The paper highlighted how the platform addressed the fragmented nature of heart failure care by enabling continuous communication between hospital specialists and community healthcare providers.

Study Design and Implementation

The AZIMUTH study (FondAZione A. Gemelli IRCCS Artificial Intelligence Empowered Digital PlatforM to sUpport paTients with Heart Failure) addressed this healthcare challenge through innovative digital health technology. It demonstrates how smartphone-based care could transform outcomes for heart failure patients. This multicenter, prospective study enrolled 300 heart failure patients across four leading Italian medical centers in Phases 1 and 2.

The study utilized Healthentia v3, a Class I Software as Medical Device (SaMD), as the core platform for remote patient monitoring and care delivery operating through two integrated components:

Patient Mobile Application: Patients used intuitive “widgets” for daily health monitoring, including mandatory weight and blood pressure tracking, validated questionnaires (Kansas Questionnaire and medication adherence assessments), and optional monitoring of heart rate, oxygen saturation, and physical activity. The app seamlessly integrated with Bluetooth-enabled devices to minimize manual entry errors.

Clinical Dashboard: Healthcare providers accessed a platform that consolidated patient data into actionable insights, featuring real-time monitoring, intelligent alert systems based on clinical thresholds, longitudinal trend analysis, and integrated patient communication tools.

The study carefully addressed the real-world implementation by establishing clear inclusion criteria that balanced technological requirements with practical applicability. Patients required basic digital literacy or caregiver support, smartphone compatibility, and received comprehensive training during enrollment. The platform was designed for intuitive use across age groups, ensuring broad accessibility.

Proven Results

The study successfully demonstrated the effectiveness of digital health solutions in heart failure management:

Patient Engagement: Achieved the primary objective with over 70% of patients successfully engaging with the digital platform throughout the study period.

Clinical Improvements: Patients showed significantly improved medication adherence and treatment engagement compared to traditional care approaches.

Healthcare Provider Value: Clinicians reported enhanced ability to monitor patients remotely, identify early warning signs, and coordinate care more effectively.

Feasibility Confirmed: The app-based model proved both technically feasible and clinically valuable across diverse patient demographics.

Impact and Validation

AZIMUTH validated that smartphone-based care could transform outcomes for heart failure patients – a population traditionally challenged by frequent hospitalizations and complex medication regimens. The study proved that digital health solutions, when properly implemented through certified medical device platforms like Healthentia, could enhance both patient outcomes and healthcare efficiency.

The successful completion of AZIMUTH Phase 1 established a new evidence base for digital health adoption in chronic disease management. Azimuth demonstrated that collaborative innovation between healthcare institutions, pharmaceutical companies, and technology providers can deliver measurable clinical value. A second paper with results from Phase II is expected soon and new partners have joined efforts in a new study named Azimusa utilizing the Azimuth care model and expanding further the scope to more medical centers in the north with more patients.

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What Is B-COMPASS?

At its core, B-COMPASS helps all stakeholders in Healthcare from clinicians, caregivers, software providers, and healthcare policymakers to identify the drivers behind patient behavior. It relies on a concise set of fewer than ten questions to generate a patient profile based on five key dimensions: health consciousness, treatment needs, personal concerns, acceptance, and perceived control.

This simplicity makes it powerful. It enables the creation of tailored communication and support strategies to address the unique challenges each patient faces, ultimately aiming to improve adherence and treatment outcomes.

How Innovation Sprint Is Contributing

As a proud partner in the BEAMER project, Innovation Sprint brings deep experience in real-world data collection, behavioral modeling, and chronic disease management through our flagship Software as a Medical Device (SaMD), Healthentia.

With years of insights from managing patient journeys, particularly around non-adherence patterns, Innovation Sprint contributes to shaping and validating the B-COMPASS model with real-world scenarios. Our involvement ensures the model reflects actual patient needs and clinical realities, making it both usable and impactful in digital health platforms.

Through Healthentia, we have seen firsthand how variable patient engagement can be, and how crucial it is to adapt digital interventions to individual needs. Integrating tools like B-COMPASS enhances our ability to design adaptive programs that not only monitor but also positively influence patient behavior over time.

