technologies into corporations’ business models and organisational structures. In general, digital transformation has influenced socio-economic recovery, that is to say economic growth, health care and income inequality (Mohamed, 2022), while its nature and pace were determined by artificial intelligence (AI), changing customer preferences and global crises such as the coronavirus pandemic (McCausland, 2021). In summary, digital transformation is a comprehensive, holistic concept that enables an overhaul of core processes and changes culture, organisation, relationships and business models. It enables both the delivery of sustainable results in the long term and the value creation for people and organisations. Undoubtedly, the COVID-19 pandemic has awakened and revolutionised how we understand digitality and demonstrated the strategic importance of its transformation (Gabryelczyk, 2020).

Digitisation of the Health Sector-Security and Stakeholder Benefits

Digital transformation in health care is essential in societies’ transition to a post-industrial, knowledge-based economy (Garcia-Perez et al., 2022). Digital technology is being deployed in health care to support and improve its traditional operations and create new value propositions for end users of health services (Ghosh et al., 2022). For patients, the digitisation of the health sector enables them to operate in a comprehensive multi-channel environment giving broad access to medical information, education and health monitoring through AI and machine learning (ML) (Kraus et al., 2021). AI technologies could address unwarranted disparities in medical care, reduce medical errors, reduce healthcare inequities, and reduce waste and low-quality, low-value care (Hashiguchi et al., 2022). ML, in turn, contributes to observing sick patients, analysing disease patterns, and diagnosing and prescribing medication. ML helps provide patient-centred care, make therapeutic decisions, and detect sepsis and high-risk emergencies in patients (Quazi, 2022). Deployment of AI systems in health care can further optimise healthcare resources, facilitate a better patient experience, reduce per capita costs and increase the satisfaction of medical professionals and patients (Dicuonzo et al., 2022).

The creation and co-creation of value for patients are mediated by digital platforms that manage the public health ecosystem. This process is taking place in collaboration with a much more comprehensive range of partners and stakeholders than was previously the case (Hermes et al., 2020). Therefore, the digitisation of health care should ensure a seamless but, at the same time, secure and protected exchange of data, such as medical data, interoperability and patient-generated data. According to Jahankhani & Kendzierskyj (2019), blockchain is a mechanism that can ensure data security and privacy in the health sector’s digitisation. Blockchain is a computerised, distributed database of records, transactions and digital events made and shared among connected users (Rejeb & Rejeb, 2020). Another definition states that blockchain is a digital, decentralised, distributed ledger that records and adds transactions chronologically to create permanent and tamper-proof records (Jain & Jain, 2022; Treiblmaier, 2018). Blockchain is shared by a network of computers, allowing customers to securely exchange financial information with suppliers without needing a third party, such as a bank (Peres et al., 2022; Swan, 2015; Yli-Huumo et al., 2016; Zheng & Yu, 2016).

In health care, a blockchain is an effective tool in preventing data breaches, improving the accuracy of medical records, reducing costs (Reddy, 2022), biomedical research, health data analytics, education, health insurance claims, remote patient monitoring or finally in pharmaceutical supply chains (Elangovan et al., 2022). Blockchain technology represents the potential for value creation in health care through compliance achievements, reduction of errors and fraud, better governance, collaborative value creation among entities, intelligent contracts, technology to support charity, greater trust, and integrity. The elements above suggest that blockchain fosters multiple tangible and intangible value creation in the study area for individuals and organisations across the health ecosystem (Spano et al., 2021). Finally, blockchain technology is crucial to developing a platform to manage the COVID-19 pandemic effectively-now and in the future. Currently, the most significant difficulty facing most nations is the lack of a precise mechanism for detecting new infections and predicting their risk. Moreover, such features of blockchain technology as decentralisation, transparency and immutability can help manage a pandemic by detecting infection outbreaks early, speeding up drug distribution and protecting users’ privacy throughout the treatment process (Jafri & Singh, 2022).

