A roadmap for empowering cardiovascular disease patients: a 5P-Medicine approach and technological integration

Literature search strategy

This article proposes a novel methodology that synergizes advanced technology with patient-centric healthcare strategies. The aim is to develop a framework that facilitates efficient healthcare delivery and empowers patients in their healthcare journey. The search methodology follows informal research representing an embedded study grounded in interdisciplinary approaches, combining insights from medicine, technology, and patient psychology. This work provides a theoretical framework for future study and helps to develop a more nuanced understanding of the research problem.

Explicit inclusion and exclusion criteria were established to focus on relevant literature from diverse sources, including Springer, Elsevier, MDPI, JMIR, IEEE, PLOS ONE, Google Scholar, and other online sources. A systematic and exhaustive search strategy was developed to cover various keywords and phrases related to patient empowerment, cardiovascular disease, digital health, and relevant technologies. It ensured that literature from various sub-disciplines and perspectives was considered.

The literature review underwent a rigorous peer review process to validate the inclusion of studies and ensure the quality and credibility of selected sources. The research team included professionals with diverse backgrounds, expertise, and perspectives. This diversity contributed to a more balanced interpretation of the literature, reducing the risk of biases associated with a singular viewpoint.

The literature review process was iterative, allowing continuous refinement based on emerging findings and insights.

Data extraction and synthesis

The study extracted data from selected studies using a standardized form, including study design, population characteristics, intervention details, outcome measures, and key findings. Quality assessment was done by assessing adaptability and mapping different categories. Authors independently rated each study, and discrepancies were resolved through discussion or consultation. A narrative synthesis summarized the findings.

Sample size determination

In this literature review, the rationale for the sample sizes used in the analyzed studies is critically evaluated to ensure the robustness and reliability of the findings. Adequate sample sizes are crucial for detecting significant effects and making valid inferences about the effectiveness of patient empowerment strategies in CVD management.

Several studies analyzed in this review conducted power analyses to determine the minimum sample size required to detect significant effects with adequate power. These guidelines helped ensure that the studies were sufficiently powered to detect small to significant effects of patient empowerment interventions on outcomes such as self-management behaviors, health literacy, and patient satisfaction.

Overall, the sample sizes in the analyzed studies were generally adequate to detect significant effects of patient empowerment strategies. Studies that followed rigorous power analysis procedures and provided detailed sample size justifications were more likely to report reliable and valid findings. This review underscores the importance of appropriate sample size determination in research on patient empowerment interventions to ensure that the reported effects are statistically and practically significant.

Statistical analysis

The study utilized descriptive analysis to summarize study characteristics, meta-analysis techniques, and conduct subgroup analyses to identify potential heterogeneity sources, such as differences in study design or population characteristics, to ensure robustness and reliability in the extracted data.

Patient empowerment strategies

In the past few decades, patient empowerment has been more popular as a proactive collaboration and patient-centered self-care approach to enhance the quality of life and health outcomes for those with chronic illnesses (Neuhauser, 2003; Rosenfield, 1992). Support groups, self-monitoring, tailored information, feedback, educational opportunities, personal exercise programs, communication (e.g., chat, email) with patients or health care providers, patient decision making, changes in health care services, and advocacy efforts are examples of empowerment interventions that have been actively pursued in the following areas: diabetes care and other chronic diseases (Howorka et al., 2000; Lorig, Ritter & González, 2003; Lorig et al., 2001; Mayer-Davis et al., 2004; Cooper, Booth & Gill, 2003), chronic obstructive pulmonary disease (Endicott et al., 2003), end-stage renal disease (Tsay & Hung, 2004), osteoporosis (Groessl, 1999), disabilities (Bhagwanjee & Stewart, 1999), cancer (Golant, Altman & Martin, 2003; Mishra et al., 1998; Davison & Degner, 1997). Two systematic reviews have demonstrated the benefits of self-management education for patients with diabetes, where eleven group-based education studies have reported improvements in diabetes control, knowledge, and medication need, which are linked to higher levels of treatment satisfaction, self-empowerment, and self-management skills (Deakin et al., 2005). With empowering features like patient decision making and small group discussion more effective than didactic sessions, seventy-two studies demonstrated short-term impacts in self-management, dietary habits, and illness control (Norris, Engelgau & Venkat Narayan, 2001).

