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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In detail Healthentia offers:

Data capture:
The Healthentia mobile app serves as a companion to the patient, facilitating objective data collection from devices but also subjective self-assessments via questionnaires. The Healthentia portal app facilitates study management, allowing healthcare professionals to define the data attributes to be collected from their patients.

Data management:
The Healthentia big data platform stores and regulates role-based access to data. It also facilitates data ingestion from 3rd parties, especially hospital information systems, providing clinical data. Data is maintained in two repositories. The Healthentia data warehouse is an Azure SQL server providing applications with access to structured data. The Healthentia data lake is an Azure cloud storage (Gen. 2), providing access to anonymized data to the Healthentia model learning services and to testbeds for affiliated 3rd party data scientists to experiment with.

Data processing & understanding:
The Healthentia smart services comprise of model learning, model inference, synthetic data generation, virtual coaching, and clinical pathway modeling services. They all operate on patient data to understand the patient context and give higher-level information to the healthcare professionals and deliver digital therapies to the patients. The model learning and inference services come in two versions: The platform versions work on Healthentia data, while the edge versions are installed in the premises of the healthcare organizations, to facilitate smart services when data cannot be ingested by a system not part of the hospital information system.

Data analytics:
Data and higher-order knowledge is visualized mainly at the Healthentia portal app, where healthcare professionals gain an understanding of the study cohort as a whole and its individual patients. Personal visualizations are also provided by the Healthentia mobile app to the patients.
Healthentia is a class I medical device, currently under evaluation for class IIa, used by large pharma and healthcare organizations across Europe. Innovation Sprint is the 2022 winner of the Disruptive Health category of the Innovation Radar Prize.

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Study Details

• Type: Research
• Disease: Pancreatic Cancer
• Sites: European countries
• Start: January 2021
• Population: 420 patients
• Duration: 36 months

Study Description

iHelp is a research study that delivers a novel personalised-healthcare framework. Enables the collection, integration, and management of health-related data from various sources (medical records, lifestyle, behaviours, social media interactions). The data is analysed using advanced AI techniques to draw adaptive learning models that are used to provide decision support in the form of early risk predictions as well as personalised prevention & intervention measures (alerts, behavioural nudges, consultations medications, therapies, screening, etc.) that are delivered through user-centric mobile and wearable applications.

Study Design & Method

Patients will receive a state-of -the-art wearable device (i.e monitoring bracelet) that will collect at a daily basis Real-World Data like activity (i.e. steps per day), sleep, and vital signs. The collected data are then transferred to the patient’s paired device through the Healthentia application downloaded on their smartphone. 

During the whole observation period, patients will be also asked to report their weekly wellbeing through the same application, completing dedicated questionnaires. Patient-reported outcomes together with the data coming from the wearable device are used to create lifestyle behavioral patterns of patients utilizing AI algorithms for understanding patients' habits and connections. 

Determine key risks associated with Pancreatic Cancer

Develop predictive models for identified risks

Develop adaptive models for targeted prevention and intervention measures

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Study Details

• Type: Non-interventional
• Disease: Haemophilia A
• Sites: 20 sites in Italy
• Start: January 2020
• Population: 150 patients
• Duration: 12 months

Study Description

POWER is a multicenter non-interventional study to evaluate the physical activity, bleeding incidence, and Health-related Quality of Life in patients with Haemophilia A without inhibitors receiving the standard of care treatment. the above outcomes will be collected through a Fitbit tracking device and Healthentia ePRO, customized for the purposes of the Study. This study is in partnership with a Clinical Research Organization (CRO) Cros NT and an eCRF technology provider Arithmos Life Sciences for a Top5 Pharma.

Study Design & Method

Patients will receive a Fitbit device (i.e monitoring bracelet) that will collect at a daily basis Real-World Data like activity (i.e. steps per day), sleep, and vital signs. The device will be delivered during the recruitment, a Fitbit account will be opened and the Application The collected data are then transferred to the patient’s paired device through the Healthentia application downloaded on their smartphone.