Technological advances in medicine and, consequently, the digital transformation of the health sector must be accompanied by parallel advances in promoting patient and public participation throughout the process. To this end, perceptions of personalised medicine (4P) and assessments of its value and risks must be better understood. The 4Ps of personalised, preventive (preemptive), predictive and participatory medicine help refocus health services from a focus on treating established diseases to maintaining health and well-being (George et al., 2022; Horne, 2017). It represents a new paradigm of holistic and integrative patient management practices with equal participation of the patient and physician in holistic health care, combining precision medicine and medical experience across the patient’s lifetime (Bartold & Ivanovski, 2022). Personalised medicine is otherwise known in the literature as precision medicine (Duffy, 2016; Hussain et al., 2021; Sharma et al., 2022; Verma et al., 2022), stratified medicine (Jorgensen, 2019; Olechno, 2016; Ruppert et al., 2016), individualised medicine (Rahimi, 2016), customised medicine (Miller & Tucker, 2017; Sarvan & Nori, 2021), molecular medicine (Ziv et al., 2016) or genomic medicine (Roden & Tyndale, 2013), which corresponds to the 4P elements listed above (Slim et al., 2021).

Digital innovations in health care provide solutions to unmet health needs. Hence they can take the form of new processes, therapies, tools, medical procedures or innovative approaches to education, training, management and procurement. Digital transformation emphasises the patient experience in delivering and improving health services to discover and identify the needs. Accordingly, healthcare users should be actively engaged in innovation to manage their health consciously. Patients are now co-producers of health services, and thanks to digital technologies, they can play a more active role in decision-making and innovation activities. Healthcare providers who continuously monitor, digitise and analyse patient data can better understand the desires and needs of healthcare users and tailor offerings and care to provide quality services (Santarsiero et al., 2022).

Practical Aspects of Implementing Digital Technologies in Health Care

The digitisation process in the health sector involves using innovative digital tools. They could improve the level of service to stakeholders and streamline the patient registration process. In addition, these IT solutions can direct patients’ movement and monitor their health inwards. Using the latest digital technology to monitor such patients helps improve their quality of life and enables attending physicians to intervene immediately in life-threatening conditions.

A critical application of AI in medicine is using algorithms to aid diagnosis in various fields-such as radiology and cardiology. The advantage of AI is that the sensitivity and specificity of the diagnosis are more significant by up to several percent than the diagnosis made by a doctor or team of medical professionals. In addition, the vast potential lies in solutions that support diagnosis at the early stages of the disease, such as cancer or cardiovascular disease (Żochowska, 2022). AI-based technology can reduce preparation times for head, neck and prostate cancers, for example, by as much as 90%, meaning that waiting times for potentially life-saving radiation therapy treatment to begin can be drastically reduced. Critical future AI applications include immunomics, synthetic biology and drug discovery. These will find revolutionary use in cancer, neurological and rare disease space, personalising the patient’s care experience (Bajwa et al., 2021). Studies further indicate that AI-based systems can outperform dermatologists in correctly classifying suspicious skin lesions. The advantage of AI systems stems from learning (more and faster) from successive cases and exposure to multiple cases per minute, which is far superior to cases evaluated by a clinician. AI-based decision-making approaches also bring applications in situations of disagreement between experts-for example, the identification of pulmonary tuberculosis on chest radiographs (Amisha et al., 2019).

Further practical applications of AI in the medical industry are support for telemedicine, body composition analysis, prediction of patient response to treatment, and democratisation of prevention (Żochowska, 2022). A key element in the development of e-health is telemonitoring of implantable devices. This situation is necessary to guarantee continuous, safe, highquality health care for patients with implantable devices. These devices are new-generation devices that, through Bluetooth technology, allow direct transmission of data from the implantable device to the patient’s configured smartphone, from which, with the dedicated application, data are transmitted to the provider through a server provided by the device manufacturer. In this case, it is not necessary to use additional transmission devices (Telemedyczna Grupa Robocza, 2021).

It is important to note that advances in wireless technology have created opportunities to provide on-demand healthcare services through healthtracking applications. Such innovative solutions have enabled a new form of healthcare delivery through remote interactions, available anywhere, anytime. Such services are essential for regions with underdeveloped infrastructure and places that lack specialists. They help reduce costs and prevent unnecessary exposure to infectious diseases at the clinic. Telehealth technology is also essential in developing countries (Bohr & Memarzadeh, 2020). In addition, it passes the test in monitoring and observing elderly and disabled patients who live far from healthcare centres (Finco et al., 2023).

In conclusion, the practical aspects of implementing innovative digital solutions into the day-to-day operations of healthcare entities can be an essential source of building a healthcare entity’s competitive advantage in the healthcare market. On the global scale, meanwhile, AI can become a vital tool for improving health equality around the world.

