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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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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CATEGORY: Healthcare insurance, Explainable AI, Personalized healthcare, Real-world data, IoT, Big data

SOURCE: Big Data and Artificial Intelligence in Digital Finance. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-94590-9_16

Risk Assessment for Personalized Health Insurance Products

Kyriazakos S., Pnevmatikakis A., Perikleous A., Kanavos S. (2022)

Abstract

The way people lead their lives is considered an important factor in health. In this chapter, we describe a system to provide risk assessment based on behavior for the health insurance sector. The system processes real-world data (RWD) of individuals from their daily life that enumerate different aspects of behavior collection. The data have been captured using the Healthentia platform and a simulator that augments the actual dataset with synthetic data. Classifiers are built to predict variations of peoples’ well-being short-term outlook. Risk assessment services are provided to health insurance professionals by processing the classifier predictions in the long term while explaining the classifiers themselves provide insights on the coaching of the users of the service.

Keywords: Healthcare insurance, Explainable AI, Personalized healthcare, Real-world data, IoT, Big data

More Publications

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At Innovation Sprint, we believe that technology can play a crucial role in creating a fairer and healthier world. Our product Healthentia is a medical decision support software that is designed to monitor, detect, and offer virtual coaching services to patients based on Real-World Data gathered from clinical investigations or from those using it as a medical or well-being device.
Healthentia helps healthcare professionals make informed decisions about patient care by providing them with real-time insights into patient health. It also helps patients take charge of their health by providing them with personalized coaching and alerts to help them manage their conditions.

In addition to using technology to improve healthcare outcomes, it’s important to also promote active living as a key component of a healthy lifestyle. Active living refers to incorporating physical activity into your daily routine, whether it’s through exercise, sports, or other physical activities. We believe that promoting active living is essential to building a fairer and healthier world. Healthentia not only offers virtual coaching services to patients, but it also encourages them to adopt healthy behaviors such as regular physical activity. We recognize that physical activity can have a positive impact on a wide range of health outcomes, including reducing the risk of chronic diseases such as heart disease, diabetes, and cancer. By promoting active living, we can help individuals improve their overall health and well-being, which can in turn lead to a fairer and healthier world for all.

On this World Health Day, let’s remember the importance of staying active and make a commitment to incorporating physical activity into our daily routines.
Together, we can use technology and promote active living to build a fairer and healthier world for everyone.

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Definition & importance

Real-World Data (RWD) is any data relating to a patient’s health status, collected during the routine delivery of care, as opposed to data collected within the controlled setup of clinical trials. Hence RWD does not differ so much in its type but in the process and population involved in its collection. The different types and sources of RWD can be:

• Clinical data from electronic health records (EHRs) and case report forms (EHRs). This data establishes who the subject is, providing demographics, family history, comorbidities, procedure and treatment history, and outcomes. Such data types are also common in clinical trials.
• Patient-generated data from patient-reported outcome (PRO) questionnaires, or measurements from wearables. This is data collected in everyday setting, providing insights directly from the patient, beyond clinic visits, procedures, and hospitalization. While patient-generated data is not unusual in clinical trials, it is collected in a centralized manner at the regular visits of the trial volunteers to the healthcare facilities. In the real-world context, the collection is done continuously at home.
• Public and government data including cost and utilization data. Such data provides information on the healthcare system and the different stakeholders therein.

Such information can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes.

While clinical trials continue to be the main tool for studying the safety and efficacy of a new medicine, their controlled environment, and well-defined cohorts constitute experimental conditions that do not represent real-world settings. RWD is a much better tool for understanding how patients react to a medicine once approved and made available in the market, i.e., in routine medical care. The lack of highly controlled settings usually results in lower levels of confidence, but the outcomes represent a wider population of subjects. Such outcomes are better suited for understanding and taking decisions in everyday medical care, in broader settings than the controlled ones in clinical trials.