During the whole observation period, patients will report through dedicated questionnaires sent to the Power App their wellbeing status in scheduled timings or ad hoc reporting of bleeding or adverse events. A quarterly EQ 5D5L questionnaire is assessing the quality of life and monthly a VAS scale is assessing pain intensity. Absences from school/work and hospitalizations. Bleeding events, treatment adherence and doses and frequency of concomitant medicines are reported monthly.

Document physical activity, (active minutes, steps count and MET) by age categories (12-17, 18-30, and 31-50 years) in patients with haemophilia A without inhibitors

Collect information on incidence and severity of adverse events (AEs)

Profile Haemophiliac patients of different categories active vs. sedentary and severe vs. moderate in terms of dif aspects

Evaluate the relationship between physical activity (type and intensity) and bleedings

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Study Details

• Type: Observational
• Disease: Cervical Cancer
• Primary Site: Fondazione Policlinico Gemelli
• Start: October 2020
• Population: 50 patients
• Duration: 24 months

Study Description

APACHE is an observational study with tracking devices, co-financed with a primary aim, to assess patients experience of using portable monitoring systems during multimodal oncological therapies and follow up period, through the use of the dedicated app Healthentia and wearable technology (i.e. monitoring bracelet), as Electronic Health Record data harvesting devices.

Monitoring oncological patients during multimodal cancer therapies may represent a significant step towards a comprehensive and reliable quality of life assessment, prevention of toxicity before its clinical onset, and treatment outcomes prediction. More specifically, women affected by locally advanced cervical cancer undergoing chemoradiotherapy followed either by surgery or brachytherapy will report experience measurements like toxicity, instrumental activities of daily living, and stress/coping levels.

The machine learning-assisted analysis of these data will allow to identify patients profile that may be used as risk categories to optimize assistance and follow-up practices.

Study Design & Method

Patients will receive a state-of -the-art wearable device (i.e monitoring bracelet) that will collect at a daily basis Real-World Data like activity (i.e. steps per day), sleep, and vital signs. The device will be delivered during the first visit prior to CRT start. The collected data are then transferred to the patient’s paired device through the Healthentia application downloaded on their smartphone.

During the whole observation period, patients will be also asked to report their weekly wellbeing through the same application, completing dedicated questionnaires. Patient reported outcomes together with the data coming from the wearable device, are used to create lifestyle behavioral patterns of patient utilizing AI algorithms. Models with neural networks will be afterward trained, making it possible to monitor deviations from the patterns, assess and predict clinical outcomes and help with the validation of therapy efficacy and compliance.

Evaluate patients Quality of Life using portable monitoring technologies during the multimodal oncological therapies and follow up period

Compare patient reported outcomes with corresponding clinical records about toxicity, instrumental activities of daily living (IADLs), and stress/coping levels

Profile patients based on their scores and activity

Train models using AI and machine learning on the patients-reported and monitored data

Testimonial

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Study Details

• Type: Interventional
• Disease: Covid19 Surveillance on Hospital personnel
• Primary Site: Fondazione Policlinico Gemelli
• Start: January 2021
• Population: Personnel
• Duration: Ongoing

Study Description

VACCINE APP is a study to record and monitor the personnel of the Agostino Gemelli IRCCS University Hospital Foundation after the administration of the first dose of vaccine. After downloading the application on the smartphone and registering, the user is asked to report any side effects that occurred on the day of vaccination and the next seven days, through a questionnaire. Healthentia collects real-world data (RWD) from each user and any reactions experienced on the day of vaccination and in the following days, through an agile questionnaire.

Study Design & Method

The application will be used by the personnel of the Agostino Gemelli IRCCS University Hospital Foundation to monitor their health status after the first dose of the COVID-19 vaccine. There is a composite questionnaire in the Healthentia application with a series of tags and alerts, which will be activated in case of any vaccine reaction.