Why B-COMPASS Matters for SaMDs like Healthentia

Tools like B-COMPASS don’t just provide insight, they enable action. For SaMDs, this means gaining the ability to segment patients more meaningfully and respond with personalized interventions, educational content, and coaching.

The result? Increased treatment adherence, improved patient satisfaction, and reduced healthcare burden—goals at the heart of both BEAMER and Innovation Sprint’s mission.

Looking Forward

As B-COMPASS matures within the BEAMER framework, its integration into digital health ecosystems like Healthentia will further personalize chronic disease management. We believe this marks a pivotal step toward scalable, human-centered digital healthcare.

To learn more about B-COMPASS and the BEAMER project, visit https://beamerproject.eu/BEAMER-model/.

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CATEGORY: Digital Health

SOURCE: European Heart Journal – Digital Health, April 2025, https://doi.org/10.1093/ehjdh/ztaf040

Study design and rationale of the AZIMUTH trial: a smartphone, app-based, E-health-integrated model of care for heart failure patients

Domenico D’Amario¹, Attilio Restivo^2,3, Renzo Laborante^2,3, Donato Antonio Paglianiti^2,3, Alfredo Cesario^2,3,4,5, Stefano Patarnello², Sofoklis Kyriazakos⁶, Alice Luraschi², Konstantina Kostopoulou⁶, Antonio Iaconelli^2,3, Enrico Incaminato¹, Gaetano Rizzo¹, Marco Gorini⁷, Stefania Marcoli⁷, Vincenzo Bartoli⁷, Thomas Griffiths⁸, Peter Fenici^3,7,9, Simona Giubilato¹⁰, Maurizio Volterrani¹¹, Giuseppe Patti¹, Vincenzo Valentini^2,3, Giovanni Scambia^2,3, Filippo Crea^3,12

More Publications

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CATEGORY: Digital Public Health

SOURCE: Frontiers – Public Health, December 2024, Volume 12-2024; https://doi.org/10.3389/fpubh.2024.1488687

Benchmarking the clinical outcomes of Healthentia SaMD in chronic disease management: a systematic literature review comparison

Sofoklis Kyriazakos¹ , ^*Aristodemos Pnevmatikakis¹, Konstantina Kostopoulou¹, Laurent Ferrière² , Kyun Thibaut² , Erika Giacobini³ , Roberta Pastorino^3,4 , Marco Gorini⁵ , Peter Fenici⁵

¹Innovation Sprint srl, Clos Chapelle-aux-Champs, Brussels, Belgium

²COVARTIM, Watermael-Boitsfort, Belgium

³Section of Hygiene, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy

⁴Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy

⁵AstraZeneca SpA, Milano Innovation District (MIND), Milano, Italy

More Publications

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Managing chronic obstructive pulmonary disease (COPD) alongside other chronic conditions (CCs) like depression requires a comprehensive and innovative approach. As part of the RE-SAMPLE research project, Healthentia, our advanced SaMD solution, addresses this challenge by integrating real-world data (RWD) to enhance disease management and improve daily life for patients. For more information about the intended use of the device and the medical modules, please consult: https://healthentia.com/medical-device/

This research-driven initiative bridges the gap between clinical research and real-world healthcare, supporting two phases of COPD and CCs management:

• Phase 1: Healthentia collects RWD to track disease progression, uncover patterns, and predict exacerbations. These insights enable proactive care, empowering healthcare providers to anticipate and address patient needs more effectively.
• Phase 2: Through its Virtual Companionship Program (VCP), the platform delivers tailored self-management tools, including lifestyle coaching, goal-setting, and real-time medical suggestions. Patients gain the confidence to manage their health actively, while healthcare providers benefit from actionable insights via a clinical dashboard. This enables personalized, adaptive care strategies, ensuring better outcomes.

Healthentia redefines the standard of COPD care in hospitals across Europe by seamlessly integrating real-world insights with technology-driven solutions. By prioritizing both patients and providers, it transforms the approach to managing not only COPD but also co-existing chronic conditions, helping patients regain control over their health and well-being.

This World COPD Day, Healthentia reaffirms its commitment to innovation, providing hope and support for millions living with COPD.