Family empowerment techniques have improved caregiver efficacy, coping mechanisms, access, efficient use of health resources, and individual patient empowerment. The most critical applications of family strategies in mental health have been reported (Sherman, 2003; Dixon et al., 2001); these include lower anxiety and depression when caring for children with chronic illnesses (Melnyk et al., 2004). Reduced sadness, anxiety, and increased empowerment were observed in support group interventions including grandparents and in a systematic evaluation of 20 parent training trials aimed at improving the psychosocial health of mothers (McCallion, Janicki & Kolomer, 2004; Barlow, Coren & Stewart-Brown, 2003).

The evidence presented in World Health Organization (2008b) indicates that patient empowerment strategies affect health outcomes in several ways: directly, through improvements in the effectiveness of individual decision making, management of disease complications, and improved health behaviors (Roberts, 1999); and indirectly, through improved access to and use of health services, with evidence of decreased utilization, improved self-education (Collins, Bybee & Mowbray, 1998), and improved mental health outcomes (Taub, Tighe & Burchard, 2001). Advocacy-based mental health empowerment programs put patients in positions of service, improving their quality of life and social support. They can also lead to changes in practice and policy, such as better recreation services (Gammonley & Luken, 2001), new respite facilities, anti-stigma coalitions, and consumer rights policies (Hess et al., 2001).

The key components of the current PE strategies for patients with CVD include shared decision making, patient involvement, health literacy, and self-management.

Impact of 5P-Medicine on PE strategies

The following sections provide a detailed yet straightforward explanation of the charts that illustrate the impact of the 5P-Medicine approach on patient empowerment. These visuals and their interpretations enhance the understanding of how patient empowerment improves through the 5P-Medicine strategies.

The data used for creating the charts was extracted from the literature review on the effectiveness of 5P-Medicine strategies on patient empowerment. The strategies included in the analysis were Predictive, Preventive, Participatory, Personalized, and Precision. The empowerment scores before and after the implementation of 5P-Medicine were compiled from the various studies included in the review. The scores were standardized and converted into percentages to facilitate comparison.

The collected data was analyzed to determine the mean empowerment scores for each strategy before and after the implementation of 5P-Medicine. It involved calculating the average scores reported across different studies to provide a single representative value for each strategy.

The bar chart presented in Fig. 2 compares patient knowledge levels across four key strategies, such as self-management, health literacy, patient involvement, and shared decision making, before and after implementing the 5P-Medicine approach.

Self-management increased knowledge from 35% to 70%, indicating that patients became more informed about managing their health independently. Health literacy rose from 45% to 85%, significantly improving patients’ understanding of health information and resources. Patient involvement jumped the patient’s knowledge from 40% to 80%, suggesting that patients became more aware of their roles in healthcare decisions. Shared decision making improved it from 30% to 65%, highlighting that patients are better informed about their options and can actively participate in treatment choices.

The 5P-Medicine approach effectively enhances patient knowledge across all strategies, empowering patients to make informed decisions about their health.

This bar chart presented in Fig. 3 illustrates the improvement in patient skills across the same four strategies before and after adopting the 5P-Medicine approach.

Self-management increased patients’ skills from 30% to 68%, indicating that patients developed better abilities to manage their health conditions.

Health literacy improved patients’ self-management and engagement from 40% to 83%, showing a significant improvement in patient’s ability to understand and use health information. Patient involvement improved from 35% to 78%, suggesting that patients became more capable of actively participating in healthcare processes. Shared decision making advanced these from 25% to 65%, highlighting that patients are more equipped to engage in discussions and decisions about their treatment options.

The 5P-Medicine approach significantly improves patient skills, enabling them to manage their health and engage in their healthcare effectively.

This bar chart presented in Fig. 4 highlights the increase in patient confidence across the four strategies before and after the implementation of the 5P-Medicine approach.

The confidence in self-management increased from 25% to 65%, indicating that patients feel more assured in managing their health independently.

Next, the confidence of health literacy rose from 35% to 80%, showing a significant improvement in patients’ confidence in understanding and using health information.

The confidence of patient involvement improved from 30% to 75%, suggesting that patients feel more capable of participating in healthcare decisions.

Finally, the confidence in shared decision making increased from 20% to 60%, highlighting that patients are more confident in discussing and deciding on their treatment options.

The 5P-Medicine approach greatly enhances patient confidence, empowering them to take charge of their healthcare more assuredly.

Implementing the 5P-Medicine approach, which includes Predictive, Preventive, Participatory, Personalized, and Precision aspects, profoundly impacts patient empowerment. Each chart shows substantial improvements in knowledge, skills, and confidence across four key empowerment strategies: self-management, health literacy, patient involvement, and shared decision making.