Generations X and Y in the Digitisation of Health Care and the Dimension of Marketing

Today’s medical market requires a change in approach to the services offered, which should be personalised and accessible on the patient’s mobile devices. The marketing dimension is critical here-namely, the design and communication of relevant medical content and digital applications that meet the expectations of demanding patient-clients. Appropriate patientcentred (patient-centric) activities should be carried out to achieve a positive patient experience. Patient experience management is now a sine qua non and a considerable challenge for the digitisation of health care.

Patient experience is the interaction between the patient and the healthcare provider, integral to healthcare quality. In general, the quality of health care services is determined by easy access to health information, timely appointments and good communication with providers, among other factors. In order to provide patient-centred care, healthcare providers need to understand the patient experience. Evaluation of the patient experience and other elements, such as the safety and effectiveness of care, constitute the only means for the creation of a complete picture of healthcare quality. (Daffodil Software, n.d.). A precise understanding of the patient experience will benefit the healthcare industry and society in many ways, including, among other things, the establishment of tailored and personalised health care (Oben, 2020).

By 2025, generations X and Y will make up about 75% of Polish society (Kozak et al., 2022); hence, there is a need to align with these generations suitable activities and marketing messages that are related to the new digital health services resulting from the ongoing digital transformation of the health sector.

Generation X consists of those born between 1961 and 1983, the communist generation, the Nothing for Real generation, the White Collar generation, the Blue Collar generation (Czerska, 2016), MTV Generation and Gen-Xers (Berk, 2013). People of this generation value work and are even attached to one employer-loyal to it. They often prioritise work responsibilities over leisure despite rejecting the 'rat race’. On the other hand, Generation X are unstable, insecure people, full of doubts-including about themselves. They are searching for the meaning of their existence and are characterised by colourlessness. When handling new technologies, this is not a problem for them (Czerska, 2016). Generation Y, or the Millennium generation, the next generation, the digital generation, the generation of flip-flops and iPods (Bilińska-Reformat & Stefańska, 2016), tech-savvy consumers (Dewalska-Opitek, 2017), generation me (Spinney, 2012), generation WHY, gaming generation, net generation, Facebook generation or iGeneration (Kelan & Lehnert, 2009), are people between 1984 and 1995. They are shrewd, overconfident and even brash at times. They are characterised by believing in their uniqueness and are intensely narcissistic.

On the other hand, generation Y cannot make decisions independently. They expect constant attention, and are also impatient as well as welleducated, with excessive expectations. Compared to Generation X, they prefer flexible employment and freedom of action, which translates into an average working time with one employer of 2 years. Millennials do not respect their bosses, treating work as an avenue for personal development. They are eager to work in teams and are open to new challenges. When it comes to new technologies, they actively use them (Czerska, 2016).

Given the above characteristics of both generations X and Y, which are open to new technological solutions, patients should be included in constructing complex health ecosystems designed to meet their needs.

One of the biggest challenges of digital transformation in the healthcare field is the final measurement of the effectiveness of the personalisation of healthcare services and the impact of patient involvement in the treatment process. Given the attitude of generations X and Y towards work and employer, it is necessary to be flexible in the design of health services and focus, on the one hand, on brand loyalty and attachment, and the other hand, on freedom of choice and frequent change of decisions. Undoubtedly, patients now actively using health services are informed and engaged. They play an active role in the decision-making process in the context of innovative health tools and services: they search for information on preventive health care, health monitoring, specialist doctors, clinics and outpatient clinics, and appointment enrolment, after which they actively use these services and consume the previously searched health services. Thus, such patients can be considered prosumers of e-health services and tools (Wolny, 2013).