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World Diabetes Day is an essential reminder of the global impact of diabetes. Especially type 2 diabetes affects millions of lives and continues to grow at an alarming rate. Currently, type 2 diabetes makes up more than 90% of the over 450 million adults worldwide that are living with diabetes. By 2030, this number is expected to reach 578 million. This increase not only strains healthcare systems but places a significant burden on individuals who must learn to live with and manage this chronic condition.

At Innovation Sprint, we believe in empowering patients by providing Healthentia, the patient companion app they need to effectively manage their condition at home. Healthentia is designed for chronic disease management, offering a personalized approach to each patient. By collecting data on daily habits, physical activity, and health metrics, Healthentia offers customized support and advice that aligns with the patient’s health goals and current state. For more information about the intended use of the device and the medical modules, please consult: https://healthentia.com/medical-device/.

For type 2 diabetes patients, effective management is key to preventing serious complications like heart disease and nerve damage. While healthcare professionals are essential, most daily care happens at home, requiring regular monitoring, a balanced diet, physical activity, and adherence to treatments. Many patients find this challenging, especially without personalized guidance.

In our ongoing research study with type 2 diabetes patients, we are taking this a step further. By gathering detailed data, we can provide personalized recommendations that evolve alongside the patient. For instance, if a patient’s activity level drops or they struggle with diet management, Healthentia can recognize these patterns and suggest targeted interventions that encourage gradual improvements. This constant, personalized feedback loop helps patients feel supported and more capable of taking control of their health.

On this World Diabetes Day, let’s acknowledge not only the challenges but also the strides being made in digital health to enhance self-management for millions of people worldwide. With solutions like Healthentia, we’re creating a future where living with diabetes doesn’t have to be so daunting.

“In our new study with Diabetes Mellitus Type 2 patients in the University of Thessaly we are integrating Healthentia, a digital health solution, to evaluate how digital health tools can enhance traditional healthcare. The patients thus far responded positively, embracing the mobile app for their support at home, while the healthcare professionals benefit from the web portal, which provides real-time data monitoring and insight into the tailored advice each patient receives.” 

Tamouridis Stefanos MD, MSc
Clinic of Endocrinology and Metabolic Diseases, General University Hospital of Larissa
University of Thessaly

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Our experiments indicate that model quality strongly depends on the data, both its volume and quality. Data volume is determined by the number of patients having already used Healthentia for a particular pathology. Data quality is determined by each patient’s adherence to the data collection plan. Both data volume and quality are seldom ideal. At least during the first studies Healthentia is used for a particular pathology, there are simply not enough patients to give us a satisfying volume of data. Moreover, patients are of varying degrees of capability to collaborate with Healthentia’s mobile app. Omissions and errors can happen, and then reduce data quality.

Training with suboptimal data volume and quality leads to suboptimal models, suffering from overfitting and bias. The alternative is to reside to the knowledge of the experts, the healthcare professionals. Their knowledge is captured within rule-based expert systems. That yield the necessary analysis of the patients. At Innovation Sprint we have been experimenting with ways to combine the information the data offers and the expertise the healthcare professionals offer. The HumAIne project offers Healthentia the novel tools to achieve this combination.

HumAIne is an EU co-funded project under the topic HORIZON-CL4-2022-HUMAN-02-01 that started in October 2023. HumAIne facilitates advanced and reliable collaboration of experts and AI towards hybrid decision making and support in a variety of industries. It delivers the HumAIne Operating System, built on four technological pillars: Active Learning, Neuro-Symbolic Learning, Swarm Learning, and eXplainable AI. The HumAine OS enables AI solution creators to build advanced Human-AI collaboration systems that outperform standalone AI and individual experts’ efforts.

Innovation Sprint leads the project’s efforts on defining the vision and specifications for human-AI collaboration, focusing on user requirements extraction and use case scenarios definition. These efforts lead on the needs to be covered by the technology developed in HumAIne. We also lead the healthcare pilot, where the HumAIne technology will be leveraged to enhance the AI modules in the patient understanding and advice delivery systems of Healthentia. More specifically, we will be employing Neuro-Symbolic Learning to learn models not simply on data, but on the combined information of data and healthcare professionals’ expertise. We will be comparing the models learnt traditionally to those employing Neuro-Symbolic Learning, both in terms of prediction metrics but also in terms of their effectiveness in the overall behavioral change framework of Healthentia.