This bar chart presented in Fig. 5 illustrates a clear and substantial improvement in patient empowerment across all five strategies after the implementation of 5P-Medicine. The most significant increases were observed in the Personalized and Participatory strategies, where empowerment scores rose by 25% and 35%, respectively. It suggests that personalized care and active patient participation are particularly effective in enhancing patient empowerment. The Preventive strategy also saw a notable increase of 20%, indicating that proactive health measures contribute significantly to patient empowerment. The Predictive and Precision strategies both saw substantial improvements of 20% and 28%, respectively, highlighting the importance of predictive analytics and precision medicine in empowering patients.

Comprehensive, the 5P-Medicine strategies have a profound positive impact on patient empowerment, leading to higher engagement, better self-management, and more active involvement in healthcare decisions. This visual and narrative description underscores the effectiveness of the 5P-Medicine approach in improving patient outcomes.

Comparison of PE with other health conditions

In terms of patient empowerment, patients with CVD may differ from patients with other medical diseases, such as diabetes, cancer, and stress-related disorders, in a few specific ways. These traits may influence how patient empowerment techniques are implemented.

As per Libby & Theroux (2005), CVD generally affects the heart and blood vessels, while cancerous illnesses mainly entail abnormal cell growth in different organs or tissues (National Care Institute, 2008). While stress-related disorders affect the nervous system and the body’s reaction to stress (American Institute of Stress, 2023), diabetes primarily affects the endocrine system, specifically insulin regulation and blood sugar control (World Health Organization, 2022b). Different kinds of tests are used in the diagnosis procedure for different illnesses. Tests like electrocardiograms (ECG/EKG), stress tests, echocardiograms, and angiographies are used in CVD diagnosis procedures. Imaging methods such as X-rays, biopsies, magnetic resonance imaging (MRI), and computed tomography (CT) scans are frequently needed for oncological disorders (Jafari et al., 2020). Blood tests that measure blood sugar levels are frequently used to diagnose diabetes (Khan et al., 2019). Psychological evaluations, assessments of stressors, and the exclusion of other possible medical explanations are all possible steps in treating stress-related illnesses (Bremner et al., 2020).

Therapies for CVD include lifestyle changes (diet, exercise, stopping smoking), drugs to control risk factors (high blood pressure, high cholesterol), cardiac procedures (bypass surgery, angioplasty), and cardiac rehabilitation (Arnett et al., 2019). To manage underlying illnesses such as diabetes and obesity, preventive approaches address risk factors and encourage healthy lifestyles (Germano et al., 2012). Treatments for oncological disorders vary greatly and are primarily based on the kind, stage, and location of the cancer, as well as the general health status of the patient. These could consist of immunotherapy, hormone treatment, targeted therapy, radiation therapy, chemotherapy, or a mix of them (Wang, Lei & Han, 2018). Regarding prevention, early detection and lifestyle modifications are frequently at the forefront. It may include routine screenings, self-examinations for specific malignancies, abstaining from tobacco and UV radiation, maintaining a healthy weight and diet, exercising regularly, and consuming less alcohol (Schiffman, Fisher & Gibbs, 2015).

Additionally, vaccinations are essential in avoiding certain cancers. For example, the Hepatitis B vaccine prevents liver cancer, and the Human Papillomavirus (HPV) vaccine prevents cervical and other HPV-related cancers (Finn, 2003). Treatment strategies for diabetes usually include regular blood sugar testing, insulin therapy, oral drugs, and lifestyle changes (diet, exercise, weight management). Changes in lifestyle, regular check-ups, and screening for prediabetes or early identification of diabetes complications are examples of preventive approaches (American Diabetes Association, 2020). Treatments for stress-related disorders involve lifestyle modifications (such as exercise, a healthy lifestyle, good sleep hygiene, and mindfulness), stress management strategies (such as relaxation exercises and mindfulness), and, in certain situations, medication for symptomatic relief. Prevention tactics aim to lower stress, increase resilience, and encourage constructive coping techniques. They include regular self-care routines like physical activity, enough sleep, a balanced diet, and social interaction. It can also be advantageous to develop constructive coping strategies, such as assertiveness, time management, and problem-solving techniques (World Health Organization, 2022a).

Each health condition has its unique characteristics, diagnostic processes, treatment approaches, preventive measures, and challenges, influencing the specific strategies for empowering patients in managing their health. Table 2 provides a comparative overview of the main aspects of CVD in contrast to other health conditions like oncological conditions, diabetes, and stress-related conditions.