RWD: Collecting in a clinical vs. everyday setting

There can be a huge quality difference between RWD collected in a clinical versus in everyday setting. In a clinical setting, the process is carried out sporadically by professionals, with subjects following strict guidelines (like time and method of collection, or diet prior to collection). In the everyday setting, the process is continuous and carried out by the subjects themselves. Whether the data is reported by the subjects or is measured by devices the subjects operate, the continuous nature and the self-supervision can lead to low quality due to device failure (usually uncharged devices, wearables not worn when they should have been, or mobile applications left unused for too long and automatically closed down) and lack of adherence (forgetting to answer instances of repeating questionnaires, amplified decline of interest in the process). Also, clinical data can be much more specialized to the medical conditions at hand, compared to most behavioral data collected in an everyday setting.

But no matter these shortcomings when dealing with data collected in an everyday setting, it is now well-established that behavior is part of the intervention. The high specialization and quality of the sporadic clinical data is complemented by the continuous nature of the behavioral, everyday data, in much the same way a low-resolution film complements the understanding offered by the occasional high-resolution photo.

Patient-generated, everyday RWD types

The behavioral, everyday RWD are categorized in terms of collection method and content. The following collection options are used:

• Patient-reported via questionnaires: This collection model is closer to the established clinical trial approach, but this time the questionnaires are digital, pushed to subjects via some companion mobile app. They mostly have to do with self-assessment of different aspects.
• Patient-reported via widgets: Similar to questionnaires, only this time rich graphical interfaces are employed. The widgets allow manual entry, or take advantage of integration with 3^rd party devices meant for occasional use like scales or blood pressure monitors to automatically collect measurements.
• Automatically reported by wearables: Continuous measurements from wearable devices is one of the most prominent sources of RWD. Ubiquitous activity trackers or more specialized devices like sleep monitors are integrated either at device level (when a Software Development Kit is available, e.g., via Apple Health Kit) or at device cloud level (when an Application Programming Interface is available).

Using any of the above methods, the following everyday RWD types are collected:

• Physiological: Data about physical activity, continuous monitoring of vitals, sleep
• Psychological: Emotions
• Social: Interactions (phone calls, social media)
• Environmental: Living and working environment

Learning on RWD

At a raw level, RWD can lead to decisions about individuals and cohorts via analytics visualizations. But a full understanding of the context of subjects is gained via processing, using machine learning techniques. Supervised algorithms facilitate learning biomarkers, while unsupervised ones lead to phenotypes.

RWD facilitates learning digital composite biomarkers. Biomarkers are quantities characterizing some disease or outcome. Digital refers to their attributes being ubiquitously available, not only as clinical data. Composite refers to the combination of multiple attributes in an attempt to predict some outcome. ML algorithms are used to learn outcome predictors as non-linear combinations of the attributes into the digital composite biomarkers.

Phenotypes characterize the way the internal conditions of subjects manifest themselves for external observation. The different RWD attributes measured constitute the observation, and clusters of the observations correspond to different phenotypes. The clusters are learned from RWD using unsupervised ML algorithms. The clusters are then modeled for efficient representation of the phenotypes.

RWD in Healthentia

Our product Healthentia is used to collect all types of patient-generated, everyday RWD types. Our subjects employ the Healthentia mobile app to answer questionnaires and to enter data via the widgets, either manually or using devices integrated via their Software Development Kits. Data collection also employs the Healthentia big data platform and ingests more subjects’ data using the Application Programming Interfaces of other device providers.
The collected RWD is analyzed using the BI analytics available at the Healthentia portal for healthcare professionals. It is also processed using the smart services of Healthentia, namely:

• The Learning Services for training models
• The Inference Services for inferring with the help of the trained models
• The Clinical Pathway for utilizing the raw RWD and the inference results in monitoring the state of subjects, and
• The Virtual Coach for utilizing all the above in personalized advice given to the subjects.