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While dialogue selection is the most important task of Healthentia’s behavioral change framework, keeping these dialogues personalized and interesting is also important in the long run. Dialogues need to be both to the point, to persuade the patients to go through them and have an impact, but also need to be interesting for the patients to keep working with them. Setting dialogue selection aside, in this blog post we discuss the two features of Healthentia dialogues that make them interesting to their recipients. We also mention a third, experimental feature based on LLMs, that is still on a lab level.

Healthentia dialogues are personalized with data from the patients. While the dialogues are selected when certain conditions arise, the content delivered is augmented with actual patient data, which could be simple – for example – static pieces of information like the patient’s name or sex. They can also be some personalized goals set for the patient by a doctor. Finally, they can also be the results of statistics on some data of the patient, like the average steps walked in the past week, or the frequency red meat is consumed in the past month. This way actual patient data can be compared to the personalized goals, while the patient is addressed by name.

Healthentia dialogues allow patients to voice their decisions and affect to some degree their therapy. While input variables augment dialogue nodes with data from the patient receiving the dialogue, output variables allow the patient to communicate some intent back to Healthentia. Such output variables can be simple feedback on whether the patient considered the dialogue useful. Or they can carry the result of some negotiation about a goal the patient has difficulty reaching. Some output variables are just stored to be part of the patient’s data, while others trigger processes that change elements of the therapy. An example of output variable creating new data is the feedback, with the dialogue selection utilizing accumulated feedback. An example of output variables triggering processes are the negotiations of goals or frequency of contact with more dialogues.

Future Healthentia dialogues can have dynamic nodes generated by LLMs. At a lab level, we experiment with two ways to add dynamically generated nodes to the dialogues. On the one hand, patient data summaries can be created using LLMs. On the other hand, LLMs can present data from reference material. In both cases, the advantage of using LLMs over manually creating the text is that the LLM text is quite variable across repetitions of the dialogues. The text variation has to do with the tone of voice requested, the data at hand and the inherent variability of the models themselves. Data summaries are simpler to create, utilizing the expressive power of a trained LLM. Selecting and presenting material from a reference library is more involved. While the expressive power of LLMs is still employed, the library of reference material is also set up and analyzed, while the LLM is restricted to get information from this library alone.

Employing the existing features in the dialogues and looking ahead to more dynamic options, Healthentia can keep the patients interested, since the dialogues are dynamic, personalized and offer two-way exchange of information, both from Healthentia to the patient, but also from the patient to Healthentia.

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Software as a Medical Devices (SaMD) and mobile health (mHealth) applications have revolutionized the healthcare landscape in the areas of Remote Patient Monitoring (RPM) and Digital Therapeutics (DTx). These technological advancements offer a range of benefits, from improved patient engagement and real-time monitoring to personalized treatment plans and enhanced clinical outcomes. During the continuous design, development, and validation of our medical device Healthentia, we have conducted a comprehensive systematic literature review to assess the current status and efficacy of these digital health interventions and benchmark the results with our finding from the clinical evidence we have collected from various studies. The results are rather positive, demonstrating how SaMDs like Healthentia can support the management of chronic diseases, satisfying patient safety and performance requirements.

The current landscape of SaMD and mHealth apps is characterized by rapid innovation and widespread adoption, especially after the Covid pandemic, which might have worked as a catalyst for digital transformation in digital health. SaMD refers to software intended to be used for medical purposes that perform these purposes without being part of a hardware medical device. mHealth applications, on the other hand, encompass a wide range of digital tools designed to support health and wellness through mobile devices. mHealth applications are not necessarily medical devices and therefore, they often don’t comply with the regulatory framework. Both SaMD and mHealth apps have seen significant advancements, driven by the increasing need for accessible, efficient, and patient-centric healthcare solutions. Regulatory frameworks have also evolved to keep pace with these innovations, ensuring that these technologies meet rigorous safety and efficacy standards. While the potential of SaMD and mHealth apps are immense, there are several challenges that need to be addressed to fully realize their benefits, such as data privacy concerns, integration with existing healthcare systems, and user adoption barriers.