According to Deloitte Digital’s 2022 report, two post-pandemic patient archetypes in Poland represent their health and digital behaviour. The first group is the so-called Traditional Patients-rarely using digital channels, using up to four apps. This group represents nearly 43% of the population. The second one is the so-called Phygital Patients-frequent users of digital channels but also interested in traditional channels. They make up more than 17% of the population. The Phygital Patient of the future expects the same level of service in all available channels, which complement each other (Deloitte Digital, 2022). This cross-channel model challenges marketing and managers to make each communication channel work smoothly and meet patient expectations, as the new standard of medical care is becoming an offering that spans multiple touchpoints across traditional and digital channels. Concerning Generation Y, Phygital patients are mainly women of the millennial generation working in large and medium-sized companies. Moreover, it is primarily to this target group that marketing messages about innovative digital solutions should be personalised, as these people are more likely to actively take care of their health when encouraged to do so by digital solutions. Besides, they need convenient access to specialists and multiple functionalities within a single application, such as automatic appointment reminders or the ability to share information about their health with a doctor (Okoniewska, 2022).

Limitations

The article is characterised by several limitations. Firstly, only articles indexed in databases were used in the analysis: Google Scholar, ResearchGate, Taylor and Francis Online and ScienceDirect, which may have resulted in the omission of valuable items on the issues under consideration. Secondly, the literature search in the databases above used a given combination of words using Boolean operators, which could have narrowed the search for relevant items. Selected industry reports and electronic sources were used for the issues under consideration to complete the analysis.

Conclusions and Practical Implications

The goal of the article, which was to present issues on digital transformation in health care and its marketing dimension, has been achieved.

The author’s findings, through a review of the literature on the subject, indicate that digital transformation in health care creates new business opportunities to solve various problems in medical practice and enables the creation of values that determine the quality of medical services. Marketing activities become helpful and even indispensable in this process.

The coronavirus pandemic has become a gas pedal, so to speak, of digital health solutions. In the health industry, which until recently was considered traditional or even conservative, the Internet is now the critical tool for learning about products and services, using them, and building opinions about healthcare providers and medical professionals. In parallel with the transformation process of the health industry, a marketing transformation process is taking place. The most critical activities in this process are patient relationship management, patient experience management, patient engagement management, patient-centred marketing, hyper-personalisation of the message/message, business to human (B2H) approach, ML and AI. In addition to the activities above, blockchain technology in the medical sector is also a new and growing phenomenon.

Several practical implications have been developed based on the analysed content of scientific and industry items. First, the transition to remote health care, if only in prevention or preventive care, requires patients to change their mentality and be open to change. Second, the availability of digital tools is impossible without marketing-through promotional campaigns for new e-solutions and presentations of mobile health products. Third, introducing innovative digital tools requires building and using new, complementary communication channels (crosschannel model) between stakeholders in the health market. Finally, blockchain technology could transform existing healthcare management into a more efficient, secure one, potentially creating value across the health ecosystem.