Each patient’s needs and specific health conditions influence patient empowerment initiatives. Cardiovascular illness emphasizes a lifetime commitment to lifestyle modifications, with the most critical component being education regarding self-monitoring and medicine. Patients with cancer who must adhere to complicated treatment plans require a great deal of emotional support in addition to thorough information about their disease, available treatments, and coping mechanisms. Diabetes management is a daily self-care practice involving technology, controlling nutrition, and checking blood sugar. Patient literacy of their condition and course of treatment is necessary. Empowerment tactics that impart efficient coping mechanisms, stress management approaches, and cognitive-behavioral therapies are necessary for treating disorders related to stress. All illnesses require an informed and proactive patient to collaborate with healthcare experts to obtain the best possible health results.

The disorders associated with stress, diabetes, cardiovascular disease, and cancerous illnesses are significant contributors to the worldwide burden of disease. These are common illnesses that cause significant morbidity and mortality, impacting a sizable section of the populace. Choosing these particular medical conditions offers a thorough understanding of patient empowerment, considering the various physiological systems, diagnostic procedures, treatment modalities, preventive measures, and the long-term commitment necessary for successful management.

This study highlights that comparing these circumstances does not provide a one-size-fits-all approach to patient empowerment. Customized strategies that consider each health condition’s distinct qualities, difficulties, and requirements are needed to obtain the best possible health results.

To empower CVD patients, the demands of a sizable and expanding patient community must be met. This calls for a multidimensional strategy that considers this patient population’s particular needs and difficulties. It offers resources, knowledge, and tools to assist patients in effectively managing their conditions, addressing co-morbidities, and enhancing their social, mental, emotional, and physical well-being.

Information and communication technologies

The introduction of new technology is impacting the delivery of healthcare. The creation of innovative healthcare models that will benefit patients and the medical community is being aided by new medical technologies. Quick and dependable ICTs are now necessary for raising the standard of healthcare globally since they offer a crucial chance to enhance coordination throughout the whole healthcare delivery system (Kierkegaard, 2012).

Information and communication technologies (ICT) are essential to patient empowerment by simplifying information interchange, granting patients access to healthcare resources, and encouraging active patient participation in their care.

ICT for health has resulted in various applications across programs, including e-prescriptions, patient portals with electronic health records (EHRs), and mobile applications. They have brought about a significant transformation in the delivery of healthcare. Patients now have access to many healthcare data, protecting patient privacy and security and enhancing the precision and safety of healthcare delivery. Patients are empowered by mobile apps created specifically for the healthcare industry, which give them access to various tools and services. Medication reminders, symptom logs, health journals, activity tracking, and access to instructional resources are just a few of the functions that these applications may offer. Through mobile apps, patients may take control of their health, monitor their development, and get individualized guidance and assistance. Secure websites or programs that give patients access to their test results, medical information, and appointment scheduling are known as online or patient portals (Lemire, Sicotte & Paré, 2008). Patients can request prescription refills, monitor their medical history, access their health information, connect with healthcare professionals, and participate in joint decision making through these portals. By giving patients electronic reminders and notifications, e-prescription systems can enhance drug adherence (Shaw & Baker, 2004). To help patients keep on track with their medications and lower the chance of missing doses, patients can receive reminders about prescription refills, drug regimens, or dosage instructions. EHRs and pharmacy databases are just two examples of the different healthcare systems that the E-rescription systems can interface with. The smooth data exchange made possible by this integration lowers the possibility of drug errors or duplicate prescriptions while improving care coordination.

With telemedicine and remote monitoring technologies, patients can check their health conditions and receive virtual consultations from the comfort of their homes, removing obstacles like geographic location, transportation problems, or a shortage of healthcare providers. Information technology facilitates contact between medical professionals at one site and patients or providers at another, known as telemedicine (Craig & Petterson, 2005). Improving communication fosters a collaborative relationship, and patients can feel heard, respected, and actively involved in their care. Better results result from patients more likely to follow their treatment programs. For patients with chronic diseases, remote monitoring makes monitoring and follow-up care easier (Alvarez et al., 2021). Patients can use wearable technology or home monitoring equipment to gather and send important health data to healthcare providers. It makes it possible to monitor health conditions continuously, identify changes or difficulties early, and take appropriate action when needed. These technologies empower patients through more access to specialized care, convenient monitoring and follow-up, patient education and self-management, improved communication, and lower healthcare costs. Technologies like telemedicine and remote monitoring improve patient outcomes, increase patient participation, and support a patient-centered approach to cardiovascular care.