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Study Description

AZIMUTH model of care is delivered through Healthentia; a Software as a Service (SaaS) medical device via a mobile application for patients and a clinical dashboard for clinicians for Remote Patient Monitoring (RPM), Patient Support Programs (PSP) and Digital Therapeutics (DTx). This innovative scalable model of personalised remote heart failure care is based on good clinical practice standards and international guidelines for heart failure management and has been designed taking into consideration both the optimization of in-hospital care delivery models as well as the often unmonitored out-of-hospital and home settings

The first phase of the study has successfully validated the feasibility, patient acceptance and perceived value of the app-based model of care, demonstrating increased user engagement in patients that leads into improved adherence to treatment.

Dr. Domenico D’Amario

Senior Cardiologist at the Gemelli IRCCS Polyclinic Foundation

Study Details

• Type:  Interventional
• Disease:  Heart Failure – Chronic Ischemic Disease
• Sites: Fondazione Policlinico Gemelli
• Start:  Phase 1: Nov-March 2021, Phase 2: Jul-Jan 2023
• Population: 300 patients
• Duration: Multiphase project

Study Design & Method

Heart failure is the leading cause of death and hospitalisation in patients aged > 65 years and a major and growing medical and economic burden, with high prevalence and incidence rates worldwide. The recent pandemic crisis has made even more evident, some critical aspects in the management of complex chronic diseases, such as heart failure, and these could be exacerbated by the increased demand for care in the future. Therefore, a significant effort must be made to align services with patients’ care needs by identifying a shared model that can exploit the most advanced technologies to enhance disease deterioration, provide adequate integration between hospital and territorial services, increase appropriateness, and reduce waiting times for specialist services.

Testimonial

The care pathway for patients with heart failure includes several steps, from acute phase treatments to chronic phase patient follow-up. This requires a close dialogue between hospital cardiologists and community medicine, especially once the patient returns home, after being discharged from a third-level hospital. But at the moment this complex path is very fragmented.

Validated the feasibility, patient acceptance and perceived value of remote patient monitoring

Demonstrating increased user engagement in patients that leads into improved adherence to treatment

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Healthentia is a digital platform by Innovation Sprint that facilitates data capture in hybrid clinical trials and enables digital therapeutics as a certified medical device. It does so by offering a smartphone application for patients and a web portal for investigators and healthcare professionals to securely access smart services and insights. Healthentia empowers patients in clinical studies and gives them an active role in taking trial actions, rather than bringing them to the trial sites. Healthentia is used by Top5 Pharma and hospitals and operates under the strict regulatory framework of Good Clinical Practice.

In 2021, Healthentia medical device entered the market of Patient Support Programs (PSP) as a decision support software intended to monitor, detect and predict outcomes, offer virtual coaching services and generate automatic alerts regarding events, based on Real World Data gathered from patients. Healthentia PSPs are offered jointly with healthcare organizations and pharmaceutical companies and follow co-development, participatory-design, and patient-centric approaches. In 2022 Healthentia is expected to collect evidence that will create clinical claims on its efficacy as a therapeutic device.

What is a Patient Support Program (PSP)?

A PSP provides an integrated approach to remote care with digital services to support patients during medical treatments. Today, digital platforms for remote patient monitoring are the technical infrastructure of PSPs, offering notifications services for therapy, functionalities for booking hospital visits, and questionnaires to be filled in. More advanced solutions enable the capturing of data from wearable and other medical devices, as well as teleconsultation and virtual coaching as a therapy.

How can a digital platform transform healthcare?

If we can better educate patients and enable them to share data between the periodic visits, we can improve their engagement in daily self-management of their health and the interactions with the care team by personalizing the care plan, making sure they understand why adherence matters, and providing ways to individually achieve it, patients will increase their understanding of care plan, medication regimen and their engagement with care plan activities and confidence in disease management. By using the PSP app and staying connected with the care team, patients will be able to stay engaged in remotely reporting their clinical metrics, wellbeing, and adherence in-between visits. Finally, by having new remote monitoring data, care plans can be adjusted at the follow-up visits, and situations that require clinical attention can be identified earlier.

Do you want to know more? Please visit www.healthentia.com

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