References

1. Amisha, F., Malik, P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328–2331. https://doi.org/10.4103/JFMPC.JFMPC_440_19
2. Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188. https://doi.org/10.7861/FHJ.2021–0095
3. Baran, G. (2013). Marketing współtworzenia wartości z klientem. Społecznotwórcza rola marketingu w procesie strukturacji interakcyjnego środowiska doświadczeń. Instytut Spraw Publicznych Uniwersytetu Jagiellońskiego.
4. Bartold, P. M., & Ivanovski, S. (2022). P4 Medicine as a model for precision periodontal care. Clinical Oral Investigations, 26(9), 5517–5533. https://doi.org/10.1007/S00784-022-04469-Y/FIGURES/7
5. Baudier, P., Kondrateva, G., Ammi, C., Chang, V., & Schiavone, F. (2022). Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs. Technovation, 102547. https://doi.org/10.1016/J.TECHNOVATION.2022.102547
6. Berk, R. A. (2013). Multigenerational diversity in the academic workplace: Implications for practice. Journal of Higher Education Management, 28(1), 10–23.
7. Bilińska-Reformat, K., & Stefańska, M. (2016). Young consumers’ behaviours in retail market and their impact on activities of retail chains. Poslovna Izvrsnost, 10(2), 123–134.
8. Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In A. Bohr & K. Memarzadeh (Eds.), Artificial Intelligence in Healthcare (pp. 25–60). Academic Press. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
9. Chawla, R. N., & Goyal, P. (2022). Emerging trends in digital transformation: A bibliometric analysis. Benchmarking, 29(4), 1069–1112. https://doi.org/10.1108/BIJ-01-2021-0009/FULL/PDF
10.Czerska, I. (2016). Pokolenie head down jako konsekwencja smartfonizacji społeczeństwa. Prace Naukowe Uniwersytetu Ekonomicznego We Wrocławiu, 459, 214–221. https://doi.org/10.15611/pn.2016.459.20
11. Daffodil Software. (n.d.). A complete guide to patient experience management. Retrieved November 20, 2022, from https://www.daffodilsw.com/healthcare/patient-experiencemanagement/
12. Darius, O. D., & Maticiuc, M. D. (2022). Digital platforms — Integrate part of company performance. Annals-Economy Series, (4), 331–336.
13. Deloitte Digital. (2022). Phygitalowy Pacjent przyszłości. W jaki sposób technologia cyfrowa ukształtuje pacjenta nowej generacji? Wyniki dla Polski.
14. Dewalska-Opitek, A. (2017). Generation Y consumer preferences and mobility choices — An empirical approach. Archives of Transport System Telematics, 10(1), 17–23.
15. Dicuonzo, G., Donofrio, F., Fusco, A., & Shini, M. (2022). Healthcare system: Moving forward with artificial intelligence. Technovation, 102510. https://doi.org/10.1016/J.TECHNOVATION.2022.102510
16. Duffy, D. J. (2016). Problems, challenges and promises: Perspectives on precision medicine. Briefings in Bioinformatics, 17(3), 494–504. https://doi.org/10.1093/bib/bbv060
17. Elangovan, D., Long, C. S., Bakrin, F. S., Tan, C. S., Goh, K. W., Yeoh, S. F., Loy, M. J., Hussain, Z., Lee, K. S., Idris, A. C., & Ming, L. C. (2022). The use of blockchain technology in the health care sector: Systematic review. JMIR Medical Informatics, 10(1), e17278. https://doi.org/10.2196/17278
18. Finco, G., Kamal, M. A., Ismail, Z., Shehata, I. M., Djirar, S., Talbot, N. C., Ahmadzadeh, S., Shekoohi, S., Cornett, E. M., Fox, C. J., & Kaye, A. D. (2023). Telemedicine, E-health, and multi-agent systems for chronic pain management. Clinics and Practice, 13(2),
470–482. https://doi.org/10.3390/CLINPRACT13020042
19. Gabryelczyk, R. (2020). Has COVID-19 Accelerated digital transformation? Initial lessons learned for public administrations. Information Systems Management, 37(4), 303–309. https://doi.org/10.1080/10580530.2020.1820633
20. Garcia-Perez, A., Cegarra-Navarro, J. G., Sallos, M. P., Martinez-Caro, E., & Chinnaswamy, A. (2022). Resilience in healthcare systems: Cyber security and digital transformation. Technovation, 102583. https://doi.org/10.1016/J.TECHNOVATION. 2022.102583
21. George, C., Shetty, A., Graduate, P., & Manjunath, N. (2022). Personalized periodontics. International Journal of Innovative Science and Research Technology, 7(3), 1376–1381.
22. Ghosh, K., Dohan, M. S., Veldandi, H., & Garfield, M. (2022). Digital transformation in healthcare: Insights on value creation. Journal of Computer Information Systems. https://doi.org/10.1080/08874417.2022.2070798
