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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At Innovation Sprint, we are advancing new features for Healthentia to improve patient analysis and care. One of our key initiatives is the development of models that provide both long-term and short-term predictions for patients. In the long-term, these models forecast a patient’s health progression based on their clinical, physiological, and behavioral data, while in the short-term, they predict the most suitable next steps for intervention. A critical focus is on ensuring these intervention techniques are personalized to each patient’s unique condition, both for long-term management and immediate care. By tailoring interventions to the individual, we aim to achieve more accurate results, enhance patient outcomes, and improve the overall care process.

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 Healthetnia’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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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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Our research paper titled “HIV Patients’ Tracer for Clinical Assistance and Research during the COVID-19 Epidemic: A Paradigm for Chronic Conditions” has been published in the Special Issue Reprint titled “Advances in AI for Health and Medical Applications“.

This publication shares insights derived from the INTERFACE study; a collaborative effort conducted by Healthentia that provided support to HIV patients during the 1^st wave of the Covid-19 pandemic.

The INTERFACE study focused on leveraging e-health and remote monitoring to enhance care for people living with HIV/AIDS (PLWHA), especially during the SARS-CoV-2 pandemic period. Using Healthentia, a mobile application with decision-making algorithms, the study collected data on symptoms, treatment adherence, and quality of life, processed through an innovative e-Clinical platform. Machine learning algorithms aimed to create a digital composite biomarker for HIV-related alerts. With over 1500 stable HIV patients in a university hospital, the study aimed to improve patient care beyond the pandemic, showing promising results in monthly data for learning predictive models.

INTERFACE, an interventional research initiative, collected real-world data (RWD) on patient outcomes. Voluntarily enrolled patients provide informed consent through the app or eConsent process. Operating within Gemelli Generator Real-World Data (G2 RWD), the study integrated Healthentia data into a tailored e-Clinical environment provided by Innovation Sprint. Healthentia combined traditional electronic patient-reported outcomes with lifestyle, behavioral, and health-related data, employing AI and machine learning to predict outcomes and generate prevention alarms, supporting clinical decision-making during trials.

This comprehensive approach proved crucial for managing HIV patients during the COVID-19 pandemic, addressing physical and mental health challenges. The e-health system identified health issues, tailored interventions, and served as a pre-screening tool for chronic patients during the epidemic. Preliminary evidence indicated effective prediction of alerts, with ongoing data collection and integration enhancing accuracy. Future development involves SHAP analysis, assessing the machine learning system’s impact on health indicators, and evaluating its application in advanced chronicity management. The study aimed to provide tailored and effective care for individuals living with HIV, especially in challenging times like the COVID-19 pandemic.

Explore the INTERFACE study for detailed insights into how Healthentia offered assistance to individuals with HIV during the Covid-19 pandemic.

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