Monitoring possible adverse events, through a series of real self-reported data

Hospital personnel surveillance for post-vaccination symptoms of COVID-19

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Topics: eHealth, Health monitoring, e-Health applications

SOURCE: MDPI Open Access Journals, 2021, Feb. ;  BOOK DOI Link, Chapter DOI Link

Risk Assessment for Personalized Health Insurance Based on Real-World Data

Aristodemos Pnevmatikakis ^1* ; Efstathios Kanavos ¹; George Matikas¹ ; Konstantina Kostopoulou¹ ; Alfredo Cesario^1,2 ; Sofoklis Kyriazakos ^1,3

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A biomarker is a naturally occurring characteristic by which a pathological or physiological process can be identified. A digital biomarker comprises of objective, quantifiable physiological and behavioral data, measured utilising digital portable, wearable, implantable or digestible devices, to be used to predict and manage health-related outcomes.

Innovation Sprint has built a composite contextual biomarker-based οn multiple aspects of Real-World Data (RWD), collected from people unobtrusively, while following-up their normal living routine. It is composite in the sense that it is not based on a single measurement, but rather on multiple diverse measurements (objective RWD) and peoples’ reports (subjective RWD). It is contextual in the sense that not only the person is measured, but also the person’s lifestyle context: social and environmental aspects complement the more traditional physiological and psychological ones.

Our RWD

At Innovation Sprint we are strong advocates of the empirical knowledge that lifestyle is a strong determinant of health. Hence our biomarker is based on RWD spanning four important aspects of a person’s lifestyle:

 Physiological RWD quantifies physical behaviour (active vs sedentary lifestyle as measured by steps walked, floors climbed, activity types, minutes in different intensity levels or heart rate zones, resting heart rate, sleep characteristics) and includes body info (height, weight, gender, race), nutrition (water, other liquids, food) and symptoms (body temperature, cough, diarrhea, headache, nausea, pain, etc.).

 Psychological RWD quantifies at a simple level mood, and in more complex situations mental state collected via elaborate, domain-specific questionnaires. Measurements can also play a role, either directly e.g. facial expression recognition, or indirectly, e.g. weather where people are living).

 Social RWD quantifies social activity of people. This can be measured indirectly from the usage of the phone (diversity, duration, frequency of calls) and social media (diversity, number, frequency of interactions). More direct information can be reported using questionnaires on activities with friends, family or co-workers.

 Environmental RWD indicates the quality of life. Usually, reported by the users. Measurements of living or working environment quality are made with commercial devices (e.g. air quality meters).

AI for discovering our biomarker

Biomarker discovery at Innovation Sprint is done in three stages

Definition stage, where the domain experts select the clinically significant outcomes that need to be predicted by the biomarker(s).

Manual RWD selection stage, where domain knowledge is applied to refine our generic RWD selection into those lifestyle aspects that are relevant to the disease/condition in question.

Iterative design stage: Machine Learning/AI algorithms are used to train a proprietary classifier using the elected RWD to predict the selected clinically significant outcomes. The classifier is applied on new data  yielding predictions and insights leading to digital therapeutics.

Validating our approach

We employed RWD collected over 7 years to train a biomarker that predicts significant weight changes. Such a biomarker is important for patients with several diseases (e.g. NAFLD), as well as for the general population interested in well-being. We achieved over 80% or correct prediction of the outcome, while we also analysed the different RWD aspects that led each individual to positive or negative outcomes, in order to offer personalized coaching services.

As we speak, we are utilising the same approach in other therapeutic areas, e.g. cervical cancer, to predict low toxicity events. Starting from 2021 we will validate this hypothesis in much larger cohorts, targeting –among others- COPD patients with Cardiovascular Disease comorbidities.

We will keep you update on our observations and findings!

Aristodemos Pnevmatikakis
R&D Director, Innovation Sprint

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