23. Hashiguchi, T. C. O., Oderkirk, J., & Slawomirski, L. (2022). Fulfilling the promise of artificial intelligence in the health sector: Let’s get real. Value in Health, 25(3), 368–373. https://doi.org/10.1016/J.JVAL.2021.11.1369
24. Hauke-Lopes, A., Ratajczak-Mrozek, M., & Wieczerzycki, M. (2022). Value co-creation and co-destruction in the digital transformation of highly traditional companies. Journal of Business & Industrial Marketing. https://doi.org/10.1108/JBIM-10-2021-0474
25. Hermes, S., Riasanow, T., Clemons, E. K., Böhm, M., & Krcmar, H. (2020). The digital transformation of the healthcare industry: Exploring the rise of emerging platform ecosystems and their influence on the role of patients. Business Research, 13(3), 1033–1069. https://doi.org/10.1007/S40685-020-00125-X/TABLES/6
26. Horne, R. (2017). The human dimension: Putting the person into personalised medicine. The New Bioethics. A Multidisciplinary Journal of Biotechnology and the Body, 23(1), 38–48. https://doi.org/10.1080/20502877.2017.1314894
27. Hussain, S., Kaur, G., & Deb, A. (2021). Mini-review on personalized medicine: A revolution in health care. Precision Medicine Research, 3(4). https://doi.org/10.53388/PMR2021080601
28. Imran, F., Shahzad, K., Butt, A., & Kantola, J. (2021). Digital Transformation of Industrial Organizations: Toward an integrated framework. Journal of Change Management. Reframing Leadership and Organizational Practice, 21(4), 451–479. https://doi.org/10.1080/14697017.2021.1929406
29. Jafri, R., & Singh, S. (2022). Blockchain applications for the healthcare sector: Uses beyond Bitcoin. In S. Tanwar (Ed.), Blockchain applications for healthcare informatics: Beyond 5G (pp. 71–92). Academic Press. https://doi.org/10.1016/B978-0-323-90615-9.00022-0
30. Jahankhani, H., & Kendzierskyj, S. (2019). Digital transformation of healthcare. In H. Jahankhani, S. Kendzierskyj, A. Jamal, G. Epiphaniou, & H. Al-Khateeb (Eds.), Blockchain and clinical trial. Advanced sciences and technologies for security applications (pp. 31–52). Springer. https://doi.org/10.1007/978-3-030-11289-9_2/COVER
31. Jain, G., & Jain, A. (2022). Blockchain for 5G-enabled networks in healthcare service based on several aspects. Blockchain Applications for Healthcare Informatics: Beyond 5G, 471–493. https://doi.org/10.1016/B978-0-323-90615-9.00018-9
32. Jorgensen, J. T. (2019). Twenty years with personalized medicine: Past, present, and future of individualized pharmacotherapy. The Oncologist, 24(7), e432–e440. https://doi.org/10.1634/THEONCOLOGIST.2019-0054
33. Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. Becoming a digitally mature enterprise. MIT Sloan Management Review. https://sloanreview.mit.edu/projects/strategy-drives-digital-transformation/
34. Kelan, E. K., & Lehnert, M. (2009). The millennial generation: Generation Y and the opportunities for a globalised, networked educational system. Beyond Current Horizons. https://www.researchgate.net/publication/255588891_The_Millennial_Generation_Gene
ration_Y_and_the_Opportunities_for_a_Globalised_Networked_Educational_System
35. Kozak, G., Kuna, B., & Żelazna, A. (2022). Personalizacja usług medycznych jako kolejny kierunek transformacji cyfrowej obszaru ochrony zdrowia. https://www2.deloitte.com/pl/pl/pages/deloitte-digital/Articles/Personalizacja-uslug-medycznych-jako-kolejny-kierunek-transformacji-cyfrowej-obszaru-ochrony-zdrowia.html
36. Kraus, S., Schiavone, F., Pluzhnikova, A., & Invernizzi, A. C. (2021). Digital transformation in healthcare: Analyzing the current state-of-research. Journal of Business Research, 123, 557–567. https://doi.org/10.1016/J.JBUSRES.2020.10.030
37. Li, S. (2021). How does COVID-19 speed the digital transformation of business processes and customer experiences? Review of Business, 41(1), 1–14.
38. Marx, E. W., & Padmanabhan, P. (2020). Healthcare digital transformation: How consumerism, technology and pandemic are accelerating the future (1st ed.). Productivity Press. https://doi.org/10.4324/9781003035695
39. Mazurek, G. (2019). Transformacja cyfrowa: perspektywa marketingu. Wydawnictwo Naukowe PWN SA.