The review involved a scientific process of identifying, selecting, and analyzing relevant studies that evaluate the effectiveness of SaMD and mHealth apps in managing chronic diseases, using the PICO (Population, Intervention, Comparison, Outcomes and Study) framework. The selected studies provide a robust evidence base, highlighting the positive impact of these technologies on patient outcomes.

This post aims to provide a comprehensive overview of the current status of SaMD and mHealth apps, highlight promising results, and discuss the potential these technologies hold for improving health outcomes and provides only an overview of a peer reviewed paper that we expect to publish in the coming months.

The safety and performances of such solutions for chronic disease patients is thoroughly discussed in various meta-analyses that have been retrieved and analyzed and include 14 meta-analyses for diabetes Type II, cardiovascular diseases, COPD and cancer. Those meta-analyses allow to identify the main clinical performances that could be expected for Healthentia to define the acceptance criteria for the risks/benefits acceptability. In addition, clinical studies originating from the selected systematic reviews were analyzed to check which features were put in place in the eHealth solutions under evaluation, allowing a comparison with those of Healthentia, resulting in prospective controlled studies on several chronic diseases:

• Diabetes Type II: 24 studies, about 1.200 patients in the intervention group
• COPD: 1 study, 36 patients
• Cancer: 3 studies, about 150 patients
• Heart diseases: 7 studies, 560 patients

Summarizing the results from these studies we provide some data both biomarkers and other clinical endpoints that demonstrate the potential:

• HbA1c level: Decreased in several of the studies.
• Physical activity & performance: Significant improvements in exercise capacity.
• Body fat: Significant improvements.
• Sleep: Significant improvements in sleep time.
• Self-care/management: Self-reported improved the self-monitoring of patients’ blood glucose levels, diet, exercise, other self-management skills, and knowledge of the disease.
• Depression & anxiety: Self-reported improved effect on their psychological status.
• 6MWD: Significant improvement.
• (HR)QoL: Difference between the DQoL–Social/Vocational Concerns subscale scores was statistically significant.
• Hospital readmissions: The 30-day hospital readmission rate was much lower, compared with the national readmission rates.
• Adherence: Significant improved medication adherence.

The list of results above is indicative and is only provided to showcase the potential and is not to be considered as a scientific outcome itself. Scientific results will be presented and analyzed in the Systematic Literature Review paper in the coming months, which will also include evidence and findings from studies that used Healthentia. By sharing our findings and experiences, we hope to contribute to the ongoing dialogue on digital health innovations and their role in transforming chronic disease management. Furthermore, the insights gained from the literature review and our benchmarking analysis may offer valuable guidance for healthcare providers, patients, and policymakers as they navigate the evolving landscape of digital health.

Looking ahead, the future of SaMD and mHealth applications appears promising, with the potential to significantly enhance the quality of care for patients with chronic diseases.

Sofoklis Kyriazakos, CEO

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Study Description

Virtual Ward study is leveraging Healthentia solution to monitor and interact with Ovarian Cancer Patients. The Phase 1 scope pertains monitoring and support through the platform for those patients who have been treated with surgery for ovarian cancer and covers the 30-days follow-up period after post-surgery discharge. This is a critical phase of the overall care pathway, with need for the patients to receive guidelines about daily habits, treatment adherence, use/state of post-surgical devices, check of wound heal, potential inflammations, etc. Likewise, the opportunity for clinical staff to have more frequent, focused remote interactions where they can capture any evolution on overall health status, mild and severe adverse symptoms, practical issues during the recovery stage, will result in better quality of care and more efficient management of the patients group.

Study Details

• Type: Virtual Ward
• Start: September 2023
• Phase 1: 30-days follow-up period, Phase 2: pre-surgery and post-surgery treatment period

Our role

Healthentia is the essential link between gynecological surgery patients and their healthcare team. It enables seamless communication, allowing patients to report symptoms, share vital data, and interact with professionals. With features like a Virtual Assistant, we provide guidance and support during the crucial post-operative phase, enhancing care effectiveness and patient experience. Through collaboration with healthcare facilities like the Gemelli Generator – Real World Data Facility, we contribute to innovative care models, ensuring personalized and timely interventions for patients.

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