Patients can obtain accurate and evidence-based information about their health issues, treatment options, and self-care practices by accessing reputable health education websites and online resource platforms, materials, interactive modules, and multimedia resources (Lemire, Sicotte & Paré, 2008). Patients can actively participate in shared decision making with healthcare practitioners, educate themselves, and better understand their health.

Online health communities and support groups allow people to interact, exchange stories, and seek help from others with comparable medical issues (Eysenbach et al., 2004). These communities empower patients by offering a forum for information sharing, peer education, and emotional support, which lessens isolation and promotes a sense of belonging.

ICTs empower patients through better access to healthcare services, health information, and resources, improved health literacy, patient-provider communication, improved self-management, and peer support. They allow patients to participate in their well-being, make educated decisions, and participate in their healthcare.

Wearables and smart devices

Consumer-grade, networked electronic gadgets known as “smart wearables” are integrated into clothing or worn as accessories on the body. These encompass a variety of devices, such as smartwatches, rings, and wristbands, among others. They are equipped with multiple advanced sensors and powerful computing capacity, enabling them to extract novel health insights (Bayoumy et al., 2021). Real-time monitoring of human physical activities and behaviors and physiological and biochemical data are made possible by wearables and smart devices (Prieto-Avalos et al., 2022). Electrocardiogram (ECG), electromyogram (EMG), heart rate (HR), body temperature, electrodermal activity (EDA), arterial oxygen saturation (SpO2), blood pressure (BP), blood glucose (BG), blood cholesterol level (BCL), and respiration rate (RR) are among the physiological signs that wearables can measure through embedded sensors (Majumder, Mondal & Deen, 2017; Reda et al., 2021).

The medical profession acknowledges numerous wearables and smart devices as valuable resources for illness diagnosis, monitoring, and treatment. The most widely used ones are fitness trackers, smartwatches, smart blood pressure monitors, smart contact lenses, pulse oximeters, sleep trackers, ECG monitors, smart inhalers, and smart pillboxes. There are numerous examples on the market, including:

• Apple Watch, Fitbit, and Garmin devices have features like stress management, activity tracking, heart rate monitoring, and sleep analysis;

• Dexcom G6 and Freestyle Libre are frequently used by individuals with diabetes to continuously monitor their blood sugar levels and provide real-time data, which can aid in effectively managing blood sugar levels;

• Users can measure and monitor their blood pressure at home with the Withings BPM Core and Omron Evolv devices.

Those with hypertension or those keeping an eye on their cardiovascular health can benefit from them; iv) The gadgets, such as those produced by Nonin or Masimo, measure the blood’s oxygen saturation levels. In addition to being useful during exercise or at high altitudes, they are frequently used by people with respiratory conditions like asthma or chronic obstructive pulmonary disease (COPD); v) Devices like Oura Ring and Beddit use sensors to monitor sleep patterns, including duration, quality, and stages of sleep. vi) Users can record electrocardiogram readings with portable ECG monitors, such as AliveCor’s KardiaMobile or Apple Watch’s ECG function. These devices can offer insights into sleep abnormalities and aid in identifying probable sleep disorders. These are useful for people who have heart problems or are at risk of having a cardiac attack; vii) Teva’s ProAir Digihaler or Propeller Health’s sensors are connected to inhalers and offer advice on how to use the device and how much medication to take at a time. They can aid in the better management of asthma and chronic obstructive pulmonary disease (COPD); viii) Pillsy and MedMinder are two examples of devices that use sensors and reminders to assist users in adhering to their prescription regimens. They can lower the possibility of missing doses by sending out alerts and reminders to take prescribed drugs on time.

These wearables and smart gadgets aim to improve self-care and empower patients. They may offer insightful information and valuable data to help preventative health care. Medical specialists should always use them to guarantee proper medical advice and decision making. It is crucial to remember that the medical community’s acceptance of these gadgets may differ depending on particular use cases and the inclinations of specific healthcare organizations or providers.