40. McCarthy, P., Sammon, D., & Alhassan, I. (2022). 'Doing’ digital transformation: Theorising the practitioner voice. Journal of Decision Systems. https://doi.org/10.1080/12460125.2022.2074650
41. McCausland, T. (2021). Digital transformation. Research-Technology Management, 64(6), 64–67. https://doi.org/10.1080/08956308.2021.1974783
42. Miller, A. R., & Tucker, C. (2017). Frontiers of health policy: Digital data and personalized medicine. Innovation Policy and the Economy, 17(1), 49–75. https://doi.org/10.1086/688844
43. Mohamed, H. A. (2022). The role of digital transformation in the socio-economic recovery post COVID-19. Applied Economics. https://doi.org/10.1080/00036846.2022.2117779
44. Mugge, P., Abbu, H., Michaelis, T. L., Kwiatkowski, A., & Gudergan, G. (2020). Patterns of digitization. A practical guide to digital transformation. Research-Technology Management, 63(2), 27–35. https://doi.org/10.1080/08956308.2020.1707003
45. Nius, B. (2022). Transformacja cyfrowa — Czym jest i po co to robić? https://global4net.com/ecommerce/transformacja-cyfrowa-czym-jest-i-po-co-to-robic/
46. Oben, P. (2020). Understanding the patient experience: A conceptual framework. Journal of Patient Experience, 7(6), 906–910. https://doi.org/10.1177/2374373520951672
47. Okoniewska, M. (2022). Pacjent phygitalowy to pacjent przyszłości. https://medycynaprywatna.pl/pacjent-phygitalowy-to-pacjent-przyszlosci/
48. Olechno, D. J. (2016). Individualized medicine vs. precision medicine. DDNews, 12(5). https://doi.org/10.1038/nbt.3514
49. Park, J. Y., Lee, K., & Chung, D. R. (2022). Public interest in the digital transformation accelerated by the COVID-19 pandemic and perception of its future impact. The Korean Journal of Internal Medicine, 37(6), 1223–1233. https://doi.org/10.3904/KJIM.2022.129
50. Pauzi, M. F., & Juhari, S. N. (2020). View of digital transformation of healthcare and medical education, within, and beyond pandemic COVID-19. Asian Journal of Medicine and Biomedicine, 4(2), 39–42. https://doi.org/10.37231/ajmb.2020.4.2.363
51. Peres, R., Schreier, M., Schweidel, D. A., & Sorescu, A. (2022). Blockchain meets marketing: Opportunities, threats, and avenues for future research. International Journal of Research in Marketing. https://doi.org/10.1016/J.IJRESMAR.2022.08.001
52. Pietronudo, M. C., Zhou, F., Caporuscio, A., La Ragione, G., & Risitano, M. (2022). New emerging capabilities for managing data-driven innovation in healthcare: The role of digital platforms. European Journal of Innovation Management, 25(6), 867–891. https://doi.org/10.1108/EJIM-07-2021-0327
53. Pihir, I., Tomièić-Pupek, K., & Furjan, M. T. (2019). Digital transformation playground — Literature review and framework of concepts. Journal of Information and Organizational Sciences, 43(1), 33–48. https://doi.org/10.31341/JIOS.43.1.3
54. Pileggi, S. F. (2021). Knowledge interoperability and re-use in Empathy Mapping: An ontological approach. Expert Systems with Applications, 180, 115065. https://doi.org/10.1016/J.ESWA.2021.115065
55. Quazi, S. (2022). Artificial intelligence and machine learning in precision and genomic medicine. Medical Oncology, 39, 120. https://doi.org/10.1007/s12032-022-01711-1
56. Rahimi, A. (2016). The promising prospects of precision medicine. Journal of Advanced Medical Sciences and Applied Technologies (JAMSAT), 2(3). https://doi.org/10.18869/nrip.jamsat.2.3.244
57. Reddy, M. (2022). Digital transformation in healthcare in 2022: 7 key trends. https://www.digitalauthority.me/resources/state-of-digital-transformation-healthcare/
58. Rejeb, A., & Rejeb, K. (2020). Blockchain and supply chain sustainability. LogForum, 16(3), 363–372. https://doi.org/10.17270/J.LOG.2020.467
59. Roden, D. M., & Tyndale, R. F. (2013). Genomic medicine, precision medicine, personalized medicine: What’s in a name? Clinical Pharmacology Therapeutics, 94(2), 169–172. https://doi.org/10.1038/clpt.2013.101
60. Ruppert, T., Sydow, S., & Stock, G. (2016). Personalized medicine: Consequences for drug research and therapy. Advances in Precision Medicine, 1(2). https://doi.org/10.18063/APM.2016.02.004
61. Santarsiero, F., Schiuma, G., Carlucci, D., & Helander, N. (2022). Digital transformation in healthcare organisations: The role of innovation labs. Technovation, 102640. https://doi.org/10.1016/J.TECHNOVATION.2022.102640