Furthermore, advancements in wireless sensor networks (WSN), artificial intelligence (AI), and the Internet of Things (IoT) have emerged as crucial allies in the creation of mHealth wearable devices. AI and neural networks are being incorporated into wearables using deep learning and signal processing techniques (Prieto-Avalos et al., 2022) to increase the accuracy of ECG and PPG signals and the dependability of ambulatory monitoring devices. Wearable technology performs better, and biological variable assessments for CVD are more accurate thanks to artificial intelligence and neural networks. Therefore, consumer adoption of wearable devices rises with their increasing accessibility and dependability (Nahavandi et al., 2022). Wearable-based technologies are becoming a more practical option for continuously monitoring CVD patients’ health while they go about their daily lives.

Artificial intelligence

AI applications in health innovation are increasing, indicating how these technologies might be used to solve issues like worker shortages and aging populations. It can lower healthcare expenditures while improving patient care significantly. Rapid advancements in artificial intelligence and robotics in healthcare are being made, particularly in early detection and diagnostic applications (Coeckelbergh, 2010). AI has a massive potential for use in the clinical setting, with applications ranging from clinical research and therapeutic decision making to the automation of diagnostic procedures.

The recent exponential growth in artificial intelligence has been fueled by large-scale annotated clinical data collecting, improvements in machine learning techniques, open-source machine learning packages, and inexpensive and quickly expanding computer power and cloud storage.

Medical diagnosis decision making systems and healthcare monitoring applications have long used computational machine learning techniques, including reinforcement, unsupervised, semi-supervised, supervised, and ensemble learning algorithms. Deep learning paradigms have been widely used in biomedical research projects and engineering applications, driven by the burgeoning cloud computing technologies and hardware infrastructures (Chen et al., 2022). Based on the encoding and decoding structures, state-of-the-art deep learning neural networks can map spatial data at different levels in numerous network layers and generate localization details of the region of interest. Convolutional neural networks, fully convolutional networks, generative adversarial networks, and recurrent neural networks are the most widely used deep learning architectures (Wu & Ghoraani, 2022).

It promises to transform the medical practice environment in a short while. AI systems can estimate patient prognosis more accurately than clinicians, perform several diagnostic tasks at the specialist level, and support surgical operations. There is a growing belief that AI has the potential to transform medicine and change the responsibilities of clinicians as machine-learning models develop.

Decision support systems

Information technology systems that provide pertinent data, information, or expertise to help people or organizations make decisions are called decision support systems (DSS). It churns large datasets to give an efficient conduit for decision making. To better organize clinical knowledge and facilitate decision making, most healthcare information systems must either incorporate clinical decision-support practices or currently do so (Hak, Guimarães & Santos, 2022). Patients with cardiovascular disease (CVD) may benefit from a DSS in several ways. Comprehensive, intelligible, and pertinent information regarding CVD can be found in a DSS. Information about the illness, its dangers, preventative measures, and available treatments may be included. By increasing their health literacy, patients can better understand their health and the effects of different decisions.

Based on available data, DSS uses machine learning and data analysis methods to forecast future health risks. It might help avert more medical issues. Tools for monitoring pertinent health metrics, including blood pressure, heart rate, weight, physical activity, and food, can be obtained through a DSS. Patients may be motivated to improve their behavior by seeing how their lifestyle decisions and self-management practices affect their health thanks to this continuous monitoring (Jovicic, Holroyd-Leduc & Straus, 2006). A DSS can help with shared decision making by giving patients and healthcare professionals a forum to discuss available treatments. An informed discussion on the appropriate course of action can be facilitated by the system’s ability to show an individual’s health data combined with evidence-based information about various treatment strategies (Ruland & Bakken, 2002). A DSS offers individualized guidance by examining a person’s health data. For example, depending on a person’s particular risk factors and health status, it can recommend specific lifestyle changes, medication adjustments, or more examinations. Some DSSs might offer a peer support network that links patients with other people dealing with similar conditions. It can offer guidance and emotional support, which increases empowerment even more.

A DSS must be carefully designed for CVD management to guarantee that it is dependable, safe, and easy to use. Healthcare professionals should assist patients in using these devices and analyzing their available data. DSS can greatly empower patients with CVD, enabling them to actively participate in their health management, enhance their quality of life, and possibly lower the risk of significant problems.

Signal processing and data analysis

Signal processing and data analysis techniques are widely used in the field to extract helpful information from the many forms of healthcare data. These methods enhance general patient care, monitoring, diagnosis, and treatment in several areas related to patient empowerment. A few of these frequently used methods are examined here.