62. Sarvan, M. S., & Nori, L. P. (2021). Personalized medicine: A new normal for therapeutic success. Indian Journal of Pharmaceutical Sciences, 83(3), 416–429. https://doi.org/10.36468/PHARMACEUTICAL-SCIENCES.790
63. Schiliro, D. (2020). Towards digital globalization and the Covid-19 challenge. International Journal of Business Management and Economic Research, 2(11), 1710–1716.
64. Schiliro, D. (2021). Digital transformation, COVID-19, and the future of work. International Journal of Business Management and Economic Research, 12(3), 1945–1952.
65. Shan, K., & John, E. P. (2022). Adoption of digital health care — A reality in future. International Journal of Innovative Research in Technology, 8(12), 370–380. https://www.researchgate.net/publication/360614780
66. Sharma, A., Guleria, V., Gupta, G., Lata, K., Patial, S. K., & Jaiswal, V. (2022). Big data analytics for personalized medicine and healthcare. In G. Gupta, V. Jaiswal, M. Khari, & N. Kumar (Eds.), Mobile health: Advances in research and applications (Vol. II, pp.
81–109). Nova Science Publishers, Inc. https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86110
67. Slim, K., Selvy, M., & Veziant, J. (2021). Conceptual innovation: 4P Medicine and 4P surgery. Journal of Visceral Surgery, 158(3), S12–S17. https://doi.org/10.1016/J.JVISCSURG.2021.01.003
68. Spano, R., Massaro, M., & Iacuzzi, S. (2021). Blockchain for value creation in the healthcare sector. Technovation, 102440. https://doi.org/10.1016/J.TECHNOVATION. 2021.102440
69. Spinney, L. (2012). The curse of generation Y. New Scientist, 214(2862), 44–47. https://doi.org/10.1016/S0262-4079(12)61104-X
70. Swan, M. (2015). Blockchain: Blueprint for a new economy (1st ed.). O’Reilly Media.
71. Telemedyczna Grupa Robocza. (2021). Wykorzystanie telemonitoringu urządzeń wszczepialnych w celu poprawy opieki nad pacjentami kardiologicznymi. Stan obecny i proponowane zmiany. https://nil.org.pl/uploaded_files/art_1620382513_raporttgr.pdf
72. Tomièić-Pupek, K., Tomièić Furjan, M., Pihir, I., & Vrèek, N. (2021). Disruptive business model innovation and digital transformation. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.3975574
73. Treiblmaier, H. (2018). The impact of the blockchain on the supply chain: A theorybased research framework and a call for action. Supply Chain Management, 23(6), 545–559. https://doi.org/10.1108/SCM-01-2018-0029/FULL/PDF
74. Vallero, D. (2019). Strategy, not technology, is at the core of digital transformation — Provided it proves agile. https://www.linkedin.com/pulse/strategy-technology-coredigital-transformation-provided-vallero
75. Verma, R., Patel, S., Minj, S. V., & Bhat, A. (2022). Healthcare 5.0: A study on improving personalized care. Proceedings — 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022, 1815–1818. https://doi.org/10.1109/ICICCS53718.2022.9788411
76. Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/J.JSIS.2019.01.003
77. Wahab, S. M. A. A., & Saad, M. (2022). Digital transformation acceleration in health sector during COVID-19: Drivers and consequences. Journal of Business and Management Sciences, 10(4), 164–179. https://doi.org/10.12691/jbms-10-4-1
78. Wolny, R. (2013). Prosumpcja i prosument na rynku e-usług. Konsumpcja i Rozwój, 1(4), 152–163.
79. Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on Blockchain technology? — A systematic review. PLoS ONE, 11(10). https://doi.org/10.1371/JOURNAL.PONE.0163477
80. Zheng, S., & Yu, B. (2016). Landsenses pattern design to mitigate gale conditions in the coastal city — A case study of Pingtan, China. International Journal of Sustainable Development & World Ecology, 24(4), 352–361. https://doi.org/10.1080/13504509.2016.1230077
81. Ziv, E., Durack, J. C., & Solomon, S. B. (2016). The importance of biopsy in the era of molecular medicine. Cancer Journal (Sudbury, Mass.), 22(6), 418. https://doi.org/10.1097/PPO.0000000000000228
82. Żochowska, D. (2022). Praktyczne zastosowanie sztucznej inteligencji (AI) w medycynie 2022. https://www.medonet.pl/magazyn-digital-health/digital-innovation,praktycznezastosowanie-sztucznej-inteligencji-ai-w-medycynie-2022,artykul,38817574.html