Physiological signals are frequently analyzed using signal-processing techniques in various domains, including biotechnology, neuroscience, and Medicine. Electrical or biological signals produced by the human body or other living things are called physiological signals, and they provide important information on the health and functions of the body. Electrocardiograms (ECG), electroencephalograms (EEG), and respiratory signals are among the health-related signals from which noise and artifacts are removed using signal processing techniques, including filtering (Colditz, Burke & Celka, 2001).

Relevant features are also extracted from health signals using signal processing techniques. In the context of ECG analysis, techniques such as wavelet transforms, heart rate variability (HRV) analysis, and QRS complex recognition, for instance, extract features that shed light on cardiac health and problems.

Signal compression techniques decrease the storage and transmission space needed for health-related signals and images. Utilizing this growing amount of data and exploring and presenting the information appropriately for medical purposes requires medical image processing.

Medical imaging modalities include computed tomography (CT) scans, ultrasound, magnetic resonance imaging (MRI), and digital pathology, frequently using signal processing techniques (Ritter et al., 2011) to extract pertinent characteristics, segment regions of interest, and improve picture quality. Image processing techniques such as edge detection, segmentation, registration, and filtering facilitate medical picture visualization, interpretation, and analysis. Health data may be efficiently stored, transmitted, and analyzed thanks to compression techniques, eliminating unnecessary or duplicate information.

Health signals are subjected to spectral analysis techniques like wavelet and Fourier transforms to determine their frequency content. By identifying particular frequency components, patterns, and signal anomalies, spectral analysis facilitates diagnosing and monitoring neurological disorders, cardiovascular diseases, and sleep problems.

Healthcare data can be analyzed using various strategies and tactics to glean insights. These methodologies employ statistical analysis, machine learning, and further computational approaches to examine many forms of healthcare data, including patient-generated data, medical imaging data, electronic health records (EHRs), and genetic data.

Healthcare organizations frequently employ machine learning (ML) techniques to evaluate and interpret large, complicated datasets, which eventually help patients take charge of their care. To create individualized treatment regimens, machine learning algorithms can evaluate patient-specific data, such as clinical factors, genetic information, and electronic health records. Machine learning models let medical professionals customize treatments for each patient by finding patterns, correlations, and treatment responses in massive datasets. Precision medicine is a method that empowers patients with therapies that are more effective and have fewer side effects, allowing for patient-centric care. Machine learning algorithms examine data from wearable devices in various healthcare applications to track and understand patients’ health states. Machine learning algorithms can identify abnormalities, forecast health concerns, and give patients immediate feedback by continuously evaluating physiological data, activity levels, and sleep patterns. It empowers people to take proactive steps to avert unfavorable health events, make educated lifestyle decisions, and actively monitor their health. Supervised learning algorithms are used for illness prediction, risk stratification, and treatment outcome prediction, including decision trees, random forests, support vector machines, and neural networks (Alanazi, 2022). Based on unsupervised learning methods, including dimensionality reduction and clustering, it aids in finding patterns and assembling related individuals or illnesses.

Information from unstructured healthcare data, such as clinical notes, medical literature, and patient questionnaires, is extracted and analyzed using natural language processing (NLP) techniques to improve patient empowerment. NLP algorithms can extract pertinent clinical data, recognize important terms or concepts, and aid in the semantic interpretation of textual data (Friedman & Elhadad, 2014). NLP makes it possible to use free-text data for text mining, sentiment analysis, information retrieval, and clinical coding. Using natural language processing (NLP) tools to extract and evaluate information from unstructured healthcare data can enhance patient empowerment in several ways. Personalized insights from clinical data can help patients and healthcare providers communicate better and make decisions together. Patients with access to current and pertinent medical material are better equipped to participate actively in discussions about their treatment options. Furthermore, by better understanding and addressing patient issues, patient feedback analysis can assist healthcare companies in raising patient happiness and engagement levels.

Data mining techniques can examine and identify links and patterns in big datasets. Classification, clustering, and association rule mining algorithms are used to extract hidden insights from healthcare data. Data mining techniques have significantly enhanced the medical industry’s capacity for prediction and decision making about diseases such as diabetes, heart disease, liver disease, cancer, and others (Patel & Patel, 2016). Data mining facilitates the identification of variables affecting patient outcomes, the discovery of co-occurrence patterns, and the generation of research hypotheses.

Techniques for time series analysis are used on data gathered over an extended period, such as patient vital signs, the course of a disease, or healthcare utilization. Time series forecasting models have been successfully used in medical applications to estimate the mortality rate, predict the course of the disease, and evaluate time-dependent risk (Bui et al., 2017). Time series techniques allow for identifying patterns, seasonality, and future value predictions (Ewusie et al., 2020). These techniques include exponential smoothing, Fourier analysis, and autoregressive integrated moving averages (ARIMA). These methods can benefit the healthcare industry by monitoring patient situations, anticipating disease outbreaks, allocating resources wisely, and promoting patient empowerment.

Data security and privacy protection solutions

Because medical data is susceptible and private, data security and privacy protection solutions are essential to healthcare technologies and patient empowerment.

Patient data privacy in healthcare is a moral, legal, and ethical requirement. Patient data protection is required by laws like the General Data Protection Regulation (GDPR) in Europe and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. Adhering to these requirements is imperative to prevent legal consequences and uphold ethical principles.

Patients with cardiovascular disease use ICT tools to exchange private medical information with healthcare professionals. Vulnerabilities in the platforms that produce and send data can be used to leak information or even take down devices. Strong patient data encryption can help to lessen it (Segura Anaya et al., 2018; Filkins et al., 2016). Concerns about user privacy also exist since wearable data can be utilized to obtain crucial user information that could be used for commercial purposes (Jiang & Shi, 2021). Research suggests that consumers might need to be aware of the privacy hazards associated with these gadgets, emphasizing the necessity of instructional programs. Additionally, the level of access to personal data consented to during the setup of these devices and the acceptance of the conditions of use was not exact. These issues can be addressed with regulations emphasizing data security, open privacy practices, and the appropriate implementation of the informed consent procedure (Kumar et al., 2022).

Patients need to feel comfortable knowing that their sensitive medical information is protected. When they have confidence that their private health information is secure, patients are more inclined to actively participate in their healthcare and share critical information with healthcare practitioners.

Emerging technologies applications and benefits: overview

Table 3 reflects a summarized view of emerging technologies, their applications, and their benefits regarding patient empowerment.

Practical application of the 5P-Medicine

The application of the 5P-Medicine approach has shown promising results in managing cardiovascular diseases (CVD). Integrating these principles into real-world settings demonstrates their effectiveness in improving patient outcomes and empowering individuals to take an active role in their health management. The predictive component leverages advanced analytics and machine learning to identify patterns and forecast health risks. For instance, a study by Alvarez et al. (2021) highlighted the use of telemedicine and remote monitoring in heart failure management. Patients using predictive analytics for continuous monitoring experienced fewer hospitalizations and improved management of their condition due to the early detection of potential health issues. Preventive strategies focus on mitigating risk factors before they develop into severe health problems. The authors of Germano et al. (2012) reported that lifestyle interventions, such as regular physical activity, dietary modifications, and smoking cessation programs, significantly reduced the incidence of cardiovascular events among high-risk populations. These preventive measures are tailored to the individual risk profiles identified through predictive analytics. Patient involvement in healthcare decisions is a cornerstone of participatory medicine. This study demonstrated that involving patients in their treatment plans through shared decision making processes led to higher satisfaction and better adherence to prescribed therapies. Patients equipped with comprehensive health information were more confident in managing their health, as evidenced by increased engagement in their care plans. Personalized medicine tailors treatments to individual genetic, environmental, and lifestyle factors. The integration of genetic profiling into treatment plans has been shown to enhance the efficacy of interventions. For example, a clinical trial integrating genetic data to personalize medication for hypertensive patients resulted in significantly better blood pressure control compared to standard treatment protocols. Precision medicine employs targeted therapies to maximize treatment effectiveness while minimizing side effects. Advanced diagnostic tools and precise therapeutic interventions have improved outcomes for patients with complex cardiovascular conditions. A notable example is the use of targeted drug delivery systems that release medication directly to the affected areas, thereby reducing systemic side effects and enhancing therapeutic efficacy.

Implementing the 5P-Medicine approach has profoundly impacted patient empowerment. Studies have shown that patients involved in self-management programs, supported by digital tools and personalized health information, exhibit higher confidence and capability in managing their conditions. Figures 2–4 illustrating patient knowledge, confidence and engagement before and after the implementation of 5P-Medicine show substantial improvements across all empowerment strategies, highlighting the approach’s success in fostering patient autonomy.

In conclusion, incorporating real-world examples of the 5P-Medicine approach in cardiovascular disease management substantiates its effectiveness. These examples reinforce the theoretical framework and provide concrete evidence of improved patient outcomes and empowerment. The shift towards a patient-centered, personalized, and proactive healthcare model promises a significant enhancement in the management of cardiovascular diseases.